No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Analysis of a changing hydrologic flood regime using the Variable Infiltration Capacity model
Abstract
The Pecatonica River and several other streams in the Wisconsin Driftless area show a decreasing trend in annual peak flows. Previous studies of the Pecatonica River detected a significant decreasing historical trend in late winter snowmelt-driven floods, while the rainfall-driven spring and summer flood peaks exhibited no significant trend during the period of record. Unlike several previous studies which attribute the decline in flood peaks mainly to changes in land management, we hypothesize that climate change had a significant contribution to the overall decrease in flood peaks. In particular, we hypothesize that the increase in winter temperatures caused the decrease in snow depth, which in turn resulted in a decreasing trend in flood peaks. In an attempt to validate this hypothesis, we used long-term daily precipitation, temperature, and river flow data observed in the watershed as inputs to the Variable Infiltration Capacity (VIC) model to generate other non-monitored climatic variables. Trends in these climatic variables were then related to the trend in flood peaks in the Pecatonica River. Due to the complexity of the hydrologic system and numerous data and modeling-related uncertainties, the above hypothesis cannot be validated with certainty. Nonetheless, the results in two different modes (event and continuous simulation) provide support to the speculation that the decreasing trend in flood peaks was a result of decreasing snow depth. The model runs resulted in a decrease in snow depths for the period of record (1915–2009), increase in sublimation and evaporation, no change in base flow, and mixed results in infiltration. These analyses also suggest that VIC can be used in other similar regions in snowmelt-driven flood peak studies. It should be recognized, however, that the success of these applications can be severely constrained by various uncertainties, including but not limited to, the poor quality or absence of snow depth data.
... The researchers attributed these trends to the adoption of various measures for soil and water conservation (Potter 1991, Kochendorfer andHubbart 2010), drainage improvement (Gebert and Krug 1996) or climate change combined with land-use changes (Juckem et al. 2008, Markus et al. 2013. To assess the contribution of climate change to the decreasing snowmelt late winter peaks in the Pecatonica River, Park and Markus (2014) developed an application of the Variable Infiltration Capacity (VIC) model (Andreadis et al. 2009, Tan et al. 2011 in two different modes: the event mode and the continuous mode. Markus et al. (2013) evaluated the snow gage records collected by the National Climatic Data Center (NCDC) of the NOAA (US National Oceanic and Atmospheric Administration) and stored in the Midwestern Regional Climate Center (MRCC) database. ...
... At these stations, the observed data include daily snow depth measurements between 1915 and 2014. In this area, the snowfall season typically occurs between December and March, and the snowpack typically melts in February and March (Park and Markus 2014). Temperatures range between −30°C in the winter and +30°C in the summer, and the growing season generally stretches from May to September. ...
... The initial parameter ranges were obtained from Mishra et al. (2010) and Andreadis et al. (2009). The VIC model calibration results used in this study are obtained in the continuous daily simulation from Park and Markus (2014), which provides descriptions of the model and calibration in more detail. Figure 5 shows the scatter between observed and simulated maximum snow depths at each gage for each event. ...
For snowmelt-driven flood studies, snow water equivalent (SWE) is frequently estimated using snow depth data. Accurate measurements of snow depth are important in providing data for continuous hydrologic simulations of such watersheds. A new hydrologic fidelity metric is proposed in this study to evaluate the potential contribution of particular snow depth datasets to flow characteristics using observed data and hydrologic modeling using the Variable Infiltration Capacity (VIC) model. Data-based hydrologic fidelity of snow depth measurements is defined as a categorical skill score between the snow depth in the watershed and the hydrograph peak or volume at the watershed outlet. Similarly, model-based hydrologic fidelity is defined as a categorical skill score between the model-simulated snow depth and the model-simulated hydrograph peak or volume. The proposed framework was illustrated using the Pecatonica River watershed in the US, indicating which sites have a higher hydrologic fidelity, which is preferred in hydrologic studies.
... The two main FEWS elements are monitoring, carried out through real-time operational instruments, and forecasting, which employs process-based and/or data-driven approaches (Cools et al. 2016). The response time of basin rainfall-runoff transformation influences the structure of FEWS (Calver 1988, Lee et al. 2005, Park and Markus 2014, Kan et al. 2017, Mosavi et al. 2018, Reichstein et al. 2019). In small basins, the forecasting procedure is based on precipitation; conversely, in larger basins, the input information in FEWS is represented by discharges recorded at specific river cross-sections. ...
... These indices quantify the importance of X j based on the expected reduction of the variance of Y when the value of X j is fixed (Pearson 1905, Saltelli and Tarantola 2002, Oakley and O'Hagan 2004). ...
... Therefore, we need to predict the flood flow of the reservoir according to the information of rainfall and flow in the upstream of the basin through time series prediction, so as to give early warning and try to avoid safety accidents caused by sudden flood peaks [1,2]. In the past, hydrological models were generally used, but these models required a lot of parameters, such as temperature, soil moisture, soil type, slope, terrain, etc., and different parameters also contained very complex relationships [3]. In recent years, machine learning technology has developed rapidly, and many researchers have found that its efficient data parallel processing ability can be applied to the field of flood prediction [4,5]. ...
... (www.preprints.org) | NOT PEER-REVIEWED | Posted: 26 January 2024 doi:10.20944/preprints202401.1867.v13 ...
Flood forecasting helps anticipate floods and evacuate people, but due to the access of a large number of iot data acquisition devices, the explosive growth of multidimensional data and the increasingly demanding prediction accuracy, classical parameter models and traditional machine learning algorithms are unable to meet the high efficiency and high precision requirements of prediction tasks. In recent years, deep learning algorithms represented by convolutional neural networks, recurrent neural networks and Informer models have achieved fruitful results in time series prediction tasks. The Informer model is used to predict the flood flow of the reservoir. At the same time, the prediction results are compared with the prediction results of the traditional method and the LSTM model, and how to apply the Informer model in the field of flood prediction to improve the accuracy of flood prediction is studied. The data of 28 floods in the Wan 'an Reservoir control basin from May 2014 to June 2020 were used, with areal rainfall in five subzones and outflow from two reservoirs as inputs and flood processes with different sequence lengths as outputs. The results show that the Informer model has good accuracy and applicability in flood forecasting. In the flood forecasting with sequence length of 4, 5 and 6, Informer has higher prediction accuracy, and the prediction accuracy is better than other models under the same sequence length, but the prediction accuracy will decline to a certain extent with the increase of sequence length. The Informer model stably predicts the flood peak better, and its average flood peak difference and average maximum flood peak difference are the smallest. As the length of the sequence increases, the number of fields with a maximum flood peak difference less than 15% increases, and the maximum flood peak difference decreases. Therefore, the Informer model can be used as one of the better flood forecasting methods, and it provides a new forecasting method and scientific decision-making basis for reservoir flood control.
... VIC has been successfully used in many studies [38][39][40]. The quality of the hydrological model parameters has a great influence on the simulation accuracy of the state variables [16]. ...
... The network is located on the northeast edge of the Tibetan Plateau. The coverage is approximately 40 80 km km × ...
Hydrological models play an essential role in data assimilation (DA) systems. However, it is a challenging task to acquire the distributed hydrological model parameters that affect the accuracy of the simulations at a grid scale. Remote sensing data provide an ideal observation for DA to estimate parameters and state variables. In this study, a special assimilation scheme was proposed to jointly estimate parameters and soil moisture (SM) by assimilating brightness temperature (TB) from the Soil Moisture and Ocean Salinity (SMOS) mission. Variable infiltration capacity (VIC) hydrological model and L-band microwave emission of the biosphere model (L-MEB) are coupled as model and observation operators, respectively. The scheme combines two stages of estimators, one for the static model parameters and the other for the dynamic state variables. The estimators approximate the posterior probability distribution of an unknown target through sequential Monte Carlo (SMC) sampling. Markov chain Monte Carlo (MCMC) and immune evolution strategy are embedded in both stages to solve particle impoverishment problems. To evaluate the effectiveness of the scheme, the estimated SM sets are compared with in-situ observations and SMOS products in Maqu on the Tibetan Plateau. Specifically, the root mean square error decreased from 0.126 to 0.087 m3m−3 for surface SM, with a slight impact on the root zone. The temporal correlation between DA results and in-situ measurements increased to 0.808 and 0.755 for surface SM (+0.057) and root zone SM (+0.040), respectively. The results demonstrate that assimilating TB has tremendous potential as an approach to improve the estimation of distributed model parameters and SMs of surface and root zone at a grid scale, and the immune evolution strategy is effective for increasing the accuracy of approximation in sampling.
... Following the approach adopted in previous works on the calibration of VIC (e.g., Dan et al., 2012;Park and Markus, 2014;Xue et al., 2015), we focus our attention on six main parameters that control the rainfall-runoff process (Table 1). These parameters are the thickness of the two soil layers (d 1 and d 2 ), the infiltration parameter (b), and three baseflow parameters (D s , D max , and W s ). ...
... 3.1.1, the choice of these parameters is already established in the literature (Dan et al., 2012;Park and Markus, 2014;Xue et al., 2015); yet, it is reasonable to expect that the use of more parameters could further improve the model accuracy. As for the use of homogeneously distributed parameters, our modeling choice is justified by the fact that the use of heterogeneously distributed parameters would largely impact the computational requirements of the calibration process. ...
During the past decades, the increased impact of anthropogenic interventions on river basins has prompted hydrologists to develop various approaches for representing human–water interactions in large-scale hydrological and land surface models. The simulation of water reservoir storage and operations has received particular attention, owing to the ubiquitous presence of dams. Yet, little is known about (1) the effect of the representation of water reservoirs on the parameterization of hydrological models, and, therefore, (2) the risks associated with potential flaws in the calibration process. To fill in this gap, we contribute a computational framework based on the Variable Infiltration Capacity (VIC) model and a multi-objective evolutionary algorithm, which we use to calibrate VIC's parameters. An important feature of our framework is a novel variant of VIC's routing model that allows us to simulate the storage dynamics of water reservoirs. Using the upper Mekong river basin as a case study, we calibrate two instances of VIC – with and without reservoirs. We show that both model instances have the same accuracy in reproducing daily discharges (over the period 1996–2005), a result attained by the model without reservoirs by adopting a parameterization that compensates for the absence of these infrastructures. The first implication of this flawed parameter estimation stands in a poor representation of key hydrological processes, such as surface runoff, infiltration, and baseflow. To further demonstrate the risks associated with the use of such a model, we carry out a climate change impact assessment (for the period 2050–2060), for which we use precipitation and temperature data retrieved from five global circulation models (GCMs) and two Representative Concentration Pathways (RCPs 4.5 and 8.5). Results show that the two model instances (with and without reservoirs) provide different projections of the minimum, maximum, and average monthly discharges. These results are consistent across both RCPs. Overall, our study reinforces the message about the correct representation of human–water interactions in large-scale hydrological models.
... Following the approach adopted in previous works on the calibration of VIC (e.g., Dan et al., 2012;Park and Markus, 2014;Xue et al., 2015), we focus our attention on six main parameters that control the rainfall-runoff process (Table 1). These parameters are the thickness of the two soil layers (d 1 and d 2 ), the infiltration parameter (b), and three baseflow parameters (D s , D max , and W s ). ...
... 3.1.1, the choice of these parameters is already established in the literature (Dan et al., 2012;Park and Markus, 2014;Xue et al., 2015); yet, it is reasonable to expect that the use of more parameters could further improve the model accuracy. As for the use of homogeneously distributed parameters, our modeling choice is justified by the fact that the use of heterogeneously distributed parameters would largely impact the computational requirements of the calibration process. ...
During the past decades, the increased impact of anthropogenic interventions on river basins has prompted hydrologists to develop various approaches for representing human-water interactions in large-scale hydrological and land surface models. The simulation of water reservoir storage and operations has received particular attention, owing to the ubiquitous presence of dams. Yet, little is known about (1) the effect of the representation of water reservoirs on the parameterization of hydrological models, and, therefore, (2) the risks associated to potential flaws in the calibration process. To fill in this gap, we contribute a computational framework based on the Variable Infiltration Capacity (VIC) model and a Multi-Objective Evolutionary Algorithm, which we use to calibrate VIC's parameters. An important feature of our framework is a novel variant of VIC's routing module that allows us to simulate the storage dynamics of water reservoirs. Using the upper Mekong river basin as a case study, we calibrate two instances of VIC-with and without reservoirs. We show that both model instances have the same accuracy in reproducing daily discharges (over the period 1996-2005); a result attained by the model without reservoirs by adopting a parameterization that compensates for the absence of these infrastructures. The first implication of this flawed parameter estimation stands in a poor representation of key hydrological processes, such as surface runoff, infiltration , and baseflow. To further demonstrate the risks associated to the use of such model, we carry out a climate change impact assessment (for the period 2050-2060), for which we use precipitation and temperature data retrieved from five Global Circulation Models (GCMs) and two Representative Concentration Pathways (RCPs 4.5 and 8.5). Results show that the two model instances (with and without reservoirs) provide different projections of the minimum, maximum, and average monthly discharges. These results are consistent across both RCPs. Overall, our study reinforces the message about the correct representation of human-water interactions in large-scale hydrological models.
... Previous studies also have shown increases in annual and seasonal precipitation and streamflow totals as well as changes in the frequency of intense rain events and the seasonality of timing of precipitation in the Midwestern United States and have suggested potential causes including large-scale climate variability and climate warming (e.g. Gupta et al., 2015;Mallakpour 20 andVillarini, 2016, 2015;Park and Markus, 2014;Yang et al., 2013). Specific attribution of the changes in Turkey River is beyond the scope of this study, but these trends nonetheless highlight the potential challenge and important considerations for FFA in a changing hydroclimate. ...
... 3a, 3c), with March-April (May-September) floods decreasing (increasing) in magnitude, leading to a shift in the seasonality of the overall distribution of annual maxima daily streamflow from a high in March prior to 1990 to a prolonged high from April-June post-1990. Although the small sample size of the annual maxima daily discharge 20 during this elevated late-spring/summertime flood period may affect the reliability of the derived PDF of flood occurrence,Park and Markus (2014) reported a significant shift toward summertime flooding in the nearby Pecatonica River.Statistically based FFA (including nonstationary methods) based on annual maxima discharges may fail to capture the impact of this shifting seasonality on flood frequency. ...
Floods are the product of complex interactions of processes including rainfall, soil moisture, and watershed morphology. Conventional flood frequency analysis (FFA) methods such as design storms and discharge-based statistical methods offer few insights into process interactions and how they shape the probability distributions of floods. Understanding and projecting flood frequency in conditions of nonstationary hydroclimate and land use requires deeper understanding of these processes, some or all of which may be changing in ways that will be undersampled in observational records. This study presents an alternative process-based FFA approach that uses stochastic storm transposition to generate large numbers of realistic rainstorm scenarios based on relatively short rainfall remote sensing records. Long-term continuous hydrologic model simulations are used to derive seasonally varying distributions of watershed antecedent conditions. We couple rainstorm scenarios with seasonally appropriate antecedent conditions to simulate flood frequency. The methodology is applied in Turkey River in the Midwestern United States, a watershed that is undergoing significant climatic and hydrologic change. We show that using only 15 years of rainfall records, our methodology can produce more accurate estimates of present-day flood frequency than is possible using longer discharge or rainfall records. We found that shifts in the seasonality of soil moisture conditions and extreme rainfall in Turkey River exert important controls on flood frequency. We also demonstrate that process-based techniques may be prone to errors due to inadequate representation of specific seasonal processes within hydrologic models. Such mistakes are avoidable, however, and our approach may provide a clearer pathway toward understanding current and future flood frequency in nonstationary conditions compared with more conventional methods.
... Therefore, we need to predict the flood flow of the reservoir according to the information of rainfall and flow in the upstream of the basin through a time series prediction, so as to give early warning and try to avoid safety accidents caused by sudden flood peaks [1,2]. In the past, hydrological models were generally used, but these models required a lot of parameters, such as temperature, soil moisture, soil type, slope, terrain, etc., and different parameters also contained very complex relationships [3]. In recent years, machine learning technology has developed rapidly, and many researchers have found that its efficient data parallel processing ability can be applied to the field of flood prediction [4,5]. ...
Flood forecasting helps anticipate floods and evacuate people, but due to the access of a large number of data acquisition devices, the explosive growth of multidimensional data and the increasingly demanding prediction accuracy, classical parameter models, and traditional machine learning algorithms are unable to meet the high efficiency and high precision requirements of prediction tasks. In recent years, deep learning algorithms represented by convolutional neural networks, recurrent neural networks and Informer models have achieved fruitful results in time series prediction tasks. The Informer model is used to predict the flood flow of the reservoir. At the same time, the prediction results are compared with the prediction results of the traditional method and the LSTM model, and how to apply the Informer model in the field of flood prediction to improve the accuracy of flood prediction is studied. The data of 28 floods in the Wan’an Reservoir control basin from May 2014 to June 2020 were used, with areal rainfall in five subzones and outflow from two reservoirs as inputs and flood processes with different sequence lengths as outputs. The results show that the Informer model has good accuracy and applicability in flood forecasting. In the flood forecasting with a sequence length of 4, 5 and 6, Informer has higher prediction accuracy, and the prediction accuracy is better than other models under the same sequence length, but the prediction accuracy will decline to a certain extent with the increase in sequence length. The Informer model stably predicts the flood peak better, and its average flood peak difference and average maximum flood peak difference are the smallest. As the length of the sequence increases, the number of fields with a maximum flood peak difference less than 15% increases, and the maximum flood peak difference decreases. Therefore, the Informer model can be used as one of the better flood forecasting methods, and it provides a new forecasting method and scientific decision-making basis for reservoir flood control.
... Soil parameters controlling the rainfall-runoff process and routing parameters in VIC-Res. The last column shows the range of each parameter considered in this study and also adopted in previous studies (e.g., Dan et al., 2012;Park and Markus, 2014;Xue et al., 2015;Wi et al., 2017 (Liang et al., 2014). Both VIC and VIC-Res consist of two modules, namely, a rainfall-runoff and a routing module (Fig. 3). ...
The calibration of macroscale hydrological models is often challenged by the lack of adequate observations of river discharge and infrastructure operations. This modeling backdrop creates a number of potential pitfalls for model calibration, potentially affecting the reliability of hydrological models. Here, we introduce a novel numerical framework conceived to explore and overcome these pitfalls. Our framework consists of VIC-Res (a macroscale model setup for the Upper Mekong Basin), which is a novel variant of the Variable Infiltration Capacity (VIC) model that includes a module for representing reservoir operations, and a hydraulic model used to infer discharge time series from satellite data. Using these two models and global sensitivity analysis, we show the existence of a strong relationship between the parameterization of the hydraulic model and the performance of VIC-Res – a codependence that emerges for a variety of performance metrics that we considered. Using the results provided by the sensitivity analysis, we propose an approach for breaking this codependence and informing the hydrological model calibration, which we finally carry out with the aid of a multi-objective optimization algorithm. The approach used in this study could integrate multiple remotely sensed observations and is transferable to other poorly gauged and heavily regulated river basins.
... To illustrate this, we compare the findings of two studies of changing flood properties in the US state of Wisconsin. Park and Markus (2014) found that flooding in the 3,400 km 2 agricultural Pecatonica River watershed has decreased due to earlier snowmelt associated with higher springtime temperatures. This earlier melt is increasingly out-of-phase with springtime rainfalls, reducing the likelihood of rain-on-snow flooding. ...
This review provides a broad overview of the current state of flood research, current challenges, and future directions. Beginning with a discussion of flood-generating mechanisms, the review synthesizes the literature on flood forecasting, multivariate and nonstationary flood frequency analysis, urban flooding, and the remote sensing of floods. Challenges and future flood research directions are outlined and highlight emerging topics where more work is needed to help mitigate flood risks. It is anticipated that the future urban systems will likely have more significant flood risk due to the compounding effects of continued climate change and land-use intensification. The timely prediction of urban floods, quantification of the socioeconomic impacts of flooding, and developing mitigation strategies will continue to be challenging. There is a need to bridge the scales between model capabilities and end-user needs by integrating multiscale models, stakeholder input, and social and citizen science input for flood monitoring, mapping, and dissemination. Although much progress has been made in using remote sensing for flood applications, recent and upcoming Earth Observations provide excellent potential to unlock additional benefits for flood applications. The flood community can benefit from more downscaled, as well as ensemble scenarios that consider climate and land-use changes. Efforts are also needed for data assimilation approaches, especially to ingest local, citizen, and social media data. Also needed are enhanced capabilities to model compound hazards and assess as well as help reduce social vulnerability and impacts. The dynamic and complex interactions between climate, societal change, watershed processes, and human factors often confronted with deep uncertainty highlights the need for transdisciplinary research between science, policymakers, and stakeholders to reduce flood risk and social vulnerability.
... Physically-based, spatially-distributed hydrological models are not only able to quantify the spatial variability of hydrological parameters, but also simplify the simulation of state variables and external fluxes. Variable Infiltration Capacity (VIC) model (Park and Markus 2014;Chawla and Mujumdar 2015;Srivastava et al. 2020), Genie Rural a 4 parameter Journalier (GR4J; Tarek et al. 2021, Kumari et al. 2021) and MIKE 11 NAM (Bisht et al. 2020Tehrani et al. 2021) were used for the assessment of impact of climate change on streamflow. Further, SWAT model is extensively utilized to address the impact of climate change on hydrological processes and extreme events in Indian river basins (Swain et al. 2020;Dixit and Jayakumar 2021a). ...
Drought characteristics and propagation of droughts have been extensively studied for future climate scenarios, but studies on drought hazard mapping in response to climate change are very limited. This study investigated the possibility and severity of drought hazard based on the meteorological and hydrological properties of the drought under changing climate change scenarios. Soil and Water Assessment Tool (SWAT) was used for future streamflow generation. Sixth International Coupled Model Intercomparison Project phage 6 (CMIP6) ensemble General Circulation Models (GCMs) were used to obtain information regarding the future precipitation and streamflow. The Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) were constructed under different Socioeconomic Shared Pathways (SSPs) to analyse the Drought Hazard Index (DHI) over the basin. The key findings of the study are: (i) hydrological and meteorological drought properties are influenced by precipitation as well as minimum and maximum temperatures; (ii) SRI showed moderate severity and remained nearly constant in all scenarios, while SPI showed a decrease in severity over the watershed; (iii) the drought severity showed regressive declining conditions and a marked decrease under different climate scenarios especially under SSP585 scenario; and (iv) the drought hazard will be lower for future scenarios compared to reference period. A novel insight to explore the drought hazard under global warming scenario is the major contribution of this study.
... To illustrate this, we compare the findings of two studies of changing flood properties in the US state of Wisconsin. Park and Markus (2014) found that flooding in the 3,400 km 2 agricultural Pecatonica River watershed has decreased due to earlier snowmelt associated with higher springtime temperatures. This earlier melt is increasingly out-of-phase with springtime rainfalls, reducing the likelihood of rain-on-snow flooding. ...
This review provides a broad overview of the current state of flood research, current
challenges, and future directions. Beginning with a discussion of flood generating
mechanisms, the review synthesizes the literature on flood forecasting, multivariate
and non-stationary flood frequency analysis, urban flooding, and the remote sensing of
floods. Challenges and future flood research directions are outlined and highlight
emerging topics where more work is needed to help mitigate flood risks. It is
anticipated that the future urban systems will likely have more significant flood risk due
to the compounding effects of continued climate change and land-use intensification.
The timely prediction of urban floods, quantification of the socio-economic impacts of
flooding, and developing mitigation strategies will continue to be challenging. There is
a need to bridge the scales between model capabilities and end-user needs by
integrating multiscale models, stakeholder input, and social and citizen science input
for flood monitoring, mapping, and dissemination. Although much progress has been
made in using remote sensing for flood applications, recent and upcoming Earth
Observations provide excellent potential to unlock additional benefits for flood
applications. The flood community can benefit from more downscaled, as well
as ensemble scenarios that consider climate and land-use changes. Efforts are also
needed for data assimilation approaches, especially, to ingest local, citizen and social
media data. Also needed are enhanced capabilities to model compound hazards and
assess as well as help reduce social vulnerability and impacts. The dynamic and
complex interactions between climate, societal change, watershed processes, and
human factors often confronted with deep uncertainty highlights the need for
transdisciplinary research between science, policymakers, and stakeholders to reduce
flood risk and social vulnerability.
... The six major parameters of the VIC model that need to be estimated are presented in Table 17.1. The choice of calibration parameters is made with reference to the previous studies of the model applications (Park and Markus 2014;Mishra et al. 2010). All other parameters From the spatial viewpoint, the calibration parameters can be broadly classified into two groups: distributed parameters and lumped parameters. ...
For construction of farm pond, the important factor is its location. Normally farmers dug out the farm ponds without considering technical aspects. At All India Coordinated Research Project for Dryland Agriculture, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola, demonstrations of site-specific farm ponds based on catchment area were planned and conducted. Based on runoff from the catchment area, the storage in the farm pond was assessed and the relationship was developed. Here, the results of the relationship of catchment, storage and command during the years 2014–2015 to 2016–2017 are presented. During 2016–2017, the runoff causing rainfall in the catchment area of 5 ha of farm pond was 301.3 mm which helps in accumulation of 2014.8 m³ runoff in the farm pond. Therefore, the catchment–storage–command relationship for the season can be given as, from 5 ha catchment, 2014.8 m³ water was stored in the farm pond which can irrigate (command) about 4.0 ha area. Moreover, the available water in the farm ponds was utilized for giving protective irrigations to different crops including vegetables. It was observed that due to utilization of stored farm pond water for protective irrigation, the yield of soybean during Kharif, chickpea during Rabi and vegetables during winter-summer had been increased.
... Hydrological model is a generalization of hydrological phenomena in nature and often used to simulate hydrological processes (Park and Markus, 2014). From the scientific and complex degree of reflecting the rules of physical motion of water flow, there are mainly three types of models (Meng et al., 2017): conceptual models, systematic models (black box models) and physical-based models (Calver, 1988;Lee et al., 2005;Kan et al., 2017). ...
The Loess Plateau is the main source of water in Yellow River, China. After 1980s, the Yellow river water presented a significant reduction, what caused the decrease of the Yellow river discharge had been debated in academic circles. We proceeded with runoff generation mechanisms to explain this phenomenon. We built saturation excess runoff and infiltration excess runoff generation mechanisms for rainfall–runoff simulation in Jingle sub-basin of Fen River basin on the Loess Plateau, to reveal the influence of land use change on flood processes and studied the changes of model parameters under different underlying conditions. The results showed that the runoff generation mechanism was mainly infiltration-excess overland flow, but the flood events of saturation-excess overland flow had an increasing trend because of land use cover change (the increase of forestland and grassland areas and the reduction of cultivated land). Some of the model parameters had physical significances，such as water storage capacity ( WM ), infiltration capacity ( f ), evapotranspiration ( CKE ), soil permeability coefficient ( k ) and index of storage capacity distribution curve ( n ) showed increasing trends, and index of infiltration capacity distribution curve ( m ) showed a decreasing trend. The above results proved the changes of runoff generation mechanism from the perspective of model parameters in Jingle sub-basin, which can provide a new perspective for understanding the discharge reduction in the Yellow River basin.
... The VIC model has been widely applied for hydroclimatic studies in India and the Indian sub-continent (Mishra et al., 2018;Mishra, 2020;. The VIC model is a semi-distributed large scale hydrologic model that has been extensively used at regional and global scales (Demaria et al., 2007;Park & Markus, 2014;Wu et al., 2007;Shah and Mishra, 2015). The VIC model is forced with gridded daily meteorological forcing including precipitation, maximum and minimum temperatures, and wind speed to simulate water budget at a daily time scale. ...
Understanding the sensitivity of water availability in the current and future climate in the Indian sub-continent is vital for food and water security. Using the Variable Infiltration Capacity (VIC) model and Budyko's framework with two observational datasets, we estimated water budget and mean annual runoff sensitivity to precipitation and potential evapotranspiration (PET) over 18 major river basins and 222 sub-basins in the Indian sub-continent. The river basins located in the north experienced a decline in mean annual precipitation while the basins in the south witnessed an increase in mean annual precipitation. Declined precipitation and increased PET resulted in a decrease in mean annual runoff in Brahmaputra, Ganga, and Indus basins during 1980-2014. On the other hand, mean annual runoff has increased in Sabarmati, South Coast, Subernarekha, Tapi, Mahanadi, East coast, Cauvery, and Brahmani river basins. Mean annual AET estimated using the Budyko’s framework was underestimated while mean annual total runoff was overestimated for the majority of the basins in comparison to the estimates from the VIC model. Moreover, the Budyko's framework with both observational datasets underestimated runoff sensitivity to the changes in precipitation and PET in comparison to the VIC model. Runoff is more sensitive to change in precipitation than PET for the majority of the river basins highlighting the importance of changes in precipitation for water availability in the Indian sub-continent. The VIC model simulated runoff and evapotranspiration are in better agreement with the observations in comparison to the estimates from the Budyko’s framework. However, a large uncertainty was found in water budget and runoff sensitivity estimated using the VIC and Budyko's models, which highlights the importance of considering multiple models for estimation of the water budget and runoff sensitivity in the sub-continental river basins.
... Table 1 lists all the important VIC-3L model parameters that are either estimated or subjected to SA and model calibration. The uncertain parameters to be included in the SA process and their ranges (Table 1) Table 1 (Demaria et al., 2007;Matheussen et al., 2002;Mishra et al., 2008;Park and Markus, 2014;Shwetha 245 and Varija, 2015; Troy et al., 2008;Xie et al., 2007) and some initial model experiments. The soil parameters Exp and Ksat were assumed to be the same for all three soil layers. ...
Several research studies have addressed the effects of future climate changes on the hydrological regime of Mahanadi river basin located in eastern part of India. However, studies investigating the effects of future land cover changes on hydrology are limited owing to the lack of availability of projected land cover scenarios. Our study investigates how the hydrology of Mahanadi river basin would respond to the current and future land cover scenarios under a large-scale hydrological modelling framework. Both historical and future land cover scenarios from the recently released, Land use Harmonisation (LUH2) project for CMIP6, indicates cropland and forest are the major land cover types in the basin with a noticeable increase in the cropland (23.3 %) at the expense of forest (22.65 %) by the end of year 2100 compared to the baseline year, 2005. A physically semi-distributed model, the Variable Infiltration Capacity has been set up and implemented over the Mahanadi river basin system for the time period 1990–2010. The uncertain model parameters were subjected to Sensitivity Analysis and calibrated within a Monte Carlo framework. The best set of calibrated models obtained is used in conjunction with the harmonized set of present and future land use scenarios from LUH2 at 25 km by 25 km resolution to generate an ensemble of model simulations that captures a range of plausible impacts of land cover changes on discharge and other hydrological components of the basin. Overall, model simulation results indicate an increase in the extreme flows (i.e., 95th percentile or higher) in the range of 0.12 to 21 % at multiple subcatchments within the basin. This increase can be attributed to the direct conversion of forested areas to agriculture (on the order of 30,000 km2) that has reduced the Leaf Area Index and subsequently reduces the Evapotranspiration (ET). These changes ultimately affect other water balance components at the land surface, resulting in an increase in surface runoff and baseflow, respectively.
... VIC has also been recently updated to include a dynamic lake/wetland model that simulates permanent lakes, seasonal flooding of vegetated land and time varying exposed fraction of land covers within a gridcell (Bowling and Lettenmaier, 2010). With these improvements, the VIC model has been widely applied to estimate effects of climate and land-cover changes on hydrologic systems (Eum et al., 2016(Eum et al., , 2017Shrestha et al., 2014), including drought (Sheffield et al., 2004) and floods (Park and Markus, 2014;Schumann et al., 2013). ...
The sustainability of grazing lands lies in the nexus of human consumption behavior, livestock productivity, and environmental footprint. Due to fast growing global food demands, many grazing lands have suffered from overgrazing, leading to soil degradation, air and water pollution, and biodiversity losses. Multidisciplinary efforts are required to understand how these lands can be better assessed and managed to attain predictable outcomes of optimal benefit to society. This paper synthesizes our understanding based on previous work done on modelling the influences of grazing of soil carbon (SC) and greenhouse gas emissions to identify current knowledge gaps and research priorities. We revisit three widely-used process-based models: DeNitrification DeComposition (DNDC), DayCent, and the Pasture Simulation model (PaSim) and two watershed models: The Soil & Water Assessment Tool (SWAT) and Variable Infiltration Capacity Model (VIC), which are widely used to simulate C, nutrient and water cycles. We review their structures and ability as process-based models in representing key feedbacks among grazing management, SOM decomposition and hydrological processes in grazing lands. Then we review some significant advances in the use of models combining biogeochemical and hydrological processes. Finally, we examine challenges of incorporating spatial heterogeneity and temporal variability into modelling C and nutrient cycling in grazing lands and discuss their weakness and strengths. We also highlight key research direction for improving the knowledge base and code structure in modelling C and nutrient cycling in grazing lands, which are essential to conserve grazing lands and maintain their ecosystem goods and services.
... TOPMODEL has the advantage in that it can easily separate surface runoff from basal runoff, and a runoff simulation can be performed for a long period. Similarly, the variable infiltration capacity (VIC) model can simulate rainfall-runoff processes and separates surface runoff and baseflow using the concept of three layers [7,8]. The VIC model simulates both water-and energy-balance equations and considers land-cover type as well as the concept of three soil layers. ...
This study analyzed the result of parameter optimization using the digital elevation model (DEM) resolution in the TOPography-based hydrological MODEL (TOPMODEL). Also, this study investigated the sensitivity of the TOPMODEL efficiency by applying the varying resolution of the DEM grid cell size. This work applied TOPMODEL to two mountainous watersheds in South Korea: the Dongkok watershed in the Wicheon river basin and the Ieemokjung watershed in the Pyeongchang river basin. The DEM grid cell sizes were 5, 10, 20, 40, 80, 160, and 300 m. The effect of DEM grid cell size on the runoff was investigated by using the DEM grid cell size resolution to optimize the parameter sets. As the DEM grid cell size increased, the estimated peak discharge was found to increase based on different parameter sets. In addition, this study investigated the DEM grid cell size that was most reliable for use in runoff simulations with various parameter sets in the experimental watersheds. The results demonstrated that the TOPMODEL efficiencies in both the Dongkok and Ieemokjung watersheds rarely changed up to a DEM grid-size resolution of about 40 m, but the TOPMODEL efficiencies changed with the coarse resolution as the parameter sets were changed. This study is important for understanding and quantifying the modeling behaviors of TOPMODEL under the influence of DEM resolution based on different parameter sets.
... Climate variability and changes in cold region watersheds are having significant impacts on the different components of the hydrologic-cycle, such as on snow accumulation and melt, soil moisture and runoff affecting local and regional hydrological regimes. Changes in any of these hydrologic processes, including precipitation intensity, snowmelt runoff and antecedent soil moisture, may cause alterations in frequency and intensity of extreme flows [1,2]. While flash floods are usually generated by intense convective rainfalls that occur in summer, snowmelt-driven extreme flows in cold regions environment are more frequent in spring and early summer [3]. ...
Flows originating from alpine dominated cold region watersheds typically experience extended winter low flows followed by spring snowmelt and summer rainfall driven high flows. In a warmer climate, there will be a temperature-induced shift in precipitation from snowfall towards rain along with changes in precipitation intensity and snowmelt timing, resulting in alterations in the frequency and magnitude of peak flow events. This study examines the potential future changes in the frequency and severity of peak flow events in the Athabasca River watershed in Alberta, Canada. The analysis is based on simulated flow data by the variable infiltration capacity (VIC) hydrologic model driven by statistically downscaled climate change scenarios from the latest coupled model inter-comparison project (CMIP5). The hydrological model projections show an overall increase in mean annual streamflow in the watershed and a corresponding shift in the freshet timing to an earlier period. The river flow is projected to experience increases during the winter and spring seasons and decreases during the summer and early fall seasons, with an overall projected increase in peak flow, especially for low frequency events. Both stationary and non-stationary methods of peak flow analysis, performed at multiple points along the Athabasca River, show that projected changes in the 100-year peak flow event for the high emissions scenario by the 2080s range between 4% and 33% depending on the driving climate models and the statistical method of analysis. A closer examination of the results also reveals that the sensitivity of projected changes in peak flows to the statistical method of frequency analysis is relatively small compared to that resulting from inter-climate model variability.
... VIC has also been recently updated to include a dynamic lake/wetland model that simulates permanent lakes, seasonal flooding of vegetated land and timely-varying exposed fraction of land covers within a grid cell (Bowling and Lettenmaier, 2010). Along with the improvements, the VIC model has been extensively applied for assessment of climate and land-cover changes on hydrologic systems (Eum et al., 2016;Shrestha et al., 2014), drought (Sheffield et al., 2004), and floods (Park and Markus, 2014;Schumann et al., 2013). ...
The sustainability of grazing lands lies in the nexus of human consumption behavior, livestock productivity, and environmental sustainability. Due to fast growing global food demands, many grazing lands have suffered from overgrazing, leading to soil degradation, air and water pollution, and biodiversity losses. Multidisciplinary efforts are required to understand how grazing lands can be better monitored, assessed and managed to attain predictable outcomes of optimal benefit to society. This paper synthesizes our understanding based on previous work done on impacts of grazing on ecosystem goods and services, identifies current knowledge gaps, and formulates a plan forward. We review the impacts of two contrasting grazing systems, continuous and multi-paddock rotational grazing, on soil carbon (C), nutrient cycling and greenhouse gas emissions (GHGs). We then extend our review to explore challenges of incorporating spatial heterogeneity and temporal variability into monitoring and modelling C and nutrient cycling in grazing lands. We revisit two process-based models (i.e., DNDC and DayCent) and two watershed models (i.e., SWAT and VIC) widely used to simulate C, nutrient and water cycles of these lands. Finally we identify research directions for improving the knowledge base which is essential to conserve grazing lands and maintain their ecosystem goods and services.
... Furthermore, both the seasonality and magnitude of flood peaks have shifted since approximately 1990 (Fig. 3a, c), with March-April (May-September) floods decreasing (increasing) in magnitude, leading to a shift in the seasonality of the overall distribution of annual maximum daily streamflow from a high in March prior to 1990 to a prolonged high from April to June post-1990. Although the small sample size of the annual maximum daily discharge during this elevated 1990-2016 late-spring and summertime flood period may affect the reliability of the derived distribution of flood occurrence, Park and Markus (2014) also reported a significant shift toward summertime flooding in the nearby Pecatonica River. Statistically based FFA (including nonstationary methods) based on annual maxima discharges may fail to capture the impact of this shifting seasonality on flood frequency. ...
Floods are the product of complex interactions among processes including
precipitation, soil moisture, and watershed morphology. Conventional flood
frequency analysis (FFA) methods such as design storms and discharge-based
statistical methods offer few insights into these process interactions and
how they “shape” the probability distributions of floods. Understanding and
projecting flood frequency in conditions of nonstationary hydroclimate and
land use require deeper understanding of these processes, some or all of
which may be changing in ways that will be undersampled in observational
records. This study presents an alternative “process-based” FFA approach
that uses stochastic storm transposition to generate large numbers of
realistic rainstorm “scenarios” based on relatively short rainfall remote
sensing records. Long-term continuous hydrologic model simulations are used
to derive seasonally varying distributions of watershed antecedent
conditions. We couple rainstorm scenarios with seasonally appropriate
antecedent conditions to simulate flood frequency. The methodology is applied
to the 4002 km2 Turkey River watershed in the Midwestern United States,
which is undergoing significant climatic and hydrologic change. We show that,
using only 15 years of rainfall records, our methodology can produce accurate
estimates of “present-day” flood frequency. We found that shifts in the
seasonality of soil moisture, snow, and extreme rainfall in the Turkey River
exert important controls on flood frequency. We also demonstrate that
process-based techniques may be prone to errors due to inadequate
representation of specific seasonal processes within hydrologic models. If
such mistakes are avoided, however, process-based approaches can provide a
useful pathway toward understanding current and future flood frequency in
nonstationary conditions and thus be valuable for supplementing existing FFA
practices.
... In hydrological processes, rainfall is taken major components and decided the drought or flooding events. Recently, there are mainly three types of models for simulating the relationship of rainfall and runoff [3,4]:conceptual models, physical-based models and black box models. A conceptual model is a representation of a system, made of the composition of concepts which are used to help us to know, understand, or simulate a subject the model represents [5]. ...
Considering the high random and non-static property of the rainfall-runoff process, lots of models are being developed in order to learn about such a complex phenomenon. Recently, Machine learning techniques such as the Artificial Neural Network (ANN) and other networks have been extensively used by hydrologists for rainfall-runoff modelling as well as for other fields of hydrology. However, deep learning methods such as the state-of-the-art for LSTM networks are little studied in hydrological sequence time-series predictions. We deployed ANN and LSTM network models for simulating the rainfall-runoff process based on flood events from 1971 to 2013 in Fen River basin monitored through 14 rainfall stations and one hydrologic station in the catchment. The experimental data were from 98 rainfall-runoff events in this period. In between 86 rainfall-runoff events were used as training set, and the rest were used as test set. The results show that the two networks are all suitable for rainfall-runoff models and better than conceptual and physical based models. LSTM models outperform the ANN models with the values of R 2 and N S E beyond 0.9, respectively. Considering different lead time modelling the LSTM model is also more stable than ANN model holding better simulation performance. The special units of forget gate makes LSTM model better simulation and more intelligent than ANN model. In this study, we want to propose new data-driven methods for flood forecasting.
... In addition, subgrid-scale heterogeneity is represented in soil moisture storage, evaporation, and runoff production (Liang 1994(Liang , 1996Nijssen et al. 2001;Nijssen 2001). In recent years, the VIC-3L model has been calibrated and applied to various basins of different scales with good performance (Mo and Lettenmaier 2014;Park and Markus 2014;Eum et al. 2014a, b;Wen et al. 2012). Liu et al. (2013), for example, used the VIC model to study the impact of land-use and climate changes on hydrologic processes in the Qingyi River Watershed, China. ...
In this study, we developed a hybrid form of rainfall-runoff model by integrating the variable infiltration capacity (VIC) model with a wavelet neural network (WNN) based on the binary gravitational search algorithm (BGSA). The streamflow of each subbasin in the Jinshajiang River Basin was first simulated by VIC model, then the simulated runoff of each subbasin and antecedent total basin runoff were decomposed via discrete wavelet transformation into a number of subseries components with different time scales. Finally, BGSA was employed to optimize the number of hidden layers and identify the appropriate subset of WNN inputs from a set of candidate subseries components. The proposed VIC_BGSA_WNN model was then compared to the traditional VIC model and reference methods based on correlation to determine effective wavelet components, and results indicated that our approach is feasible and effective.
... The variable infiltration capacity (VIC) land surface model (Liang 1994 [4], 1996 [5]) is a hydrological based land surface scheme that explicitly represents the effects of the spatial variability of infiltration, precipitation, and vegetation on water fluxes through the landscape. In recent years, the VIC model have been applied to various basins of different scales with good performance (Park and Markus 2014 [6]; Yan et al. 2015 [7]; Shrestha et al. 2015 [2]). The applications also have covered many research areas, from studies of simulating ensembles of streamflow, global flood estimation, uncertainty analysis of climate data and model parameter sets, and the influence of land-use and climate change on streamflow processes. ...
... Furthermore, using the available information on triggering basin-average precipitation, we have computed multiple descriptors (summarized in Table 1) for each event, including runoff coefficient, base flow index, and first-and second-order moments of both precipitation and flow (Zoccatelli et al. 2011)-parameters that do not exist in current flooding catalogs. These descriptors broaden the applicability of this database to varying f lood studies, including hydrological modeling (Jayakrishnan et al. 2005;Park and Markus 2014;Shen et al. 2016a), flood risk analyses (Apel et al. 2009), and geomorphological and geophysical impact analyses (Costa 1987;Xu et al. 2004). ...
Notwithstanding the rich record of hydrometric observations compiled by the U.S. Geological Survey (USGS) across the contiguous United States (CONUS), flood event catalogues are sparse and incomplete. Available databases or inventories are mostly survey- or report-based, impact-oriented, or limited to flash floods. These data do not represent the full range of flood events occurring in CONUS in terms of geographical locations, severity, triggering weather, or basin morphometry. This study describes a comprehensive dataset consisting of more than half a million flood events extracted from 6,301 USGS flow records and radar-rainfall fields from 2002 to 2013, using the characteristic point method. The database features event duration; first- (mass center) and second- (spreading) order moments of both precipitation and flow; flow peak and percentile; event runoff coefficient; base flow; and information on the basin geomorphology. It can support flood modeling, geomorphological and geophysical impact studies, and instantaneous unit hydrograph and risk analyses, among other investigations. Preliminary data analysis conducted in this study shows that the spatial pattern of flood events affected by snowmelt correlates well with the mean annual snowfall accumulation pattern across CONUS; the basin morphometry affects the number of flood events and peak flows; and the concentration time and spreadness of the flood events can be related to the precipitation first- and second-order moments.
... VIC has been widely used (Liang et al. 1994, Xia et al. 2012, Park & Markus 2014, Zhang et al. 2014, Vano & Lettenmaier 2014 for water resources management, land-atmosphere interactions, and climate change. Although VIC can balance both water and energy budgets (in the water-and-energy mode, or the "full mode"), it can also be used in a "water-only" mode that only solves the water budget. ...
The objective of this dissertation research is to better understand the hydrological impacts of climate variability and climate change. This objective is first addressed in a two-part study focusing on the Northeast US using the Connecticut River Basin as a case study. Changes to the hydrological cycle are investigated for the past several decades using precipitation and river discharge data from observations and soil moisture and evapotranspiration (ET) from the VIC hydrological model. From 1950-2011 a clear increase of precipitation intensity is identified, together with increasing precipitation amount, discharge, runoff ratios, and soil moisture. The ET trend is negligible. This study of the past is followed by projections of the future using the VIC model driven by a bias-corrected climate for the period of 2046-2065 from three climate models. The projected future changes that had not yet manifested in the past include enhanced ET for all four seasons and a change to the seasonality of snow melt and discharge. There are also indications of wetter winters, changing characteristics of flood events, and a consistently increasing mean intensity of precipitation which continues from the past analysis. Compared to the past, the future foods are projected to be less frequent but last longer. Among all hydrological variable, ET is the most difficult to simulate. In this dissertation research, an innovative approach to improving the accuracy of ET estimations is developed, which combines hydrological models with data derived from satellite remote sensing including leaf area index and ET. This model-data integration leads to a more accurate reconstruction of historic river flow and different future hydrological trends that include an increase of summer droughts. This dissertation research also explores the mechanisms underlying the recently discovered decline of the ET trend in many regions focusing on the continental U.S. using the Community Land Model 4.5. Experimental simulations are conducted to isolate the effects of the most influential factors on ET. It is found that the changing characteristics of precipitation, precipitation amount in particular, are the primary cause of the ET trend decline. The roles of wind speed and temperature changes are found to be negligible.
... Subgrid-scale heterogeneity is also represented in regards to soil moisture storage, evaporation and runoff production [1,2,[4][5][6][7]. In recent studies, the VIC model has been applied to several different scales of watersheds [8][9][10]. The VIC model has also been applied in several other research fields, for example to simulate streamflow ensembles [11], snowmelt [12], global flood events [13] and to conduct uncertainty analysis of climate data and model parameter sets [14]. ...
A hybrid rainfall-runoff model was developed in this study by integrating the variable infiltration capacity (VIC) model with artificial neural networks (ANNs). In the proposed model, the prediction interval of the ANN replaces separate, individual simulation (i.e., single simulation). The spatial heterogeneity of horizontal resolution, subgrid-scale features and their influence on the streamflow can be assessed according to the VIC model. In the routing module, instead of a simple linear superposition of the streamflow generated from each subbasin, ANNs facilitate nonlinear mappings of the streamflow produced from each subbasin into the total streamflow at the basin outlet. A total of three subbasins were delineated and calibrated independently via the VIC model; daily runoff errors were simulated for each subbasin, then corrected by an ANN bias-correction model. The initial streamflow and corrected runoff from the simulation for individual subbasins serve as inputs to the ANN routing model. The feasibility of this proposed method was confirmed according to the performance of its application to a case study on rainfall-runoff prediction in the Jinshajiang River Basin, the headwater area of the Yangtze River.
... Applications of data assimilation techniques (such as input updating, state updating, parameter updating and output updating) and the real-time monitoring data arise in the field of real-time flood forecasting to present an actual state of the system which can get better results of forecast (Sene 2013). For example, Liu et al. (2010), Khatibi et al. (2011) and Park and Markus (2014) used monitored data (such as daily precipitation, temperature, and river flow observation) to improve the accuracy of flood modelling with the data assimilation that is available for flood forecasting. However, simulation errors were not completely eliminated due to the uncertainties existed in parameters, structure and boundary conditions of such model. ...
Flood is the worst weather-related hazard in Taiwan because of steep terrain and storm. The tropical storm often results in disastrous flash flood. To provide reliable forecast of water stages in rivers is indispensable for proper actions in the emergency response during flood. The river hydraulic model based on dynamic wave theory using an implicit finite-difference method is developed with river roughness updating for flash flood forecast. The artificial neural network (ANN) is employed to update the roughness of rivers in accordance with the observed river stages at each time-step of the flood routing process. Several typhoon events at Tamsui River are utilized to evaluate the accuracy of flood forecasting. The results present the adaptive n-values of roughness for river hydraulic model that can provide a better flow state for subsequent forecasting at significant locations and longitudinal profiles along rivers.
... No empirical methods estimate hourly U accurately because of low correlation with other forcings (Parlange and Katz 2000). Therefore, we used NCEP-NCAR reanalyses data (Kalnay et al. 1996), which were the basis for the popular Maurer et al. (2002) dataset and have been used in recent snow model applications (Kang et al. 2014;Park and Markus 2014). We estimated hourly U based on the long-term (1981-2010) mean 6-hourly dataset of scalar wind speed at each site to capture typical diurnal and seasonal cycles. ...
Physically based models facilitate understanding of seasonal snow processes but require meteorological
forcing data beyond air temperature and precipitation (e.g., wind, humidity, shortwave radiation, and long- wave radiation) that are typically unavailable at automatic weather stations (AWSs) and instead are often represented with empirical estimates. Research is needed to understand which forcings (after temperature and precipitation) would most benefit snow modeling through expanded observation or improved estimation techniques. Here, the impact of forcing data availability on snow model output is assessed with data- withholding experiments using 3-yr datasets at well-instrumented sites in four climates. The interplay between forcing availability and model complexity is examined among the Utah Energy Balance (UEB), the Dis- tributed Hydrology Soil Vegetation Model (DHSVM) snow submodel, and the snow thermal model (SNTHERM). Sixty-four unique forcing scenarios were evaluated, with different assumptions regarding availability of hourly meteorological observations at each site. Modeled snow water equivalent (SWE) and snow surface temperatureTsurf diverged most often because of availability of longwave radiation, which is the least frequently measured forcing in cold regions in the western United States. Availability of longwave radiation (i.e., observed vs empirically estimated) caused maximum SWE differences up to 234 mm (57% of peak SWE), mean differences up to 6.28 C in Tsurf, and up to 32 days difference in snow disappearance timing. From a model data perspective, more common observations of longwave radiation at AWSs could benefit snow model development and applications, but other aspects (e.g., costs, site access, and maintenance) need consideration.
*
... The VIC model has been successfully applied and evaluated in a number of major river basins within the US (e.g., Abdulla et al., 1996;Bowling et al., 2004;Bowling and Lettenmaier, 2010;Cherkauer and Lettenmaier, 1999;Haddeland et al., 2006aHaddeland et al., ,b, 2007Maurer et al., 2001Maurer et al., , 2002Mishra et al., 2011;Nijssen et al., 1997Nijssen et al., , 2001aTang et al., 2009Tang et al., , 2012, the Mekong River Basin (Costa-Cabral et al., 2008,b;Haddeland et al., 2006a,b), and at a global scale (Nijssen et al., 2001a,b,c). Moreover, the VIC model has been widely used in land use change research (Matheussen et al., 2000) and flood peaks in a snowmelt-driven hydrological regime (Park and Markus, 2014). The groundwater is not considered within this study. ...
... More accurate results was obtained in daily modelling as it uses soil moisture conditions. Park and Markus (2014) made an analysis of flood regime and suggest that VIC can be used in snow melt driven flood peak studies. ...
Various ongoing researches are there on topics like which model will give more compatible results with that of observed discharges. It was argued that even complex modeling does not provide better results. Climate change and soil heterogeneity has got an important role in finding out surface runoff. In this paper, we are going to discuss briefly about variable infiltration capacity model (VIC), TOPMODEL, HBV, MIKESHE and soil and water assessment tool (SWAT) model. VIC performs well in moist areas and can be efficiently used in the water management for agricultural purposes. Requirement of large data and physical parameters makes the use of MIKE SHE model limited to smaller catchments. Only a little direct calibration is required for SWAT model to obtain good hydrologic predictions. HBV model gives satisfactory results and TOPMODEL can be used in catchments with shallow soil and moderate topography.
... VIC has been widely used (Liang et al., 1994;Xia et al., 2012;Park and Markus, 2014;Vano et al., 2014;Zhang et al., 2014) for water resource management, land-atmosphere interactions, and climate change. Although VIC can balance both water and energy budgets (in the water-and-energy mode, or the "full mode"), it can also be used in a "water-only" mode that only solves the water budget. ...
Increase of precipitation intensity is the most definite and detectable hydrological consequence of a warmer climate. Among all U.S. regions, the Northeast has witnessed the strongest increase of extreme precipitation in the past five decades. This study examines the impact of climate changes during 1950-2011 on hydrological processes in the Northeast using the Connecticut River Basin as a case study. In addition to precipitation and river discharge data from observations, the Variable Infiltration Capacity (VIC) hydrological model is used to derive hydrological variables for which long-term observational data are not readily available. Our results show a clear increase of precipitation intensity, with substantial increase in both the number of days with greater than 10 mm precipitation and the simple daily intensity index. From 1950 to 2011, extreme precipitation amount (which is the total amount of precipitation from the upper 1% of daily precipitation) increased substantially, by 240% relative to the 1950 level. The weight of extreme precipitation as a fraction of total precipitation also increased, from about 10.6% in the 1950s to 30.4% in the 2000s. Despite the increase of precipitation extremes, the consecutive dry days experienced a slight decrease. Mean trend analysis shows indications of increasing precipitation amount, increasing discharge, increasing runoff ratios, increasing soil moisture, and a negligible evapotranspiration trend. Our simulations suggest that the basin is entering a wetter regime more subject to meteorological flood conditions than to drought conditions. A companion paper will investigate how these trends may persist or differ in the mid-21st Century under continued warming.
Hidrologi dan Agroklimatologi adalah dua cabang ilmu yang krusial dalam bidang keteknikan dan pertanian. Keduanya berperan penting dalam memahami dan mengelola sumber daya air dan iklim untuk keberlanjutan lingkungan dan pertanian yang efektif dan efisienDengan menguasai bidang hidrologi diharapkan dapat memberikan dasar yang kokoh untuk memahami dan mengelola sumber daya air yang sangat penting bagi kehidupan manusia dan ekosistem. Dengan mengintegrasikan pengetahuan hidrologi dalam kebijakan dan praktik, kita dapat menghadapi tantangan terkait air dan lingkungan dengan lebih efektif dan berkelanjutan. Sementara itu cuaca dan iklim adalah faktor faktor yang mempengaruhi ketersediaan air tersebut di alam.
Hidrologi dan agroklimatologi memiliki kontribusi pada pengelolaan sumber daya air yang berkelanjutan dan efisien. Ini sangat penting dalam memenuhi kebutuhan air bagi manusia, pertanian, dan lingkungan. Selain itu dengan melakukan analisis pada dua bidang pengetahuan ini memungkinkan kita untuk merencanakan infrastruktur pengendalian banjir dan pengelolaan air yang efektif, sehingga dapat mengurangi dampak buruk dari peristiwa cuaca ekstrim. Dalam menghadapi perubahan iklim global, analisis hidrologi dan agroklimatologi diperlukan untuk mengetahui bagaimana pola cuaca dan aliran air berubah akan membantu dalam merencanakan strategi adaptasi untuk pertanian dan ekosistem yang rentan terhadap perubahan iklim.
The calibration of macro-scale hydrological models is often challenged by the lack of adequate observations of river discharge and infrastructure operations. This modelling backdrop creates a number of potential pitfalls for model calibration, potentially affecting the reliability of hydrological models. Here, we introduce a novel numerical framework conceived to explore and overcome these pitfalls. Our framework consists of VIC-Res (a macro-scale model setup for the Upper Mekong River Basin) and a hydraulic model used to infer discharge time series from satellite data. Using these two models and Global Sensitivity Analysis, we show the existence of a strong relationship between the parameterization of the hydraulic model and the performance of VIC-Res – a co-dependence that emerges for a variety of performance metrics we considered. Using the results provided by the sensitivity analysis, we propose an approach for breaking this co-dependence and informing the hydrological model calibration, which we finally carry out with the aid of a multi-objective optimization algorithm. The approach used in this study could integrate multiple remote-sensed observations and is readily transferable to other basins.
Citation: Thakuri, S.; Parajuli, B.P.; Shakya, P.; Baskota, P.; Pradhan, D.; Chauhan, R. Open-Source Data Alternatives and Models for Flood Risk Management in Nepal. Remote Sens. 2022, 14, 5660. https://doi.
Availability and applications of open-source data for disaster risk reductions are increasing. Flood hazards are a constant threat to local communities and infrastructures (e.g., built-up environment and agricultural areas) in Nepal. Due to its negative consequences on societies and economic aspects, it is critical to monitor and map those risks. This study presents the open access earth observation (EO) data, geospatial products, and different analytical models available for flood risk assessment (FRA) and monitoring in Nepal. The status of flood risk knowledge and open-source data was reviewed through a systematic literature review. Multispectral optical data are widely used, but use of microwave data is extremely low. With the recent developments in this field, especially optical and microwave data, the monitoring, mapping, and modeling of flood hazards and risk have been more rapid and precise and are published in several scientific articles. This study shows that the choice of appropriate measurements and data for a flood risk assessment and management involves an understanding of the flood risk mechanism, flood plain dynamics, and primary parameter that should be addressed in order to minimize the risk. At the catchments, floodplains, and basin level, a variety of open data sources and models may be used under different socioeconomic and environmental limitations. If combined and analyzed further, multi-source data from different models and platforms could produce a new result to better understand the risks and mitigation measures related to various disasters. The finding of this study helps to select and apply appropriate data and models for flood risk assessment and management in the countries like Nepal where the proprietary data and models are not easily accessible.
Annual cropping systems are common in the Driftless Region of the U.S. Midwest, but soil degradation is prone to happen in such systems due to the rugged topography of the region. Recent rapid increases in row crop area have been noted in this region, with annual precipitation and hydrologic extremes on the rise in recent decades. The aim of this research to use geospatial datasets and tools in order to assess the regional trends in land use, precipitation, and hydrologic change and quantify the relationship between these environmental trends. Between 2006 and 2017, substantial row crop expansion of 10,000 ha or more was common across HUC 8 (Hydrologic Unit Code 8) watersheds in our study area. Expansion occurred mainly on steeper slopes, converting existing grasslands or alfalfa (Medicago sativa L.) to row crops. Classifying land as planted (in row crops), plantable (in row crops or could be converted), and unplantable (unable to be converted) revealed that Driftless Region watersheds have ∼30–50% of plantable land available for future expansion. Annual precipitation was highly variable during this time period but had a general increasing trend. On average, precipitation showed higher correlation to streamflow compared to row crop expansion across 27 USGS river gage drainage basins in our study area. However, when the increase in row crop area was significant and was accompanied by increasing precipitation, stronger correlation between row crop area and annual streamflow was exhibited. This finding suggests that row crop expansion acts to enhance the effects of increasing precipitation on local hydrology.
Evaluation of Gridded Multi-Satellite Precipitation
Estimation (TRMM-3B42-V7) Performance in the
Upper Indus Basin (UIB)
Climate change and human activities have been widely recognized as two major factors that deeply influenced hydrological processes. It has great importance to evaluate and identify the contributions among distinct driving factors, which could help carry out better regional water resources management regulation. In this study, we used the Variable Infiltration Capacity (VIC) model to quantify the contributions for different climatic variables and human activities to runoff in the Luanhe River basin (LRB) with semi-arid climate, northern China. Our intention is to detect the multiple impacts of climate change (CC), such as precipitation change (PC) and temperature rising (TR), and human activities (HA), including land use/ cover change (LUCC) and direct human activities (i.e., water withdrawal and inter-basin water diversion, DHA), on runoff variance. The “natural period” (1961–1979), “weak impact period” (1980–1997) and “strong impact period” (1998–2016) were divided based on the double-mass curves and Mann-Kendall’s test of observed runoff data from 1961 to 2016. The results indicated that, 1) compared with climate change, human activities were the major factors decreasing the runoff, which contributed more than 60% to the runoff reduction in two impact periods; 2) the runoff variation affected by climate was ruled by the precipitation change positively and strongly, and the impacts of precipitation and temperature strengthened with time. 3) During the strong impact period, the direct human activities have presented the most contribution on runoff reduction, which may result from the water diversion and water withdrawal. The influence of LUCC on runoff was mainly due to the conversion between grassland and cropland. These results would be helpful for policymakers and researchers to better understand the responses of runoff to this changing environment conditions and provide implications for future water resources planning and management at the basin scale.
To overcome the drawbacks faced by the traditional manual calibration of hydrological models, this study employs an adaptive differential evolution (DE) algorithm for automatic calibration of Variable Infiltration Capacity (VIC) hydrological model. In the DE algorithm, proper tuning of its control parameters is laborious and generally needs a great amount of time and resources. Therefore, a self-adaptive scheme is presented to enhance the efficacy of the basic DE. The proposed automatic parameter estimation scheme is applied for a case study and evaluated its performance using standard performance measures of coefficient of correlation (R²), Nash–Sutcliffe coefficient (NSE), percent bias (PBIAS), and index of agreement (IoA). The findings from the study revealed that the adaptive DE was successful to optimize the unknown parameters of the VIC model accurately, which signified that the automatic calibration scheme is a credible alternative to the manual approach.
The Lancang-Mekong River Basin (LMRB) is one of the most important transboundary river basins in Asia. While climate change perturbs the streamflow and affects flood events, reservoir operation may mitigate or aggravate this impact. Therefore, quantitative assessment of the climate change impact and reservoir effect on the LMRB is a vital prerequisite for future hydropower development and environmental protection. This study aimed to estimate the variation of the streamflow and flood characteristics affected by climate change and reservoir operation within the LMRB. A reservoir module was incorporated into the Variable Infiltration Capacity (VIC) model to simulate the streamflow susceptible to the reservoirs. It was found that the reservoirs had a substantial influence on the streamflow during 2008–2016, when many reservoirs were constructed in the LMRB. The reservoirs across the Lancang River (the upper Mekong River located in China) reduced the annual average streamflow by 5% at Chiang Sean station (northern Thailand) in 2008–2016, whereas their influence became undetectable downstream of Vientiane station (northern Laos). The streamflow changes downstream of Mukdahan station at southern Laos (including the stations in Cambodia and southern Vietnam) were mainly attributed to the local reservoirs and climate change. Compared with the baseline period of 1985–2007, the upstream reservoir operation dramatically affected streamflow at the midstream stations with higher dry season streamflow (+15% to +37%), but lower wet season streamflow was less affected (−2% to −24%) in 2008–2016. Climate change increased the magnitude and frequency of the flood by up to 14% and 45%, respectively, whereas the reservoir operation reduced them by 16% and 36%, respectively. Our findings provide insights into the interaction between climate change and reservoir operation and their integrated effects on the streamflow, informing and supporting water management and hydropower development in the LMRB.
The conventional abrupt change-based assessments of climate- and human-induced impacts on streamflow require the existence of change point(s) and stationarity assumption. However, hydrological conditions may not change abruptly at a certain time, but rather evolve gradually over a period. We propose a trend-based time-varying approach that does not require these prerequisites to assess the climate- and human-induced impacts on hydrological conditions in the Pearl River Basin (PRB), China, which can be applied in other basins. The trend-based time-varying approach detects human activities exert a significant seasonal regulation on streamflow (i.e. 113% decreases in wet season and 93% increases in dry season) and 101% reductions in flood peaks the East River Basin, the sub-basin with the highest ratio of total reservoir storage capacity to river discharge in the PRB. Climate change contributes to 77% increases in flood peaks in the West River Basin, a large sub-basin with lower flood control levels.
Cold region hydrology is conditioned by distinct cryospheric and hydrological processes. While snowmelt is the main contributor to both surface and subsurface flows, seasonally frozen soil also influences the partition of meltwater and rain between these flows. Cold regions of the Northern Hemisphere midlatitudes have been shown to be sensitive to climate change. Assessing the impacts of climate change on the hydrology of this
region is therefore crucial, as it supports a significant amount of population relying on hydrological services and subjected to changing hydrological risks. We present an exhaustive review of the literature on historical and projected future changes on cold region hydrology in response to climate change. Changes in snow, soil, and streamflow key metrics were investigated and summarized at the hemispheric scale, down to the basin scale. We found substantial evidence of both historical and projected changes in the reviewed hydrological metrics. These metrics were shown to display different sensitivities to climate change, depending on the cold season temperature regime of a given region. Given the historical and projected future warming during the 21st century, the most drastic changes were found to be occurring over regions with near-freezing air
temperatures. Colder regions, on the other hand, were found to be comparatively less sensitive to climate change. The complex interactions between the snow and soil metrics resulted in either colder or warmer soils, which led to increasing or decreasing frost depths, influencing the partitioning rates between the surface and subsurface flows. The most consistent and salient hydrological responses to both historical and projected climate change were an earlier occurrence of snowmelt floods, an overall increase in water availability and streamflow during winter, and a decrease in water availability and streamflow during the warm season, which calls for renewed assessments of existing water supply and flood risk management strategies.
Cold region hydrology is conditioned by distinct cryospheric and hydrological processes. While snowmelt is the main contributor to both surface and subsurface flows, seasonally frozen soil also influences the partition of meltwater and rain between these flows. Cold regions of the Northern Hemisphere midlatitudes have been shown to be sensitive to climate change. Assessing the impacts of climate change on the hydrology of this region is therefore crucial, as it supports a significant amount of population relying on hydrological services and subjected to changing hydrological risks. We present an exhaustive review of the literature on historical and projected future changes on cold region hydrology in response to climate change. Changes in snow, soil, and streamflow key metrics were investigated and summarized at the hemispheric scale, down to the basin scale. We found substantial evidence of both historical and projected changes in the reviewed hydrological metrics. These metrics were shown to display different sensitivities to climate change, depending on the cold season temperature regime of a given region. Given the historical and projected future warming during the 21st century, the most drastic changes were found to be occurring over regions with near-freezing air temperatures. Colder regions, on the other hand, were found to be comparatively less sensitive to climate change. The complex interactions between the snow and soil metrics resulted in either colder or warmer soils, which led to increasing or decreasing frost depths, influencing the partitioning rates between the surface and subsurface flows. The most consistent and salient hydrological responses to both historical and projected climate change were an earlier occurrence of snowmelt floods, an overall increase in water availability and streamflow during winter, and a decrease in water availability and streamflow during the warm season, which calls for renewed assessments of existing water supply and flood risk management strategies.
To evaluate the accuracy and applicability of the TMPA 3B42-V7 precipitation product for the Lancang River basin, we used different statistical indices to explore the performance of the product in comparison to gauge data. Then, we performed a hydrologic simulation using the Variable Infiltration Capacity (VIC) hydrological model with two scenarios (Scenario I: streamflow simulation using gauge-calibrated parameters; Scenario II: streamflow simulation using 3B42-V7-recalibrated parameters) to verify the applicability of the product. The results of the precipitation analysis show good accuracy of the V7 precipitation data. The accuracy increases with the increase of both space and time scales, while time scale increases cause a stronger effect. The satellite can accurately measure most of the precipitation but tends to misidentify non-precipitation events as light precipitation events (<1 mm/day). The results of the hydrologic simulation show that the VIC hydrological model has good applicability for the Lancang River basin. However, 3B42-V7 data did not perform as well under Scenario I with the lowest Nash–Sutcliffe coefficient of efficiency (NSCE) of 0.42; Scenario II suggests that the error drops significantly and the NSCE increases to 0.70 or beyond. In addition, the simulation accuracy increases with increased temporal scale.
The Fraser River Basin (FRB) of British Columbia is one of the largest and most important watersheds in western North America, and home to a rich diversity of biological species and economic assets that depend implicitly upon its extensive riverine habitats. The hydrology of the FRB is dominated by snow accumulation and melt processes, leading to a prominent annual peak streamflow invariably occurring in May–July. Nevertheless, while annual peak daily streamflow (APF) during the spring freshet in the FRB is historically well correlated with basin-averaged, 1 April snow water equivalent (SWE), there are numerous occurrences of anomalously large APF in below- or near-normal SWE years, some of which have resulted in damaging floods in the region. An imperfect understanding of which other climatic factors contribute to these anomalously large APFs hinders robust projections of their magnitude and frequency.
We employ the Variable Infiltration Capacity (VIC) process-based hydrological model driven by gridded observations to investigate the key controlling factors of anomalous APF events in the FRB and four of its subbasins that contribute nearly 70 % of the annual flow at Fraser-Hope. The relative influence of a set of predictors characterizing the interannual variability of rainfall, snowfall, snowpack (characterized by the annual maximum value, SWEmax), soil moisture and temperature on simulated APF at Hope (the main outlet of the FRB) and at the subbasin outlets is examined within a regression framework. The influence of large-scale climate modes of variability (the Pacific Decadal Oscillation (PDO) and the El Niño–Southern Oscillation – ENSO) on APF magnitude is also assessed, and placed in context with these more localized controls. The results indicate that next to SWEmax (univariate Spearman correlation with APF of ρ ^ = 0.64; 0.70 (observations; VIC simulation)), the snowmelt rate (ρ ^ = 0.43 in VIC), the ENSO and PDO indices (ρ ^ = −0.40; −0.41) and (ρ ^ = −0.35; −0.38), respectively, and rate of warming subsequent to the date of SWEmax (ρ ^ = 0.26; 0.38), are the most influential predictors of APF magnitude in the FRB and its subbasins. The identification of these controls on annual peak flows in the region may be of use in understanding seasonal predictions or future projected streamflow changes.
Because hydrological models are so important for addressing environmental problems, parameter calibration is a fundamental task for applying them. A broadly used method for obtaining model parameters for the past 20 years is the evolutionary algorithm. This approach can estimate a set of unknown model parameters by simulating the evolution process. The ant colony optimization (ACO) algorithm is a type of evolutionary algorithm that has shown a strong ability in tackling combinatorial problems and is suitable for hydrological model calibration. In this study, an ACO based on the grid partitioning strategy was applied to the parameter calibration of the variable infiltration capacity (VIC) model for the Upper Heihe River basin and Xitiaoxi River basin, China. The shuffled complex evolution (SCE-UA) algorithm was used to test the applicability of the ACO. The results show that ACO is capable of model calibration of the VIC model; the Nash-Sutcliffe coefficient of efficiency is 0.62 and 0.81 in calibration and 0.65 and 0.86 in validation for the Upper Heihe River basin and Xitiaoxi River basin respectively, which are similar to the SCE-UA results. Despite the encouraging results obtained thus far, further studies could still be performed on the parameter optimization of an ACO to enlarge its applicability to more distributed hydrological models.
Variable Infiltration Capacity hydrology model is a physically based, Semi-distributed macroscale hydrological model that represents surface and subsurface hydrologic processes on spatially distributed grid cell. In mountainous watersheds Snow melt can have a significant impact on the water balance and at certain times of the year it could be the most important contribution to runoff. In this study the Variable Infiltration Capacity Hydrology model has been successfully applied for Alaknanda River Basin. As input to the model long-term(1999-2008) daily meteorological dataset such as temperature, precipitation, wind speed and geospatial dataset such as land cover data, Elevation data , soil data were provided from multiple sources (NRSC,NBSS&LUP,NOAA and IMD). In addition, the spatial distribution of runoff, snow cover and snow depth were analyzed and compared with the monthly stream flow data obtained from rudraprayag (lat-30.285, lon-78.98), MODIS 8 day snow cover product (MOD10A2) and AMSRE snow depth product. The model runs resulted in an increase in Snowmelt Runoff for the period of record (2001–2006), as a result of decrease in Snow Cover and Snow Depth for the monsoon period. In this study Nash–Sutcliffe efficiency is 0.92 which indicate a good fit between observed and simulated runoff. 1. Introduction In snow covered area, snow melt runoff is predominant during summer, which when failed to be managed properly leads to inadequate fresh water supply in mountainous region, downstream flooding and consequent rise in the sea level. Uttarkhand state receives considerable amount of rainfall & snowfall. It also serves as origin for major rivers like Yamuna, Alaknanda & Bhagirathi. Still the state is facing severe water scarcity due to improper management of water. It also faces disastrous events owing to its topography. In order to overcome these problems, proper management practices have to be implemented, for which an accurate estimate of total runoff from the basin is to be estimated, which can be achieved through hydrological modeling. In India, the perennial Himalayan rivers are fed by snowmelt and glacier melt runoff. The regular mapping and monitoring of snow cover and glaciers remain a challenge in these hilly areas due to inaccessibility and few ground observation sites. Therefore the importance of seasonal snow cover, glaciers and their associated melt runoff of this region is to be considered. The objective of this study is to carry out macro scale hydrological modeling for snow clad basin to estimate the runoff generated from the snow covered area using VIC model. Hydrological modeling is one efficient way for consistent long term behavioural studies. Hydrological modeling is a mathematical representation of natural processes that influence primarily the energy and water balances of a watershed. The fundamental objective of hydrological modeling is to gain an understanding of the hydrological system in order to provide reliable information for managing water resources in a sustained manner. Powerful spatially-distributed models are based on physical principles governing the movement of water within a catchment area, but they need detailed high-quality data to be used effectively. Some of the basic data requirements of hydrological modeling are: i) Meteorological data (precipitation, temperature, wind speed, relative humidity, atmospheric pressure, albedo, longwave radiation, shortwave radiation, atmospheric density, cloud cover) ii) Terrain data (elevation, slope, flow direction, flow accumulation) iii) Land use / land cover data (land use classes & their area, vegetation classes & its properties like root depth, root distribution, height, leaf area index, roughness, displacement, canopy resistance) iv) Soil data (layer-wise physical, hydraulic & textural properties like soil size, thickness of each layer, soil temperature, particle density, bulk density, bubbling pressure, texture) The conversion of snow and ice into water is called snowmelt, which needs input of energy (heat). The physics of melting snow and transformation of melt water into runoff are very important aspect of snow hydrology. Snowmelt is the overall result of different heat transfer processes to the snow pack. The sun is the ultimate source of energy responsible for the melting of snow pack. There is a complex interaction between the incoming solar radiation, earth's atmosphere and terrain surface. Hence a number of intermediate steps in the process of energy transfer to the snow surface have to be considered to understand the process of snowmelt and also to make quantitative estimations of the melt.
This paper describes an energy balance snowmelt model developed for the prediction of rapid snowmelt rates responsible for soil erosion and water input to a distributed water balance model. The model uses a lumped representation of the snowpack with two primary state variables, namely, water equivalence and energy content relative to a reference state of water in the ice phase at 0°C. This energy content is used to determine snowpack average temperature or liquid fraction. This representation of the snowpack is used in a distributed version of the model with each of these state variables modeled at each point on a rectangular grid corresponding to a digital elevation model. Inputs are air temperature, precipitation, wind speed, humidity, and radiation at hourly time steps. The model uses physically-based calculations of radiative, sensible, latent, and advective heat exchanges. An equilibrium parameterization of snow surface temperature accounts for differences between snow surface temperature and average snowpack temperature without having to introduce additional state variables. Melt outflow is a function of the liquid fraction, using Darcy's law. This allows the model to account for continued outflow even when the energy balance is negative. A detailed description of the model is given together with results of tests against data collected at the Central Sierra Snow Laboratory, California; Reynolds Creek Experimental Watershed, Boise Idaho; and at the Utah State University drainage and evapotranspiration research farm, Logan, Utah. The testing includes comparisons against melt outflow collected in melt lysimeters, surface snow temperatures collected using infrared temperature sensors and depth and water equivalence measured using snow core samplers.
Multi-Resolution Land Characterization 2001 (MRLC 2001) is a second-generation Federal consortium designed to create an updated pool of nation-wide Landsat 5 and 7 imagery and derive a second-generation National Land Cover Database (NLCD 2001). The objectives of this multi-layer, multi-source database are two fold: first, to provide consistent land cover for all 50 States, and second, to provide a data framework which allows flexibility in developing and applying each independent data component to a wide variety of other applications. Components in the database include the following: (1) normalized imagery for three time periods per path/row, (2) ancillary data, including a 30 m Digital Elevation Model (DEM) derived into slope, aspect and slope position, (3) perpixel estimates of percent imperviousness and percent tree canopy, (4) 29 classes of land cover data derived from the imagery, ancillary data, and derivatives, (5) classification rules, confidence estimates, and metadata from the land cover classification. This database is now being developed using a Mapping Zone approach, with 66 Zones in the continental United States and 23 Zones in Alaska. Results from three initial mapping Zones show single-pixel land cover accuracies ranging from 73 to 77 percent, imperviousness accuracies ranging from 83 to 91 percent, tree canopy accuracies ranging from 78 to 93 percent, and an estimated 50 percent increase in mapping efficiency over previous methods. The database has now entered the production phase and is being created using extensive partnering in the Federal government with planned completion by 2006.
Multi-Resolution Land Characterization 2001 (MRLC 2001) is a second-generation Federal consortium designed to create an updated pool of nation-wide Landsat 5 and 7 imagery and derive a second-generation National Land Cover Database(NLCD 2001). The objectives of this multi-layer, multi-source database are two fold: first, to provide consistent land cover for all 50 States, and second, to provide a data framework which allows flexibility in developing and applying each independent data component to a wide variety of other applications. Components in the database include the following: (1) normalized imagery for three time periods per path/row, (2) ancillary data, including a 30 m Digital Elevation Model(DEM) derived into slope, aspect and slope position, (3) per-pixel estimates of percent imperviousness and percent tree canopy, (4) 29 classes of land cover data derived from the imagery, ancillary data, and derivatives, (5) classification rules,
confidence estimates, and metadata from the land cover classification. This database is now being developed using a Mapping Zone approach, with 66 Zones in the continental United States and 23 Zones in Alaska. Results from three initial mapping Zones show single-pixel land cover accuracies ranging from 73 to 77 percent, imperviousness accuracies ranging from 83 to 91 percent, tree canopy accuracies ranging from 78 to 93 percent, and an estimated 50 percent increase in mapping efficiency over previous methods. The database has now entered the production phase and is being created using extensive partnering in the Federal government with planned completion by 2006.
Recent studies have shown substantial declines in snow water equivalent (SWE) over much of the western United States in the last half century, as well as trends toward earlier spring snowmelt and peak spring streamflows. These trends are influenced both by interannual and decadal-scale climate variability, and also by temperature trends at longer time scales that are generally consistent with observations of global warming over the twentieth century. In this study, the linear trends in 1 April SWE over the western United States are examined, as simulated by the Variable Infiltration Capacity hydrologic model implemented at 1/8° latitude longitude spatial resolution, and driven by a carefully quality controlled gridded daily precipitation and temperature dataset for the period 1915 2003. The long simulations of snowpack are used as surrogates for observations and are the basis for an analysis of regional trends in snowpack over the western United States and southern British Columbia, Canada. By isolating the trends due to temperature and precipitation in separate simulations, the influence of temperature and precipitation variability on the overall trends in SWE is evaluated. Downward trends in 1 April SWE over the western United States from 1916 to 2003 and 1947 to 2003, and for a time series constructed using two warm Pacific decadal oscillation (PDO) epochs concatenated together, are shown to be primarily due to widespread warming. These temperature-related trends are not well explained by decadal climate variability associated with the PDO. Trends in SWE associated with precipitation trends, however, are very different in different time periods and are apparently largely controlled by decadal variability rather than longer-term trends in climate.
There is an increasing interest in examining long-term trends in measures of snow climatology. An examination of the U.S. daily snowfall records for 1900–2004 revealed numerous apparent inconsistencies. For example, long-term snowfall trends among neighboring lake-effect stations differ greatly from insignificant to +100% century−1. Internal inconsistencies in the snow records, such as a lack of upward trends in maximum seasonal snow depth at stations with large upward trends in snowfall, point to inhomogeneities. Nationwide, the frequency of daily observations with a 10:1 snowfall-to-liquid-equivalent ratio declined from 30% in the 1930s to a current value of around 10%, a change that is clearly due to observational practice. There then must be biases in cold-season liquid-equivalent precipitation, or snowfall, or both. An empirical adjustment of snow-event, liquid-equivalent precipitation indicates that the potential biases can be statistically significant.
Examples from this study show that there are nonclimatic issues that complicate the identification of and significantly change the trends in snow variables. Thus, great care should be taken in interpretation of time series of snow-related variables from the Cooperative Observer Program (COOP) network. Furthermore, full documentation of optional practices should be required of network observers so that future users of these data can properly account for such practices.
Traditional approaches to the direct estimation of snow properties from passive microwave remote sensing have been plagued by limitations such as the tendency of estimates to saturate for moderately deep snowpacks and the effects of mixed land cover within remotely sensed pixels. An alternative approach is to assimilate satellite microwave emission observations directly, which requires embedding an accurate microwave emissions model into a hydrologic prediction scheme, as well as quantitative information of model and observation errors. In this study a coupled snow hydrology (Variable Infiltration Capacity (VIC)) and microwave emission (Dense Media Radiative Transfer (DMRT)) model are evaluated using multiscale brightness temperature (TB) measurements from the Cold Land Processes Experiment (CLPX). The ability of VIC to reproduce snowpack properties is shown with the use of snow pit measurements, while TB model predictions are evaluated through comparison with Ground-Based Microwave Radiometer (GBMR), air- craft (Polarimetric Scanning Radiometer (PSR)), and satellite (Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E)) TB measurements. Limitations of the model at the point scale were not as evident when comparing areal estimates. The coupled model was able to reproduce the TB spatial patterns observed by PSR in two of three sites. However, this was mostly due to the presence of relatively dense forest cover. An interesting result occurs when examining the spatial scaling behavior of the higher-resolution errors; the satellite-scale error is well approximated by the mode of the (spatial) histogram of errors at the smaller scale. In addition, TB prediction errors were almost invariant when aggregated to the satellite scale, while forest-cover fractions greater than 30% had a significant effect on TB predictions.
Monitoring continuous changes in snowpack dynamics and its meteorological drivers is critical for understanding key aspects of water resources, climate variability, and ecology. While manual snow surveys have traditionally been used to evaluate snow processes, their high costs and discrete measurements can lead to biased estimations of accumulation and ablation rates. Ultrasonic range sensors offer an alternative to continuously monitor snow depth but their widespread employment has been limited because of high prices. This paper describes the development of an inexpensive prototype ultrasonic sensor suite characterized by a ready-to-use stand-alone design and flexibility to incorporate additional meteorological instruments. The performance of 48 units was tested during a winter season in central British Columbia, recording snow depth and air temperature data consistent with those from nearby weather stations and manual measurements. Despite a relatively small underestimation of snow depth due to known, repairable reasons, the sensor system demonstrated reliability for research and operations.
The Northern Hemisphere has large areas that are forested and seasonally snow covered. Compared with open areas, forest canopies strongly influence interactions between the atmosphere and snow on the ground by sheltering the snow from wind and solar radiation and by intercepting falling snow; these influences have important consequences for the meteorology, hydrology, and ecology of forests. Many of the land surface models used in meteorological and hydrological forecasting now include representations of canopy snow processes, but these have not been widely tested in comparison with observations. Phase 2 of the Snow Model Intercomparison Project (SnowMIP2) was therefore designed as an intercomparison of surface mass and energy balance simulations for snow in forested areas. Model forcing and calibration data for sites with paired forested and open plots were supplied to modeling groups. Participants in 11 countries contributed output from 33 models, and the results are published here for sites in Canada, the United States, and Switzerland. On average, the models perform fairly well in simulating snow accumulation and ablation, although there is a wide intermodal spread and a tendency to underestimate differences in snow mass between open and forested areas. Most models capture the large differences in surface albedos and temperatures between forest canopies and open snow well. There is, however, a strong tendency for models to underestimate soil temperature under snow, particularly for forest sites, and this would have large consequences for simulations of runoff and biological processes in the soil.
Not Available Bibtex entry for this abstract Preferred format for this abstract (see Preferences) Find Similar Abstracts: Use: Authors Title Return: Query Results Return items starting with number Query Form Database: Astronomy Physics arXiv e-prints
Since the mid-1930s a variety of soil conservation practices have been
applied to agricultural lands throughout the United States. While
intended to reduce soil erosion, if effective, these practices should
alter the hydrology of streams which drain the treated lands. This
hypothesis was explored for the East Branch of the Pecatonica River, a
gaged 221 square mile agricultural catchment in southwestern Wisconsin.
On the basis of the analysis of peak and daily flow data there has been
a decrease in flood peaks and in winter/spring flood volumes and an
increase in hydrologic rise times and in the contribution of
winter/spring snowmelt events to base flow. These changes do not appear
to be due to climatic variations, reservoir construction, or major land
use changes. Instead, they appear to have resulted from the adoption of
various soil conservation practices, particularly those involving the
treatment of gullies and the adoption of conservation tillage.
Recent research points to changing frequency and intensity of heavy rainfall events and consequently, flood flows in Northeastern Illinois. Flood frequency modeling is a key component of managing floodwaters in this highly populated metropolitan area, which includes Chicago and surrounding urban areas, and has enormous economic and environmental significance. In this research, 100-year 24-h precipitation totals in Northeastern Illinois were quantified using the L-moments method with 1948 2004 hourly precipitation data at six stations in the region for comparison with published results from the US Weather Bureau Technical Paper No. 40 (TP-40), Illinois State Water Survey Bulletin 70 (Bulletin 70) and National Oceanic and Atmospheric Administration Atlas-14 (NOAA-14). Sensitivity analyses were conducted to examine the effects of various factors on 100-year, 24-h precipitation at Aurora College station, in particular the effects of selecting different periods of the precipitation record, different regions, and different underlying distributions. The sensitivity analyses used 1900 2004 daily precipitation data at 12 stations in the region. Finally, the HEC-HMS rainfall-runoff model was used to illustrate relative impacts of changing estimates of design precipitation on flood peaks at 12 small watersheds in the region. It was demonstrated that the oldest source, TP-40, produced significantly smaller 100-year, 24-h rainfall totals, than Bulletin 70, NOAA-14, and the current study. It was also shown that the variability in design rainfall calculated based on different 50-year records (nearly 200%) was much larger than those based on the choice of underlying statistical distribution (50%), or the selection of region (25%). The average relative increase in hydrologic peaks exceeded that of rainfall peaks. This could partly be explained by the non-linear nature of the hydrologic system.
The variable infiltration capacity (VIC) macroscale hydrologic model was
modified to improve its performance in cold regions by adding frozen
soil and energy balance snow accumulation and ablation algorithms.
Frozen soil penetration was determined by solving thermal fluxes through
the soil column. Infiltration and runoff response parameterizations were
modified to reflect the simulated ice content of the soil. The revised
model was tested using point data from the University of Minnesota
Rosemount Agricultural Experiment Station and subsequently applied to
two subcatchments in the upper Mississippi River basin. The point tests
showed that the model was able to reproduce observations of the
snowpack, soil liquid water content, and freezing and thawing front
depths. Comparisons of simulated discharge from the two subbasins showed
that the frozen soil algorithm reduced infiltration during winter and
spring thaws and increased rapid runoff response. However, the magnitude
of the increased runoff response is relatively modest, at the scale of
the two upper Mississippi subbasins tested.
The role of cold season climate variability on lakes and wetlands in the Great Lakes region of the United States was examined over a period of 91 years (1917-2007) using the variable infiltration capacity (VIC) land surface hydrologic model. Statistically significant trends in observed cold season precipitation and air temperature indicated that both have significantly increased during the last 91 years. Results also showed that despite the significant increase in the cold season (December-May) precipitation, snowfall is significantly decreased during the period 1917-2007, suggesting the change in the distribution of the cold season precipitation. Both total runoff and evapotranspiration during the spring (March-May) season are increased; however, the trend associated with evapotranspiration was significant. These changes in cold season precipitation and temperature resulted in an increasing trend in domain-averaged fractional inundation extent during the spring season. The inundation extent of lakes and wetlands during the spring season showed sensitivity to periods of extreme climate. Driest years and those with the lowest snowfall resulted in the lowest inundation extents, while years with highest snowfalls resulted in the greatest inundation extents. Five year composites of extreme dry; wet; cold; warm; low snow; high snow; low snow, high temperature; and high snow, low temperature showed the mean domain average fractional inundation extent in spring to be 0.17, 0.22, 0.23, 0.20, 0.17, 0.24, 0.21, and 0.22, respectively. The fractional inundation extent in spring was significantly correlated with snowfall, the amount of snowmelt in the cold season, and the total runoff (surface runoff + base flow) in spring. Spring inundation extent was negatively correlated with the cold season air temperature, suggesting that higher air temperature could lead to lower inundation.
Abstract Analyses of streamflow, snowfall temperature, and precipitation in snow - melt dominated,river basins in the western US indicates an advance in the timing of peak spring flows over the past fifty years. Warm temperature spells in spring have occurred much earlier in recent years, which partly explains the trend in the timing of the spring peak flow. In addition, a decrease in snow water equivalent and a general increase in winter precipitation is evident for many,weather stations in the western U.S. It appears that in recent decades more of the precipitation is coming as rain rather than snow. The trends are strongest at lower elevations and in the Pacific Northwest region, where winter temperatures are closer to the freezing-point; it appears that in this region in particular, modest,shifts in temperature are capable of forcing large shifts in basin hydrologic response. We speculate that these trends could be potentially a manifestation of the general global warming,trend in recent decades and also due to enhanced ENSO activity. The observed,trends in hydroclimatol ogy,over the western,US can have significant
Annual flood peaks on 12 small urbanizing watersheds in northeastern Illinois increased over the past several decades. This is explained by intensive urbanization and increasing trends of heavy rainfall in the region. Average urbanization of those watersheds increased from 10.6% in 1954 to 61.8% in 1996. In addition to land-cover changes, numerous studies reported increasing frequency and intensity of heavy precipitation in the region. As a consequence, older studies produced lower design rainfall estimates than more recent sources. In this research, 100-year, 24-hour precipitation totals in northeastern Illinois were quantified using the L-moments method with 1948-2004 hourly precipitation data at six stations in the region for comparison with published results from the U.S. Weather Bureau Technical Paper No. 40 (TP-40), Illinois State Water Survey Bulletin 70 (Bulletin 70) and National Oceanic and Atmospheric Administration Atlas-14 (NOAA-14). Sensitivity analyses were conducted to examine effects of various factors on 100-year, 24-hour precipitation at the Aurora College station, particularly effects of selecting different periods of precipitation record, different regions, and different underlying distributions. It was demonstrated that the oldest source, TP-40, published in 1961,
Recent trends in precipitation and streamflow in the United States have become a particular focus of hydroclimatic research. The U.S. Hydro-Climatic Data Network (HCDN) has proven to be especially useful for the analysis of long-term streamflow trends. The U.S. Geological Survey (USGS) scientists selected sites for inclusion in the HCDN from the USGS streamgauge network on the basis of streamflows being relatively free of nonclimatic anthropogenic influences. Consequently, most previous analyses of flow trends at those sites have either implicitly or explicitly attributed the trends to climate change and variability. In this paper, trends in seasonal and annual precipitation, and annual 7-day low, mean, and peak flows are examined for 48 medium-sized HCDN streams in the upper Mississippi (UM) water-resource region over 1939-2008. Using the concept of precipitation elasticity of flow, it is shown that the observed magnitudes of statistically significant increases in mean and low flows were up to a factor of 3 greater than expected from observed precipitation increases alone. Peak flows increased less than expected, and in the case of the Driftless Area at the center of the UM basin, decreased despite increased precipitation. It is proposed that the differences between expected and observed changes in streamflow can be explained by rural land-use changes in this principally agricultural region.
Evidence gleaned from the instrumental record of climate data identifies a robust, recurring pattern of ocean–atmosphere climate variability centered over the midlatitude North Pacific basin. Over the past century, the amplitude of this climate pattern has varied irregularly at interannual-to-interdecadal timescales. There is evidence of reversals in the prevailing polarity of the oscillation occurring around 1925, 1947, and 1977; the last two reversals correspond to dramatic shifts in salmon production regimes in the North Pacific Ocean. This climate pattern also affects coastal sea and continental surface air temperatures, as well as streamflow in major west coast river systems, from Alaska to California.
The present study examines the effects of historic climate variability on cold-season processes, including soil temperature, frost depth, and the number of frost days and freeze–thaw cycles. Considering the impor-tance of spatial and temporal variability in cold-season processes, the study was conducted in the midwestern United States using both observations and model simulations. Model simulations used the Variable In-filtration Capacity (VIC) land surface model (LSM) to reconstruct and to analyze changes in the long-term (i.e., 1917–2006) means of soil frost variables. The VIC model was calibrated using observed streamflow records and near-surface soil temperatures and then evaluated for streamflow, soil temperature, frost depth, and soil moisture before its application at the regional scale. Soil frost indicators—such as the number of frost days and freeze–thaw cycles—were determined from observed records and were tested for the presence of significant trends. Overall trends in extreme and mean seasonal soil temperature from 1967 onward indicated a warming of soil temperatures at a depth of 10 cm—specifically in northwest Indiana, north-central Illinois, and southeast Minnesota—leading to a reduction in the number of soil frost days. Model simulations in-dicated that by the late-century period (1977–2006), soil frost duration decreased by as much as 36 days compared to the midcentury period (1947–76). Spatial averages for the study area in warm years indicated shallower frost penetration by 15 cm and greater soil temperatures by about 38C at 10-cm soil depth than in the cold years.
1] Historical observations indicate a shift toward shorter winters and increased average air temperatures in the midwestern United States. Furthermore, a rise in soil temperatures is likely to be enhanced under projections of increased air temperature; however, reduced snow cover during winter may lead to colder soil temperatures in the future. Cumulatively, these changes will affect cold season processes in the region. Therefore the impact of such changes on cold season processes were analyzed under two climate models (Geophysical Fluid Dynamics Laboratory version CM2.1.1 (GFDL) and UK Met Office Hadley Center Climate Model, version 3.1 (HadCM3)) and three scenarios (B1, A1B, and A2) by implementing the variable infiltration capacity land surface model from 1977 to 2099. Ensemble averages of the two models for the three scenarios indicated that both air temperature and precipitation would increase in the cold season (December–May), with the greatest increases projected under the A2 scenario by late in the 21st century (2070–2099). Also during this period, the median number of days when air temperature was below 0°C reduced in comparison to the base period (1977–2006) by 25, 35, and 38 days for the B1, A1B, and A2 scenarios, respectively. The number of freeze‐thaw cycles increased in the south‐central Wisconsin and the northern regions of Michigan by up to 3 cycles, while the duration of soil frost decreased by between 2 weeks and nearly 2 months during 2070–2099 with respect to the base period. Citation: Sinha, T., and K. A. Cherkauer (2010), Impacts of future climate change on soil frost in the midwestern United States, J. Geophys. Res., 115, D08105, doi:10.1029/2009JD012188.
Statistical relationships between annual floods at 200 long-term (85–127 years of record) streamgauges in the coterminous United States and the global mean carbon dioxide concentration (GMCO2) record are explored. The streamgauge locations are limited to those with little or no regulation or urban development. The coterminous US is divided into four large regions and stationary bootstrapping is used to evaluate if the patterns of these statistical associations are significantly different from what would be expected under the null hypothesis that flood magnitudes are independent of GMCO2. In none of the four regions defined in this study is there strong statistical evidence for flood magnitudes increasing with increasing GMCO2. One region, the southwest, showed a statistically significant negative relationship between GMCO2 and flood magnitudes. The statistical methods applied compensate both for the inter-site correlation of flood magnitudes and the shorter-term (up to a few decades) serial correlation of floods.Citation Hirsch, R.M. and Ryberg, K.R., 2012. Has the magnitude of floods across the USA changed with global CO2 levels? Hydrolological Sciences Journal, doi: 10.1080/02626667.2011.621895.
Many studies have developed snow process understanding by exploring the impact of snow model complexity on simulation performance. This paper revisits this topic using several recently developed land surface models, including the Simplified Simple Biosphere Model (SSiB); Noah; Variable Infiltration Capacity (VIC); Community Land Model, version 3 (CLM3); Snow Thermal Model (SNTHERM); and new field measurements from the Cold Land Processes Field Experiment (CLPX). Offline snow cover simulations using these five snow models with different physical complexity are performed for the Rabbit Ears Buffalo Pass (RB), Fraser Experimental Forest headquarters (FHQ), and Fraser Alpine (FA) sites between 20 September 2002 and 1 October 2003. These models simulate the snow accumulation and snowpack ablation with varying skill when forced with the same meteorological observations, initial conditions, and similar soil and vegetation parameters.
All five models capture the basic features of snow cover dynamics but show remarkable discrepancy in depicting snow accumulation and ablation, which could result from uncertain model physics and/or biased forcing. The simulated snow depth in SSiB during the snow accumulation period is consistent with the more complicated CLM3 and SNTHERM; however, early runoff is noted, owing to neglected water retention within the snowpack. Noah is consistent with SSiB in simulating snow accumulation and ablation at RB and FA, but at FHQ, Noah underestimates snow depth and snow water equivalent (SWE) as a result of a higher net shortwave radiation at the surface, resulting from the use of a small predefined maximum snow albedo. VIC and SNTHERM are in good agreement with each other, and they realistically reproduce snow density and net radiation. CLM3 is consistent with VIC and SNTHERM during snow accumulation, but it shows early snow disappearance at FHQ and FA. It is also noted that VIC, CLM3, and SNTHERM are unable to capture the observed runoff timing, even though the water storage and refreezing effects are included in their physics.
A set of sensitivity experiments suggest that Noah’s snow simulation is improved with a higher maximum albedo and that VIC exhibits little improvement with a larger fresh snow albedo. There are remarkable differences in the vegetation impact on snow simulation for each snow model. In the presence of forest cover, SSiB shows a substantial increase in snow depth and SWE, Noah and VIC show a slight change though VIC experiences a later onset of snowmelt, and CLM3 has a reduction in its snow depth. Finally, we observe that a refined precipitation dataset significantly improves snow simulation, emphasizing the importance of accurate meteorological forcing for land surface modeling.
1] The effects of forest canopies on snow accumulation and ablation processes can be very important for the hydrology of midlatitude and high-latitude areas. A mass and energy balance model for snow accumulation and ablation processes in forested environments was developed utilizing extensive measurements of snow interception and release in a maritime mountainous site in Oregon. The model was evaluated using 2 years of weighing lysimeter data and was able to reproduce the snow water equivalent (SWE) evolution throughout winters both beneath the canopy and in the nearby clearing, with correlations to observations ranging from 0.81 to 0.99. Additionally, the model was evaluated using measurements from a Boreal Ecosystem-Atmosphere Study (BOREAS) field site in Canada to test the robustness of the canopy snow interception algorithm in a much different climate. Simulated SWE was relatively close to the observations for the forested sites, with discrepancies evident in some cases. Although the model formulation appeared robust for both types of climates, sensitivity to parameters such as snow roughness length and maximum interception capacity suggested the magnitude of improvements of SWE simulations that might be achieved by calibration.
Past heavy precipitation events in the Chicago metropolitan area have caused significant flood-related economic and environmental
damages. A key component in flood management policies and actions is determining flood magnitudes for specified return periods.
This is a particularly difficult task in areas with a complex and changing climate and land-use, such as the Chicago metropolitan
area. The standard design storm methodology based on the NOAA Atlas 14 and ISWS Bulletin 70 has been used in the past to estimate
flood hydrographs with variable return periods in this region. In a changing climate, however, these publications may not
be accurate. This study presents and illustrates a methodology for diagnostic analysis of future climate scenarios in the
framework of urban flooding, and assesses the corresponding uncertainties. First, the design storms are calculated using data
downscaled by a regional climate model (RCM) at 30-km spacing for the present and 2050s under the IPCC A1Fi (high) and B1
(low) emissions scenarios. Next, the corresponding flood discharges at six watersheds in suburban Chicago are estimated using
a hydrologic event model. The resulting scenarios in flood frequency were first assessed through a set of diagnostic tests
for precipitation timing and frequency. The study did not reveal any significant changes in the 2050s in the average timing
of heavy storms, but their regularity decreased. The average timing did not exhibit any significant spatial variability throughout
the region. The precipitation frequency analysis revealed distinct differences between the northern and southeastern subregions
of the Chicago metropolitan area. The quantiles in the northern subregion averaged for 2-year, 5-year, and 10-year return
periods exhibited a 20% and 16% increase in daily precipitation for scenarios B1 and A1Fi, respectively. The southeastern
subregion, however, exhibited a decrease of 12% for scenario B1 and a minor increase of 3% for scenario A1Fi. The hydrologic
effects of changing precipitation on the flood quantiles were illustrated using six small watersheds in the region. The relative
increases or decreases in precipitation translated into even larger relative increases or decreases in flood peaks, due to
the nonlinear nature of the rainfall-runoff process. Simulations using multiple climate models, for longer periods, finer
spatio-temporal resolution, and larger areal coverage could be used to more accurately account for numerous uncertainties
in the precipitation and flood projections.
Climatic changes at the Earth's surface propagate slowly downward into the ground and modify the ambient ground thermal regime. However, causes of soil temperature changes in the upper few meters are not well documented. One major obstacle to understanding the linkage between the soil thermal regime and climatic change is the lack of long-term observations of soil temperatures and related climatic variables. Such measurements were made throughout the former Soviet Union with some records beginning at the end of the 19th century. In this paper, we use records from Irkutsk, Russia, to demonstrate how the soil temperature responded to climatic changes over the last century. Both air temperature and precipitation at Irkutsk increased from the late 1890s to the 1990s. Changes in air temperature mainly occurred in winter, while changes in precipitation happened mainly during summer. There was an anti-correlation between mean annual air temperature and annual total precipitation, i.e., more(less) precipitation during cold (warm) years. There were no significant trends of changes in the first day of snow on the ground in autumn, but snow steadily disappeared earlier in spring, resulting in a reduction of the snow cover duration. A grass-covered soil experiences seasonal freezing for more than nine months each year and the long-term average maximum depth of seasonally frozen soils was about 177 cm with a range from 91 cm to 260 cm.The relatively lower soil temperature at shallow depths appears to represent the so-called `thermal offset' in seasonally frozen soils. Changes in mean annual air temperature and soil temperature at 40 cm depth were about the same magnitude (2.0 C to 2.5 C) over the common period of record, but the patterns of change were substantially different. Mean annual air temperature increased slightly until the 1960s, while mean annual soil temperature increased steadily throughout the entire period. This leads to the conclusion that changes in air temperature alone cannot explain the changes in soil temperatures at this station. Soil temperature actually decreased during summer months by up to 4 C, while air temperature increased slightly.This cooling in the soil may be explained by changes in rainfall and hence soil moisture during summer due to the effect of a soil moisture feedback mechanism. While air temperature increased about 4 C to 6 C during winter, soil temperature increased by up to 9 C. An increase in snowfall during early winter (October and November) and early snowmelt in spring may play a major role in the increase of soil temperatures through the effects of insulation and albedo changes. Due to its relatively higher thermal conductivity compared to unfrozen soils, seasonally frozen ground may enhance the soil cooling, especially in autumn and winter when thermal gradient is negative.
The main focus of this paper is the time series analysis of the precipitation-runoff process with transfer functions. Starting from there, a horizontal routing model is constructed to be coupled to the existing land surface parametrization (LSP) schemes which provide the lower boundary conditions in numerical weather prediction and atmospheric general circulation models. As these models currently have a resolution of 10 km−300 km (what we some kind of arbitrary define as the “large scale”), it will be assumed that the horizontal routing process can be lumped as a linear time invariant system. While the main physical properties of the soil (temperature, moisture) and all physical processes (partition of the energy and water fluxes) have to be represented by an LSP scheme, the coupling with a simple routing scheme allows the direct comparison of predicted and measured streamflow data as an integrated quantity and validation tool for both, the atmospheric and the LSP model. The main task of the routing scheme is to preserve the horizontal travel time of water within each grid box as well as from grid box to grid box in the coupled model to first order, while the correct amount of runoff must be given by the LSP scheme. Inverse calculation also allows the direct estimation of runoff which should have been produced by an LSP scheme. As we don't want to deal with snow processes the scheme is applied from February to November. DOI: 10.1034/j.1600-0870.1996.t01-3-00009.x
Decreasing annual maximum flood peaks on the rivers and streams in the Wisconsin driftless area have been reported in recent studies. Various explanations have been offered, generally suggesting different episodes or change points separating the early periods of higher peak flows from the more recent lower peak flows. The present research used two statistical tests to detect a change point in annual flood peaks at Freeport on the Pecatonica River for the period 1914-2008. Both tests indicated that the most significant change occurred in 1954. Next, to find an explanation for the decreasing peaks, this research carried out a seasonal analysis of flood timing. The decrease in winter flood peaks was partly explained by the decrease in snow depth and the increase in winter temperature, providing less favorable conditions for winter flooding. In turn, the decrease in winter peak flows made once smaller summer peak flows more dominant in recent years, causing the shift in flood timing. Similar analysis showed a significant degree of resemblance between the Pecatonica River and several streams in its vicinity.
The availability of long-term gridded datasets of precipitation, temperature, and other surface meteorological variables offers the potential for deriving a range of land surface conditions that have not been directly observed. These include, for instance, soil moisture, snow water equivalent, evapotranspiration, runoff, and subsurface moisture transport. However, gridding procedures can themselves introduce artificial trends due to incorporation of stations with different record lengths and locations. Hence, existing gridded datasets are in general not appropriate for estimation of long-term trends. Methods are described here for adjustment of gridded daily precipitation and temperature maxima and minima over the continental United States based on newly available (in electronic form) U.S. Cooperative Observer station data archived at the National Climatic Data Center from the early 1900s on. The intent is to produce gridded meteorological datasets that can be used, in conjunction with hydrologic modeling, for long-term trend analysis of simulated hydrologic variables.
A physically based hydrology model is used to produce time series for the period 1916-2003 of evapotranspiration (ET), runoff, and soil moisture (SM) over the western United States from which long-term trends are evaluated. The results show that trends in ET in spring and summer are determined primarily by trends in precipitation and snowmelt that determine water availability. From April to June, ET trends are mostly positive due primarily to earlier snowmelt and earlier emergence of snow-free ground, and secondarily to increasing trends in spring precipitation. From July to September trends in ET are more strongly influenced by precipitation trends, with the exception of areas (most notably California) that receive little summer precipitation and have experienced large changes in snowmelt timing. Trends in the seasonal timing of ET are modest, but during the period 1947-2003 when temperature trends are large, they reflect a shift of ET from midsummer to early summer and late spring. As in other studies, it is found that runoff is occurring earlier in spring, a trend that is related primarily to increasing temperature, and is most apparent during 1947-2003. Trends in the annual runoff ratio, a variable critical to western water management, are determined primarily by trends in cool season precipitation, rather than changes in the timing of runoff or ET. It was found that the signature of temperature-related trends in runoff and SM is strongly keyed to mean midwinter [December-February (DJF)] temperatures. Areas with warmer winter temperatures show increasing trends in the runoff fraction as early as February, and colder areas as late as June. Trends toward earlier spring SM recharge are apparent and increasing trends in SM on 1 April are evident over much of the region. The 1 July SM trends are less affected by snowmelt changes and are controlled more by precipitation trends.
A distributed hydrology-vegetation model is described that includes canopy interception, evaporation, transpiration, and snow accumulation and melt, as well as runoff generation via the saturation excess mechanisms. Digital elevation data are used to model topographic controls on incoming solar radiation, air temperature, precipitation, and downslope water movement. Canopy evapotranspiration is represented via a two-layer Penman-Monteith formulation that incorporates local net solar radiation, surface meteorology, soil characteristics and moisture status, and species-dependent leaf area index and stomatal resistance. Snow accumulation and ablation are modeled using an energy balance approach that includes the effects of local topography and vegetation cover. Saturated subsurface flow is modeled using a quasi three-dimensional routing scheme. The model was applied at a 180-m scale to the Middle Fork Flathead River basin in northwestern Montana. This 2900-km2, snowmelt-dominated watershed ranges in elevation from 900 to over 3000 m. The model was calibrated using 2 years of recorded precipitation and streamflow. The model was verified against 2 additional years of runoff and against advanced very high resolution radiometer based spatial snow cover data at the 1-km2 scale. Simulated discharge showed acceptable agreement with observations. The simulated areal patterns of snow cover were in general agreement with the remote sensing observations, but were lagged slightly in time.
Global and regional trends in drought for 1950–2000 are analyzed using a soil moisture–based drought index over global terrestrial areas, excluding Greenland and Antarctica. The soil moisture fields are derived from a simulation of the terrestrial hydrologic cycle driven by a hybrid reanalysis–observation forcing dataset. Drought is described in terms of various statistics that summarize drought duration, intensity, and severity. There is an overall small wetting trend in global soil moisture, forced by increasing precipitation, which is weighted by positive soil moisture trends over the Western Hemisphere and especially in North America. Regional variation is nevertheless apparent, and significant drying over West Africa, as driven by decreasing Sahel precipitation, stands out. Elsewhere, Europe appears to have not experienced significant changes in soil moisture, a trait shared by Southeast and southern Asia. Trends in drought duration, intensity, and severity are predominantly decreasing, but statistically significant changes are limited in areal extent, of the order of 1.0%–7.0% globally, depending on the variable and drought threshold, and are generally less than 10% of continental areas. Concurrent changes in drought spatial extent are evident, with a global decreasing trend of between 0.021% and 0.035% yr 1 . Regionally, drought spatial extent over Africa has increased and is dominated by large increases over West Africa. Northern and East Asia show positive trends, and central Asia and the Tibetan Plateau show decreasing trends. In South Asia all trends are insignificant. Drought extent over Australia has decreased. Over the Americas, trends are uniformly negative and mostly significant. Within the long-term trends there are considerable interannual and decadal variations in soil moisture and drought characteristics for most regions, which impact the robustness of the trends. Analysis of de-trended and smoothed soil moisture time series reveals that the leading modes of variability are associated with sea surface temperatures, primarily in the equatorial Pacific and secondarily in the North Atlantic. Despite the overall wetting trend there is a switch since the 1970s to a drying trend, globally and in many regions, especially in high northern latitudes. This is shown to be caused, in part, by concurrent increasing temperatures. Although drought is driven primarily by variability in precipitation, projected continuation of temperature increases during the twenty-first century indicate the potential for enhanced drought occur-rence.
The spatial distribution of frozen soil and snow cover at the start of
the spring melt season plays an important role in the generation of
spring runoff and in the exchange of energy between the land surface and
the atmosphere. Field observations were made at the University of
Minnesota's Rosemount Agricultural Experiment Station to identify
statistical distributions that can be used to describe the spatial
variability of frozen soil and snow in macroscale hydrology models.
These probability distributions are used to develop algorithms that
simulate the subgrid spatial variability of snow and soil ice content
for application within the framework of the variable infiltration
capacity macroscale hydrologic model. Point simulations show that the
new snow algorithm increases the melt rate for thin snowpacks, and the
new soil frost algorithm allows more drainage through the soil during
the winter. Simulations of the Minnesota River show that the new snow
algorithm makes little difference to regional streamflow but does play
an important role in the regional energy balance, especially during the
spring snowmelt season. The new soil frost algorithm has a larger impact
on spring streamflow and plays a minor role in the surface energy
balance during the spring soil thaw season.
Evidence gleaned from the instrumental record of climate data identifies a robust, recurring pattern of ocean-atmosphere climate variability centered over the midlatitude North Pacific basin. Over the past century, the amplitude of this climate pattern has varied irregularly at interannual-to-interdecadal timescales. There is evidence of reversals in the prevailing polarity of the oscillation occurring around 1925, 1947, and 1977; the last two reversals correspond to dramatic shifts in salmon production regimes in the North Pacific Ocean. This climate pattern also affects coastal sea and continental surface air temperatures, as well as streamflow in major west coast river systems, from Alaska to California.
Changes in the amount and timing of the discharge of major Eurasian
Arctic rivers have been well documented, but whether or not these
changes can be attributed to climatic factors or to the construction of
manmade reservoirs remains unclear. Here we endeavor to identify the key
processes (snow cover and air temperature) that have regulated seasonal
streamflow fluctuations in the Eurasian Arctic over the last
half-century (1958-1999) and to understand the regional coherence of
timing trends, using a set of Eurasian Arctic rivers selected
specifically because they are free of known effects of dams. We find a
shift toward earlier onset of spring runoff as measured by a modest
change in the spring pulse onset (26 of 45 stations) and a strong change
in the centroid of timing (39 of 45 stations). Winter streamflows
increased over the period of record in most rivers, suggesting that
trends observed by others in larger regulated Eurasian Arctic rivers may
not be entirely attributable to reservoir construction. Upward trends in
air temperature appeared to have had the largest impact on spring and
summer flows for tributaries in the coldest of the major Eurasian Arctic
river basins (e.g., the Lena). While the overall duration of snow cover
has not significantly changed across the Eurasian Arctic, snow cover
disappearance has trended earlier in the year and appears to be related
to the increased May and snowmelt season fractional flows.
Secular trends in streamflow are evaluated for 395 climate-sensitive streamgaging stations in the conterminous United States using the non-parametric Mann-Kendall test. Trends are calculated for selected quantiles of discharge, from the 0th to the 100th percentile, to evaluate differences between low-, medium-, and high-flow regimes during the twentieth century. Two general patterns emerge; trends are most prevalent in the annual minimum (Q0) to median (Q50) flow categories and least prevalent in the annual maximum (Q100) category; and, at all but the highest quantiles, streamflow has increased across broad sections of the United States. Decreases appear only in parts of the Pacific Northwest and the Southeast. Systematic patterns are less apparent in the Q100 flow. Hydrologically, these results indicate that the conterminous U.S. is getting wetter, but less extreme.
Droughts can be characterized by their severity, frequency and duration, and areal extent. Depth-area-duration analysis, widely used to characterize precipitation extremes, provides a basis for the evaluation of drought severity when storm depth is replaced by an appropriate measure of drought severity. Gridded precipitation and temperature data were used to force a physically based macroscale hydrologic model at 1/ 2° spatial resolution over the continental United States, and construct a drought history from 1920 to 2003 based on the model-simulated soil moisture and runoff. A clustering algorithm was used to identify individual drought events and their spatial extent from monthly summaries of the simulated data. A series of severity-area-duration (SAD) curves were constructed to relate the area of each drought to its severity. An envelope of the most severe drought events in terms of their SAD characteristics was then constructed. The results show that (a) the droughts of the 1930s and 1950s were the most severe of the twentieth century for large areas; (b) the early 2000s drought in the western United States is among the most severe in the period of record, especially for small areas and short durations; (c) the most severe agricultural droughts were also among the most severe hydrologic droughts, however, the early 2000s western U.S. drought occupies a larger portion of the hydrologic drought envelope curve than does its agricultural companion; and (d) runoff tends to recover in response to precipitation more quickly than soil moisture, so the severity of hydrologic drought during the 1930s and 1950s was dampened by short wet spells, while the severity of the early 2000s drought remained high because of the relative absence of these short-term phenomena.
The least squares estimator of a regression coefficient β is vulnerable to gross errors and the associated confidence interval is, in addition, sensitive to non-normality of the parent distribution. In this paper, a simple and robust (point as well as interval) estimator of β based on Kendall's [6] rank correlation tau is studied. The point estimator is the median of the set of slopes (Yj - Yi)/(tj-ti) joining pairs of points with ti ≠ ti, and is unbiased. The confidence interval is also determined by two order statistics of this set of slopes. Various properties of these estimators are studied and compared with those of the least squares and some other nonparametric estimators.
This report provides technical documentation for the computer code SNTHERM.89, which is one-dimensional mass and energy balance model of snow and frozen soil. The model is structured using a simplified mixture theory and addresses coupled mass and heat flow, phase change and snow metamorphism. The underlying theory and numerical equations are presented. Included are detailed descriptions of the computation of the energy fluxes at the air/snow interface and of optional routines for estimating short- and long-wave radiation on horizontal and sloped surfaces.
This is the original first edition published as a physical book by Elsevier. It is woefully out of date. An updated electronic version was published in 2002 by the U.S. Geological Survey, and a completely revised 2020 version with updated methods and supporting materials is listed in my publication list, and is available for download at https://doi.org/10.3133/tm4a3 .
This study investigates trends in the timing and magnitude of seasonal maximum flood events across Canada. A new methodology for analyzing trends in the timing of flood events is developed that takes into account the directional character and multi-modality of flood occurrences. The methodology transforms the directional series of flood occurrences into new series by defining a new location of the origin. A test of flood seasonality (multi-modality) is then applied to identify dominant flood seasons. Floods from the dominant seasons are analyzed separately by a seasonal trend analysis. The Mann–Kendall test in conjunction with the method of pre-whitening is used in the trend analysis. Over 160 streamflow records from one common observation period are analyzed in watersheds with relatively pristine and stable land-use conditions. The results show weak signals of climate variability and/or change present in the timing of floods in Canada during the last three decades. Most of the significant trends in the timing of spring snowmelt floods are negative trends (earlier flood occurrence) found in the southern part of Canada. There are no significant trends identified in the timing of fall rainfall floods. However, the significance of the fall, rainfall-dominated flood season has been increasing in several analyzed watersheds. This may indicate increasing intensity of rainfall events during the recent years. Trends in the magnitude of floods are more pronounced than the trends in the timing of floods. Almost one fifth of all the analyzed stations show significant trends in the magnitude of snowmelt floods. Most of the significant trends are negative trends, suggesting decreasing magnitudes of snowmelt floods in Canada over the last three decades. Significant negative trends are found particularly in southern Ontario, northern Saskatchewan, Alberta and British Columbia. There are no significant trends in the magnitude of rainfall floods found in the analyzed streamflow records. The results support the outcomes of previous streamflow trend studies conducted in Canada.
The Variable Infiltration Capacity (VIC) macroscale hydrologic model is distinguished from other Soil – Vegetation – Atmosphere Transfer schemes (SVATS) by its focus on runoff processes. These are represented via the variable infiltration curve, a parameterization of the effects of subgrid variability in soil moisture holding capacity, from which the model takes its name, and a representation of nonlinear baseflow. Recent upgrades to the model have improved its representation of cold land processes, and the effects of surface storage in lakes and wetlands. Specific improvements described in this paper include the following: (1) explicit representation of the canopy energy balance separate from the land surface when snow is intercepted in the canopy; (2) parameterization of the effects of spatial variability in soil freeze – thaw state and snow distribution on moisture and energy fluxes; and (3) effects of advection on snowmelt under conditions of partial snow cover. The effects of these model updates are demonstrated using data from the PILPS Phase 2(e) validation catchments within the Torne-Kalix River basin, Sweden. D 2003 Elsevier Science B.V. All rights reserved.
Trends in streamflow characteristics were analyzed for streams in southwestern Wisconsin's Driftless Area by using data at selected gaging stations. The analyses indicate that annual low flows have increased significantly, whereas annual flood peaks have decreased. The same trends were not observed for forested areas of northern Wisconsin. Streamflow trends for other streams in southeastern Wisconsin draining predominantly agricultural land were similar to trends for Driftless Area streams for annual low flows. The causes for the trends are not well understood nor are the effects. Trends in annual precipitation do not explain the observed trends in streamflow. Other studies have found that erosion rates decreased significantly in the Driftless Area, and have attributed this reduction to a change of agricultural practices, which increase infiltration, decrease flood peaks, and increase low flows.
The main focus of this paper is the time series analysis of the precipitation-runoff process with transfer functions. Starting from there, a horizontal routing model is constructed to be coupled to the existing land surface parametrization (LSP) schemes which provide the lower boundary conditions in numerical weather prediction and atmospheric general circulation models. As these models currently have a resolution of 10 km−300 km (what we some kind of arbitrary define as the “large scale”), it will be assumed that the horizontal routing process can be lumped as a linear time invariant system. While the main physical properties of the soil (temperature, moisture) and all physical processes (partition of the energy and water fluxes) have to be represented by an LSP scheme, the coupling with a simple routing scheme allows the direct comparison of predicted and measured streamflow data as an integrated quantity and validation tool for both, the atmospheric and the LSP model. The main task of the routing scheme is to preserve the horizontal travel time of water within each grid box as well as from grid box to grid box in the coupled model to first order, while the correct amount of runoff must be given by the LSP scheme. Inverse calculation also allows the direct estimation of runoff which should have been produced by an LSP scheme. As we don't want to deal with snow processes the scheme is applied from February to November.
The Parallel Climate Model (PCM) has been used in the Accelerated ClimatePrediction Initiative (ACPI) Program to simulate the global climateresponse to projected CO2, sulfate, and other greenhouse gasforcingunder a business-as-usual emissions scenario during the 21st century. In these runs, the oceans were initialized to 1995 conditions by a group from the Scripps Institution of Oceanography and other institutions. An ensemble of three model runs was then carried out to the year 2099 using the projected forcing. Atmospheric data fromthese runs were saved at 6-hourly intervals (hourly for certain criticalfields) to support the ACPI objective of accurately modeling hydrologicalcycles over the western U.S. It is shown that the initialization to1995 conditions partly removes the un-forced oceanic temperature and salinity drifts that occurred in the standard 20th century integration. The ACPI runs show a global surface temperature increase of 3–8 C over northern high-latitudes by the end of the 21st century, and 1–2 C over the oceans. This is generally within 0.1Cof model runs without the 1995 ocean initialization. The exception is in theAntarctic circumpolar ocean where surface air temperature is cooler in theACPI run; however the ensemble scatter is large in this region. Althoughthe difference in climate at the end of the 21st century is minimalbetween the ACPI runs and traditionally spun up runs, it might be largerfor CGCMs with higher climate sensitivity or larger ocean drifts. Ourresults suggest that the effect of small errors in the oceans (such asthose associated with climate drifts) on CGCM-simulated climate changesfor the next 50–100 years may be negligible.
HAPEX-MOBILHY data, consisting of one year of hourly atmospheric forcing data at Caumont (SAMER No. 3, 43.68°N, 0.1°W) were used repeatedly to run the two-layer Variable Infiltration Capacity (VIC-2L) land-surface scheme until the model reached equilibrium in its water and energy balance. The equilibrium results are compared with one year of weekly soil moisture measurements at different depths, the estimated latent heat fluxes for 35 days of the intensive observation period (IOP), and the accumulated evaporation, runoff and drainage for the entire soya crop season. The latent heat flux comparisons show that VIC-2L tends to underestimate the evaporation due to the low soil moisture in its upper layer. The soil moisture comparison shows that the total soil water content is well simulated in general, but the soil water content in the top 0.5 m is underestimated, especially in May and June. These comparisons suggest that the lack of a mechanism for moving moisture from the lower to the upper soil layer in VIC-2L is the main cause for model error in the HAPEX-MOBILHY application. A modified version of VIC-2L, which has a new feature that allows diffusion of moisture between soil layers, and a 0.1 m thin layer on top of the previous upper layer, is described. In addition, the leaf area index (LAI) and the fraction vegetation cover are allowed to vary at each time step in a manner consistent with the rest PILPS-RICE Workshop, rather than being seasonally fixed. With these modifications, the VIC-2L simulations are re-evaluated. These changes are shown to resolve most of the structural deficiencies in the original version of the model. The sensitivity analysis of the new version of the model to the choices of soil depths and root distribution show that the evapotranspiration and soil moisture at the model equilibrium state are more sensitive to the root distribution than to the soil depth.