Figure 10 - uploaded by Kurt C. Kornelsen
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Density-scatter plots satellite scale and MESH-CMEM grid scale data for all 615 grid cells (urban areas and open water removed) for the study period of Apr 1 – Oct 31 2010. High density indicates more individual grid cells in the scatter plot at a particular XY co- ordinate.
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The assimilation of soil moisture and brightness temperature (TB) are expected to improve the modeling of land surface processes, but are only available at a resolution that is far coarser than the scale of many hydrological processes. Due to systematic differences between model states and satellite observations, a bias correction operator is a nec...
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To minimize systematic differences between soil moisture (SM) time series derived from remote sensing (RS) and land surface model (LSM), RS-based SM climatology information is typically discarded during land data assimilation (DA). However, recent studies have demonstrated that SM climatology estimates provided by L-band microwave RS retrievals can...
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... By carrying multiple sensors on the same flight platform and collecting images from one vertical and multiple different angles at the same time, through a series of internal work such as image recognition, solution, and mapping, a true 3D model is established, and the real world can be quickly restored, allowing users to acquire images from multiple angles. In view of this research problem, China's remote sensing monitoring of mines has also developed from the ground remote sensing platform to the aviation and aerospace remote sensing platform, and the monitoring data have also changed from the initial thermal infrared data to the medium-low resolution data and then to the high-resolution remote sensing image data monitoring [5]. Yutaka et al. put forward the thermal infrared radiation of coal seam by taking coal mine as an experiment and investigated Coalfield investigation, prediction, coal prospecting, and geological hazard detection [6]. ...
Ecological restoration with the assistance of certain artificial measures is to restore the original ecological function and productivity of the ecosystem or to make the ecosystem develop to a virtuous circle, which is a complex systematic project. Based on this, combined with the actual needs of a mine ecological restoration project, this paper puts forward the technical process and method of quickly acquiring the geographic information of abandoned mines by using the oblique photography technology of unmanned aerial vehicles. The verification results show that the maximum plane/elevation residual error of the checkpoints measured in the field is 0.221 m−0.181 m, and the median error is 0.030 m–0.112 m. According to the encryption requirement of 1∶500 scale topographic mapping in hilly land, the plane position error of the inspection point relative to the field control point should be less than 0.175 m. The elevation should be less than 0.280 m, and the experimental results in this paper can meet the requirements. Oblique photography can provide abundant digital results, and it can play an important role in mine restoration scheme design, restoration construction, and monitoring and management after restoration.
... The infiltration and aquifer parameters were obtained from Table A.2 in Rossman and Huber (2016). Other parameter values were chosen as they were similar to real-world urban watershed parameters (Liu et al., 2013 (Kornelsen et al., 2016). After filtering, there were 43 urban watersheds of the original 121 that had adequate data for the experiment; their locations are shown in Fig. 3. ...
Data assimilation is well suited for merging observed and simulated values to improve hydrologic forecasts. Typically, observation data that is assimilated into hydrologic models can be obtained from in situ gauges, radar, and remote sensing. In an urban environment, the benefits of soil moisture assimilation are not clearly defined. This work aims to identify the feasible imperviousness range at which it is advantageous to assimilate remotely sensed soil moisture to improve hydrologic forecasting in an urban watershed. A synthetic experiment was set up to simulate the retrieval of soil moisture onto a spatial grid and assimilate it into a hydrologic model. Sub-catchments were set up such that they would represent areas within that retrieval grid with varying levels of development. Multiple rainfall events were simulated with forecasts of up to 12 hours for each sub-catchment. The results of these simulations indicate that when areal average soil moisture is assimilated into an urban sub-catchment model which has a level of impervious that exceeds a threshold value, there is a decrease in model forecast performance, indicating that soil moisture assimilation is no longer beneficial. A quick way to determine the imperviousness threshold was then derived using a modified NRCS-CN method which matches the results of the synthetic experiments. This methodology was then further tested using real-world urban watersheds and shown to be a valid approach that can be used to quickly determine whether soil moisture assimilation would be beneficial for the watershed of interest. In general, this work has shown that hydrologic forecasting in an urban watershed can benefit from soil moisture assimilation even when imperviousness reaches 65–75%. Additionally, this work found a quick way to validate further if soil moisture assimilation is beneficial using some simple watershed characteristics.
... This filtering was done for both the ascending overpass, which is retrieved in the mornings, and descending overpass, which is retrieved during the evenings, soil moisture data. Retrievals with RFI probability and data quality index values greater than 0.1 were removed (Kornelsen et al., 2016). Assimilation of soil moisture 3only occurred during the non-freezing months of May to October to avoid the impact of retrievals of frozen soils. ...
... When updating using SMOS soil moisture the ascending pass data was prioritized over the descending pass such that the descending retrieval was assimilated only if there was no soil moisture data available from the ascending retrieval. Since the models are run at a daily timestep the ascending overpass (6 am retrieval) is assumed to provide a better representation of the antecedent soil moisture conditions of the day, additionally the ascending has been shown to perform better than the descending pass (Jackson et al., 2012;Kornelsen et al., 2016). Since data assimilation is used to help remove random error in the model outputs, the SMOS soil moisture data was bias corrected using CDF matching to each of the models' soil moisture states (ascending and descending separately). ...
The goal of this study was to determine if assimilating a combination of various derived data products can help circumvent some of the difficulties associated with urban watershed modeling. Combinations of the SNODAS (Snow Data Assimilation System) snow water equivalent data, the SMOS (Soil Moisture and Ocean Salinity) L2 soil moisture, and streamflow observations were used for the data assimilation schemes. Combinations of these observation data sets were assimilated into lumped conceptual rainfall-runoff models of the highly-urbanized Don River basin (in southern Ontario) to determine if assimilation of geophysical variables will have a significant impact on simulations and forecasting in an urbanized watershed. The Ensemble Kalman Filter (EnKF) data assimilation method was used for these analyses, with various rainfall-runoff models that include GR4J, HyMod, MAC-HBV, and SAC-SMA models. The best data assimilation scheme for hydrologic modeling involved using a combination of streamflow, soil moisture, and snow water equivalent while performing both state and parameter updating. These results suggest that using a combination of soil moisture and snow water equivalent from the SMOS and SNODAS data products can improve simulations and ensemble forecasts in an urban basin.
... CLASS is also linked with the Canadian Terrestrial Ecosystem Model (CTEM; Arora, 2003;Arora & Boer, 2003) to produce fluxes of energy, water, and CO 2 at the land surface using vegetation and ecosystem dynamics. Since 2007, MESH has also been widely used for various water resources applications and large-scale hydrological modelling in Canada (e.g., Deacu, Fortin, Klyszejko, Spence, & Blanken, 2012;Dornes, Pomeroy, Pietroniro, & Verseghy, 2008;Haghnegahdar et al., 2014;Kornelsen, Davison, & Coulibaly, 2016;Xu et al., 2015). ...
Complex hydrological models are being increasingly used nowadays for many purposes such as studying the impact of climate and land-use change on water resources. However, building a high-fidelity model, particularly at large scales, remains a challenging task, due to complexities in model functioning and behavior and uncertainties in model structure, parameterization, and data. Global Sensitivity Analysis (GSA), which characterizes how the variation in the model response is attributed to variations in its input factors (e.g., parameters, forcing data), provides an opportunity to enhance the development and application of these complex models. In this paper, we advocate using GSA as an integral part of the modelling process by discussing its capabilities as a tool for diagnosing model structure and detecting potential defects, identifying influential factors, characterizing uncertainty, and selecting calibration parameters. Accordingly, we conduct a comprehensive GSA of a complex land surface-hydrology model, Modélisation Environmentale–Surface et Hydrologie (MESH), which combines the Canadian Land Surface Scheme (CLASS) with a hydrological routing component, WATROUTE. Various GSA experiments are carried out using a new technique, called Variogram Analysis of Response Surfaces (VARS), for alternative hydroclimatic conditions in Canada using multiple criteria, various model configurations, and a full set of model parameters. Results from this study reveal that, in addition to different hydroclimatic conditions and SA criteria, model configurations can also have a major impact on the assessment of sensitivity. GSA can identify aspects of the model internal functioning that are counter-intuitive, and thus, help the modeler to diagnose possible model deficiencies and make recommendations for improving development and application of the model. As a specific outcome of this work, a list of the most influential parameters for the MESH model is developed. This list, along with some specific recommendations, is expected to assist the wide community of MESH and CLASS users, to enhance their modelling applications.
... Successful examples of SMOS Tb assimilation using a variety of simplifying assumptions are illustrated in Lievens et al. (2015); De Lannoy and ; Kornelsen et al. (2016). These studies use a radiative transfer model (RTM) to dynamically invert Tb information into corrections to modeled soil moisture estimates. ...
Three different data products from the Soil Moisture Ocean
Salinity (SMOS) mission are assimilated separately into the Goddard Earth
Observing System Model, version 5 (GEOS-5) to improve estimates of surface
and root-zone soil moisture. The first product consists of multi-angle,
dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere
extracted from Level 1 data. The second product is a derived SMOS Tb product
that mimics the data at a 40° incidence angle from the Soil Moisture
Active Passive (SMAP) mission. The third product is the operational SMOS
Level 2 surface soil moisture (SM) retrieval product. The assimilation system
uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally
varying climatological bias mitigation for Tb assimilation, whereas a
time-invariant cumulative density function matching is used for SM retrieval
assimilation. All assimilation experiments improve the soil moisture
estimates compared to model-only simulations in terms of unbiased
root-mean-square differences and anomaly correlations during the period from
1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in
areas where the satellite data are most sensitive to surface soil moisture,
large skill improvements (e.g., an increase in the anomaly correlation by
0.1) are found in the surface soil moisture. The domain-average surface and
root-zone skill metrics are similar among the various assimilation
experiments, but large differences in skill are found locally. The
observation-minus-forecast residuals and analysis increments reveal large
differences in how the observations add value in the Tb and SM retrieval
assimilation systems. The distinct patterns of these diagnostics in the two
systems reflect observation and model errors patterns that are not well
captured in the assigned EnKF error parameters. Consequently, a localized
optimization of the EnKF error parameters is needed to further improve Tb or
SM retrieval assimilation.
Soil moisture (SM) and precipitation play an essential part in land surface and atmospheric processes. Availability of high-resolution SM and precipitation data are quintessential for regional and global studies in hydro climatology. There exists a strong interconnection between the varying amounts of SM and the precipitation over a given area, giving rise to the bottom-up approach of estimating precipitation from SM2RAIN. This study examines precipitation derived from SM, downscaled using a unique cognation of land surface temperature (LST), vegetation index (VI), and SM. For this work, MODIS optical data having one km spatial resolution has been used to downscale the SMOS (25 km) and ESA CCI (25 km) SM data to generate four fine-resolution SM products SMOS-LST-NDVI, SMOS-LST-EVI, ESA CCI-LST-NDVI, and ESA CCI-LST-EVI, respectively. In this study, the Ganga basin in India is used as a case study area to assess the bottom-up SM2RAIN approach with efficiency. The correlation analysis of SM products with LST, NDVI, and EVI, shows that LST has a robust negative correlation, whereas NDVI and EVI have a significant positive correlation. Results obtained from the SM2RAIN simulation from downscaled SM managed to capture the spatial variation of the high precipitation event. Validation with IMERG GPM precipitation data shows that the SMOS-LST-NDVI precipitation product best captures the spatial variation of observed precipitation. The present study results provide convincing evidence that high-resolution precipitation can be estimated using satellite-derived SM data at basin level, as long as better calibration and validation data are available.
Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at 40° incidence angle from the Soil Moisture Active Passive mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations during the period 1 July 2010 to 1 May 2015 and for 187 sites across the United States. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g. increase in anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.
For an accurate estimation of land surface state variables through remote sensing data assimilation, it is important to estimate the forecast and observation biases as well. This study focuses on the evaluation of a methodology to estimate land surface state variables, together with model forecast and observation biases. Two conceptual rainfall-runoff models (HBV and GRKAL) are used for this purpose. Soil moisture data, retrieved by the Soil Moisture Ocean Salinity (SMOS) mission, are assimilated into these models for 59 unregulated sub-basins of the Murray-Darling basin in Australia. When both models simulate similar soil moisture values, the methodology results in similar forecast and observation bias estimates for both models. The same behavior is obtained when the temporal evolution of the soil moisture simulations is different, but with a similar long-term mean climatology. However, when the long-term mean climatology of both models is different, but with a similar temporal evolution, the bias estimates from both models have a different climatology as well, but with a high temporal correlation. The overall conclusion from this paper is that observation bias estimation is of key importance when updating internal state variables in a conceptual rainfall-runoff system that is calibrated to produce realistic discharge output for possibly biased internal state variables, and that the relative partitioning of bias into forecast and observation bias remains a model-dependent challenge.
Soil moisture is an important parameter among the fifty “Essential Climate Variables” according to Global Climate Observing System (GCOS). It allows to perform several applications in different fields, especially hydrological, meteorological and agricultural ones. There are several methods for measuring this parameter in two main categories: in-situ methods and remote sensing. In this sense, two well-known satellites are invested, namely Soil Moisture and Ocean Salinity (SMOS) from European Space Agency (ESA) launched in 2009 and Soil Moisture Active Passive (SMAP) from National Aeronautics and Space Administration (NASA) launched in 2015. This work will be dedicated to the state of the art of soil moisture downscaling and applications across various regions of the world, including Canada, USA and Spain to take advantage of these studies for a future effective exploitation of soil moisture mapping in a Moroccan context.
Near real-time quantitative precipitation estimates are required for many applications including weather forecasting, flood forecasting, crop management, forest fire prevention, hydropower production, and dam safety. Since April 2011, such a product has been available from Environment and Climate Change Canada for a domain covering all North America. This product, known as the Regional Deterministic Precipitation Analysis, is generated using the Canadian Precipitation Analysis (CaPA) system. Although it was designed for near real-time use, an archive of pre-operational and operational products going back to 2002 is now available and has been used in numerous studies. This paper presents a review of the various scientific publications that have reported either using or evaluating CaPA products. We find that the product is used with success both for scientific studies and operational applications and compares well with other precipitation datasets. We summarize the strengths and weaknesses of the system as reported in the literature. We also provide users with information on how the system works, how it has changed over time, and how the archived and near real-time analyses can be accessed and used. We finally briefly report on recent and upcoming improvements to the product based, in part, on the results of this literature review.
