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Climate change in the Pacific Northwest and in particular, the Salmon River Basin (SRB), is expected to bring about 3–5 °C rise in temperatures and an 8 % increase in precipitation. In order to assess the impacts due to these changes at the basin scale, this study employed an improved version of Variable Infiltration Capacity (VIC) model, which inc...
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... The modification of LULC is also a significant parameter of climatic change ( Li et al. 2022 ). Additionally, the change in climatic ( Sridhar et al. 2013( Sridhar et al. , 2019, geomorphology ( Sujatha and Sridhar 2018 ), and LULC patterns always disturbs https://doi.org/10.1016/j.rama.2023.10.007 1550-7424/© 2023 The Society for Range Management. ...
Google Earth Engine (GEE) is presently the most innovative international open-source platform for the advanced-level analysis of geospatial big data. In this study, we used three machine learning algorithms to apply this cloud platform for Land Use Land Cover (LULC) research in the Mardan, Pakistan. The machine learning algorithm in GEE is the most advanced technique to generate reliable and informative LULC maps from various satellite data to present reliable results. The primary goal of the present study is to compare the performance of various machine learning models (i.e., classification and regression trees [CART], support vector machine [SVM], and random forest [RF]) in GEE for the reliable four classes LULC maps using the Sentinel-2 imageries of 2022. In the current study, three satellite indices like the Normalized Difference Vegetation Index, Modified Normalized Difference Water Index, and Normalized Difference Built Index were applied to detect the features (i.e., vegetation, built, barren land, and water bodies in the study area). The performance of all three models was evaluated by validation and accuracy assessments. The Kappa coefficients of CART, SVM, and RF for Sentinel-2 images were 94%, 95%, and 97%, while the average overall accuracy is 96.25%, 97%, and 98.68%, respectively. The present study illustates that in this classification and comparison, RF performed better than SVM and CART. The current research study revealed that GEE has speedily processed the satellite imageries to develop the four classes of reliable LULC maps of the study area with the best accuracy results and deliver excellent support for further analysis.
... Therefore, more attention has been paid to studying the importance of extreme rainfall. However, extreme rainfall in different regions can vary greatly in spatial distribution, scope, frequency, duration, and severity [8,9]. Moreover, the responses to extreme rainfall can vary greatly in different regions. ...
Analyzing the hydrological sequence from the non-stationary characteristics can better understand the responses and significances of changes in extreme rainfall to global climate change. Taking the plain area in the middle and lower reaches of the Yangtze River basin (MLRYRB) as the study area, this study adopted a set of extreme rainfall indices and used the Bernaola-Galvan Segmentation Algorithm (BGSA) method to test the non-stationarity of extreme rainfall events. The General Pareto Distribution (GPD) was used to fit extreme rainfall under different thresholds and the range of return period was calculated to select the optimal threshold of extreme rainfall. In addition, the cross-wavelet technique was used to explore the correlations of extreme rainfall with El Niño-Southern Oscillation (ENSO) and Western Pacific Subtropical High (WPSH) events. The results show that: (1) extreme rainfall under different thresholds had different non-stationary characteristics, and 40-60 mm was more suitable as the optimal threshold for extreme rainfall. (2) The GPD distribution could well fit the extreme rainfall in the MLRYRB. By comparing the uncertainty of the return period, 40-60 mm was also considered as the suitable optimal threshold for extreme rainfall; however, different sub-regions had different optimal thresholds. (3) ENSO and WPSH had significant periodic effects on the extreme rainfall in the MLRYRB. These findings highlighted the significance of non-stationary assumptions in hydrological frequency analysis, which were of great importance for hydrological forecasting and water conservancy project management.
... Numerous studies have revealed that because of land-use change and agricultural increase affect the entire process of the hydrological cycle majorly such as interception, infiltration, and evapotranspiration (ET), resulting in a change of surface and subsurface flows (Gashaw et al., 2018;Jaksa & Sridhar, 2015;Ahiablame et al., 2017;Welde & Gebremariam, 2017;Wang et al., 2014;Niraula et al., 2015;Seong & Sridhar, 2017;Sridhar and Wedin, 2009;Sridhar & Anderson, 2017). 8.7 Conclusions 196 Acknowledgments 197 References 197 In the same way, because of climate change, the entire hydrological system gets disturbed and affects the spatial and temporal distribution of water resources in an area (Paul et al., 2018;Ghosh et al., 2012;Sridhar et al., 2013;Jin & Sridhar, 2012). Over the global scale of climate, India has experienced a significant increase in maximum rainfall intensity, and spatial variability has been observed over the last 50 years (Vinnarasi & Dhanya, 2016;Bisht et al., 2017a, b). ...
... Global land surface models and hydrological models can simulate historical streamflow, soil moisture and energy fluxes using long-term climate datasets (precipitation and air temperature) (Rodell et al., 2004;Sridhar et al., 2013a;Sridhar et al., 2013b). The model simulations can then be used to estimate long-term TWS. ...
The Gravity Recovery and Climate Experiment (GRACE) satellite mission began in 2002 and ended in June 2017. GRACE applications are limited in their ability to study long-term water cycle behavior because the data is limited to a short period, i.e., from 2002 to 2017. In this study, we aim to reconstruct (1960–2002) GRACE total water storage anomalies (TWSA) to obtain a continuous TWS time series from 1960 to 2016 over four river basins of South India, namely the Godavari, Krishna, Cauvery and Pennar River basins, using Multilayer Perceptrons (MLP). The Seasonal Trend Decomposition using Loess procedure (STL) method is used to decompose GRACE TWSA and forcing datasets into linear trend, interannual, seasonal, and residual parts. Only the de-seasoned (i.e., interannual and residual) components are reconstructed using the MLP method after the linear trend and seasonal components are removed. Seasonal component is added back after reconstruction of de-seasoned GRACE TWSA to obtain complete TWSA series from 1960 to 2016. The reconstructed GRACE TWSA are converted to groundwater storage anomalies (GWSA) and compared with nearly 2000 groundwater observation well networks. The results conclude that the MLP model performed well in reconstructing GRACE TWSA at basin scale across four river basins. Godavari (GRB) experienced the highest correlation (CC = 0.96) between the modelled TWSA and GRACE TWSA, followed by Krishna (KRB) with CC = 0.93, Cauvery (CRB) with CC = 0.91, and Pennar (PCRB) with CC = 0.92. The seasonal GWSA from GRACE (GWSAGRACE) correlated well with the GWSA from groundwater observation wells (GWSAOBS) from 2003 to 2016. KRB exhibited the highest correlation (r=0.85) followed by GRB (r=0.81), PCRB (r=0.81) and CRB (r=0.78). The established MPL technique could be used to reconstruct long-term TWSA. The reconstructed TWSA data could be useful for understanding long-term trends, as well as monitoring and forecasting droughts and floods over the study regions.
... As a result, sub-basin scale sediment yield analysis using a physically based distributed hydrological model is required for identifying accurate sediment source areas and controlling sediment yield through soil and water conservation practices. Many physically based hydrological models, such as VIC [25,26], ANSWERS [27], AGNPS [28], WEPP [29] and SWAT [30] have been in use over the past three decades to understand the hydrological processes. Roti et al. [31] reviewed the applicability of physically distributed models and concluded that the SWAT model outperformed AGNPS, ANSWERS and WEPP models [32][33] in both small and large areas [34]. ...
With increased demand for water and soil in this Anthropocene era, it is necessary to understand the water balance components and critical source areas of land degradation that lead to soil erosion in agricultural dominant river basins. Two medium-sized east-flowing rivers in India, namely Nagavali and Vamsadhara, play a significant role in supporting water supply and agriculture demands in parts of the Odisha districts of Kalahandi, Koraput and Rayagada, as well as the Andhra Pradesh districts of Srikakulam and Vizianagaram. Floods are more likely in these basins as a result of cyclones and low-pressure depressions in the Bay of Bengal. The water balance components and sediment yield of the Nagavali and Vamsadhara river basins were assessed using a semi-distributed soil and water assessment tool (SWAT) model in this study. The calibrated model performance revealed a high degree of consistency between observed and predicted monthly streamflow and sediment load. The water balance analysis of Nagavali and Vamsadhara river basins showed the evapotranspiration accounted for 63% of the average annual rainfall. SWAT simulated evapotranspiration showed a correlation of 0.78 with FLDAS data. The calibrated SWAT model showed that 26.5% and 49% of watershed area falling under high soil erosion class over Na-gavali and Vamsadhara river basins, respectively. These sub watersheds require immediate attention to management practices to improve the soil and water conservation measures.
... Extreme rainfall will have a huge impact on the local ecology, industry, and social economy, which has motivated more and more studies to emphasize the importance of extreme rainfall. However, the temporal and spatial distributions of extreme weather events in different regions are quite different (Sridhar et al., 2013;Weldegerima et al., 2018), normally, with different impact ranges, frequencies, durations, and severities (Swain et al., 2019;Fagnant et al., 2020;Tong et al., 2020;Ndlovu et al., 2021). Therefore, it is of great significance to investigate the spatiotemporal variations of extreme rainfall and its potential driving factors for different regions. ...
Extreme rainfall can be affected by various climatic factors such as the large-scale climate patterns (LCPs). Understanding the changing LCPs can improve the accuracy of extreme rainfall prediction. This study explores the variation trend of extreme rainfall in the middle and lower reaches of the Yangtze River Basin (MLRYRB) and the telecorrelation with four LCPs, namely WPSHI (Western Pacific Subtropical High Index), EAMI (East Asia Monsoon Index), ENSO (El Niño-Southern Oscillation) and PDO (Pacific Decadal Oscillation), through modified Mann-Kendall (MMK) analysis, Pearson correlation coefficient, wavelet coherence analysis (WTC) and improved partial wavelet analysis (PWC). Previous studies have ignored the interdependence between these climate indices when analyzing their effects on precipitation. This study introduces the improved PWC, which can remove the correlations between them and reveal the influence of a single LCP. The results show that: 1) extreme rainfall in the MLRYRB has an obvious increasing trend and has a significant correlation with the LCPs; 2) the LCPs have a significant cyclical relationship with extreme rainfall, which can be significantly affected by the intergenerational variation of the LCPs; and 3) the improved PWC can accurately reveal the influence of a single LCP. EAMI is the main influencing factor in the 1-year cycle, while WPSHI is the main influencing factor in the 5-year cycle. ENSO and PDO can always influence extreme rainfall by coupling WPSHI or EAMI.
... There is evidence that spring snowpack has already declined as a result of increases in air temperature throughout the northern hemisphere (Stewart 2009) and in the Pacific Northwest in particular (Stoelinga et al. 2010, Mote et al. 2018, Zeng et al. 2018. Climate models suggest that declines in snowpack accumulation and trends towards earlier melt in the Pacific Northwest are likely to accelerate with climate change as winter air temperatures continue to increase (Sridhar et al. 2012, Chegwidden et al. 2019, Marshall et al. 2019. Future declines in snowpack are predicted to lead to decreases in streamflow during the warm season (Vano et al. 2015), particularly in slow-draining streams with substantial snowpack and groundwater exchange (Tague and Grant 2009). ...
Water temperature plays a primary role in driving ecological processes in streams due to its direct impact on biogeochemical cycles and the physiological processes of stream fauna, such as growth, development, and the timing of life history events. Streams influenced by snowpack melt are generally cooler in the summer and demonstrate less sensitivity to climate variability in what is commonly referred to as “climate buffering”. Despite the substantial influence of snowpack on stream temperature and expected changes in snowpack accumulation and melt timing with climate change, methods for representing snowpack in statistical models for stream temperature have not been well explored. In this investigation, we quantified the extent of stream temperature buffering in free-flowing streams across a geographically diverse region in the Pacific Northwest USA. We demonstrated that statistical models of daily mean stream temperature can be improved by explicitly accounting for temporal variability in a small number of climate covariates believed to be mechanistically related to stream temperature. Our novel statistical approach included as predictors combinations and interactions between the following variables: (1) air temperature, (2) lagged air temperature (where the lag duration varied according to its relationship with flow on a given day at that site), (3) flow, (4) snowpack in the upstream catchment, and (5) day of year. We found that sites with substantial snow influence were associated with increased air temperature buffering during the warm season and longer air temperature lags (>30 days during spring high flows and ∼ 10 days during late summer low flows) compared to sites where precipitation predominantly fell as rain (
... Among these observations, satellites and weather radars provide qualitative forecasts, while NWP models provide quantitative forecasts (Sridevi et al., 2020). Hence, quantitative rainfall forecasts from NWP models that represents land-atmospheric interactions continue to be the primary source of rainfall data for input into any hydrological model for flood forecasting, water management and disaster assessments among other applications (Shahrban et al., 2016;Sridhar et al., 2013;Sridhar and Valayamkunnath, 2018;Sujatha and Sridhar, 2017). NWP forecasts were first used in Europe and the United States, but are now used all over the world (Saulo et al., 2001). ...
Rainfall forecasting and its spatio-temporal variability is important for many hydrological applications. It is critical to understand the uncertainty and verify the quality of rainfall forecasts provided by Numerical Weather Prediction (NWP) models. In the present study, the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS) model performance is evaluated for day-1 to day-5 forecast with a threshold of 1 mm/day in the Nagavali and Vamsadhara river basins, India. From the results, the model predicted the rainfall with a correlation coefficient of >0.3 and probability of detection >0.6 for day-1 and day-3 forecasts. The bias in rainfall prediction shifted from overestimation to underestimation by 30% as forecast lead time increased. The total mean error is decomposed into hit, false, and missed bias. The main sources of total mean error are hit bias and false bias. However, missed bias influenced total mean error as lead time increased. Bias correction is applied for the rainfall events with a rainfall intensity >12 mm/day. RMSE improved by >18% for day-1 forecast in both the Nagavali and Vamsadhara basins, and the improvement ranged between 3% to 9% for other days. In the Nagavali basin, BIAS and ME improved and ranged from 44% to 65% for day-1 to day-5 forecast, whereas in the Vamsadhara basin, it ranged from 65% to 93%. Our findings are useful for early warning dissemination during the flood events, resource mobilization to protect communities, and sustainable water resources planning and management.
... It could be done by maintaining the hydrometric networks and utilising its datasets for hydrological and hydrodynamic modelling, which would help generate flood scenarios and forecast floods during extreme events. Additionally, the interaction of land use land cover with climate change and hydrology should be adopted in the research [72][73][74]. Furthermore, increasing inter-sectoral communication through enhancing human and institutional capacities must be promoted. ...
Uttarakhand, an Indian Himalayan state in India, is famous for its natural environment, health rejuvenation, adventure, and a pilgrimage centre for various religions. It is categorised into two major regions, i.e., the Garhwal and the Kumaon, and geographically, the Bhabar and the Terai. Floods, cloudbursts, glacier lake outbursts, and landslides are the major natural hazards that cause the highest number of mortalities and property damage in this state. After becoming a full 27th state of India in 2000, the developmental activities have increased many folds, which has added to such calamities. This study briefly summarises the major incidents of flood damage, describes the fragile geology of this Himalayan state, and identifies the natural as well as the anthropogenic causes of the flood as a disaster. It also highlights the issue of climate change in the state and its adverse impact in the form of extreme precipitation. Besides these, it reviews the challenges involved in flood management and highlights the effective flood risk management plan that may be adopted to alleviate its adverse impacts.
... From the recent studies about the impact of climate change on a basin scale, it is understood that trend analysis of climatic variables plays a vital role in understanding its eAects (Dubey and Sharma 2018;Venkat et al. 2020). Rainfall and temperature are the most important climate variables that vary from place to place and time to time (Sridhar et al. 2013;Seong and Sridhar 2017;Weldegerima et al. 2018). Variations in precipitation have impact on region's water resources by aAecting streamCow, soil moisture, and groundwater recharge (Adarsh and Reddy 2015). ...
Long-term trend analysis of meteorological variables is required for implementing any hydrological model or water resources management model to a basin. Spatio-temporal variations in precipitation and temperature of a basin are helpful for meteorologists, agriculturists and policymakers to take appropriate decisions. This study performs long-term trend analysis for gridded precipitation (1901-2019) and temperature (1951-2019) datasets of 0.258 9 0.258 resolution in the Munneru river basin, India using Bve different trend tests for annual, seasonal and monthly time steps in sub-basin wise. An increasing trend is observed in annual precipitation in the Brst sub-basin (S1) at 8.6 mm/decade and in the third sub-basin (S3) at 11.6 mm/decade. An increasing trend is observed for both annual average maximum and minimum temperature at a rate of 1.58C and 0.068C per decade for the basin, respectively. Twenty different climate extreme indices are calculated using daily data of precipitation and temperature. Increasing trend is observed for PCPTOT, R20mm, RX5DAY, R95PTOT, SDII, TX90P, TXX and WSDI indices. Decreasing trend is observed for CWD, CSDI, TN10P and TX10P at particular grid points. Results from this study are useful to understand the climate variability and its impact on water resources in the future periods and hydrological assessment in the basin.
