Groundwater Storage Change in the Jinsha River Basin from GRACE, Hydrologic Models, and In Situ Data

Ground Water ◽  
2019 ◽  
Author(s):  
Nengfang Chao ◽  
Gang Chen ◽  
Jian Li ◽  
Longwei Xiang ◽  
Zhengtao Wang ◽  
...  
Keyword(s):  
River Basin ◽  
Hydrologic Models ◽  
Groundwater Storage ◽  
Jinsha River ◽  
In Situ Data ◽  
Storage Change

Groundwater storage change in the Jinsha River basin from GRACE, hydrologic models, and in situ data

2020 ◽  
Author(s):  
Nengfang Chao
Keyword(s):  
Sediment Transport ◽  
River Basin ◽  
Human Activities ◽  
Water Storage ◽  
Hydrologic Models ◽  
Groundwater Storage ◽  
Jinsha River ◽  
In Situ Data ◽  
Storage Change

<p>Groundwater plays a major role in the hydrological processes driven by climate change and human activities, particularly in upper mountainous basins. The Jinsha River Basin (JRB) is the uppermost region of the Yangtze River and the largest hydropower production region in China. With the construction of artificial cascade reservoirs increasing in this region, the annual and seasonal flows are changing and affecting the water cycles. Here, we first infer the groundwater storage changes (GWSC), accounting for sediment transport in JRB, by combining the Gravity Recovery and Climate Experiment (GRACE) mission, hydrologic models and in situ data. The results indicate: (1) the average estimation of the GWSC trend, accounting for sediment transport in JRB, is 0.76±0.10 cm/year during the period 2003–2015, and the contribution of sediment transport accounts for 15%; (2) precipitation (P), evapotranspiration (ET), soil moisture change (SMC), GWSC and land water storage changes (LWSC) show clear seasonal cycles; the interannual trends of LWSC and GWSC increase, but P, runoff (R), surface water storage change (SWSC) and SMC decrease, and ET remains basically unchanged; (3) the main contributor to the increase in LWSC in JRB is GWSC, and the increased GWSC may be dominated by human activities, such as cascade damming, and climate variations (such as snow and glacier melt due to increased temperatures). This study can provide valuable information regarding JRB in China for understanding GWSC patterns and exploring their implications for regional water management.</p>

Analysis of groundwater storage changes in main Polish river basins using GRACE observations, in-situ data, and hydrological and climate models

2020 ◽  
Author(s):  
Jolanta Nastula ◽  
Justyna Śliwińska ◽  
Zofia Rzepecka ◽  
Monika Birylo
Keyword(s):  
Climate Models ◽  
Water Storage ◽  
Coupled Model ◽  
Research Programme ◽  
Climate Data ◽  
Groundwater Storage ◽  
In Situ Data ◽  
Snow Water ◽  
Land Data Assimilation System

<p>The Gravity Recovery and Climate Experiment (GRACE) measurements have provided global observations of total water storage (TWS) changes at monthly intervals for almost 20 years. They are useful for estimating changes in groundwater storage (GWS) after extracting other water storage components like soil water or snow water.</p><p>In this study, we analyse the GWS variations of two main Polish basins, the Vistula and the Odra, using GRACE observations, in-situ wells measurements, GLDAS (Global Land Data Assimilation System) hydrological models, and CMIP5 (the World Climate Research Programme’s Coupled Model Intercomparison Project Phase 5) climate data. The research is conducted for the period between September 2006 and October 2015.</p><p>Here, TWS is taken directly from GRACE measurements and also computed from all considered models. GWS is obtained by subtracting the modelled sum of soil moisture and snow water from the GRACE-based TWS. The resultant GWS series are validated by comparing with appropriately calibrated in-situ wells measurements. For each GWS time series, the trends, spectra, amplitudes, and seasonal components were computed and analysed. The results suggest that in Poland there has been generally no major GWS depletion. The results can contribute toward selection of an appropriate model that, in combination with GRACE observations, would provide information on groundwater changes in regions with limited or inaccurate in-situ groundwater storage measurements.</p>

scholarly journals Improving the Accuracy of Groundwater Storage Estimates Based on Groundwater Weighted Fusion Model

2022 ◽  
Vol 14 (1) ◽  
pp. 202
Author(s):  
Kai Su ◽  
Wei Zheng ◽  
Wenjie Yin ◽  
Litang Hu ◽  
Yifan Shen
Keyword(s):  
Human Factors ◽  
River Basin ◽  
Groundwater Level ◽  
Efficiency Coefficient ◽  
Fusion Model ◽  
Groundwater Storage ◽  
Effective Measure ◽  
Weighted Fusion ◽  
Grace Data

It is an effective measure to estimate groundwater storage anomalies (GWSA) by combining Gravity Recovery and Climate Experiment (GRACE) data and hydrological models. However, GWSA results based on a single hydrological model and GRACE data may have greater uncertainties, and it is difficult to verify in some regions where in situ groundwater-level measurements are limited. First, to solve this problem, a groundwater weighted fusion model (GWFM) is presented, based on the extended triple collocation (ETC) method. Second, the Shiyang River Basin (SYRB) is taken as an example, and in situ groundwater-level measurements are used to evaluate the performance of the GWFM. The comparison indicates that the correlation coefficient (CC) and Nash-Sutcliffe efficiency coefficient (NSE) are increased by 9–40% and 23–657%, respectively, relative to the original results. Moreover, the root mean squared error (RMSE) is reduced by 9–28%, which verifies the superiority of the GWFM. Third, the spatiotemporal distribution and influencing factors of GWSA in the Hexi Corridor (HC) are comprehensively analyzed during the period between 2003 and 2016. The results show that GWSA decline, with a trend of −2.37 ± 0.38 mm/yr from 2003 to 2010, and the downward trend after 2011 (−0.46 ± 1.35 mm/yr) slow down significantly compared to 2003–2010. The spatial distribution obtained by the GWFM is more reliable compared to the arithmetic average results, and GWFM-based GWSA fully retain the advantages of different models, especially in the southeastern part of the SYRB. Additionally, a simple index is used to evaluate the contributions of climatic factors and human factors to groundwater storage (GWS) in the HC and its different subregions. The index indicates that climate factors occupy a dominant position in the SLRB and SYRB, while human factors have a significant impact on GWS in the Heihe River Basin (HRB). This study can provide suggestions for the management and assessments of groundwater resources in some arid regions.

Exploring meso-scale soil water and groundwater storage changes within the USA through a Bayesian combination of GRACE data with monthly 12.5 km model simulations

2020 ◽  
Author(s):  
Nooshin Mehrnegar ◽  
Owen Jones ◽  
Michael B. Singer ◽  
Maike Schumacher ◽  
Thomas Jagdhuber ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Soil Water ◽  
Water Storage ◽  
Balance Model ◽  
Soil Water Storage ◽  
Groundwater Storage ◽  
In Situ Data ◽  
Grace Data ◽  
The Usa

<p>Climatic changes in precipitation intensity across the United States (USA) may also affect the frequency and magnitude of drought and flooding events, with potentially serious consequences for water supply across this country. Reliable estimation of water storage changes in the soil root zone and groundwater aquifers is important for predicting future water availability, drought and flood monitoring and weather prediction. In this study, we assimilate Terrestrial Water Storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into a water balance model with 12.5-km spatial resolution. Our goal is to explore meso-scale surface and deep-level soil water storage, as well as groundwater changes within the USA covering the period 2003-2017. A new Bayesian approach is formulated and implemented in this study, which provides a dynamic solution for a state-space equation between hydrological model outputs and TWS observations, while considering their error structures. The unknown state parameters and temporal dependency between them are estimated through a combination of forward/backward Kalman Filtering and Markov Chain Monto Carlo (MCMC) methods.</p><p>The outputs of this methodological approach are evaluated using in situ data from historical USGS groundwater data (over 6600 wells) and the ESA CCI surface soil moisture data. The results indicate that our GRACE data assimilation generally improves the simulation of groundwater and soil moisture across the USA. For example, the long-term linear trend fitted to the Bayesian-derived groundwater and soil water storage are in a same direction as those of in situ data in 63% and 58% of regions studied across the USA, respectively. However, this vale is estimated less than 51% for both water storage estimates derived from the original water balance model, which suggesting that the data assimilation modulates the hydrological models to perform more realistically. The biggest improvements are observed in the southeast USA with considerably large inter-annual variability in precipitation, where modelled groundwater apparently responded too strongly to the changes in atmospheric forcing. The Bayesian data assimilation method also improves the temporal correlation coefficients between the in situ USGS and ESA CCI data and model outputs after merging with GRACE TWS estimates. For instance, the correlation coefficient between groundwater storage and USGS observation increased from -0.52 to 0.48 and from -0.28 to 0.25 in southeast and southwest of USA, respectively. Finally, we will explore changes in Bayesian-derived groundwater and soil water storage within the Florida, California and South of Mississippi regions and interpret their relations with climate-induced factors such as precipitation and ENSO index.</p><p><strong>Keywords:</strong> USA; Data Assimilation; Bayesian Method; Kalman Filtering; MCMC; GRACE; W3RA; groundwater storage; soil water storage; USGS; ESA CCI.</p><p> </p>

scholarly journals Validation study of TMPA 3B42V6 in a typical alpine and gorge region: Jinsha River basin, China

2013 ◽  
Vol 13 (12) ◽  
pp. 3479-3492 ◽  
Author(s):  
Y. C. Yang ◽  
G. W. Cheng ◽  
J. H. Fan ◽  
W. P. Li ◽  
J. Sun ◽  
...  
Keyword(s):  
River Basin ◽  
Validation Study ◽  
Probability Of Detection ◽  
Temporal Scale ◽  
Rain Event ◽  
Temporal Scales ◽  
Jinsha River ◽  
Daily Scale ◽  
The Mean

Abstract. Because of density and distribution flaws inherent with in situ rainfall measurements, satellite-based rainfall products, especially the Tropical Rainfall Measuring Mission (TRMM), were expected to offer an alternative or complement for modeling of hydrological processes and water balance analysis. This study aims at evaluating the validity of a standard product, the TRMM Multi-satellite Precipitation Analysis (TMPA) 3B42V6, by comparing it with in situ ground gauge datasets on a typical alpine and gorge region in China, the Jinsha River basin. The validation study involved the performance of the 3B42V6 product on 3 h, daily and monthly temporal scales. Statistical analysis methods were used for rainfall and rain event estimation. The results affirmed that the 3B42V6 product demonstrated increasing accuracy when the temporal scales were increased from 3 h to daily to monthly. The mean correlation coefficient of rainfall time series between the 3B42V6 product and the gauge over the Jinsha River basin reached 0.34 on the 3 h scale, 0.59 on the daily scale, and 0.90 on the monthly scale. The mean probability of detection (POD) of the 3B42V6 product reached 0.34 on the 3 h scale and 0.63 on the daily scale. The 3B42V6 product of 80.4% of stations obtained an acceptable bias (± 25%) over the investigation area. A threshold of nearly 5.0 mm d−1 in daily rainfall intensity split the 3B42V6 product into overestimates (< 5.0 mm d−1) and underestimates (> 5.0 mm d−1). The terrain elements of altitude, longitude, and latitude were the major influencing factors for 3B42V6 performance. In brief, the 3B42V6 dataset has great potential for research on hydrologic processes, especially daily or large temporal scale. As for fine temporal scale applications, such as flood predictions based on a 3 h scale dataset, it is necessary to conduct adjustments or to combine the 3B42V6 product with gauges to be more accurate regarding the issues in the study area or in analogous regions with complicated terrains.

scholarly journals Downscaling of SMAP Soil Moisture in the Lower Mekong River Basin

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 56 ◽  
Author(s):  
Chelsea Dandridge ◽  
Bin Fang ◽  
Venkat Lakshmi
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
River Basin ◽  
River Basins ◽  
Large River ◽  
Mekong River ◽  
Mekong River Basin ◽  
In Situ Data ◽  
Large River Basins

In large river basins where in situ data were limited or absent, satellite-based soil moisture estimates can be used to supplement ground measurements for land and water resource management solutions. Consistent soil moisture estimation can aid in monitoring droughts, forecasting floods, monitoring crop productivity, and assisting weather forecasting. Satellite-based soil moisture estimates are readily available at the global scale but are provided at spatial scales that are relatively coarse for many hydrological modeling and decision-making purposes. Soil moisture data are obtained from NASA’s soil moisture active passive (SMAP) mission radiometer as an interpolated product at 9 km gridded resolution. This study implements a soil moisture downscaling algorithm that was developed based on the relationship between daily temperature change and average soil moisture under varying vegetation conditions. It applies a look-up table using global land data assimilation system (GLDAS) soil moisture and surface temperature data, and advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and land surface temperature (LST). MODIS LST and NDVI are used to obtain downscaled soil moisture estimates. These estimates are then used to enhance the spatial resolution of soil moisture estimates from SMAP 9 km to 1 km. Soil moisture estimates at 1 km resolution are able to provide detailed information on the spatial distribution and pattern over the regions being analyzed. Higher resolution soil moisture data are needed for practical applications and modelling in large watersheds with limited in situ data, like in the Lower Mekong River Basin (LMB) in Southeast Asia. The 1 km soil moisture estimates can be applied directly to improve flood prediction and assessment as well as drought monitoring and agricultural productivity predictions for large river basins.

scholarly journals Improved Understanding of Groundwater Storage Changes under the Influence of River Basin Governance in Northwestern China Using GRACE Data

2021 ◽  
Vol 13 (14) ◽  
pp. 2672
Author(s):  
Xin Liu ◽  
Litang Hu ◽  
Kangning Sun ◽  
Zhengqiu Yang ◽  
Jianchong Sun ◽  
...  
Keyword(s):  
River Basin ◽  
Groundwater Level ◽  
Regional Scale ◽  
Water Storage ◽  
Northwestern China ◽  
In Situ Observation ◽  
Observation Data ◽  
Groundwater Storage ◽  
Grace Data

Groundwater is crucial for economic development in arid and semiarid areas. The Shiyang River Basin (SRB) has the most prominent water use issues in northwestern China, and overexploited groundwater resources have led to continuous groundwater-level decline. The key governance planning project of the SRB was issued in 2007. This paper synthetically combines remote-sensing data from Gravity Recovery and Climate Experiment (GRACE) data and precipitation, actual evapotranspiration, land use, and in situ groundwater-level data to evaluate groundwater storage variations on a regional scale. Terrestrial water storage anomalies (TWSA) and groundwater storage anomalies (GWSA), in addition to their influencing factors in the SRB since the implementation of the key governance project, are analyzed in order to evaluate the effect of governance. The results show that GRACE-derived GWS variations are consistent with in situ observation data in the basin, with a correlation coefficient of 0.68. The GWS in the SRB had a slow downward trend from 2003 to 2016, and this increased by 0.38 billion m³/year after 2018. As the meteorological data did not change significantly, the changes in water storage are mainly caused by human activities, which are estimated by using the principle of water balance. The decline in GWS in the middle and lower reaches of the SRB has been curbed since 2009 and has gradually rebounded since 2014. GWS decreased by 2.2 mm EWH (equivalent water height) from 2011 to 2016, which was 91% lower than that from 2007 to 2010. The cropland area in the middle and lower reaches of the SRB also stopped increasing after 2011 and gradually decreased after 2014, while the area of natural vegetation gradually increased, indicating that the groundwater level and associated ecology significantly recovered after the implementation of the project.

scholarly journals Assessment of the accuracy of snow water equivalent calculation with the use of global numerical weather prediction models and SnoWE snowpack model (by the example of the Kama River basin)

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Andrey N. Shikhov ◽  
◽  
Evgenii V. Churiulin ◽  
Rinat K. Abdullin ◽  
◽  
...  
Keyword(s):  
River Basin ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Entire Area ◽  
Numerical Weather ◽  
In Situ Data ◽  
Snow Water

The paper discusses the results of snow cover formation and snowmelt modeling in the Kama river basin (S = 507 km2) using two approaches previously developed by the authors. The first one is the SnoWE snowpack model developed at the Hydrometeorological Center of the Russian Federation and used in quasi-operational mode since 2015, and the second is GIS-based empirical technique which was previously implemented for the Kama river basin. Both methods are based on a combination of numerical weather prediction (NWP) models data with operational synoptic observations at the weather stations. The study was performed for the winter seasons 2018/19 and 2019/20. To assess the reliability of simulated snow water equivalent (SWE), we obtained in-situ data from 68 locations (snow survey routes) distributed over the entire area of ​​the river basin. As a result of the study, the main advantages and limitations of two methods for SWE calculation were identified. As for the maximum values of SWE, the root mean square error (RMSE) of simulated SWE ranges from 14% to 28% of the average observed SWE according to in-situ data. It was found, that the SnoWE model more reliably reproduces SWE in the lowland part of the river basin. Simultaneously, SWE was substantially underestimated according to the SnoWE model in the northern and mountainous parts of the basin,. The second method provides a more realistic estimate of the spatial distribution of SWE over the area, as well as a higher accuracy of calculation for its northern part of the river basin. The main drawback of the method is the substantial overestimation of the intensity of snowmelt and snow sublimation. Consequently, the accuracy of SWE calculations sharply decreases in the spring season. Wherein, SWE calculation accuracy in the winter season 2019/20 was substantially lower than in 2018/19 due to frequent thaws.

scholarly journals Evaluation of Satellite-Based Rainfall Estimates in the Lower Mekong River Basin (Southeast Asia)

2019 ◽  
Vol 11 (22) ◽  
pp. 2709 ◽  
Author(s):  
Chelsea Dandridge ◽  
Venkat Lakshmi ◽  
John Bolten ◽  
Raghavan Srinivasan
Keyword(s):  
Southeast Asia ◽  
River Basin ◽  
Watershed Modeling ◽  
Rain Gauge ◽  
Mekong River ◽  
Coefficient Of Determination ◽  
Mekong River Basin ◽  
In Situ Data ◽  
Rainfall Estimates

Satellite-based precipitation is an essential tool for regional water resource applications that requires frequent observations of meteorological forcing, particularly in areas that have sparse rain gauge networks. To fully realize the utility of remotely sensed precipitation products in watershed modeling and decision-making, a thorough evaluation of the accuracy of satellite-based rainfall and regional gauge network estimates is needed. In this study, Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 v.7 and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily rainfall estimates were compared with daily rain gauge observations from 2000 to 2014 in the Lower Mekong River Basin (LMRB) in Southeast Asia. Monthly, seasonal, and annual comparisons were performed, which included the calculations of correlation coefficient, coefficient of determination, bias, root mean square error (RMSE), and mean absolute error (MAE). Our validation test showed TMPA to correctly detect precipitation or no-precipitation 64.9% of all days and CHIRPS 66.8% of all days, compared to daily in-situ rainfall measurements. The accuracy of the satellite-based products varied greatly between the wet and dry seasons. Both TMPA and CHIRPS showed higher correlation with in-situ data during the wet season (June–September) as compared to the dry season (November–January). Additionally, both performed better on a monthly than an annual time-scale when compared to in-situ data. The satellite-based products showed wet biases during months that received higher cumulative precipitation. Based on a spatial correlation analysis, the average r-value of CHIRPS was much higher than TMPA across the basin. CHIRPS correlated better than TMPA at lower elevations and for monthly rainfall accumulation less than 500 mm. While both satellite-based products performed well, as compared to rain gauge measurements, the present research shows that CHIRPS might be better at representing precipitation over the LMRB than TMPA.

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