A New GRACE Downscaling Approach for Deriving High‐Resolution Groundwater Storage Changes Using Ground‐Based Scaling Factors DOI Creative Commons
Huixiang Li, Yun Pan, Pat J.‐F. Yeh

et al.

Water Resources Research, Journal Year: 2024, Volume and Issue: 60(11)

Published: Nov. 1, 2024

Abstract To compensate for the coarse resolution of groundwater storage (GWS) estimation by Gravity Recovery and Climate Experiment (GRACE) satellites make better use available observed groundwater‐level (GWL) data in some aquifers, a ground‐based scaling factor (SF) method is proposed here to derive high‐resolution GRACE GWS estimates. Improvement achieved using gridded SF derived from assimilating GWL observations. The tested on North China Plain (NCP, ∼140,000 km 2 ), where dense network observation wells consistently estimated specific yield (SY) set are available, demonstrate its effectiveness practical applications. sensitivities SF‐estimated accuracy specification SY assimilation explored through four designed numerical experiments. Results show that this novel can reduce impact uncertainty estimates, particularly regions with more pronounced regional trends. primarily determined whether assimilated reflect regionally averaged trend. less dependent number assimilated. trend (2004–2015) NCP −32.6 ± 1.3 mm/yr (−4.6 0.2 3 /yr), contrasting trends found west Piedmont (∼54,000 , loss −66.8 mm/yr) coastal Eastern (∼20,000 gain +7.2 mm/yr). Despite limitations time scale dependence inherent method, study highlights benefits situ instead model simulations estimating downscale higher‐resolution desired local water resources management.

Language: Английский

The analysis on groundwater storage variations from GRACE/GRACE-FO in recent 20 years driven by influencing factors and prediction in Shandong Province, China DOI Creative Commons
Wanqiu Li, Lifeng Bao, Guobiao Yao

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 9, 2024

Monitoring and predicting the regional groundwater storage (GWS) fluctuation is an essential support for effectively managing water resources. Therefore, taking Shandong Province as example, data from Gravity Recovery Climate Experiment (GRACE) GRACE Follow-On (GRACE-FO) used to invert GWS January 2003 December 2022 together with Watergap Global Hydrological Model (WGHM), in-situ volume level data. The spatio-temporal characteristics are decomposed using Independent Components Analysis (ICA), impact factors, such precipitation human activities, which also analyzed. To predict short-time changes of GWS, Support Vector Machines (SVM) adopted three commonly methods Long Short-Term Memory (LSTM), Singular Spectrum (SSA), Auto-Regressive Moving Average (ARMA), comparison. results show that: (1) loss intensity western significantly greater than those in coastal areas. From 2006, increased sharply; during 2007 2014, there exists a rate - 5.80 ± 2.28 mm/a GWS; linear trend change 5.39 3.65 2015 2022, may be mainly due effect South-to-North Water Diversion Project. correlation coefficient between WGHM 0.67, consistent level. (2) has higher positive monthly Precipitation Climatology Project (GPCP) considering time delay after moving average, similar energy spectrum depending on Continuous Wavelet Transform (CWT) method. In addition, influencing facotrs annual analyzed, including consumption mining, farmland irrigation 0.80, 0.71, respectively. (3) For prediction, SVM method analyze, training samples 180, 204 228 months established goodness-of-fit all 0.97. coefficients 0.56, 0.75, 0.68; RMSE 5.26, 4.42, 5.65 mm; NSE 0.28, 0.43, 0.36, performance model better other short-term prediction.

Language: Английский

Citations

9
Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets DOI Open Access
F. M. Hasan,

Paul Medley,

Jason Drake

et al.

Water, Journal Year: 2024, Volume and Issue: 16(13), P. 1904 - 1904

Published: July 3, 2024

Machine learning (ML) applications in hydrology are revolutionizing our understanding and prediction of hydrological processes, driven by advancements artificial intelligence the availability large, high-quality datasets. This review explores current state ML hydrology, emphasizing utilization extensive datasets such as CAMELS, Caravan, GRDC, CHIRPS, NLDAS, GLDAS, PERSIANN, GRACE. These provide critical data for modeling various parameters, including streamflow, precipitation, groundwater levels, flood frequency, particularly data-scarce regions. We discuss type methods used significant successes achieved through those models, highlighting their enhanced predictive accuracy integration diverse sources. The also addresses challenges inherent applications, heterogeneity, spatial temporal inconsistencies, issues regarding downscaling LSH, need incorporating human activities. In addition to discussing limitations, this article highlights benefits utilizing high-resolution compared traditional ones. Additionally, we examine emerging trends future directions, real-time quantification uncertainties improve model reliability. place a strong emphasis on citizen science IoT collection hydrology. By synthesizing latest research, paper aims guide efforts leveraging large techniques advance enhance water resource management practices.

Language: Английский

Citations

8
Impacts of groundwater storage variability on soil salinization in a semi-arid agricultural plain DOI Creative Commons
Geng Cui, Yan Liu,

Xiaojie Li

et al.

Geoderma, Journal Year: 2025, Volume and Issue: 454, P. 117162 - 117162

Published: Jan. 6, 2025

Language: Английский

Citations

0
A novel generative adversarial network and downscaling scheme for GRACE/GRACE-FO products: Exemplified by the Yangtze and Nile River Basins DOI
Jielong Wang, Yunzhong Shen, Joseph L. Awange

et al.

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 969, P. 178874 - 178874

Published: Feb. 24, 2025

Language: Английский

Citations

0
Reanalysis and Forecasting of Total Water Storage and Hydrological States by Combining Machine Learning With CLM Model Simulations and GRACE Data Assimilation DOI Creative Commons
Fupeng Li, Anne Springer, Jürgen Kusche

et al.

Water Resources Research, Journal Year: 2025, Volume and Issue: 61(2)

Published: Feb. 1, 2025

Abstract Hydrological Models face limitations in simulating the water cycle due to deficiencies process representation and such problems also weaken their forecasting skills. Here, we use Machine Learning (ML) forecast Gravity Recovery Climate Experiment (GRACE) derived total storage anomaly (TWSA) up 1 year ahead over Europe with near real‐time meteorological observations as predictors. Subsequently, assimilate forecasted GRACE TWSA into Community Land Model (CLM) enhance its performance both reanalysis forecast. As found five hindcast experiments, ML for following fits quite well actual Europe, an average correlation of 0.91, 0.92, 0.94 Iberian peninsula, Danube, Volga basins. Validation by data suggests that assimilating can improve CLM's capacity not only hydrological states but droughts. Additionally, is a viable alternative terms enhancing on seasonal annual scales through Data assimilation (DA). We highlight contribution DA generating CLM based overcomes purely model‐based TWSA. This study drought or resource services might consider integrate would benefit from constraining models ML‐forecasted At shorter timescales, forecasts could be useful quick‐look analysis processing suggested upcoming satellite gravity missions.

Language: Английский

Citations

0
Statistical downscaling of GRACE terrestrial water storage changes based on the Australian Water Outlook model DOI Creative Commons
Ikechukwu Kalu, Christopher E. Ndehedehe, Vagner G. Ferreira

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: May 2, 2024

Abstract The coarse spatial resolution of the Gravity Recovery and Climate Experiment (GRACE) dataset has limited its application in local water resource management accounting. Despite efforts to improve GRACE resolution, achieving high downscaled grids that correspond hydrological behaviour patterns is still limited. To overcome this issue, we propose a novel statistical downscaling approach GRACE-terrestrial storage changes (ΔTWS) using precipitation, evapotranspiration (ET), runoff data from Australian Water Outlook. These budget components drive column much global land area. Here, original 1.0° × 0.05° over large hydro-geologic basin northern Australia (the Cambrian Limestone Aquifer—CLA), capturing sub- grid heterogeneity ΔTWS region. results are validated 12 in-situ groundwater monitoring stations estimates CLA’s April 2002 June 2017. change time (ds/dt) estimated model was weakly correlated (r = 0.34) with ΔTWS. weak relationship attributed possible uncertainties inherent ET datasets used budget, particularly during summer months. Our proposed methodology provides an opportunity freshwater reporting enhances feasibility for other strengthen local-scale applications.

Language: Английский

Citations

3
Machine learning downscaling of GRACE/GRACE-FO data to capture spatial-temporal drought effects on groundwater storage at a local scale under data-scarcity DOI Creative Commons

Christopher Shilengwe,

Kawawa Banda, Imasiku Nyambe

et al.

ENVIRONMENTAL SYSTEMS RESEARCH, Journal Year: 2024, Volume and Issue: 13(1)

Published: Sept. 3, 2024

The continued threat from climate change and human impacts on water resources demands high-resolution continuous hydrological data accessibility for predicting trends availability. This study proposes a novel threefold downscaling method based machine learning (ML) which integrates: normalization; interaction of hydrometeorological variables; the application time series split cross-validation that produces high spatial resolution groundwater storage anomaly (GWSA) dataset Gravity Recovery Climate Experiment (GRACE) its successor mission, GRACE Follow-On (GRACE-FO). In study, relationship between terrestrial (TWSA) other land surface variables (e.g., vegetation coverage, temperature, precipitation, in situ level data) is leveraged to downscale GWSA. predicted downscaled GWSA datasets were tested using monthly observations, results showed model satisfactorily reproduced temporal variations area, with Nash-Sutcliffe efficiency (NSE) correlation coefficient values 0.8674 (random forest) 0.7909 (XGBoost), respectively. Evapotranspiration was most influential predictor variable random forest model, whereas it rainfall XGBoost model. particular, excelled aligning closely observed patterns, as evidenced by positive correlations lower error metrics (Mean Absolute Error (MAE) 54.78 mm; R-squared (R²) 0.8674). 5 km (based decreasing trend associated variability pattern. An increase drought severity during El Niño lengthened full recovery historical trends. Furthermore, lag occurrence precipitation recharge likely controlled intensity characteristics aquifer. Projected increases could further times response droughts changing climate, resetting new tipping condition. Therefore, adaptation strategies must recognise less will be available supplement supply droughts.

Language: Английский

Citations

2
Bridging the Temporal Gaps in GRACE/GRACE–FO Terrestrial Water Storage Anomalies over the Major Indian River Basins Using Deep Learning DOI

Pragay Shourya Moudgil,

G. Srinivasa Rao, Kosuke Heki

et al.

Natural Resources Research, Journal Year: 2024, Volume and Issue: 33(2), P. 571 - 590

Published: Feb. 22, 2024

Language: Английский

Citations

1
Impacts of climate change and human activities on global groundwater storage from 2003-2022 DOI

Jiawen Zhang,

Tanja Liesch, Nico Goldscheider

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 26, 2024

Abstract Groundwater is integral to land surface processes, significantly influencing water and energy cycles, it an important resource for drinking ecosystems. Climate change anthropogenic impacts have ever-increasing influence on the cycle groundwater storage in recent decades. This study leverages GRACE ERA5 data analyze variability from 2003 2022, with a 1° spatial resolution. Approximately 81% of global regions shown significant changes, 48% experiencing declines 52% observing increases. 3.2 billion people live where has declined over past 20 years. Findings indicate considerable depletion hotspots (> mm/year) northern India, North China Plain, eastern Brazil, Middle East, around Caspian Sea. Analysis by climatic region showed that most pronounced occurred arid semi-arid areas aridity index between 0.1 0.5, highlighting sparse vegetation fragile In terms climate change, compared precipitation, meteorological drought wetness are primary factors distribution storage. primarily caused unsustainable extraction, especially irrigation. facilitates monitoring, underscoring need long-term dynamic observation inform sustainable management policies crucial facing ensure freshwater sustainability.

Language: Английский

Citations

0
A New GRACE Downscaling Approach for Deriving High‐Resolution Groundwater Storage Changes Using Ground‐Based Scaling Factors DOI Creative Commons
Huixiang Li, Yun Pan, Pat J.‐F. Yeh

et al.

Water Resources Research, Journal Year: 2024, Volume and Issue: 60(11)

Published: Nov. 1, 2024

Abstract To compensate for the coarse resolution of groundwater storage (GWS) estimation by Gravity Recovery and Climate Experiment (GRACE) satellites make better use available observed groundwater‐level (GWL) data in some aquifers, a ground‐based scaling factor (SF) method is proposed here to derive high‐resolution GRACE GWS estimates. Improvement achieved using gridded SF derived from assimilating GWL observations. The tested on North China Plain (NCP, ∼140,000 km 2 ), where dense network observation wells consistently estimated specific yield (SY) set are available, demonstrate its effectiveness practical applications. sensitivities SF‐estimated accuracy specification SY assimilation explored through four designed numerical experiments. Results show that this novel can reduce impact uncertainty estimates, particularly regions with more pronounced regional trends. primarily determined whether assimilated reflect regionally averaged trend. less dependent number assimilated. trend (2004–2015) NCP −32.6 ± 1.3 mm/yr (−4.6 0.2 3 /yr), contrasting trends found west Piedmont (∼54,000 , loss −66.8 mm/yr) coastal Eastern (∼20,000 gain +7.2 mm/yr). Despite limitations time scale dependence inherent method, study highlights benefits situ instead model simulations estimating downscale higher‐resolution desired local water resources management.

Language: Английский

Citations

0