The understanding of water resource changes and a proper projection of their future availability are necessary elements of sustainable water planning. Monitoring GWS change and future water resource availability are crucial, especially under changing climatic conditions. Traditional methods for in situ groundwater well measurement are a significant challenge due to data unavailability. The present investigation utilized the Long Short Term Memory (LSTM) networks to monitor and forecast Terrestrial Water Storage Change (TWSC) and Ground Water Storage Change (GWSC) based on Gravity Recovery and Climate Experiment (GRACE) datasets from 2003–2025 for five basins of Saudi Arabia. An attempt has been made to assess the effects of rainfall, water used, and net budget modeling of groundwater. Analysis of GRACE-derived TWSC and GWSC estimates indicates that all five basins show depletion of water from 2003–2020 with a rate ranging from −5.88 ± 1.2 mm/year to −14.12 ± 1.2 mm/year and −3.5 ± 1.5 to −10.7 ± 1.5, respectively. Forecasting based on the developed LSTM model indicates that the investigated basins are likely to experience serious water depletion at rates ranging from −7.78 ± 1.2 to −15.6 ± 1.2 for TWSC and −4.97 ± 1.5 to −12.21 ± 1.5 for GWSC from 2020–2025. An interesting observation was a minor increase in rainfall during the study period for three basins. 

https://file.techscience.com/ueditor/files/cmc/TSP_CMC-70-3/TSP_CMC_20495/TSP_CMC_20495.pdf

Deep Learning Based Modeling of Groundwater Storage Change

Aim The aim was to evaluate fire activity for the entire Amazon and Amazon regions within each country/department from 2003 to 2020, assessing the potential contributions of drought and deforestation and contrasting 2020 with the previous years. Location Amazonia sensu lato. Time period Annually from 2003 to 2020. Major taxa studied Terrestrial plants. Methods We collected time series of MODIS active fire detections and burned area and assessed the yearly burned area of several land-use/land-cover types. We also divided the Amazon territory into 10 km × 10 km grid cells to identify annual anomalies in active fire occurrence, rainfall, maximum cumulative water deficit (MCWD) and deforestation. Rainfall and MCWD anomalies for a given cell each year consisted of annual values at least one standard deviation below average for that cell from 2003 to 2020, whereas fire and deforestation anomalies had annual values at least one standard deviation above the average for that cell. Results Brazil and Bolivia have contributed, on average, >70% and about 15%, respectively, of annual active fire detections in the Amazon, in addition to more than half and about one-third of annual burned areas in the region. On average, 32% of annual burned areas in the Amazon have consisted of agricultural lands, 29% of natural grasslands and 16% of old-growth forests. The annual extent of areas with fire anomalies was significantly associated with the annual extent of areas with deforestation anomalies, but not significantly associated with the annual extent of areas experiencing water deficit anomalies. In 2020, the total burned area in the Amazon was the greatest since 2010, and the ratio of burned area per active fire was the second greatest of the time series, despite a much lower extent of areas with anomalously high water deficit in comparison to the 2015–2016 megadrought. Main conclusions Our findings suggest that the majority of anomalously high fire occurrences in the Amazon since 2003 did not occur in anomalous drought conditions. The intensification of agricultural fires and deforestation aggravated the burning of Amazonian ecosystems in 2020. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/geb.13577

Amazon fires in the 21st century: The year of 2020 in evidence

Accurately assessing groundwater storage changes in Niger is critical for long-term water resource management but is difficult due to sparse field data. We present a study of groundwater storage changes and recharge in Southern Niger, computed using data from NASA Gravity Recovery and Climate Experiment (GRACE) mission. We compute a groundwater storage anomaly estimate by subtracting the surface water anomaly provided by the Global Land Data Assimilation System (GLDAS) model from the GRACE total water storage anomaly. We use a statistical model to fill gaps in the GRACE data. We analyze the time period from 2002 to 2021, which corresponds to the life span of the GRACE mission, and show that there is little change in groundwater storage from 2002–2010, but a steep rise in storage from 2010–2021, which can partially be explained by a period of increased precipitation. We use the Water Table Fluctuation method to estimate recharge rates over this period and compare these values with previous estimates. We show that for the time range analyzed, groundwater resources in Niger are not being overutilized and could be further developed for beneficial use. Our estimated recharge rates compare favorably to previous estimates and provide managers with the data required to understand how much additional water could be extracted in a sustainable manner.

/pdf/evaluating-groundwater-storage-change-and-recharge-using-47v68qyw.pdf

Evaluating Groundwater Storage Change and Recharge Using GRACE Data: A Case Study of Aquifers in Niger, West Africa

Abstract Drought is a costly natural disaster that impacts economies and ecosystems worldwide, so monitoring drought and communicating its impacts to individuals, communities, industry, and governments is important for mitigation, adaptation, and decision‐making. This research describes a novel machine learning framework to predict and understand the Canadian Drought Monitor (CDM). This fully automated approach is trained on nearly two decades of expert analysis and would assist the comprehensive monitoring of drought impacts without the continued requirement of ground support, a benefit in many data‐limited areas across the country. The framework also integrates the Shapley Additive Explanation (SHAP) variable importance metric to provide insight into drought dynamics in near real‐time, demonstrating its usefulness for understanding the value of different data sets for drought assessments and dispelling the commonly held misconception that machine learning models are not useful for inference. The results demonstrate that the model can effectively predict the CDM maps and realistically capture the evolution of drought events over time. A SHAP analysis found that the Prairie drought of 2015 was related to a strong El Niño event that reduced water supply to a region already facing long‐term water deficits, and the subsequent reduction in groundwater availability was detected by the Gravity Recovery and Climate Experiment satellite. Overall, this research shows strong potential to streamline the CDM methodology, integrate scientific insight into operations in near real‐time using SHAP values, and provide an avenue to retrospectively extend the CDM for evaluating current and future drought events in a historical context. 

A Machine Learning Framework for Predicting and Understanding the Canadian Drought Monitor

The climate anomaly in the Amazon River basin (ARB) has a very important influence on global climate change and has always been the focus of scientists from all over the world. To fill the 11-month data gap between Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions, we fused the TWSC results from six GRACE solutions by using the generalized three-cornered hat and the least square method to improve the reliability of TWSC results, and then combined Swarm data to construct an uninterrupted long time series of a TWSC-based drought index (GRACE/Swarm-DSI). The drought index was used to detect and characterize the drought events in the ARB between 2003 and 2020. The results show that GRACE/Swarm-DSI has a strong correlation with Self-Calibrating Palmer Drought Severity Index (SCPDSI) (0.6345), Standardized Precipitation Evapotranspiration Index-3 (SPEI-3) (0.5411), SPEI-6 (0.6377) and SPEI-12 (0.6820), and the Nash–Sutcliffe efficiency between GRACE/Swarm-DSI and the above four drought indices are 0.3348, 0.2786, 0.4044 and 0.4627, respectively. Eleven drought events were identified in the ARB during the study period, and the 2005, 2010 and 2016 droughts are the most severe and the longest. The correlation between GRACE/Swarm-DSI and precipitation (PPT) (the correlation coefficient is 0.55 with a 2-month delay) is higher than that of evapotranspiration (ET) (the correlation coefficient is −0.18 with a 12-month delay). It explains that less PPT is the main cause of drought events in the ARB. The influence of PPT is greater in the plains than the one in the mountains and the response time of GRACE/Swarm-DSI to PPT is 1~2 months in most regions. Our results provide a certain reference for the hydrological application of the Swarm model in filling the gap between GRACE and GRACE-FO missions.

/pdf/the-drought-events-over-the-amazon-river-basin-from-2003-to-vh1eyyi9.pdf

The Drought Events over the Amazon River Basin from 2003 to 2020 Detected by GRACE/GRACE-FO and Swarm Satellites

The Gravity Recovery and Climate Experiment (GRACE) mission has measured total water storage change (TWSC) and interpreted drought patterns in an unparalleled way since 2002. Nevertheless, there are few sources that can be used to understand drought patterns prior to the GRACE era. In this study, we extended the gridded GRACE TWSC to 1993 by combining principal component analysis (PCA), least square (LS) fitting, and multiple linear regression (MLR) methods using climate variables as input drivers. We used the extended (climate-driven) TWSC to interpret drought patterns (1993–2019) over the Amazon basin. Results showed that, in the Amazon area with the resolution of 0.5°, GRACE, GRACE follow on, and Swarm had correlation coefficients of 0.95, 0.92, and 0.77 compared with climate-driven TWSCS, respectively. The drought patterns assessed by the climate-driven TWSC were consistent with those interpreted by the Palmer Drought Severity Index and GRACE TWSC. We also found that the 1998 and 2016 drought events in the Amazon, both induced by strong El Nino events, showed similar drought patterns. This study provides a new perspective for interpreting long-term drought patterns prior to the GRACE period.

Drought Events over the Amazon River Basin (1993–2019) as Detected by the Climate-Driven Total Water Storage Change

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Drought Events over the Amazon River Basin (1993–2019) as Detected by the Climate-Driven Total Water Storage Change