Short-term trends in Africa's freshwater resources: Rates and drivers

Synergistic Effect of Neighboring Superhydrophilic Patterns on Superhydrophobic Surfaces for Enhanced Fog Collection

In this work, a superhydrophobic surface loaded with elliptical superhydrophilic patterns in a V-shaped arrangement has been fabricated with the help of shape memory membranes with uniform vertically penetrative channels (i.e., SMEUVs, as a mask). The special geometry (elliptical) and arrangement (V-shaped) of superhydrophilic patterns play important roles in the enhancement of fog collection. The former not only facilitates droplet detachment from superhydrophilic regions but also dominates its directional transport. The latter promotes the coalescence of tiny droplets based on directional flow pathway toward collection area, minimizing the risk of re-evaporation of them and providing fresh sites for subsequent nucleation and growth of droplets. The combination of them contributes to the synergistic effect of neighboring superhydrophilic patterns on the superhydrophobic surface. As a result, the optimal specimen (V-shaped arrangement of elliptical superhydrophilic patterns) in this work exhibits much higher fog collection efficiency (∼4 times) relative to the reference (superhydrophobic or superhydrophilic surface). Our results are significant for the design and fabrication of fog collection systems.

Comparison of groundwater storage changes over losing and gaining aquifers of China using GRACE satellites, modeling and in-situ observations

Groundwater depletion in intensively exploited aquifers of China has been widely recognized, whereas an overall examination of groundwater storage (GWS) changes over major aquifers remains challenging due to limited data and notable uncertainties. Here, we present a study to explore GWS changes over eighteen major aquifers covering an area of 1,680,000 km2 in China using data obtained from the Gravity Recovery and Climate Experiments (GRACE), global models, and in-situ groundwater level observations. The analysis aims to reveal the discrepancy in annual trends, amplitudes, and phases associated with GWS changes among different aquifers. It is found that GWS changes in the studied aquifers represent a spatial pattern of 'Wet-gets-more, Dry-gets-less'. An overall decreasing trend of -4.65 ± 0.34 km3/yr is observed by GRACE from 2005 to 2016, consisting of a significant (p < 0.05) increase of 47.28 ± 3.48 km3 in 7 aquifers and decrease of 103.56 ± 2.4 km3 (∼2.6 times the full storage capacity of the Three Gorges Reservoir) in 10 aquifers summed over the 12 years. The annual GWS normally reaches a peak in late July with an area-weighted average annual amplitude of 19 mm, showing notable discrepancy in phases and amplitudes between the losing aquifers (12 mm in middle August) in northern China and gaining aquifers (28 mm in early July) mostly in southern China. GRACE estimates are generally comparable, but can be notably different, with the results obtained from model simulations and in-situ observations at aquifer scale, with the area-weighted average correlation coefficients of 0.6 and 0.5, respectively. This study highlights different GWS changes of losing and gaining aquifers in response to coupled impacts of hydrogeology, climate and human interventions, and calls for divergent adaptions in regional groundwater management.

Combining downscaled-GRACE data with SWAT to improve the estimation of groundwater storage and depletion variations in the Irrigated Indus Basin (IIB)

The growth of agricultural production systems is a major driver of groundwater depletion worldwide. Balancing groundwater supply and food production requires localized understanding of groundwater storage and depletion variations in response to diverse cropping systems and surface water availability for irrigation. While advances through Gravity Recovery and Climate Experiment (GRACE) have facilitated estimating the groundwater storage (GWS) changes in recent years, the coarse resolution of GRACE data hinders the characterization of GWS variation hotspots. Herein, we present a novel spatial water balance approach to improve the distributed estimation of groundwater storage and depletion changes at a spatial scale that can detect the hotspots of GWS variation. We used a mixed geographically weighted regression (MGWR) model to downscale GRACE Level-3 data from coarse resolution (1° × 1°) to fine scale (1 km × 1 km) based on high resolution environmental variables. We then combined the downscaled GRACE-based GWS variations with results from a calibrated Soil and Water Assessment Tool (SWAT) model. We demonstrate an application of the approach in the Irrigated Indus Basin (IIB). Between 2002 and 2019, total loss of groundwater reserves varied in the IIB's 55 canal command areas with the highest loss observed in Dehli Doab by >50 km³ followed by 7.8-49 km³ in the upstream, and 0.77-7.77 km³ in the downstream canal command areas. GWS declined by -325.55 mm/year at Dehli Doab, followed by -186.86 mm/year at BIST Doab, -119.20 mm/year at BARI Doab, and -100.82 mm/year at JECH Doab. The rate of groundwater depletion is increasing in the canal command areas of Delhi Doab and BIST Doab by 0.21-0.35 m/year. Larger groundwater depletion in some canal command areas (e.g., RACHNA, BIST Doab, and Delhi Doab) is associated with the rice-wheat cropping system, low rainfall, and low flows from tributaries.

Leveraging machine learning methods to quantify 50 years of dwindling groundwater in India

Global compilations and regional studies, indicative of the unsustainable extraction and subsequent unremittingly depleting groundwater (GW) in India, either provide bulk estimates or are confined to the river basins and therefore conceal inferences from a nationwide policymaking perspective. Here, we provide the state-wise past (2000-2020) and future (2030-2050) assessment of dwindling groundwater in India utilizing in-situ groundwater levels (GWL) from 54,112 wells, remote sensing products, and hydrological simulations. By employing three machine learning methods, we show a decline in GWL of over 80% in North India with a notable shift towards the eastern state of Uttar Pradesh and a cumulative groundwater loss (169.96 ± 19.67 km³) equivalent to the water storage capacity of the world's biggest dam (Kariba Dam, Zimbabwe). Its likely contribution to sea-level rise (0.47 ± 0.06 mm) is about 64% of that from annual global glacier melt. Our results typically contrast the GW recovery paradox in South India (e.g., a declining trend of -84.48 ± 38.81 mm/a (p < 0.05) in Andhra Pradesh during 2000-2020), reveal high seasonal variability (e.g., up to ~6 m in Maharashtra), and illustrate the skewed effect of survivor bias in the traditional assessments. We infer the significant impact of underlying hydrogeology and the implementation of water-related policies and projects on the GWL dynamic and variability in the region. Projected GWL reveals a likely water scarcity situation for about 2.8 million km² area and one billion residents of the country up to 2050. Our observation-based analysis offers insights into the state-level monthly GW dynamics, which is critical for efficient interstate resource allocation, development plans, and policy interventions with broad methodological implications for the water-scarce countries.

Factors controlling groundwater radioactivity in arid environments: An automated machine learning approach

Groundwater resources in the Kingdom of Saudi Arabia (KSA) have high levels of natural radioactivity. Within the northwestern KSA, gross alpha (α) and gross beta (β) levels exceed national and international drinking-water limits. In this study, we developed and used an automated machine learning (AML) approach to quantify relationships between gross α and gross β activities and different geological, hydrogeological, and geochemical conditions. Two AML model groups (group I for gross α; group II for gross β) were constructed, using water samples collected from 360 irrigation and water supply wells, to define a robust model that explains the spatial variability in gross α and gross β activities, as well as variables that control the gross activities. Each group contained four model families: deep neural network (DNN), gradient boosting machine (GBM), generalized linear model (GLM), and distributed random forest (DRF). Model inputs include chemical compositions as well as geological and hydrogeological conditions. Three performance metrics were used to evaluate the models during training and testing: normalized root mean square error (NRMSE), Pearson's correlation coefficient (r), and Nash-Sutcliff efficiency (NSE) coefficient. Results indicate that (1) the GBM model outperformed (training: NRMSE: 0.37 ± 0.10; r: 0.92 ± 0.05; NSE: 0.85 ± 0.09; testing: NRMSE: 0.71 ± 0.08; r: 0.72 ± 0.08; NSE: 0.49 ± 0.12) the DNN, DRF, and GLM models when modelling gross α activities; (2) gross α activities are controlled by pH, stream density, nitrate, manganese, and vegetation index; (3) the DRF model outperformed (training: NRMSE: 0.41 ± 0.05; r: 0.92 ± 0.02; NSE: 0.83 ± 0.04; testing: NRMSE: 0.67 ± 0.09; r: 0.77 ± 0.07; NSE: 0.54 ± 0.12) the GBM, DNN, and GLM models when modelling gross β activities; (4) input variables that affect the gross β actives are pH, temperature, stream density, lithology, and nitrate; and (5) no single model could be used to model both gross α and gross β activities-instead, a combination of AML models should be used. Our computationally efficient approach provides a framework and insights for using AML techniques in water quality investigations and promotes more and improved use of different geological, hydrogeological, and geochemical datasets by the scientific community and decision makers to develop guidelines for mitigation.

High-Performance Hydrogel Based on Modified Chitosan for Removal of Heavy Metal Ions in Borehole: A Case Study from the Bahariya Oasis, Egypt

Globally, there is a rising demand for water purification. This demand is driven by numerous factors, including economic growth, increasing population, water shortage, and deterioration of water quality. The current work highlights the manufacturing of environmentally friendly and highly efficient sorbent based on chitosan nanoparticles after successive crosslinking (using glutaraldehyde) and modification through grafting of 4-aminoazobenzene-3,4′-disulfonic acid (AZDS) as a source of sulfonic groups. First, the produced sorbent was thoroughly specified using FTIR, TGA, SEM, SEM-EDX, pHpzc, BET (nitrogen sorption desorption isotherms), and elemental analyses (EA). The sorbent was tested for the sorption of Fe(III) before application to highly contaminated iron water well samples. Next, the sorption was improved as the sulfonation process was conducted under the selected experimental conditions within 25 and 20 min with a maximum capacity of 2.7 and 3.0 mmol Fe g−1 in visible light and under UV, respectively. Then, the uptake kinetics for both techniques were fitted by the pseudo-first-order rate equation (PFORE), in which the effect of the resistance to intraparticle diffusion has remained an unneglected factor, while the Langmuir equation has fitted the sorption isotherms. After that, the efficient desorption was achieved by using 0.2 M hydrochloric acid solution, and the desorption process was as fast as the sorption process; 15 min was sufficient for complete desorption. The sorbent shows high selectivity for heavy metal ions compared to the representative elements. Finally, the sorbent was used for the removal of heavy metal ions from a highly contaminated water well in the Bahariya Oasis and appeared to be highly efficient for heavy metal removal even in a diluted solution. Accordingly, it can be implemented in the task of water treatment.

Virtual Water Flow Pattern in the Yellow River Basin, China: An Analysis Based on a Multiregional Input-Output Model

Research on the Yellow River Basin's virtual water is not only beneficial for rational water resource regulation and allocation, but it is also a crucial means of relieving the pressures of a shortage of water resources. The water stress index and pull coefficient have been introduced to calculate the implied virtual water from intraregional and interregional trade in the Yellow River Basin on the basis of a multi-regional input-output model; a systematic study of virtual water flow has been conducted. The analysis illustrated that: (1) Agriculture is the leading sector in terms of virtual water input and output among all provinces in the Yellow River Basin, which explains the high usage. Therefore, it is important to note that the agricultural sector needs to improve its water efficiency. In addition to agriculture, virtual water is mainly exported through supply companies in the upper reaches; the middle reaches mainly output services and the transportation industry, and the lower reaches mainly output to the manufacturing industry. Significant differences exist in the pull coefficients of the same sectors in different provinces (regions). The average pull coefficients of the manufacturing, mining, and construction industries are large, so it is necessary to formulate stricter water use policies. (2) The whole basin is in a state of virtual net water input, that is, throughout the region. The Henan, Shandong, Shanxi, Shaanxi, and Qinghai Provinces, which are relatively short of water, import virtual water to relieve local water pressures. However, in the Gansu Province and the Ningxia Autonomous Region, where water resources are not abundant, continuous virtual water output will exacerbate the local resource shortage. (3) The Yellow River Basin's virtual water resources have obvious geographical distribution characteristics. The cross-provincial trade volume in the downstream area is high; the virtual water trade volume in the upstream area is low, as it is in the midstream and downstream areas; the trade relationship is insufficient. The Henan and Shandong Provinces are located in the dominant flow direction of Yellow River Basin's virtual water, while Gansu and Inner Mongolia are at the major water sources. Trade exchanges between the midstream and downstream and the upstream should be strengthened. Therefore, the utilization of water resources should be planned nationwide to reduce water pressures, and policymakers should improve the performance of agricultural water use within the Yellow River Basin and change the main trade industries according to the resource advantages and water resources situation of each of them.

Filling Temporal Gaps within and between GRACE and GRACE-FO Terrestrial Water Storage Records: An Innovative Approach

Temporal gaps within the Gravity Recovery and Climate Experiment (GRACE) (gap: 20 months), between GRACE and GRACE Follow-On (GRACE-FO) missions (gap: 11 months), and within GRACE-FO record (gap: 2 months) make it difficult to analyze and interpret spatiotemporal variability in GRACE- and GRACE-FO-derived terrestrial water storage (TWSGRACE) time series. In this study, an overview of data and approaches used to fill these gaps and reconstruct the TWSGRACE record at the global scale is provided. In addition, the study provides an innovative approach that integrates three machine learning techniques (deep-learning neural networks [DNN], generalized linear model [GLM], and gradient boosting machine [GBM]) and eight climatic and hydrological input variables to fill these gaps and reconstruct the TWSGRACE data record at both global grid and basin scales. For each basin and grid cell, the model performance was assessed using Nash–Sutcliffe efficiency coefficient (NSE), correlation coefficient (CC), and normalized root-mean-square error (NRMSE), a leader model was selected based on the model performance, and variables that significantly control leader model outputs were defined. Results indicate that (1) the leader model reconstructed the TWSGRACE with high accuracy over both grid and local scales, particularly in wet and low anthropogenically active regions (grid scale: NSE = 0.65 ± 0.20, CC = 0.81 ± 0.13, and NSE = 0.56 ± 0.16; basin scale: NSE = 0.78 ± 0.14, CC = 0.89 ± 0.07, and NRMSE = 0.43 ± 0.14); (2) no single model was flawless in reconstructing the TWSGRACE over all grids or basins, so a combination of models is necessary; (3) basin-scale models outperform grid-scale models; (4) the DNN model outperforms both GLM and GBM at the basin scale, whereas the GBM outperforms at the grid scale; (5) among other inputs, the Global Land Data Assimilation System (GLDAS)-derived TWS controls the model performance on both basin and grid scales; and (6) the reconstructed TWSGRACE data captured extreme climatic events over the investigated basins and grid cells. The developed approach is robust, effective, and could be used to accurately reconstruct TWSGRACE for any hydrologic system across the globe.

Quantifying Changes in Groundwater Storage and Response to Hydroclimatic Extremes in a Coastal Aquifer Using Remote Sensing and Ground-Based Measurements: The Texas Gulf Coast Aquifer

With the increasing vulnerability of groundwater resources, especially in coastal regions, there is a growing need to monitor changes in groundwater storage (GWS). Estimations of GWS have been conducted extensively at regional to global scales using GRACE and GRACE-FO observations. The major goal of this study was to evaluate the applicability of uninterrupted monthly GRACE-derived terrestrial water storage (TWSGRACE) records in facilitating detection of long- and short-term hydroclimatic events affecting the GWS in a coastal area. The TWSGRACE data gap was filled with reconstructed values from multi-linear regression (MLR) and artificial neural network (ANN) models and used to estimate changes in GWS in the Texas coastal region (Gulf Coast and Carrizo–Wilcox Aquifers) between 2002 and 2019. The reconstructed TWSGRACE, along with soil moisture storage (SMS) from land surface models (LSMs), and surface water storage (SWS) were used to estimate the GRACE-derived GWS (GWSGRACE), validated against the GWS estimated from groundwater level observations (GWSwell) and extreme hydroclimatic event records. The results of this study show: (1) Good agreement between the predicted TWSGRACE data gaps from the MLR and ANN models with high accuracy of predictions; (2) good agreement between the GWSGRACE and GWSwell records (CC = 0.56, p-value < 0.01) for the 2011–2019 period for which continuous GWLwell data exists, thus validating the approach and increasing confidence in using the reconstructed TWSGRACE data to monitor coastal GWS; (3) a significant decline in the coastal GWSGRACE, at a rate of 0.35 ± 0.078 km3·yr−1 (p-value < 0.01), for the 2002–2019 period; and (4) the reliable applicability of GWSGRACE records in detecting multi-year drought and wet periods with good accuracy: Two drought periods were identified between 2005–2006 and 2010–2015, with significant respective depletion rates of −8.9 ± 0.95 km3·yr−1 and −2.67 ± 0.44 km3·yr−1 and one wet period between 2007 and 2010 with a significant increasing rate of 2.6 ± 0.63 km3·yr−1. Thus, this study provides a reliable approach to examine the long- and short-term trends in GWS in response to changing climate conditions with significant implications for water management practices and improved decision-making capabilities.

Assessing Freshwater Changes over Southern and Central Africa (2002–2017)

In large freshwater river basins across the globe, the composite influences of large-scale climatic processes and human activities (e.g., deforestation) on hydrological processes have been studied. However, the knowledge of these processes in this era of the Anthropocene in the understudied hydrologically pristine South Central African (SCA) region is limited. This study employs satellite observations of evapotranspiration (ET), precipitation and freshwater between 2002 and 2017 to explore the hydrological patterns of this region, which play a crucial role in global climatology. Multivariate methods, including the rotated principal component analysis (rPCA) were used to assess the relationship of terrestrial water storage (TWS) in response to climatic units (precipitation and ET). The use of the rPCA technique in assessing changes in TWS is warranted to provide more information on hydrological changes that are usually obscured by other dominant naturally-driven fluxes. Results show a low trend in vegetation transpiration due to deforestation around the Congo basin. Overall, the Congo (r2 = 76%) and Orange (r2 = 72%) River basins maintained an above-average consistency between precipitation and TWS throughout the study region and period. Consistent loss in freshwater is observed in the Zambezi (−9.9 ± 2.6 mm/year) and Okavango (−9.1 ± 2.5 mm/year) basins from 2002 to 2008. The Limpopo River basin is observed to have a 6% below average reduction in rainfall rates which contributed to its consistent loss in freshwater (−4.6 ± 3.2 mm/year) from 2006 to 2012.Using multi-linear regression and correlation analysis we show that ET contributes to the variability and distribution of TWS in the region. The relationship of ET with TWS (r = 0.5) and rainfall (r = 0.8) over SCA provides insight into the role of ET in regulating fluxes and the mechanisms that drive precipitation in the region. The moderate ET–TWS relationship also shows the effect of climate and anthropogenic influence in their interactions.

Using GRACE satellite observations for separating meteorological variability from anthropogenic impacts on water availability

Gravity Recovery and Climate Experiment (GRACE) observations provide information on Total Water Storage Anomaly (TWSA) which is a key variable for drought monitoring and assessment. The so-called Total Water Storage Deficit Index (TWSDI) based on GRACE data has been widely used for characterizing drought events. Here we show that the commonly used TWSDI approach often exhibits significant inconsistencies with meteorological conditions, primarily upon presence of a trend in observations due to anthropogenic water use. In this study, we propose a modified version of TWSDI (termed, MTWSDI) that decomposes the anthropogenic and climatic-driven components of GRACE observations. We applied our approach for drought monitoring over the Ganges–Brahmaputra in India and Markazi basins in Iran. Results show that the newly developed MTWSDI exhibits consistency with meteorological drought indices in both basins. We also propose a deficit-based method for drought monitoring and recovery assessment using GRACE observations, providing useful information about volume of deficit, and minimum and average time for drought recovery. According to the deficit thresholds, water deficits caused by anthropogenic impacts every year in the Ganges–Brahmaputra basin and Markazi basins is almost equal to an abnormally dry condition and a moderate drought condition, receptively. It indicates that unsustainable human water use have led to a form of perpetual and accelerated anthropogenic drought in these basins. Continuation of this trend would deplete the basin and cause significant socio-economic challenges.

Multi-Scale Hydrologic Sensitivity to Climatic and Anthropogenic Changes in Northern Morocco

Natural and human-induced impacts on water resources across the globe continue to negatively impact water resources. Characterizing the hydrologic sensitivity to climatic and anthropogenic changes is problematic given the lack of monitoring networks and global-scale model uncertainties. This study presents an integrated methodology combining satellite remote sensing (e.g., GRACE, TRMM), hydrologic modeling (e.g., SWAT), and climate projections (IPCC AR5), to evaluate the impact of climatic and man-made changes on groundwater and surface water resources. The approach was carried out on two scales: regional (Morocco) and watershed (Souss Basin, Morocco) to capture the recent climatic changes in precipitation and total water storage, examine current and projected impacts on total water resources (surface and groundwater), and investigate the link between climate change and groundwater resources. Simulated (1979–2014) potential renewable groundwater resources obtained from SWAT are ~4.3 × 108 m3/yr. GRACE data (2002–2016) indicates a decline in total water storage anomaly of ~0.019m/yr., while precipitation remains relatively constant through the same time period (2002–2016), suggesting human interactions as the major underlying cause of depleting groundwater reserves. Results highlight the need for further conservation of diminishing groundwater resources and a more complete understanding of the links and impacts of climate change on groundwater resources.

Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate

The water cycle is the most essential supporting physical mechanism ensuring the existence of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is composed of evaporation, evapotranspiration, sublimation, water vapor transport, condensation, precipitation, runoff, infiltration and percolation, groundwater flow, and plant uptake. For a correct closure of the global water cycle, observations are needed of all these processes with a global perspective. In particular, precipitation requires continuous monitoring, as it is the most important component of the cycle, especially under changing climatic conditions. Passive and active sensors on board meteorological and environmental satellites now make reasonably complete data available that allow better measurements of precipitation to be made from space, in order to improve our understanding of the cycle’s acceleration/deceleration under current and projected climate conditions. The article aims to draw an up-to-date picture of the current status of observations of precipitation from space, with an outlook to the near future of the satellite constellation, modeling applications, and water resource management.