Water Storage Research Articles

An Abrupt Decline in Global Terrestrial Water Storage and Its Relationship with Sea Level Change

AbstractAs observed by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) missions, global terrestrial water storage (TWS), excluding ice sheets and glaciers, declined rapidly between May 2014 and March 2016. By 2023, it had not yet recovered, with the upper end of its range remaining 1 cm equivalent height of water below the upper end of the earlier range. Beginning with a record-setting drought in northeastern South America, a series of droughts on five continents helped to prevent global TWS from rebounding. While back-to-back El Niño events are largely responsible for the South American drought and others in the 2014–2016 timeframe, the possibility exists that global warming has contributed to a net drying of the land since then, through enhanced evapotranspiration and increasing frequency and intensity of drought. Corollary to the decline in global TWS since 2015 has been a rise in barystatic sea level (i.e., global mean ocean mass). However, we find no evidence that it is anything other than a coincidence that, also in 2015, two estimates of barystatic sea level change, one from GRACE/FO and the other from a combination of satellite altimetry and Argo float ocean temperature measurements, began to diverge. Herein, we discuss both the mechanisms that account for the abrupt decline in terrestrial water storage and the possible explanations for the divergence of the barystatic sea level change estimates.

Intermittent Drip Irrigation Soil Wet Front Prediction Model and Effective Water Storage Analysis

The depth and width of drip infiltration play a critical role in designing effective irrigation strategies. However, existing models primarily focus on continuous irrigation and fail to predict wetting patterns under intermittent drip irrigation. This study developed an infiltration model to estimate soil moisture depth and width under intermittent drip irrigation and identified strategies that enhance effective water storage. Indoor soil box simulations were conducted, with continuous drip irrigation as the control. Results showed that intermittent irrigation increased infiltration width and reduced depth, maximizing water storage efficiency. We recommend adopting an intermittent irrigation system with 1.5 h of irrigation followed by a 0.5 h interval, repeated four times. This system increased effective water storage by up to 16.23% compared to continuous irrigation. The proposed method is suitable for sandy loam farmland in southern Xinjiang and can significantly improve water use efficiency in arid regions.

Mapping the Potential of Managed Aquifer Recharge in Africa: GIS-based Multi-Criteria Decision Analysis Approach

Africa faces numerous challenges related to rainfall variability, droughts, water scarcity, and climate change. Managed Aquifer Recharge (MAR)- groundwater recharge and underground water storage for later use or environmental support presents a viable alternative for water storage and may provide an effective tool for coping with such challenges. However, the potential area where MAR can be feasibly implemented has not been identified. This study mapped MAR feasibility using a Geographic Information System-based Multi-Criteria Decision Analysis (GIS-MCDA) and assessed MAR potential in Africa. The methodology focused on three key pillars of MAR feasibility: intrinsic suitability based on biophysical parameters, water source availability, and water demand. Maps responding to these pillars were developed and combined to create a composite MAR feasibility map. Results show that 18% of the continental area falls into the low feasibility class, 73% into the moderate feasibility class, and 7% into the high feasibility class. The feasibility map was validated against 17 existing MAR schemes in Africa, demonstrating a good correlation between their locations and areas with MAR potential. Results of sensitivity analysis of criteria weights of the biophysical parameters show that geology is the most influential criterion, followed by slope. In general, this first feasibility assessment shows good potential for MAR implementation in Africa. Therefore, MAR should be considered prominently among other water storage options for resilience building in Africa and policymakers should ensure adequate resource allocation for its implementation. The feasibility map can be used to guide MAR planning and investment decisions.

Assessing Groundwater Storage Change in the Great Artesian Basin Using GRACE and Groundwater Budgets

AbstractLarge, confined aquifer systems play a vital role in sustaining human settlements and industries in many regions. Understanding the sustainability of these water resources requires the evaluation of groundwater storage change. Direct in‐situ observation of groundwater storage is limited by the distribution and availability of groundwater level and aquifer storativity data. Here, we use and compare two auxiliary methods, applied at basin and sub‐basin scales, to assess groundwater storage changes in the Great Artesian Basin (GAB), one of the World's largest confined aquifer systems. The first, the groundwater budget, derives storage change as the residual of fluxes in and out of the GAB, assuming they are all accounted for and accurately estimated. The second uses time‐variable gravity data from GRACE satellites to estimate temporal changes in groundwater mass, assuming that all other components of the terrestrial water mass change detected by GRACE are correctly subtracted. Despite the depletion observed during the 20th century, groundwater storage is mostly stable during 2002–2022. An increase in storage is detected in the Surat sub‐basin, a major recharge area. This increase is attributed to an over‐representation of large recharge events during the study period and/or storage recovery following rehabilitation of free‐flowing bores. The approach consisting in disaggregating GRACE data assumes that water storage changes in confined aquifers is dominated by changes in the GAB, and as such, it may overestimate the increase in the GAB by incorrectly attributing the increase occurring in overlying aquifers to the GAB. In contrast, the recharge estimates used in the groundwater budgets do not account for flood recharge and might underestimate storage increase in the GAB.

A Theoretical Framework to Quantify Ecosystem Pressure-Volume Relationships.

'Water potential' is the biophysically relevant measure of water status in vegetation relating to stomatal, canopy and hydraulic conductance, as well as mortality thresholds; yet, this cannot be directly related to measured and modelled fluxes of water at plot- to landscape-scale without understanding its relationship with 'water content'. The capacity for detecting vegetation water content via microwave remote sensing further increases the need to understand the link between water content and ecosystem function. In this review, we explore how the fundamental measures of water status, water potential and water content are linked at ecosystem-scale drawing on the existing theory of pressure-volume (PV) relationships. We define and evaluate the concept and limitations of applying PV relationships to ecosystems where the quantity of water can vary on short timescales with respect to plant water status, and over longer timescales and over larger areas due to structural changes in vegetation. As a proof of concept, plot-scale aboveground vegetation PV curves were generated from equilibrium (e.g., predawn) water potentials and water content of the above ground biomass of nine plots, including tropical rainforest, savanna, temperate forest, and a long-term Amazonian rainforest drought experiment. Initial findings suggest that the stored water and ecosystem capacitance scale linearly with biomass across diverse systems, while the relative values of ecosystem hydraulic capacitance and physiologically accessible water storage do not vary systematically with biomass. The bottom-up scaling approach to ecosystem water relations identified the need to characterise the distribution of water potentials within a community and also revealed the relevance of community-level plant tissue fractions to ecosystem water relations. We believe that this theory will be instrumental in linking our detailed understanding of biophysical processes at tissue-scale to the scale at which land surface models operate and at which tower-based, airborne and satellite remote sensing can provide information.

Does succulence in woody plants delay desiccation, and is stored water used to maintain physiological function during drought conditions?

Succulence is a trait that describes water storage in plant organs and tissues regardless of life form. Plants use the stored water to maintain physiological function and delay desiccation. However, it is unclear whether succulence in leaves, stems and roots of woody plants delays desiccation, whether it provides 'utilizable water' to maintain physiological function, or buffers changes in water status in drying soils through capacitance. We conducted a pot dry-down experiment with nine shrub species to determine whether woody plants with greater leaf, stem, or root succulence have greater shoot utilizable water or capacitance. We also investigated whether greater succulence delays desiccation, represented by cumulative VPD, until evapotranspiration ceased or until utilizable water was exhausted. Greater leaf and stem succulence were strongly related to greater shoot utilizable water and capacitance. However, desiccation time was not delayed in plants with greater total shoot succulence, utilizable water, or capacitance. Instead, woody plants with greater root succulence had longer desiccation times. This suggests that woody plants use aboveground succulence to maintain physiological function and water status during drought, whereas root succulence extends desiccation time. Our study improves the mechanistic understanding of how woody plants use stored water to survive in dryland ecosystems.

Texas Water Observatory: A Distributed Network for Monitoring Water, Energy, and Carbon Cycles under Variable Climate and Land Use on Gulf Coast Plains

Abstract In the Gulf Coastal Plains of Texas, a state-of-the-art distributed network of field observatories, known as the Texas Water Observatory (TWO), is developed to better understand the water, energy, and carbon cycles across the critical zone (encompassing aquifers, soils, plants, and atmosphere) at different spatiotemporal scales. Using more than 300 advanced real-time/near-real-time sensors, this observatory monitors high-frequency water, energy, and carbon storage and fluxes in the Brazos River corridor, which are critical for coupled hydrologic, biogeochemical, and land–atmosphere process understanding in the region. TWO provides a regional resource for better understanding and/or managing agriculture, water resources, ecosystems, biodiversity, disasters, health, energy, and weather/climate. TWO infrastructure spans common land uses in this region, including traditional/aspirational cultivated agriculture, rangelands, native prairie, bottomland hardwood forest, and coastal wetlands. Sites represent landforms from low-relief erosional uplands to depositional lowlands across climatic and geologic gradients of central Texas. We present the overarching vision of TWO and describe site design, instrumentation specifications, data collection, and quality control protocols. We also provide a comparison of water, energy, and carbon budget across sites, including evapotranspiration, carbon fluxes, radiation budget, weather, profile soil moisture and soil temperature, soil hydraulic properties, hydrogeophysical surveys, groundwater levels, and groundwater quality reported at TWO primary sites for 2018–20 (with certain data gaps). In conjunction with various Earth-observing remote sensing and legacy databases, TWO provides a master testbed to evaluate process-driven or data-driven critical zone science, leading to improved natural resource management and decision support at different spatiotemporal scales. Significance Statement We provide the vision, design setup, and data acquisition of a state-of-the-art network of field observatories across the Gulf Coastal Plains of Texas. This observatory provides a wealth of measurements of the water, energy, and carbon fluxes, thereby providing a critical testbed for improving the understanding of terrestrial hydrological, biogeochemical, and atmospheric processes across diverse land-use and climate conditions.

Provenance and pretreatment effect on seed germination of twenty-six provenances of Xanthoceras sorbifolium

ABSTRACT Due to the long dormancy period and hard seed coat, the natural germination percentage of yellowhorn (Xanthoceras sorbifolium) seeds is low, which seriously hinders the large-scale cultivation of the yellowhorn. In this study, the seeds of 26 provenances with different climatic and geographical conditions were collected and the seed germination was tested, which was conducted to determine the effects of seed pretreatment methods on the germination of yellowhorn. This study showed that the germination percentage (GP), mean germination time (MGT) and germination index (GI) of 26 provenances of yellowhorn seeds in different pretreatment methods were highly significant (p < 0.01). The sand storage + clean water and sand storage + PEG almost significantly promote the GP and GI, and reduce the MGT of all provenances, while the pretreatments of warm water and cold water have almost no promoting effect on the GP and GI. In addition, this study indicates that provenances are as important as pretreatments in germination ability. Correlation between germination ability and geo-climatic data of provenances showed that low temperature can improve seed germination ability. This study showed that the seed germination ability of yellowhorn can be improved by using combined pretreatments for provenances with genetically low germination potential.

Estimation of Terrestrial Water Storage Changes in Brazil From the Joint Inversion of GRACE‐Based Geopotential Difference and GNSS Vertical Displacement Data

AbstractGravity Recovery and Climate Experiment (GRACE) satellite gravimetry and Global Navigation Satellite System (GNSS) surface displacement measurements offer complementary advantages for monitoring terrestrial water storage (TWS) changes. We propose a new joint inversion model based on the combination of GRACE‐based geopotential difference (GPD) observations using the mascon method and GNSS vertical displacements through the Green's function method to obtain reliable TWS changes in Brazil. The performance of the jointly inverted TWS changes is assessed through closed‐loop simulations and comparisons with hydrometeorological data (precipitation‐P, evapotranspiration‐ET, and runoff‐R) using water budget closure (P‐ET‐R) and river water level observations from satellite altimetry. The simulation results indicate that the joint inversion results exhibit higher accuracy and reliability than GRACE GPD‐based mascon (GPD‐mascon) and GNSS solutions, and the standard deviations (STDs) of joint results decrease by ∼6.98 and ∼37.5 mm compared to those from GPD‐mascon and GNSS solutions. The joint inversion of real GRACE and GNSS data demonstrates notably lower uncertainty than that of GPD‐mascon solutions and exhibits significant improvement than GNSS‐only solutions. The STDs and correlation coefficients between monthly R time series derived from three inversion methods (joint inversion, GPD‐mascon and GNSS solutions, combined with P and ET through water budget closure) and in situ observations are 19.607 mm and 0.912, 20.879 mm and 0.904, and 31.370 mm and 0.778, respectively. The joint estimates also yield better correlation with river water level observations compared to GPD‐mascon and GNSS solutions. Furthermore, at the weekly time scale, the joint inversion results show better consistency with P‐ET‐R data than those from GPD‐mascon and GNSS solutions.

Global patterns in vegetation accessible subsurface water storage emerge from spatially varying importance of individual drivers

Abstract Vegetation roots play an essential role in regulating the hydrological cycle by removing water from the subsurface and releasing it to the atmosphere. However, the present understanding of the drivers of ecosystem-scale root development and their spatial variability globally is limited. This study investigates the varying roles of climate, landscape, and vegetation on the magnitude of root zone storage capacity ( S r ) worldwide, which is defined as the maximum volume of subsurface moisture accessible to vegetation roots. To this aim, we quantified S r and evaluated 21 possible climate, landscape, and vegetation controls for 3612 river catchments worldwide using a random forest machine learning model. Our findings reveal climate as primary, but spatially varying, driver of ecosystem scale S r with landscape and vegetation characteristics playing a minor role. More specifically, we found the mean inter-storm duration as most dominant control of S r globally, followed by mean temperature, mean precipitation, and mean topographic slope. While the inter-storm duration, temperature, and slope exhibit a consistent relation with S r globally, the relation between precipitation and S r varies spatially. Based on this spatial variability, we classified two different regimes: precipitation driven and energy limited. The precipitation-driven regime exhibits a positive relation between precipitation and S r for precipitation of up to 3 mm d − 1 , above which the relation flattens and eventually becomes negative. The energy-limited regime exhibits a strictly negative relation between precipitation and S r . Using the random forest model based on these three dominant climate variables and the landscape variable slope, we generated a global gridded dataset of S r , which closely resembles other global datasets of root characteristics. This suggests that our parsimonious approach based on four globally available variables to estimate S r on a global scale has the potential to be readily and easily integrated into the parameterization of S r in global hydrological and land surface models. This may enhance the accuracy of global predictions of land–atmosphere exchange fluxes and hydrological extremes by providing a robust representation of both spatial and temporal variability in vegetation root characteristics.

Assessing rainwater harvesting scenarios to mitigate potable water storage needs in Algerian individual houses under intermittent supply.

Urban areas in Algeria are increasingly suffering from water scarcity due to climate change and rapid urbanization, prompting the government to introduce an intermittent water supply system. This situation requires households to store potable water during supply interruptions. This study investigates the potential of rainwater harvesting (RWH) to reduce the need for such storage in single-family homes in the municipality of Beni-Mared (north of Algeria). Through five simulated scenarios: "Current" which represents the existing situation without RWH; "Maximum" in which all available rainwater is captured; "Switch" in which the existing potable water storage tanks are used for rainwater; "Optimal" in which the size of the storage tanks and efficiency are balanced; and "Outdoor" in which the focus is on rainwater for external purposes such as watering. The results show that the "Maximum" scenario has the greatest potential, as it reduces the need for potable water storage by up to 84.5%, although it requires large and potentially expensive tanks. The "Optimal" scenario offered a practical solution in which a 2.7 m³ tank covers up to 33% of the storage. The "Switch" scenario, where existing tanks were repurposed, reduced storage requirements by 15.07%, while the "Outdoor" scenario, designed for external uses, achieved a more modest reduction of 5.85% to 6.71%. These results demonstrate the importance of implementing an adapted RWH approach supported by a comprehensive development strategy, community involvement and financial support to effectively address water scarcity in Algeria.

Drainage estimation across mountainous regions from large-scale soil moisture observations

Drainage is a crucial soil hydrological process that governs the partitioning of rainfall into runoff, groundwater recharge, soil water storage and evapotranspiration. Despite its significance, the drainage process is poorly understood due to the difficulty in direct measurements and insufficient understanding of its underlying physical mechanisms. To address these challenges, we present an innovative, physically-based, data-driven approach, SM2D (Soil Moisture to Drainage), to estimate drainage. SM2D was applied and examined using soil moisture data from a large-scale observation network over mountainous areas during 2014–2020. The soil moisture threshold governing drainage initiation proves to be significantly lower than the commonly employed field capacity metric in hydrological models. This threshold is influenced by factors such as mean soil moisture, bulk density, residual soil moisture, soil organic carbon, and parameters n and α of soil retention curve. Notably, field capacity has minimal impact on this threshold. Additionally, our analysis reveals that the drainage process is more influenced by the Soil Water Storage Increment (SWSI) than by mean soil moisture (MSM) that has traditionally been recognized as a key factor in drainage control. In comparison to commonly used exponential equations and those in models such as the Soil & Water Assessment Tool (SWAT), SM2D demonstrates superior performance in estimating drainage. The exponential equation derived from the SWSI outperforms those derived from other soil moisture metrics, including the commonly utilized MSM, challenging prevailing norms in drainage equations. SM2D holds the potential to generate extensive drainage datasets from satellite or large-scale soil moisture observations, advancing large-scale hydrological studies.

Resilience and Resistance of Vegetation in Response to Droughts in a Subtropical Humid Region Dominated by Karst

The resilience and resistance of vegetation are important indicators of the vegetation’s response to droughts. Owing to the uniqueness of the environment in humid karst areas, results from studies on other climatic zones may not necessarily present the status of vegetation resilience and resistance in humid karst areas. Herein, We calculated vegetation resilience and resistance by autoregressive modeling using Enhanced Vegetation Index (EVI), Total Water Storage Anomaly (TWSA), temperature (TA), precipitation (PRE) data, An analysis of variance (ANOVA) was then conducted to compare the differences in resilience and resistance of different vegetation types in the study area, as well as the differences in resilience and resistance of vegetation in different sub-geomorphic zones. Finally, natural factors affecting vegetation resilience and resistance were quantified using partial least squares structural equation modeling (PLS-SEM). The results demonstrate the following points. First, vegetation resilience, total-water-storage anomaly resistance, and vegetation resistance against precipitation anomalies were lower in karst areas of the study area than in non-karst areas of the study area (except for vegetation resistance against temperature anomalies). Second, vegetation resilience was the lowest in some sub-geomorphic zones within karst areas, and it was still comparable to that in semiarid areas. Third, precipitation and temperature were important factors that affected the resilience and resistance of vegetation in karst areas, and the geochemical indicators (CaO, MgO, and SiO2) of soil parent material were major factors that affected the resistance and resilience of vegetation in non-karst areas. In summary, this study was undertaken to reveal the natural characteristics of vegetation resilience and resistance in humid karst regions. Our findings complement and expand the existing body of knowledge on vegetation resilience and resistance in other ecologically fragile zones limited by moisture.

Flexible regulation of airport air-conditioning systems: Impact of cooling load variations on temperature stability

The integration of renewable energy sources has led to notable supply-demand imbalances due to their intermittent nature. Air-conditioning systems, as significant energy consumers with considerable flexibility, play a crucial role in integrating renewable energy and regulating power systems. This paper delves into the capability of air-conditioning systems in airports to flexibly adjust, particularly examining how varying cooling loads impact indoor temperatures. The energy flexibility of the air-conditioning system is evaluated based on three fundamental cooling load variations (incremental, translational, and discrete), and several evaluation parameters are defined. For areas with smaller cooling load fluctuations, there is a greater adjustment margin, but long adjustment periods should be avoided. For areas with larger fluctuations, the original cooling load curve should be considered in the adjustment strategy. The simulation results show that advancing or delaying the supply by 1 hour during peak cooling load periods can ensure indoor temperature stability, and step-type cooling load curves do not adversely affect indoor comfort levels. Practical case studies further demonstrate that utilizing water storage as an active regulation strategy can reduce operational costs by 26%, and with flexible adjustments, this reduction can increase to 35-54%. The study offers a framework to optimize air-conditioning flexibility in airports, facilitating better renewable energy integration and grid stability, which holds significant potential to reduce costs and improve energy efficiency, thus contributing to sustainable energy management.

Improving long-term prediction of terrestrial water storage through integration with CMIP6 decadal prediction

Improving long-term prediction of terrestrial water storage through integration with CMIP6 decadal prediction

Hydrology on Solid Grounds? Integration Is Key to Closing Knowledge Gaps Concerning Landscape Subsurface Water Storage Dynamics

ABSTRACTIndividual approaches to observe water dynamics across our landscape, from the land surface to groundwater, are many though they individually only provide glimpses into the real world due to their specific space–time scales. Comprehensive integration across all available observations is still largely lacking, limiting both our ability to reduce scientific knowledge gaps, and to guide land and water management using the best available scientific evidence. We argue that a stronger focus on integration of observational products, while utilising machine learning and accounting for current perceptual understanding is urgently needed to overcome this limitation. Since Europe is warming faster than any other continent, central Europe is undergoing a dramatic hydroclimatic transition about which such integrated observations would provide timely and valuable insights. Here, we present potential and gaps of current and planned observational methods. We argue that hyperresolution (sub km) integrated estimates of landscape water dynamics are feasible, which could significantly improve our ability to simulate vadose zone and groundwater dynamics, ultimately closing gaps in our current perception of hydrological processes in a temperate region under strong influence from climate change. We close by arguing that an interdisciplinary effort of various scientific communities is needed to enable this advancement.

A Spatially‐Distributed Machine Learning Approach for Fractional Snow Covered Area Estimation

AbstractSnowpack in mountainous areas often provides water storage for summer and fall, especially in the Western United States. In situ observations of snow properties in mountainous terrain are limited by cost and effort, impacting both temporal and spatial sampling, while remote sensing estimates provide more complete spacetime coverage. Spatial estimates of fractional snow covered area (fSCA) at 30m are available every 16 days from the series of multispectral scanning instruments on Landsat platforms. Daily estimates at 463m spatial resolution are also available from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra satellite. Fusing Landsat and MODIS fSCA images creates high resolution daily spatial estimates of fSCA that are needed for various uses: to support scientists and managers interested in energy and water budgets for water resources and to understand the movement of animals in a changing climate. Here, we propose a new machine learning approach conditioned on MODIS fSCA, as well as a set of physiographic features, and fit to Landsat fSCA over a portion of the Sierra Nevada USA. The predictions are daily 30m fSCA. The approach relies on two stages of spatially‐varying models. The first classifies fSCA into three categories and the second yields estimates within (0, 100) percent fSCA. Separate models are applied and fitted within sub‐regions of the study domain. Compared with a recently‐published machine learning model (Rittger, Krock, et al., 2021), this approach uses spatially local (rather than global) random forests, and improves the classification error of fSCA by 16%, and fractionally‐covered pixel estimates by 18%.

Agricultural land around river confluences could strongly suppress floods occurrences

Flooding poses a significant threat to socioeconomic systems, with risks expected to escalate in the future due to factors such as climate change and urbanization. Ecosystem-based disaster risk reduction (Eco-DRR) emphasizes the role of natural and seminatural ecosystems, particularly agricultural ecosystems, in disaster impact mitigation such as by flood prevention benefits. This study evaluates the effectiveness of Eco-DRR in agricultural land around river confluences across Japan, where frequent flooding occurs. Using flood occurrence data from 2010 to 2018 in 1,917 municipalities across Japan, statistical models were established incorporating agricultural land, urbanized areas, and the number of dams as explanatory variables to predict flood frequency. Previous studies have demonstrated that agricultural land located in the water storage zone contributes to flood suppression, whereas high land in urbanized areas is associated with increased flooding risk. Therefore, this condition was incorporated into the models. Two patterns were tested: one using all agricultural land and another focusing on agricultural land within a 1-km buffer around river confluences. The results demonstrate that agricultural land near river confluences significantly enhances flood suppression compared to all agricultural land. In contrast, urbanized areas within water storage zones were associated with increased flood risks. The findings suggest that conserving agricultural land in flood-prone confluence areas offers substantial flood mitigation benefits, whereas urban expansion in these areas heightens flood risks. Therefore, prioritizing the preservation of agricultural land around river confluences is crucial for flood risk reduction.

Dynamic mode decomposition of GRACE satellite data

Advancements in satellite technology yield environmental data with ever improving spatial coverage and temporal resolution. This necessitates the development of techniques to discern actionable information from large amounts of such data. We explore the potential of dynamic mode decomposition (DMD) to discover the dynamics of spatially correlated structures present in global-scale data, specifically in observations of total water storage anomalies provided by GRACE satellite missions. Our results demonstrate that DMD enables data compression and extrapolation from a reduced set of dominant spatiotemporal structures. The accuracy of its predictions of global system dynamics is preserved in its reconstruction of local time series. These findings suggest potential uses of DMD in analysis of remote-sensing data for hydrologic applications.

Reconstructed centennial precipitation-driven water storage anomalies in the Nile River Basin using RecNet and their suitability for studying ENSO and IOD impacts

While the Gravity Recovery And Climate Experiment (GRACE) and its Follow-On (GFO) missions have offered valuable observations for monitoring total water storage anomalies (TWSA), their short record constrains our ability to study the complete range and long-term variability of TWSA in the Nile River Basin (NRB). Previous studies reconstructing TWSA in this region either relied on specific hydrological models or did not consider spatial correlations among the TWSA grids. Here, we employ RecNet, a deep learning model capable of providing independent TWSA observations without relying on hydrological models while considering spatial correlations, to reconstruct precipitation-driven TWSA in the NRB from 1923 to 2022. The reconstructed data are validated by comparisons with the Global Land Data Assimilation System (GLDAS), the WaterGAP Global Hydrology Model (WGHM), the water balance budget, long-term runoff data, and GRACE-REC (i.e., a global reconstruction dataset freely available online). Subsequently, the suitability of the reconstructed data for studying El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) impacts within the NRB is assessed. Dividing the NRB into four sub-regions, i.e., the Lake Victoria Basin (LVB), the Bahr el Jebel and Bahr el Ghazal basins (BJBG), Ethiopian Highlands region (EH), and the Lower Nile River Basin (LNRB), it is shown that RecNet successfully reconstructs precipitation-driven TWSA over BJBG and EH, achieving correlation coefficient (CC), normalized root mean square error (NRMSE), and Nash–Sutcliffeefficiency (NSE) of 0.94/0.11/0.88 and 0.96/0.09/0.91 during the testing period, respectively. Additionally, RecNet’s reconstruction shows better agreement with GLDAS and WGHM than GRACE-REC, correlating well with runoff and the water balance budget in these regions. The relatively poor performance in the LVB and LNRB regions could be attributed to the substantial influence of Lake Victoria and the arid climate, respectively. Correlation analysis and wavelet coherence analysis identify significant coherence between ENSO/IOD and the reconstructed TWSA in BJBG and EH, with CC values of −0.68/0.34 and −0.82/0.56, respectively. This study provides centennial reconstructed TWSA data that could be useful in climate change/variability studies and water resource management within the NRB.