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The quantity and quality of surface water are inherently connected yet are overwhelmingly studied separately in the field of remote sensing. Remotely observable water quantity (e.g., water extent, water elevation, lake/reservoir volume, and river discharge) and water quality (e.g., color, turbidity, total suspended solids, chlorophyll a, colored dissolved organic matter, and temperature) parameters of inland waterbodies interact through a series of hydrological and biogeochemical processes. In this review, we analyzed trends in remote sensing publications to understand the prevalence of studies on the quantity versus quality of open‐surface inland waterbodies (rivers, streams, lakes, and reservoirs) as well as identified opportunities for integrating both water quality and quantity sensing in future work. Our bibliometric analysis found that despite the increasing number of publications using remote sensing for inland waterbodies, few studies to date have used remote sensing tools or approaches to simultaneously study water quantity and quality. Ultimately, by providing insights into potential integration of the water quality and quantity studies, we aim to identify a pathway to advance the understanding of inland water dynamics and freshwater resources through remote sensing. This article is categorized under:Water and Life > MethodsScience of Water > Water QualityWater and Life > Nature of Freshwater EcosystemsScience of Water > Methods [ABSTRACT FROM AUTHOR]

Although reservoirs serve as a lifeline for drought mitigation and adaptation, reservoir‐based droughts (RBD) have received limited attention. Especially RBD that incorporate reservoir evaporation have remained overlooked. Here, we develop an Integrated Reservoir Drought Index (IRDI) using a Frank copula by combining the effects of reservoir storage and evaporation rate for 164 global reservoirs. Results show that Africa, South America, and Australia have experienced relatively longer RBD, whereas Asia and Africa have experienced more intense RBD. Furthermore, when significant (p‐value < 0.05) trends in inflow and IRDI have shown opposite signs at a given reservoir, it is implied that human influence was the dominant driver of the long‐term changes in RBD. We found that RBD was driven by climate in 71% of the 38 reservoirs examined and by human activities in the remaining 29% of reservoirs. Our study provides reservoir specific drought outlook that has significant environmental and sustainability implications. Plain Language Summary: Reservoirs are the prime source of water during droughts. However, water supply from reservoirs depends on how much water reservoirs have. Reservoirs lose significant amounts of water through evaporation. Hence, understanding reservoir droughts by incorporating evaporation information—will help water managers to better regulate water resources and optimize reservoir operations. With the ongoing trends of an increasing population and a changing climate, reservoirs will undergo more frequent and intense droughts in the future. Here, we developed an integrated reservoir drought index (IRDI) based on reservoir storage and evaporation that can help to identify, detect, and characterize droughts more efficiently. We found that reservoirs in Africa, South America, and Australia experienced relatively longer droughts, whereas reservoirs in Asia and Africa experienced more intense droughts. While comparing trends of IRDI and inflow coming into the reservoirs, 29% of the 38 reservoirs examined showed opposite trends. This highlights the fact that how reservoirs are operated or managed can also influence drought characteristics within them. Key Points: Evaporation information should be included in reservoir‐based drought frameworksThe Integrated Reservoir Drought Index (IRDI) helps to identify, detect, and characterize drought characteristics efficientlyReservoir‐based droughts were driven by climate in 71% of the 38 reservoirs analyzed, and by humans in the remaining 29% of the reservoirs [ABSTRACT FROM AUTHOR]

Surface water reservoirs are increasingly being relied upon to meet rising demands in the context of growing population and changing climate. However, the amount of water available in reservoirs (and the corresponding trends) have not been well quantified at the global scale. Here we use satellite observations to estimate the storage variations of 7245 global reservoirs from 1999 to 2018. Total global reservoir storage has increased at a rate of 27.82 ± 0.08 km3/yr, which is mainly attributed to the construction of new dams. However, the normalized reservoir storage (NS)—the ratio of the actual storage to the storage capacity—has declined by 0.82 ± 0.01%. The decline of NS values is especially pronounced in the global south, while the global north mainly exhibits an NS increase. With predicted decreasing runoff and increasing water demand, these observed diminishing storage returns of reservoir construction will likely persist into the future. Despite the continually increasing global reservoir storage, the normalized storage—the ratio of the actual storage to the storage capacity—has declined over the past two decades, indicating diminishing storage returns from reservoir construction. [ABSTRACT FROM AUTHOR]

Hydrological drought quantification using surface water information has been lacking at a large scale due to limited in situ data. We introduce a framework for monitoring hydrological droughts using a global, long‐term, monthly, remotely sensed reservoir surface area dataset. At regional scales, a new index—the reservoir area drought index (RADI)—is defined as the monthly normalized reservoir area time series. RADI was validated using an in situ reservoir storage based index (average R2 = 0.83). RADI not only helps to characterize drought propagation from meteorological and agricultural droughts to hydrological droughts but also fills the information gap between streamflow/runoff based and groundwater based drought indices. The surface area dataset was further used to characterize the recovery rate (and to estimate the recovery time) at the individual reservoir scale during droughts. This across‐scale drought monitoring framework can help mitigate drought impacts and increase water use efficiency among multi‐reservoir systems. Plain language summary: Drought is a global phenomenon that can have substantial impacts on regional economies and ecosystems. Quantification of droughts is of great importance for mitigating losses. Leveraging an existing remotely sensed global reservoir area dataset, we introduce a new hydrological drought index–the reservoir area drought index, RADI—that can serve as an indicator of surface water availability at a regional scale. Nine drought‐prone regions were chosen to evaluate the performance of RADI. Results suggest that RADI can contribute new information to hydrological drought monitoring and can complement other indices to fully depict the propagation of different kinds of droughts. In addition, reservoir area time series can also be used for quantifying the responses of individual reservoirs to droughts. Key Points: A new hydrological drought index was developed using remotely sensed reservoir surface area time seriesThe drought evaluation framework permits drought quantification at both regional and local scalesThe new index is informative about reservoir water availability and contributes to the evaluation of drought propagation. [ABSTRACT FROM AUTHOR]

Knowledge of reservoir bathymetry is essential for many studies on terrestrial hydrological and biogeochemical processes. However, there are currently no cost-effective approaches to derive reservoir bathymetry at the global scale. This study explores the potential of generating high-resolution global bathymetry using elevation data collected by the 532-nm Advanced Topographic Laser Altimeter System (ATLAS) onboard the Ice, Cloud, and Land Elevation Satellite (ICESat-2). The novel algorithm was developed and tested using the ICESat-2 airborne prototype, the Multiple Altimeter Beam Experimental Lidar (MABEL), with Landsat-based water classifications (from 1982 to 2017). MABEL photon elevations were paired with Landsat water occurrence percentiles to establish the elevation–area (E–A) relationship, which in turn was applied to the percentile image to obtain partial bathymetry over the historic dynamic range of reservoir area. The bathymetry for the central area was projected to achieve the full bathymetry. The bathymetry image was then embedded onto the digital elevation model (DEM). Results were validated over Lake Mead against survey data. Results over four transects show coefficient of determination (${R} ^{2}$) values from 0.82 to 0.99 and root-mean-square error (RMSE) values from 1.18 to 2.36 m. In addition, the E–A and elevation–storage (E–S) curves have RMSEs of 1.56 m and 0.08 km3, respectively. Over the entire dynamic reservoir area, the derived bathymetry agrees very well with independent survey data, except for within the highest and lowest percentile bands. With abundant overpassing tracks and high spatial resolution, the newly launched ICESat-2 should enable the derivation of bathymetry over an unprecedented number of reservoirs. [ABSTRACT FROM AUTHOR]

Abstract Evaporation from open surface water is a critical and continuous process in the water cycle. Globally, evaporation losses from reservoirs are estimated to be greater than the combined consumption from industrial and domestic water uses. However, this large volume of water loss is only coarsely considered in modern water resources management practices due to the complexities involved with quantifying these losses. By fusing remote sensing and modeling approaches, this study developed a novel method to accurately estimate the evaporation losses from 721 reservoirs in the contiguous United States (CONUS). Reservoir surface areas were extracted and enhanced from the Landsat based Global Surface Water Dataset (GSWD) from March 1984 to October 2015. The evaporation rate was modeled using the Penman Equation in which the lake heat storage term was considered. Validation results using in situ observations suggest that this approach can significantly improve the accuracy of the simulated monthly reservoir evaporation rate. The evaporation losses were subsequently estimated as the product of the surface area and evaporation rate. This paper presents a first of its kind, comprehensively validated, locally practical, and continentally consistent reservoir evaporation dataset. The results suggest that the long term averaged annual evaporation volume from these 721 reservoirs is 33.73 × 109 m3, which is equivalent to 93% of the annual public water supply of the United States (in 2010). An increasing trend of the evaporation rate (0.0076 mm/d/year) and a slightly decreasing trend of the total surface area (−0.011 × 109 m2/year) were both detected during the study period. As a result, the total evaporation shows an insignificant trend, yet with significant spatial heterogeneity. This new reservoir evaporation dataset can help facilitate more efficient water management practices. Highlights • A long term, comprehensive evaporation dataset was developed for 721 US reservoirs. • The calculated evaporation rate was improved by including the heat storage effect. • Driven by global brightening, evaporation rate has increased over most reservoirs. • The long term annual evaporation amount from these reservoirs is 33.73 × 109 m3/year. [ABSTRACT FROM AUTHOR]

During the past several decades, numerous reservoirs have been built across the world for a variety of purposes such as flood control, irrigation, municipal/industrial water supplies, and hydropower generation. Consequently, the timing and magnitude of natural streamflow have been altered significantly by reservoir operations. In addition, the hydrological cycle is also modified by land-use/land-cover change and by climate change. To understand the fine-scale feedback between hydrological processes and water management decisions, a distributed hydrological model embedded with a reservoir component is desired. In this study, a multi-purpose reservoir module with predefined complex operational rules was integrated into the Distributed Hydrology Soil Vegetation Model (DHSVM). Conditional operating rules, which are designed to reduce flood risk and enhance water supply reliability, were adopted in this module. The performance of the integrated model was tested over the upper Brazos River Basin in Texas, where two U.S. Army Corps of Engineers managed reservoirs, Lake Whitney and Aquilla Lake, are located. The integrated model was calibrated and validated using observed reservoir inflow, outflow, and storage data. The error statistics were summarized for both reservoirs on a daily, weekly, and monthly basis. Using the weekly reservoir storage for Lake Whitney as an example, the coefficient of determination (R 2 ) was 0.85 and the Nash-Sutcliff Efficiency (NSE) was 0.75. These results suggest that this reservoir module holds promise for use in sub-monthly hydrological simulations. With the new reservoir component, the DHSVM provides a platform to support adaptive water resources management under the impacts of evolving anthropogenic activities and substantial environmental changes. [ABSTRACT FROM AUTHOR]

Man-made reservoirs play a key role in the terrestrial water system. They alter water fluxes at the land surface and impact surface water storage through water management regulations for diverse purposes such as irrigation, municipal water supply, hydropower generation, and flood control. Although most developed countries have established sophisticated observing systems for many variables in the land surface water cycle, long-term and consistent records of reservoir storage are much more limited and not always shared. Furthermore, most land surface hydrological models do not represent the effects of water management activities. Here, the contribution of reservoirs to seasonal water storage variations is investigated using a large-scale water management model to simulate the effects of reservoir management at basin and continental scales. The model was run from 1948 to 2010 at a spatial resolution of 0.25° latitude-longitude. A total of 166 of the largest reservoirs in the world with a total capacity of about 3900 km3 (nearly 60% of the globally integrated reservoir capacity) were simulated. The global reservoir storage time series reflects the massive expansion of global reservoir capacity; over 30 000 reservoirs have been constructed during the past half century, with a mean absolute interannual storage variation of 89 km3. The results indicate that the average reservoir-induced seasonal storage variation is nearly 700 km3 or about 10% of the global reservoir storage. For some river basins, such as the Yellow River, seasonal reservoir storage variations can be as large as 72% of combined snow water equivalent and soil moisture storage. [ABSTRACT FROM AUTHOR]

We studied 34 global reservoirs for which good quality surface elevation data could be obtained from a combination of five satellite altimeters for the period from 1992 to 2010. For each of these reservoirs, we used an unsupervised classification approach using the Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day 250 m vegetation product to estimate the surface water areas over the MODIS period of record (2000 to 2010). We then derived elevation-area relationships for each of the reservoirs by combining the MODIS-based estimates with satellite altimeter-based estimates of reservoir water elevations. Through a combination of direct observations of elevation and surface area along with documented reservoir configurations at capacity, we estimated storage time histories for each reservoir from 1992 to 2010. We evaluated these satellite-based data products in comparison with gauge observations for the five largest reservoirs in the United States (Lakes Mead, Powell, Sakakawea, Oahe, and Fort Peck Reservoir). The storage estimates were highly correlated with observations (R = 0.92 to 0.99), with values for the normalized root mean square error (NRMSE) ranging from 3% to 15%. The storage mean absolute error (expressed as a percentage of reservoir capacity) for the reservoirs in this study was 4%. The multidecadal reconstructed reservoir storage variations are in accordance with known droughts and high flow periods on each of the five continents represented in the data set. [ABSTRACT FROM AUTHOR]

Although accurate 3-D reservoir bathymetry is a key input for multiple applications (such as global hydrological models, local water resources related studies, and others), such information is only available for very few locations. To fill this knowledge gap, we generated a 30 m resolution bathymetry dataset for 347 global reservoirs, representing a total volume of 3123 km3 (50% of the global reservoir capacity). First, Area–Elevation (A–E) relationships for the identified reservoirs were derived by combining altimetry data from multiple satellites with Landsat imagery data. Next, the resulting A–E relationships were applied to the Surface Water Occurrence (SWO) data from the JRC Global Surface Water (GSW) dataset to obtain bathymetry values for the dynamic areas of the reservoirs. Lastly, an extrapolation method was adopted to help achieve the full bathymetry dataset. The remotely sensed bathymetry results were primarily validated against the following: (1) They were validated against Area–Elevation (A–E) and Elevation–Volume (E-V) relationships derived from the in situ elevations and volumes for 16 reservoirs, with root-mean-square error (RMSE) values of elevation from 0.06 m to 1.99 m, and normalized RMSE values of storage from 0.56% to 4.40%. (2) They were also validated against survey bathymetric maps for four reservoirs, with R2 values from 0.82 to 0.99 and RMSE values from 0.13 m to 2.31 m. The projected portions have relatively large errors and uncertainties (compared to the remotely sensed portions) because the extrapolated elevations cannot fully capture the underwater topography. Overall, this approach performs better for reservoirs with a large dynamic area fractions. It can also be applied to small reservoirs (e.g., reservoirs with surface areas of a few square kilometers or less), where ICESat observations are available, and to large natural lakes. With the contribution of ICESat-2, this dataset has the potential to be expanded to thousands of reservoirs and lakes in the future. • A high-resolution bathymetry dataset for global reservoirs is generated and evaluated. • The bathymetry contains a remotely sensed dynamic part with the rest extrapolated. • It includes 347 reservoirs, representing 50% of the overall global storage capacity. • Accurate Area–Elevation (A–E) and Elevation–Volume (E–V) curves are provided. [ABSTRACT FROM AUTHOR]

Satellite remote sensing of near real-time reservoir storage variations has important implications for flood monitoring and water resources management. However, satellite altimetry data, which are essential for estimating storage variations, are only available for a limited number of reservoirs. This lack of high-density spatial coverage directly hinders the potential use of remotely sensed reservoir information for improving the skills of hydrological modeling over highly regulated river basins. To solve this problem, a reservoir storage dataset with high-density spatial coverage was developed by combining the water surface area estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) imageries with the Digital Elevation Model (DEM) data collected by the Shuttle Radar Topography Mission (SRTM). By including more reservoirs, this reservoir dataset represents 46.6% of the overall storage capacity in South Asia. The results were validated over five reservoirs where gauge observations are accessible. The storage estimates agree well with observations, with coefficients of determination ranging from 0.47 to 0.91 and normalized root mean square errors (NRMSE) ranging from 15.46% to 37.69%. Given the general availability of MODIS and SRTM data, this algorithm can be potentially applied for monitoring global reservoirs at a high density. [ABSTRACT FROM AUTHOR]

The magnitude and frequency of hydrometeorological extremes are expected to increase in the conterminous United States (CONUS) over the rest of this century, and their increase will significantly impact water resource management. In this study, we evaluated the large-scale climate change effects on extreme hydrological events and their implications for reservoir inflows in 138 headwater subbasins located upstream of reservoirs across CONUS using the Variable Infiltration Capacity (VIC) hydrologic model. The VIC model was forced with a 10-member ensemble of global circulation models under the Representative Concentration Pathway 8.5 that were dynamically downscaled using a regional climate model (RegCM4) and bias-corrected to 1/24° grid cell resolution. Four commonly used indices, including mean annual flow, annual center timing, 100-year daily high streamflow, and 10-year 7-day average low streamflow were used for evaluation. The results projected an increase in the high streamflow by 44% for a majority of subbasins upstream of flood control reservoirs in the central United States (US) and a decrease in the low streamflow by 11% for subbasins upstream of hydropower reservoirs across the western US. In the eastern US, frequencies of both high and low streamflow were projected to increase in the majority of subbasins upstream of both hydropower and flood control reservoirs. Increased frequencies of both high and low streamflow events can potentially make reservoirs across CONUS more vulnerable to future climate conditions. This study estimates reservoir inflow changes over the next several decades, which can be used to optimize water supply management downstream. [ABSTRACT FROM AUTHOR]