ABSTRACT Globally, many wetlands and lakes are at risk for further loss, which can amplify downstream consequences of flood and drought events. We derived remotely sensed based time series of surface water storage (SWstorage) to determine when and where accounting for SWstorage dynamics improves predictions of river discharge. We trained four long short-term memory (LSTM) models, that differed in their inclusion of storage data and catchment characteristics, to simulate daily river discharge (2016–2023) for select watersheds across the conterminous United States. Adding SWstorage to a meteorology-only or meteorology-and-catchment characteristics model improved upon model Nash-Sutcliffe efficiency (NSE) in 80.6% of the watersheds. Residuals during low-flow (Q70) events decreased by 47.6% when adding storage to meteorological data. Improvements were most consistent in ecoregions with a greater abundance of non-floodplain lakes and wetlands. This effort represents the first exploration to train a multi-watershed LSTM on landscape-scale remotely sensed time series of SWstorage.

Lakes on the Qinghai–Tibet Plateau are important indicators of global climate change, and variations in their water storage strongly influence regional hydrological cycles and ecosystems. However, existing studies have largely focused on relative changes in lake volume, while the precise quantification of absolute water storage remains insufficient, largely due to the lack of long-term, high-accuracy water storage time series. Constrained by harsh natural conditions and limited in situ observations, conventional approaches struggle to achieve the accurate long-term monitoring of lake water storage across the Plateau. To address this challenge, we propose a DEM-based underwater topography extrapolation method. Under the assumption of continuity between surrounding onshore terrain and submerged lakebed morphology, nearshore DEM data are extrapolated to reconstruct lake bathymetry. By integrating multi-source remote sensing observations of lake area and water level, we estimate and reconstruct 30-year absolute water storage time series for 120 Plateau lakes larger than 50 km2. This method does not require measured water depth data and is particularly suitable for data-scarce, topographically complex, high-altitude lake regions, effectively overcoming key limitations of conventional methods used for absolute water storage monitoring. Validation shows strong agreement between our estimates and an independent validation dataset, with an overall correlation coefficient of 0.95; the reconstructed time series are highly reliable, with correlation coefficients exceeding 0.6. During the study period, the total lake water storage of the Qinghai–Tibet Plateau exhibited a significant increasing trend, with a cumulative growth of approximately 137.297 billion m3, representing a 20.73% increase, and showing notable spatial heterogeneity. The water storage dataset constructed in this study provides reliable data support for research on water cycles, climate change assessment, and regional water resource management on the Qinghai–Tibet Plateau.

Central East African lakes This study investigates the variations in lake water level, surface area, and water storage in East Africa. Lake water levels were derived from ICESat-2 altimetry data, while surface areas were extracted using Sentinel-2 optical imagery. Water storage changes were estimated using the frustum volume formula. A normalization approach was applied to analyze the spatiotemporal evolution of the lakes, and the driving factors of lake changes were further explored in conjunction with meteorological data. The results indicate that ICESat-2 effectively meets the requirements for monitoring lake water levels in Central East Africa. Between 2018 and 2024, only Lake Turkana and Lake Abhe exhibited a continuous rise in water levels. During the monitoring period, lake water level, surface area, and water storage volume showed a synchronous bimodal pattern. Further analysis reveals that runoff is the primary driver of lake water storage variation, followed by precipitation, while temperature generally shows a negative correlation with water storage. • ICESat-2 effectively retrieved water levels for 18 lakes in Central East Africa. • A normalized approach revealed spatiotemporal patterns of lake variations. • Water level, area, and storage showed synchronous bimodal variations. • Runoff was the main driver of lake water storage changes, followed by precipitation.

Abstract Seasonally frozen ground regulates groundwater–surface water interactions in saline lake basins, altering water balance, salinity gradients, and biogeochemical processes. Using density‐dependent reactive transport simulations in the Badain Jaran Desert, China, we evaluate how freeze–thaw cycles affect groundwater flow, salt dynamics, and nutrient fluxes under varying salinity conditions. Our results show that seasonal freezing suppresses evaporation and enhances down‐gradient groundwater flow, shifting the fresh–saline interface lakeward and limiting inland saltwater intrusion. During the cold season, both fresh and recirculated groundwater to lakes increase, offsetting evaporative losses and enhancing lake water storage. Simultaneously, nutrient fluxes to lakes intensify, reflecting enhanced mobilization from groundwater reservoirs. These findings emphasize the hydrogeological and biogeochemical significance of seasonal freezing in saline basins.

Abstract We estimate the solid Earth's elastic response and change in equivalent water thickness produced by 983 global natural lakes and artificial reservoirs. Using the altimetry and LandSat based lake water storage compilation of Yao et al. (2023b, https://zenodo.org/records/7946043 ), we assemble the following data products: (a) change in water volume for 287 natural lakes and 696 artificial reservoirs interpolated to be continuous from October 1992 to October 2020, (b) maps of change in lake water storage each month, (c) lake‐generated change in equivalent water thickness as seen by Gravity Recovery and Climate Experiment (GRACE) in spherical harmonic coefficients and 3‐degree mass concentration elements (mascons), (d) lake‐generated east, north, and up components of elastic displacement at 19,827 Global Navigation Satellite System (GNSS) sites that the Nevada Geodetic Laboratory analyzes. Removing estimates of lake water storage reduces the variance of 79% of the 333 affected 3‐degree mascons in JPL's mascon solution, with a mean reduction of 2 cm (7%). In northern North America and Asia, lake water storage is predicted to reach its seasonal maximum 4–6 months after observed terrestrial water storage from GRACE, which can increase GRACE's variance when lake water is removed. Removing lake‐generated elastic displacements from GNSS station displacements reduces the weighted root mean square of residuals relative to a trended sinusoid with a seasonal period by an average of 4 mm (12%). Applications of GNSS displacements observations, such as glacial isostatic adjustment modeling and tectonic reconstructions can be biased due to lake water loading in the case of stations with short records and large lake water changes.

In the Inner Mongolia Plateau Lake Zone (IMP), situated in China’s semi-arid region, its lake water storage change plays a critical role in wetland ecosystem conservation and regional water security through its lake water storage dynamics. To investigate long-term lake water storage (LWS) changes, this study proposes a novel lake monitoring framework that reconstructs historical lake level time series and estimates water level variations in lakes without altimetry data. Using multi-source satellite data, we quantified LWS variations (2000–2021) across 109 lakes (≥5 km2) on the IMP and examined their spatiotemporal patterns. Our results reveal a net decline of 1.21 Gt in total LWS over the past two decades, averaging 0.06 Gt/yr. A distinct shift occurred around 2012: LWS decreased by 10.82 Gt from 2000 to 2012 but increased by 9.61 Gt from 2013 to 2021. Spatially, significant LWS reductions were concentrated in the central and eastern IMP, resulting from intensive water diversion and groundwater exploitation. In contrast, increases were observed mainly in the western and southern regions, driven by enhanced precipitation and reduced aridity. The findings improve understanding of lake dynamics in semi-arid China over the last two decades and offer technical guidance for sustainable water resource management.

Lake topography, which serves as a crucial basis for water resource monitoring, has been extensively applied in hydrological and geomorphological research. However, monitoring lake dynamics in data-scarce regions remains challenging due to the limited revisit frequency of altimetry satellites and uncertainties in estimating submerged depths based on surrounding terrain. Focusing on the Shapotou region of Ningxia, this study collected bathymetric data using an unmanned surface vessel and employed interpolation methods and machine learning (XGBoost) to determine the most effective approach for constructing the lake digital elevation model (DEM). The relationship curve between water area, water level, and water reserve, derived from DEM analysis and calculation, is integrated with remote sensing imagery, thus facilitating the efficient monitoring of lake water dynamics. This approach provides valuable scientific and practical support for water resource management in remote or data-scarce regions lacking conventional hydrological infrastructure. The results indicate that: (1) Despite the theoretical potential of machine learning for underwater terrain prediction, this study demonstrates that traditional spatial interpolation methods offer greater advantages in areas characterized by data scarcity and significant anthropogenic terrain modification, thereby providing empirical evidence to guide method selection under similar conditions. (2) The average annual water storage of lakes in the study area was estimated at 336.249 × 104m3 by integrating relationship curves with remote sensing imagery. Total storage reached a minimum of 307.246 × 104m3 in 2016 and a maximum of 411.802 × 104m3 in 2024. Water level fluctuations were generally less than 1m, revealing the relative stability of lakes in arid regions.

Monitoring the hydrological processes of lakes can provide reliable data for regional water resources assessment. This paper analyzed changes in the lake area and water level of Hala Lake from 2011 to 2023, subsequently estimating its lake water storage change (LWSC). We used image data from Landsat series satellites and multi-source satellite altimetry data, and then quantitatively assessed the influence of various driving factors on the LWSC in combination with hydrological and meteorological models. The results show three stages of parallel changes in the area, water level and LWSC of Hala Lake in the past 13 years. The first stage is from 2011 to 2014, when the lake expanded slightly, the second stage is from 2015 to 2019, when the lake expanded rapidly, and the last stage is from 2020 to 2023, with relatively stable conditions. Over the entire study period, the LWSC increased with a trend of 0.192 ± 0.009 km3/a. Lake surface precipitation, precipitation-caused runoff, and glacier meltwater contributed to the total recharge input by 51%, 40.96%, and 8.04%, respectively, while the lake surface evaporation accounted for 59.37% of the total recharge input as water loss. Thus, the left 40.63% of the input caused the LWSC increase. Although lake surface precipitation provided the primary contribution to the Hala Lake LWSC, precipitation-caused runoff was the key factor forming the three stages in the LWSC. The results of this study provide valuable information for the rational development and utilization of water resources by government departments and are also beneficial to the study of global change.

ABSTRACT The underwater terrain data of lakes serves as a crucial foundational dataset for estimating water storage capacity. Although the traditional measurement approaches are capable of precisely acquiring underwater terrain data, they are costly and time-consuming, making them impractical for widespread application to all lake water bodies. Consequently, only a limited number of lakes worldwide possess underwater terrain measurement data. To address this issue, the urgent need to develop a low-cost, user-friendly observation and simulation methods is needed. Hence, this study proposes a three-dimensional underwater terrain simulation method by using the machine learning technique Extreme Gradient Boosting (XGBoost). The method leverages the similarity between underwater and surface topography by utilizing a digital elevation model (DEM) as input data. Precision evaluation and analysis were conducted using eight representative lakes from the Tibetan Plateau. The results demonstrate that the algorithm achieves a relative error of −11.83% in average depth, −26.94% in maximum depth, and 19.36% in water storage capacity, all estimated based on the simulated underwater terrain. In regions where direct measurements are challenging or data is unavailable, this algorithm provides relatively accurate simulations of lake underwater terrain and water storage capacity, offering critical foundational data to support lake water resource management and scientific research.

The hydrological cycle in the Tibetan Plateau is experiencing notable changes in recent decades under a changing climate. The hydrological changes, however, are not well investigated due to the limitations in the availability of ground-based observations. In this study, by incorporating satellite-based observations into a hydrological modeling framework, seasonal and inter-annual dynamics of water balance for lakes in the northwest Tibetan Plateau are examined systematically for the period of 1990 to 2022. Satellite-based observations, including lake water area and water level, have been used to calibrate the hydrological model and to estimate lake water storage. The hydrological model performs satisfactorily, with the Nash–Sutcliffe efficiency coefficient (NSE) exceeding 0.5 for all 15 studied lakes. It is found that inflow contributes over 70% of annual water gain for most lakes, while percolation accounts for a larger portion (>60%) of total water loss than evaporation. The studied lakes have expanded substantially, with regional average increasing rates in lake level and water storage of 0.38 m/a and 3.12 × 108 m3/a, respectively. Some lakes transitioned from shrinking to expanding around 1999, and expansion in most lakes has further accelerated since around 2012, primarily because of increased precipitation over the lake catchments, leading to greater inflow to the lakes. These findings provide important insights into understanding and predicting responses of lake water balance to climate change as well as for developing adaptative strategies.

ABSTRACT Lake water storage variations on the Tibetan Plateau (TP) serve as crucial indicators of regional hydrological dynamics and climate changes, providing more comprehensive insights than discrete measurements of lake area or water level alone. While accurate bathymetric data is fundamental for quantifying lake water storage, conventional bathymetric surveys are often constrained by logistical challenges and high operational costs in the remote region like the TP. The high altitude and minimal human activity on the TP result in exceptional lake water clarity, allowing laser altimetry to penetrate water depths of several tens of metres. In this study, we used data from Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) laser altimetry data collected from 2019 to 2023 to map five shallow, elongated lakes on the TP. First, we applied the DBSCAN denoising algorithm to eliminate anomalous photons and then fitted polynomial functions to the lakebed elevation profiles for individual tracks. Subsequently, we merged the profiles from all valid tracks within each lake area to derive comprehensive lakebed topography and depth estimates. Comparative analysis with depth measurements from previous studies revealed strong agreement in both absolute depths and spatial patterns of bottom topography. Our results showed that the water depths of the five studied lakes range from 0 to 47 m, with Puma Yumco identified as the deepest (maximum depth of 47 m) and Pelrap Tso as the shallowest (maximum depth of 26 m. The shoreline of Puma Yumco exhibited steeper topography compared to the other four lakes. This study demonstrated the capability of ICESat-2 laser altimetry as a cost-effective and reliable tool for lake bathymetry estimation on the TP. The approach presented in this study holds promise for broader applications in other regions with optically clear water bodies, thereby contributing to improve monitoring of lake dynamics and understanding of regional water storage dynamics and climate change impacts.

The remarkable expansion of lake areas across the Changtang Plateau (CTP, located in the central Tibetan Plateau) since the late 1990s has drawn considerable scientific interest, presenting a striking contrast to the global decline in natural lake water storage observed during the same period. This study systematically investigates the mechanisms underlying lake area variations on the CTP by integrating glacierized area changes derived from the Google Earth Engine (GEE) platform with atmospheric circulation patterns from the ERA5 reanalysis dataset. Our analysis demonstrates that the limited glacier coverage on the CTP exerted significant influence only on glacial lakes in the southern region (r = −0.65, p < 0.05). The widespread lake expansion across the CTP predominantly stems from precipitation increases (r = 0.74, p < 0.01) associated with atmospheric circulation changes. Enhanced Indian summer monsoon (ISM) activity facilitates anomalous moisture transport from the Indian Ocean to the southwestern CTP, manifesting as increased specific humidity (Qa) in summer. Simultaneously, the weakened westerly jet stream reinforces moisture convergence across the CTP, driving enhanced annual precipitation. By coupling glacier coverage variations with atmospheric processes, this research establishes that precipitation anomalies rather than glacial meltwater primarily govern the extensive lake expansion on the CTP. These findings offer critical insights for guiding ecological security strategies and sustainable development initiatives on the CTP.

Thermokarst lakes, serving as significant sources of methane (CH4), play a crucial role in affecting the feedback of permafrost carbon cycle to global warming. However, accurately assessing CH4 emissions from these lakes remains challenging due to limited observations during lake ice melting periods. In this study, by integrating field surveys with machine learning modeling, we offer a comprehensive assessment of present and future CH4 emissions from thermokarst lakes on the Tibetan Plateau. Our results reveal that the previously underestimated CH4 release from lake ice bubble and water storage during ice melting periods is 11.2 ± 1.6 Gg C of CH4, accounting for 17 ± 4% of the annual total release from lakes. Despite thermokarst lakes cover only 0.2% of the permafrost area, they annually emit 65.5 ± 10.0 Gg C of CH4, which offsets 6.4% of the net carbon sink in alpine grasslands on the plateau. Considering the loss of lake ice, the expansion of thermokarst lakes is projected to lead to 1.1–1.2 folds increase in CH4 emissions by 2100. Our study allows foreseeing future CH4 emissions from the rapid expanding thermokarst lakes and sheds new lights on processes controlling the carbon-climate feedback in alpine permafrost ecosystems.

The dramatic expansion of lake areas on the Changtang Plateau (CTP) since the late 1990s has attracted significant attention, as it contrasts sharply with the global decline in natural lake water storage during the same period. This study investigates the mechanisms behind lake area changes on the CTP by combining glacier change information obtained from the Google Earth Engine (GEE) platform with atmospheric circulation patterns derived from the ERA5 reanalysis dataset. Results indicate that the lake area on the CTP over the past six decades (1960–2020) exhibited three distinct phases: (1) a period of slow expansion from the 1960s to the 1980s; (2) a contraction phase from the 1980s to the late 1990s; and (3) a period of rapid expansion from the late 1990s to 2020. Further analysis revealed that the limited number of glaciers on the CTP significantly affected only a small fraction of glacial lakes near the southern part of the CTP (r = -0.65, p < 0.05). However, this effect is insufficient to explain the extensive expansion of lakes on CTP, particularly since most lakes are not located near glaciers. Instead, the expansion of lakes on the CTP is primarily attributed to anomalous increases in precipitation (r = 0.74, p < 0.01) caused by atmospheric circulation anomalies. This is evidenced by the enhanced Indian summer monsoon, which transports anomalous moisture from the Arabian Sea to the southwestern TP, leading to increased specific humidity at 500 hPa over the CTP during summer. Concurrently, the weakening of the westerly jet stream enhances moisture convergence, further contributing to increased summer precipitation. By integrating glacier change data with atmospheric circulation analysis, this study demonstrates that the extensive lake expansion on the CTP is driven primarily by precipitation anomalies rather than glacial melt. These findings provide valuable insights for future ecological safety planning and sustainable development in the region.

Abstract. Moraine-dammed glacial lakes (MDLs) are not only vital sources of freshwater but also a hazard to mountain communities if they drain in sudden glacial lake outburst floods (GLOFs). Accurately measuring the water storage of these lakes is crucial to ensure sustainable use and safeguard mountain communities downstream. However, thousands of glacial lakes still lack a robust estimate of their water storages because bathymetric surveys in remote regions are difficult and expensive. Here we geometrically approximate the shape and depths of moraine-dammed lakes and provide a cost-effective model to improve lake water storage estimation. Our model uses the outline and the terrain surrounding a glacier lake as input data, assuming a parabolic lake bottom and constant hillslope angles. We initially validate our model using data from four newly surveyed glacial lakes on the Qinghai–Tibet Plateau. Subsequently, we incorporate data from 40 additional measured lakes as a sample set to compare and evaluate the model's performance against other existing models. Our model overcomes the autocorrelation issue inherent in earlier area/depth–water storage relationships and incorporates an automated calculation process based on the topography and geometrical parameters specific to moraine-dammed lakes. Compared to other models, our model achieved the lowest average relative error of approximately 14 % when analyzing a dataset of 44 observed lakes, surpassing the > 44 % average relative error from alternative models. Finally, the model is used to calculate the water storage change in moraine-dammed lakes in the past 30 years in High-mountain Asia. The model has been proven to be robust and can be utilized to update the water storage of lake water for conducting further management of glacial lakes with the potential for outburst floods in the world.

This study reconstructed the annual lake surface area (LSA) and absolute lake water storage (LWS) changes of Lake Sarez, the world’s largest high-altitude landslide-dammed lake, from 1992 to 2023 using multi-source remote sensing data. All available Landsat images were used to extract the LSA using an improved multi-index threshold method, which incorporates a slope mask and threshold adjustment to enhance the boundary delineation accuracy (Kappa coefficient = 0.94). By combining the LSA with high-resolution DEM and the GLOBathy bathymetry dataset, the absolute LWS was reconstructed, fluctuating between 12.3 × 109 and 12.8 × 109 m3. A water balance analysis revealed that inflow runoff (IRO) was the primary driver of LWS changes, contributing 54.57%. The cross-wavelet transform and wavelet coherence analyses showed that the precipitation (PRE) and snow water equivalent (SWE) were key climatic factors that directly influenced the variability of IRO, impacting the interannual water availability in the lake, with PRE having a more sustained impact. Temperature indirectly regulated IRO by affecting SWE and potential evapotranspiration. Furthermore, IRO exhibited different resonance periods and time lags with various atmospheric circulation factors, with the Pacific Decadal Oscillation and North Atlantic Oscillation having the most significant influence on its interannual variations. These findings provide crucial insights into the hydrological behavior of Lake Sarez under climate change and offer a novel approach for studying water storage dynamics in high-altitude landslide-dammed lakes, thereby supporting regional water resource management and ecological conservation.

Monitoring long-term water storage of lakes and reservoirs in arid ungauged basin based on underwater topography derived from multi-source satellite data.

Study region: Dongting Lake, Southern China. Study focus: Lakes play a crucial role in Sustainable Development Goal (SDG) 6, which focuses on water-related ecosystems. Here, we devised a methodology to estimate Dongting Lake water storage and analyze their dynamic changes for monitoring SDG 6.6.1 from 2001 to 2020. This study not only provide a geospatial perspective for monitoring water storage for SDG 6.6.1 under the climate oscillations and human impacts in a large floodplain lake but also provide an important scientific basis for improving the optimal allocation of water resources of the Dongting Lake area. New hydrological insights: Remote sensing data are preferable for establishing water storage models during dry seasons, whereas water level is more reliable during wet seasons. Over the past 20 years, average annual water storage in the lake regions has displayed relatively stable fluctuations with an upward trend since 2015. During the wet season, East Dongting Lake has the largest storage capacity, while most of the water resources are concentrated in South Dongting Lake during the dry season. The water storage deviation reached up to 20 %, with a notable negative deviation of more than 10 % during 2011–2015 caused by a severe drought. Related to the stable operation of the Three Gorges Project and the conservation policy initiated by the Chinese government, Dongting Lake is in a stable and recovery period.

Lake storage in Nganga Rinco serves as a crucial indicator of climate change and is significant for the ecological environment. This study combines ICESat-2, the Global Surface Water Dataset (GSWD), and geographic interpolation to derive lake bathymetry and assess 30-year water storage variation of Nganga Rinco. Bathymetry in dynamic regions offers higher spatial resolution and more precise elevation data compared to the SRTM DEM. When compared to in-situ bathymetry, the derived bathymetry showed an average error of 3.64 m, with errors concentrated in deeper regions. The underwater elevation of Nganga Rinco ranges from 4,628.18 m to 4,718.87 m, with an average of 4,698.62 m, showing a total depth variation of 90.69 m. The lake's volume grew from 11.23 km3 in 1992 to 12.06 km3 in 2021, peaking at 12.16 km3 in 2020, reflecting an overall fluctuation increase of 0.82 km3 (7%). Gray Relation Analysis (GRA) was applied to examine how water storage responds to climate change factors. The analysis revealed that precipitation significantly impacts water storage capacity, while air temperature indirectly affects storage changes through snow/ice melting and evapotranspiration. This research offers valuable insights into lake storage estimation in regions that have no direct underwater terrain measurements, providing important implications for water resource management and advancing our understanding of climate change impacts on high-altitude lakes.

Spatiotemporal Characteristics of Lake Water Level, Surface Area, and Storage Changes in Central East Africa from 2018 to 2024