Explore the vast range of titles available.

Enhanced reservoir evaporation has become an emerging concern regarding water loss, especially when compounded with the ever‐increasing water demand. In this study, we evaluated the evaporation rates and losses for 678 major reservoirs (representing nearly 90% of total storage capacity) in the Contiguous United States over historical baseline (1980–2019), near‐term (2020–2039), and mid‐term (2040–2059) future periods. The evaporation rate was estimated using the Lake Evaporation Model (LEM), an advanced lake evaporation model that addresses both heat storage and fetch effects, driven by multi‐ensemble downscaled Coupled Model Intercomparison Projects 6 (CMIP6) projections under the SSP585 emission scenario. The results project that the evaporation loss may increase by 2.5 × 107 m3/yr through the research period (1980–2059). Among all regions, the Rio Grande is projected to have the largest increasing rate in the near‐term and mid‐term future, with values of 7.11% of 10.25%, respectively. At the seasonal scale, the most significant increase in the evaporation rate is projected during the fall. The evaporation is projected to increase faster than the streamflow over many of the regions in the southwestern US during the summer/fall, suggesting that the shortage of water will be further exacerbated. The climate models contribute the most to the variance, as compared to the other components related to the projection of evaporation losses (e.g., hydrological model, downscaling method, and historical meteorological data set). These findings demonstrate the need to consider accelerated water loss through open water evaporation in long‐term water resources planning across various spatiotemporal scales. Plain Language Summary Evaporative water loss from reservoirs is unavoidable in arid/semi‐arid regions worldwide. The expected exacerbation of evaporation losses under climate change has become an emerging issue in water resource planning. We project the future evaporation rate and losses with Lake Evaporation Model under CMIP6‐based future climate and hydrological scenario. For 678 major reservoirs over the Contiguous United States, both evaporation rates and losses are projected to increase, with much more severe conditions during the mid‐term future (2040–2059) than in the near‐term future (2020–2039). Future exacerbation of evaporation will be much more substantial in the southwestern US, and is expected to be more severe in the fall. This research supports both short‐term extreme water event responses and long‐term water supply management strategies. Key Points Reservoir evaporation losses in the Contiguous United States are projected to increase by 2.5 × 107 m3/yr under the SSP585 emission scenario The increase of reservoir evaporation will further exacerbate the water shortage in the southwestern regions Compared to the other components related to the projection of the evaporation losses, the climate models contribute the most to the variance

An in-depth understanding of a key element such as lake evaporation is particularly beneficial in developing the optimal management approach for reservoirs. In this study, we first aim to evaluate the applicability of regressors Random Forest (RF), Gradient Booting (GB), and Decision Tree (DT), K Nearest Neighbor (kNN), and XGBoost architectures to predict daily lake evaporation of five reservoirs in the Awash River basin, Ethiopia. The best performing models, Gradient Boosting and XGBoost, are then explained through an explanatory framework using daily climate datasets. The interpretability of the models was evaluated using the Shapley Additive explanations (SHAP). The GB model performed better with (RMSE = 0.045, MSE = 0.031, MAE = 0.002, NSE = 0.997, KGF = 0.991, RRMSE = 0.011) for Metehara Station, (RMSE = 0.032, MSE = 0.024, MAE = 0.001, NSE = 0.998, KGF = 0.999, RRMSE = 0.008) at Melkasa Station, and Dubti Station (RMSE = 0.13, MSE = 0.09, MAE = 0.017, NSE = 0.982, KGF = 0.977,RRMSE = 0.022) as the same as of XGBoost. The factors with the greatest overall impact on the daily evaporation for GB and XGboost Architecture were the SH, month, Tmax, and Tmin for Metehara and Melkasa, and Tmax, Tmin, and month had the greatest impact on the daily evaporation for Dubti. Furthermore, the interpretability of the models showed good agreement between the MLAs simulations and the actual hydro-climatic evaporation process. This result allows decision makers to not only rely on the results of an algorithm, but to make more informed decisions by using interpretable results for better control of the basin reservoir operating rules.

Lake evaporation is a critical component of the hydrological cycle. Quantifying lake evaporation in hyper-arid regions by measurement and estimation can both provide reliable potential evaporation (ET0) reference and promote a deeper understanding of the regional hydrological process and its response towards changing climate. We placed a floating E601 evaporation pan on East Juyan Lake, which is representative of arid regions’ terminal lakes, to measure daily evaporation and conducted simultaneous bankside synoptic observation during the growing season of 2013–2015. A semi-empirical evaporation model derived from Dalton model was parameterized and validated with measured data. The model was then used to estimate lake evaporation during 2002–2015. According to in situ measurements, maximum, minimum and mean lake evaporation were 8.1, 3.7 and 6.5 mm/day, and growing season evaporation was 1183.3 mm (~80% of the annual amount). Adding up non-growing season evaporation that we converted from φ20 pan evaporation at Ejina weather station, the annual mean lake evaporation, 1471.3 mm, was representative of lower Heihe River’s ET0. Model inter-comparison implied our model performed well both in simplicity and accuracy and has potential utilization in a data-sparse area. In 2002–2015, estimated mean daily evaporation was 6.5 mm/day and growing season evaporation was 1233.7 mm. Trend analysis of estimated evaporation proved the evaporation paradox’s existence in this hyper-arid region and validated complementary relationship theory’s adaptability.

The evaporative loss from global lakes (natural and artificial) is a critical component of the terrestrial water and energy balance. However, the evaporation volume of these water bodies—from the spatial distribution to the long-term trend—is as of yet unknown. Here, using satellite observations and modeling tools, we quantified the evaporation volume from 1.42 million global lakes from 1985 to 2018. We find that the long-term average lake evaporation is 1500 ± 150 km 3 year −1 and it has increased at a rate of 3.12 km 3 year −1 . The trend attributions include an increasing evaporation rate (58%), decreasing lake ice coverage (23%), and increasing lake surface area (19%). While only accounting for 5% of the global lake storage capacity, artificial lakes (i.e., reservoirs) contribute 16% to the evaporation volume. Our results underline the importance of using evaporation volume, rather than evaporation rate, as the primary index for assessing climatic impacts on lake systems. While the evaporative water loss from global lakes is invisible, the volume is substantial. In recent decades, lake evaporation volume has been significantly increasing due to enhanced evaporation rate, melting lake ice, and expansion of water extent.

Lake evaporation is a sensitive indicator of the hydrological response to climate change. Variability in annual lake evaporation has been assumed to be controlled primarily by the incoming surface solar radiation. Here we report simulations with a numerical model of lake surface fluxes, with input data based on a high-emissions climate change scenario (Representative Concentration Pathway 8.5). In our simulations, the global annual lake evaporation increases by 16% by the end of the century, despite little change in incoming solar radiation at the surface. We attribute about half of this projected increase to two effects: periods of ice cover are shorter in a warmer climate and the ratio of sensible to latent heat flux decreases, thus channelling more energy into evaporation. At low latitudes, annual lake evaporation is further enhanced because the lake surface warms more slowly than the air, leading to more long-wave radiation energy available for evaporation. We suggest that an analogous change in the ratio of sensible to latent heat fluxes in the open ocean can help to explain some of the spread among climate models in terms of their sensitivity of precipitation to warming. We conclude that an accurate prediction of the energy balance at the Earth’s surface is crucial for evaluating the hydrological response to climate change.

Open water evaporation, which often consumes a large fraction of annual storage (especially in arid and semi‐arid regions), is a controlling variable for modern water resource management. Developing a daily reservoir evaporation data set is necessary for reservoir operations to consider the influence of evaporation in a timely manner. However, over the past few decades, the quantification of reservoir evaporation has primarily relied on monthly Class A pan evaporation observations, which might lead to largely biased estimations because they do not incorporate the effects of heat storage and fetch. In this study, we developed a general Daily Lake Evaporation Model (DLEM) based on Penman's equation combined with daily atmospheric reanalysis data sets, to improve the validity and frequency of reservoir evaporation monitoring. Compared with daily evaporation estimates from eddy covariance systems, the DLEM showed good quality and reliability, with R2 values ranging from 0.50 to 0.73 and RMSE values ranging from 1.13 to 1.97 mm day−1 in four different sites. By applying DLEM to all 188 major reservoirs in Texas, we generated a long‐term data set of (1 January 1981–31 December 2021) daily evaporation rates. The results reveal a clear geographic distribution and strong seasonality of evaporation throughout Texas, where the mean evaporation rate is highest during July, with 6.85 mm day−1. Trend analysis indicates that the annual average evaporation rate has significantly increased since 1981 at a rate of 0.076 mm day−1 decade−1. This study provides sufficient data support for water resource operations and management, and regional water planning. Key Points A general Daily Lake Evaporation Model (DLEM) is developed on modified‐Penman evaporation and compared with in situ eddy covariance measurements A first long‐term (40+ years) regional scale daily reservoir evaporation data set is generated for 188 major reservoirs in Texas (US) While shortwave radiation affects the long‐term spatial distribution and trends, high wind speed is likely to cause daily extremes

Global warming is considered a major threat to Earth’s lakes water budgets and quality. However, flow regulation, over-exploitation, lack of hydrological data, and disparate evaluation methods hamper comparative global estimates of lake vulnerability to evaporation. We have analyzed the stable isotope composition of 1257 global lakes and we find that most lakes depend on precipitation and groundwater recharge subsequently altered by catchment and lake evaporation processes. Isotope mass-balance modeling shows that ca. 20% of water inflow in global lakes is lost through evaporation and ca. 10% of lakes in arid and temperate zones experience extreme evaporative losses >40 % of the total inflow. Precipitation amount, limnicity, wind speed, relative humidity, and solar radiation are predominant controls on lake isotope composition and evaporation, regardless of the climatic zone. The promotion of systematic global isotopic monitoring of Earth’s lakes provides a direct and comparative approach to detect the impacts of climatic and catchment-scale changes on water-balance and evaporation trends. An isotope synthesis of 1257 global lakes revealed on average 20% of inflow is lost to evaporation, but 10% of Earth’s lakes show extreme evaporative losses. Stable water isotope monitoring is an effective way to detect comparative climatic and catchment-scale impacts on lake water-balance budgets.

Rivers and lakes are intrinsically connected waterbodies yet they are rarely used to hydrologically constrain one another with remote sensing. Here we begin to bridge the gap between river and lake hydrology with the introduction of the LakeFlow algorithm. LakeFlow uses river‐lake mass conservation and observations from the Surface Water and Ocean Topography (SWOT) satellite to provide river discharge estimates of lake and reservoir inflows and outflows. We test LakeFlow performance at three lakes using a synthetic SWOT data set assuming the maximum measurement errors defined by the mission science requirements, and we include modeled lateral inflow and lake evaporation data to further constrain the mass balance. We find that LakeFlow produces promising discharge estimates (median Nash‐Sutcliffe efficiency = 0.88, relative bias = 14%). LakeFlow can inform water resources management by providing global lake inflow and outflow estimates, highlighting a path for recognizing rivers and lakes as an interconnected system. Plain Language Summary Effective water resource management depends on our ability to monitor and understand lake and reservoir inflows and outflows. Satellite remote sensing of lakes and rivers has become increasingly important for water management but little work has been done to estimate streamflow at river‐lake interfaces. Here we present the LakeFlow algorithm that leverages satellite observations of lakes and rivers to estimate streamflow at lake inflows and outflows. We test LakeFlow using synthetic data at three U.S. lakes in Georgia, Arizona and Kansas, and find that it yields promising estimates of streamflow at river‐lake boundaries. LakeFlow provides valuable insights into river‐lake streamflow dynamics, which can inform water management decisions and is a step forward in the integration of river and lake studies. Key Points LakeFlow is a new algorithm that uses Surface Water and Ocean Topography (SWOT) satellite data to estimate river inflow and outflow at lakes via mass conservation Applying LakeFlow to three sample lake systems shows promising performance for estimating lake inflows and outflows (median Nash‐Sutcliffe Efficiency = 0.88) Including modeled estimates of non SWOT‐observed evaporation and tributary inflows can further improve LakeFlow discharge estimates

Lakes are experiencing ice declines and fundamental changes in winter conditions. For Earth's largest lakes that experience seasonal ice cover, the relationship between ice conditions and evaporation is critical to water balance estimates and global freshwater storage. Here, we analyze robust data sets of net basin supplies, satellite‐derived products, and model estimates of surface turbulent heat flux for the Laurentian Great Lakes during the period 1973–2022. We show that ice cover does not have a strong relationship with lake evaporation in winter months and that often the magnitude of the ice effect on moisture flux reduction is within the range of natural variability and the uncertainty of water budget estimates. This suggests that differences in lake evaporation between cold and warm winters is driven by seasonal overlake atmospheric conditions, more broadly, and that ice cover reduces but does not determine the resultant evaporation.

Projected changes in lake-effect snowfall by the mid- and late twenty-first century are explored for the Laurentian Great Lakes basin. Simulations from two state-of-the-art global climate models within phase 5 of the Coupled Model Intercomparison Project (CMIP5) are dynamically downscaled according to the representative concentration pathway 8.5 (RCP8.5). The downscaling is performed using the Abdus Salam International Centre for Theoretical Physics (ICTP) Regional Climate Model version 4 (RegCM4) with 25-km grid spacing, interactively coupled to a one-dimensional lake model. Both downscaled models produce atmospheric warming and increased cold-season precipitation. The Great Lakes’ ice cover is projected to dramatically decline and, by the end of the century, become confined to the northern shallow lakeshores during mid-to-late winter. Projected reductions in ice cover and greater dynamically induced wind fetch lead to enhanced lake evaporation and resulting total lake-effect precipitation, although with increased rainfall at the expense of snowfall. A general reduction in the frequency of heavy lake-effect snowstorms is simulated during the twenty-first century, except with increases around Lake Superior by the midcentury when local air temperatures still remain low enough for wintertime precipitation to largely fall in the form of snow. Despite the significant progress made here in elucidating the potential future changes in lake-effect snowstorms across the Great Lakes basin, further research is still needed to downscale a larger ensemble of CMIP5 model simulations, ideally using a higher-resolution, nonhydrostatic regional climate model coupled to a three-dimensional lake model.