Lake Storage Changes Research Articles

Predicting Future Lake Water Storage Changes on the Tibetan Plateau under Different Climate Change Scenarios

The variation of lake water storage is an important indicator for studying both climate change and ecological environment changes. Previous studies have mainly focused on the lake storage changes in recent decades, and predicting future lake storage changes on the Tibetan Plateau under climate change scenarios remains a crucial gap. We addressed this gap by establishing prediction models for water storage changes in nine lakes using historical water storage and climate data from the past 29 years and predicting the water storage changes for the next 80 years under three scenarios based on Phase 6 of the Coupled Model Intercomparison Project (CMIP6) data. The Quantile-mapping (QM) method was applied to correct the precipitation data of CMIP6 with assimilated data. The results indicated that the prediction model performed well, with high correlation (R2 > 0.7 for the training set) and low mean absolute error (MSE < 0.1 km3). The results suggest that most lakes will experience a slight increase in water storage until 2050, followed by a rapid rise until 2100 under all three SSP (Shared Socioeconomic Pathways) scenarios, including SSP126, SSP245, and SSP585. By the end of the century, the total projected increase in lake water storage is estimated to be 189.676 ± 16.266 km3, 191.762 ± 10.683 km3, and 186.212 ± 6.441 km3 until 2100, respectively.

Human or climate? Differentiating the anthropogenic and climatic drivers of lake storage changes on spatial perspective via remote sensing data

Lakes are an essential part of the terrestrial water system in which storage changes are controlled by water balance and human impact. Although there are some attempts to define storage changes on a global scale, examination of spatial relations is poorly quantified. In this study, therefore, lake storage changes have been investigated using remote-sensing-derived data around the globe. Hence, 372 artificial/natural lakes were obtained, covering between 1992 and 2019. Watersheds belong to river was extracted via HydroSHED data. Based on watershed, dominant climate types were determined via Köppen-Geiger classification. Similarly, the areal average CRU TS v.4.05 monthly gridded precipitation time series and human footprint data based on watersheds were obtained to understand the drivers of lake storage changes. The nonparametric Mann-Kendall and Sen's slope trend analyses were applied to the lake storage change and precipiation values in order to determine long-term increases and decreases. A bivariate map was constructed between storage changes trend vs precipitation trend and human footprint to reveal the drivers of lake storage changes in terms of spatial aspects. The trend analysis and bivariate map results show that North America, the East African Highlands, and the Tibet plateau are important increasing hotspots, where precipitation is a significant driver for storage oscillations, except for the Tibet plateau. Besides, the Brazilian Highlands, Pacific Mountain System, and Intermontane of conterminous USA are other decreasing hotspots in which human footprint and decreasing precipitation collectively affect these changes. Furthermore, results clearly show that anthropogenic influence is low in the northern and mountainous areas, and storage changes have a linear relationship with precipitation. In contrast, intense human climate interaction influences lake changes in plains areas and arid/temperate climates.

Estimation of Lake Storage Based on the Surrounding Topography around the Lake from the SRTM DEM

The estimation of lake depth and water storage is crucial for water resource management in inland waters. In previous studies, lake water storage estimation required in situ bathymetry data; however, the high cost of fieldwork prevented the extension of this method to all lakes. This study aims to solve this problem by studying how to estimate lake water storage without using bathymetric data. According to the similarity between the topography surrounding the lake and the underwater topography, we present an approach to estimate the lake water storage. The principle of the method is to extend the surrounding terrain into the underwater area to establish an underwater model. In this study, six lakes of the Tibetan Plateau were selected, and the results showed that the average R2 between the estimated and measured water depths of the six lakes was about 0.76. Among them, the estimated water depth of Taiyang Lake (maximum depth of 61.57 m) had the largest deviation from the measured water depth, with an RMSE (root mean square error) of 19.77 m, while the estimated water depth of Bangda Co (maximum depth of 41.04 m) had the best result, with an RMSE of 7.83 m. The relative errors of all water storage estimations were less than 20%. The lake with the best accuracy was Zigetang Co (measured water storage of 39.6 Gt), and the estimated water storage was 42.2 Gt, with a relative error of 6.57%. The lake with the worst accuracy was Taiyang Lake (measured water storage of 31.94 Gt), and the estimated water storage was 36.47 Gt, with a relative error of 14.18%. This study provides an effective method for lake depth estimation and improves the ability to monitor changes in lake storage, especially for lakes with harsh environments or in remote areas.

Understanding groundwater behaviors and exchange dynamics in a linked catchment-floodplain-lake system

Groundwater and surface water are hydrologically interconnected systems that exhibit dynamic water, heat and mass exchanges. In this study, a conceptual framework was used to investigate groundwater behaviors and associated hydrological exchanges by combining field measurements, digital filtering and analytical approaches, exemplified by a linked catchment-floodplain-lake system (Poyang Lake, China). The results show that the hydrological regime for both groundwater and surface water exhibit a seasonal variability in the lake catchment. Topographically, the lake catchment can be divided into the mountainous baseflow, ungauged lateral groundwater and floodplain groundwater that contribute to the lake storage changes. Although groundwater flow is generally from the mountainous catchment to the lake floodplain areas due to topographic effects, precipitation provides an additional input for the shallow groundwater and is expected to enhance the groundwater dynamics in terms of spatially heterogeneous responses. The estimation indicates that about 40 % of the catchment river discharge may be coming from the mountainous baseflow (~290 × 108 m3/yr) and discharged into the lake through a surface flow pathway. The ungauged groundwater-lake interaction shows the annual discharge volume is up to 10 × 108 m3/yr and associated exchange fluxes tend to be stronger during spring-summer months (23–45 m3/s) than those of autumn-winter months (9–22 m3/s). Additionally, the floodplain groundwater-lake exchange (~9.5 × 108 m3/yr) indicates that groundwater generally receives the lake water during summer months (mean flux = 110 m3/s) and discharges into the lake during other months (90 m3/s) through a subsurface pathway. This study highlights the importance of groundwater's contributions to the surface river-lake system in terms of the flux variability and different transport pathways. The outcomes of this work will benefit future water resources management and applications by providing a methodology for predicting the groundwater hydrology of large lake-catchment systems.

Variations in lake water storage over Inner Mongolia during recent three decades based on multi-mission satellites

Lake shrinkage is a frequent water resource problem in arid and semi-arid regions that can affect the hydrological cycle and damage the local aquatic environment. Since the 1990 s, many lakes in Inner Mongolia have shrunk significantly. However, the causes of the shrinkage and long-term storage trends of lakes in Inner Mongolia are still poorly understood. Using observations from multi-mission satellites, we analyzed water storage changes in six major lakes in Inner Mongolia over the past 30 years and explored the underlying causes. The results reveal divergent changes between the two lake categories. For each of the tectonic lakes (including Lake Hulun, Lake Dalinuoer, Lake Daihai, and Lake Hongjiannao), the water storage has continuously decreased with annual rates of 0.067 km3/yr, 0.012 km3/yr, 0.021 km3/yr, and 0.01 km3/yr, respectively. In contrast, for oxbow lakes (including Lake Hasuhai and Lake Wuliangsuhai), the storage trends were insignificant (0.001 km3/yr for both). Human activities were the most significant driving factors, especially coal mining, agricultural irrigation, and grazing, which explained more than 70% of the storage changes in Lake Hulun, Lake Dalinuoer, and Lake Hasuhai. Among the natural factors, evapotranspiration and runoff were secondary influences on these lake storage changes. This study provides a systematic analysis and attribution of water storage changes for major lakes in Inner Mongolia and provides scientific guidance for local water resource management and planning. Our method provides a useful example of using multi-mission satellite data for estimating water storage changes.

Satellite Laser Altimetry Reveals a Net Water Mass Gain in Global Lakes With Spatial Heterogeneity in the Early 21st Century

AbstractLake water storage changes are important factors that influence the climate, hydrological cycle, and environments. However, long‐term estimation of global lake storage changes is challenging because historical in‐situ hydrological observations worldwide are rarely available. Benefiting from the laser altimeter ICESat and ICESat‐2, we comprehensively assessed water level and volume changes in global natural lakes larger than 10 km2 during 2003–2020. The 6,567 lakes observable by ICESat/ICESat‐2, which account for ∼94% of the total global lake volume, showed a total water storage increase of 10.88 ± 16.45 Gt/yr during 2003–2020, and the estimate reaches 16.12 ± 20.41 Gt/yr when also taking account of the remaining unobserved lakes. Despite water gains in most natural lakes, large lakes under dry and high water‐stress conditions experienced dramatic water loss in general. Presumably, these drying lakes may continue to shrink with a warming climate and continuously increasing water demands in the future without further action.

Potential and Limitations of Satellite Altimetry Constellations for Monitoring Surface Water Storage Changes—A Case Study in the Mississippi Basin

Remote sensing data are essential for monitoring the Earth’s surface waters, especially since the amount of publicly available in-situ data is declining. Satellite altimetry provides valuable information on the water levels and variations of lakes, reservoirs and rivers. In combination with satellite imagery, the derived time series allow the monitoring of lake storage changes and river discharge. However, satellite altimetry is limited in terms of its spatial resolution due to its measurement geometry, only providing information in the nadir direction beneath the satellite’s orbit. In a case study in the Mississippi River Basin (MRB), this study investigates the potential and limitations of past and current satellite missions for the monitoring of basin-wide storage changes. For that purpose, an automated target detection is developed and the extracted lake surfaces are merged with the satellites’ tracks. This reveals that the current altimeter configuration misses about 80% of all lakes larger than 0.1 km2 in the MRB and 20% of lakes larger than 10 km2, corresponding to 30% and 7% of the total water area, respectively. Past altimetry configurations perform even more poorly. From the larger water bodies represented by a global hydrology model, at least 91% of targets and 98% of storage changes are captured by the current altimeter configuration. This will improve significantly with the launch of the planned Surface Water and Ocean Topography (SWOT) mission.

Seventy-year long record of monthly water balance estimates for Earth\u2019s largest lake system

We develop new estimates of monthly water balance components from 1950 to 2019 for the Laurentian Great Lakes, the largest surface freshwater system on Earth. For each of the Great Lakes, lake storage changes and water balance components were estimated using the Large Lakes Statistical Water Balance Model (L2SWBM). Multiple independent data sources, contributed by a binational community of research scientists and practitioners, were assimilated into the L2SWBM to infer feasible values of water balance components through a Bayesian framework. A conventional water balance model was used to constrain the new estimates, ensuring that the water balance can be reconciled over multiple time periods. The new estimates are useful for investigating changes in water availability, or benchmarking new hydrological models and data products developed for the Laurentian Great Lakes Region. The source code and inputs of the L2SWBM model are also made available, and can be adapted to include new data sources for the Great Lakes, or to address water balance problems on other large lake systems.

Reconciling the water balance of large lake systems

Water balance models are commonly employed to improve understanding of drivers behind changes in the hydrologic cycle across multiple space and time scales. Generally, these models are physically-based, a feature that can lead to unreconciled biases and uncertainties when a model is not encoded to be faithful to changes in water storage over time. Statistical methods represent one approach to addressing this problem. We find, however, that there are very few historical hydrological modeling studies in which bias correction and uncertainty quantification methods are routinely applied to ensure fidelity to the water balance. Importantly, we know of none (aside from preliminary applications of the model we advance in this study) applied specifically to large lake systems. We fill this gap by developing and applying a Bayesian statistical analysis framework for inferring water balance components specifically in large lake systems. The model behind this framework, which we refer to as the L2SWBM (large lake statistical water balance model), includes a conventional water balance model encoded to iteratively close the water balance over multiple consecutive time periods. Throughout these iterations, the L2SWBM can assimilate multiple preliminary estimates of each water balance component (from either historical model simulations or interpolated in situ monitoring data, for example), and it can accommodate those estimates even if they span different time periods. The L2SWBM can also be executed if data for a particular water balance component are unavailable, a feature that underscores its potential utility in data scarce regions. Here, we demonstrate the utility of our new framework through a customized application to the Laurentian Great Lakes, the largest system of lakes on Earth. Through this application, we find that the L2SWBM is able to infer new water balance component estimates that, to our are knowledge, are the first ever to close the water balance over a multi-decadal historical period for this massive lake system. More specifically, we find that posterior predictive intervals for changes in lake storage are consistent with observed changes in lake storage across this period over simulation time intervals of both 6 and 12 months. In additional to introducing a framework for developing definitive long-term hydrologic records for large lake systems, our study provides important insights into the origins of biases in both legacy and state-of-the-art hydrological models, as well as regional and global hydrological data sets.

Lake water and glacier mass gains in the northwestern Tibetan Plateau observed from multi-sensor remote sensing data: Implication of an enhanced hydrological cycle

Lakes and glaciers are widely distributed in the northwestern Tibetan Plateau. A synchronized examination of lake and glacier mass variations there has not previously been conducted, which has limited the understanding of the lake water balance and the hydrologic cycle in the region. In this study, we comprehensively examine lake area and volume, and glacier mass changes using multi-sensor satellite data. We find that lake expansion in the northwestern Tibetan Plateau was more robust from 1976 to 2018 when compared to other regions of the plateau, especially for the glacier-fed and endorheic lakes. Lake volume changes show that most of the lakes had an increase in water volume, particularly in 2000–2018 with a total water storage gain of ~28.6 Gt. By using high-resolution KH-9 and TanDEM-X data, we observe that the glacier surface elevation of the western Kunlun Mountains had a slight thinning rate of −0.07 ± 0.16 m/yr in 1973–2000, but a positive rate of 0.002 ± 0.003 m/yr in 2000–2018. The heterogeneous pattern of glacier elevation changes between the north (N) and south (S) slopes are revealed, i.e. -0.02 ± 0.01 m/yr (N) against −0.12 ± 0.03 m/yr (S) in 1973–2000 and − 0.05 ± 0.02 m/yr (N) against 0.06 ± 0.02 m/yr (S) in 2000–2018. Overall, the glaciers trend to a stable state (~0.05 m/yr) in both the south slope of the western Kunlun Mountains and Aru Co regions between 2000 and 2018. Similar patterns are also found for basin-wide examinations of lake storage changes (mass gains) and glacier mass budgets (positive or close to stable). The seasonal snow cover area changes, derived from cloud-free MODIS snow cover products, present a variable and insignificant trend between 2003 and 2017. Snow depth derived from passive microwave remote-sensing data, exhibits a decreasing trend between 1979 and 2015, but the water equivalent could contribute only an insignificant amount to the observed lake changes. The lake water gains, and almost positive glacier mass balance imply that the hydrological cycle in the northwestern Tibetan Plateau has become enhanced.