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Effects of lake evaporation (EW) on basin evapotranspiration (ETB) and lake water storage change (LWSC) at lake‐basin scale have never been reported for most basins on the inner Tibetan Plateau (IB). In this study, EW of 118 large lakes in 95 closed lake‐basins were estimated, and its effects on ETB and LWSC over 2001–2018 were examined using a derivative‐guided framework from the aspects of EW amount, rate, trend slope and inter‐annual variability. We found that EW amount has a high effect (17%) on regional ETB amount compared to the average lake area ratio (α) (∼5%), and the effect has increased significantly (2%/10 a). The spatial pattern of the effect is mainly controlled by α, and the increasing trend of α (0.6%/10 a) also dominated the increasing trend in regional ETB rate (0.30 mm/a) though with large spatial heterogeneity. Variance in α and EW rate have a minor effect (∼3%) on ETB variance, especially for the basins with lower α. The combination of quasi lake inflow (RL, 41%) and lake surface precipitation (PW, 16%) offset the depletion of EW (−43%), resulting in the surplus of regional lake water (LWSC > 0). The increase in EW mount, which is mainly from lake area expansion (90%), caused a decreasing trend in LWSC (i.e., slower growth rate) with a contribution of −59%. This suggests a negative feedback between lake area expansion and EW amount in the IB, and the feedback may continue with the predicted area increases. Key Points: Amount of evaporation for 118 lakes was estimated using two open water evaporation algorithms with annual lake areaA new derivative‐guided framework was presented to examine lake effects on basin evapotranspiration and lake water storage changeBasin scale lake inflow of 95 ungauged lake‐basins was estimated based on lake water balance [ABSTRACT FROM AUTHOR]

Lake storage change serves as a unique indicator of natural climate change on the Tibetan Plateau (TP). However, comprehensive lake storage data, especially for lakes smaller than 10 km2, are still lacking in the region. In this dataset, we completed a census of annual relative lake volume (RLV) for 976 lakes, which are larger than 1 km2, on the endorheic basin of the Tibetan Plateau (EBTP) during 1989–2019 using Landsat imagery and digital terrain models. Our method first identifies individual lakes, determines their analysis extents and calculates annual lake area from Landsat imagery. It then derives lake area-elevation relationship, estimates lake surface elevation, and calculates RLV. Validation and comparison with several existing datasets indicate our data are more reliable and comprehensive. Our study complements existing lake datasets by providing a complete and long-term lake water volume change data for the region. Measurement(s) Relative lake volume Technology Type(s) Remote sensing image and DEM data Sample Characteristic - Environment endorheic lake Sample Characteristic - Location Tibetan Plateau [ABSTRACT FROM AUTHOR]

[Display omitted] • WUE increased significantly for main vegetation types in the Tibetan Plateau during 1982–2018; • Increased LAI boosted WUE in grassland while decreased VPD and elevated CO 2 derived WUE increase in broad-leaf forests; • Regression-based methods are too simplified and cannot fully reflect nonlinear effects of different drivers on WUE change. Ecosystem water use efficiency (WUE) is a key indicator for understanding the response of carbon–water processes to environmental changes. However, as affected by uncertainty of WUE estimation and attribution, the individual contributions of environmental drivers to WUE changes and the underlying mechanisms remain unclear on the Tibetan Plateau (TP). Here, a theory-based analytical WUE model was modified by introducing an optimal stomatal behavior model, and used to estimate monthly WUE and their drivers during 1982–2018 for main vegetation types on the Tibetan Plateau (TP). Rationality of three mainstream analytical attribution methods—analytical-based partial derivative method (APDM), regression-based partial derivative method (NPDM), and climate elasticity method (CEM)—was examined. Furtherly, the contributions of various environmental drivers to WUE variation trend were estimated for various vegetation types. Results indicate that: (1) The modified model performed well in estimating WUE against with observed WUE at five eddy-covariance (EC) flux stations, which decreased the uncertainty of WUE estimation. WUE increased significantly in the TP with a slope of 0.49 × 10-2 g C kg−1 H 2 O yr−1 during 1982–2018 with forests increased most rapidly; (2) Only APDM captured both the variation and magnitude of WUE trends, and both NPDM and CEM failed to reproduce the magnitude of WUE trends, though CEM was better than NPDM benefited from its nonlinear structure. Through comparing these methods, we found that the nonlinear direct effects of these forcing variables on WUE change could be higher than their indirect effects caused by their interactions. (3) There are some differences in the main drivers of WUE trends for various vegetation types. Increase in WUE was mainly driven by the increase in leaf area index (LAI) for both steppes and meadows, while decreased air water vapor pressure deficit (VPD) and elevated CO 2 showed higher positive effects for broad-leaf forests. The distinct response and extremely scarce EC observations of forests suggest that more attention should be paid to forests in the TP. This study could help in selection of attribution methods and understanding the mechanisms of carbon–water coupling of alpine ecosystems under a changing climate. [ABSTRACT FROM AUTHOR]

• Lake effect on TWSC was estimated using a newly developed long-term lake volume data. • Contributions of water storage components to TWSC were estimated using both RF model and water balance approach. • The RF method was used to estimate both TWSA and TWSC. • Regional TWSA decreased in the pre-mutation period and then quickly increased in the post-mutation period with an intensified hydrological cycle. Terrestrial water storage (TWS) and its change (TWSC) are extremely sensitive to climate change, and are good indicators of the response of the hydrological system to climate change in the Tibetan Plateau (TP). However, long-term variation in TWS and its components, as well as contributions of these component changes to TWSC, are still not well documented. In this study, we extended monthly TWS anomaly (TWSA) and TWSC from 1989 to 2019 using the random forest (RF) method, analyzed the long-term contribution rates of each component change to TWSC using a mass balance approach, and compared them with the rates estimated by the RF method. Our major findings are: (1) the warming and wetting trend in the entire analysis period (PA) and in the pre-mutation period (P1) was slowed down or even reversed in the post-mutation period (P2), which was caused mainly by the decrease in temperature and precipitation in the north-eastern Inner Basin (IB) and southern Qaidam Basin (QB); (2) the RF method is capable of estimating monthly TWSA and TWSC (0.872 ≤ R2 ≤ 0.957 in training data, 0.528 ≤ R2 ≤ 0.860 in test data). Variable selection has an important impact on the method, and TWSC can be used as an alternative when TWSA estimate is poor; (3) Significant annual TWSA increase (2.20 mm/a, P < 0.05) in the IB during PA comes from a higher increase during P2 (3.62 mm/a, P < 0.05) and a small variation during P1 (−0.65 m/a, P > 0.1). Spatially, the increase is attributed to the higher increase trends (> 4.50 mm/a, P < 0.05) in north-eastern IB during PA and P2, and slight decrease in western and southern IB during PA. The spatial and temporal heterogeneity was mainly caused by the differences in precipitation amount, glacier proportions, and permafrost types. (4) During PA, TWSC in the IB was mainly determined by lake water storage change (LWSC) with a contribution rate of 62 %, while in the QB it was mainly determined by soil water change (−43 %), and LWSC had a contribution rate of −27 %. LWSC impact on TWSC was more significant in the IB, especially in the southeastern sub-regions with regional water storage surplus (TWSC > 0). The overall increase in water storages and fluxes, and exchanges imply an intensified hydrologic cycle in the region. [ABSTRACT FROM AUTHOR]