The contrasting patterns of lake-level fluctuations across the Tibetan Plateau (TP) are indicators of differences in the water balance over the TP. However, little is known about the key hydrological factors controlling this variability. The purpose of this study is to contribute to a more quantitative understanding of these factors for four selected lakes in the southern-central part of the TP: Nam Co and Tangra Yumco (increasing water levels), and Mapam Yumco and Paiku Co (stable or slightly decreasing water levels). We present the results of an integrated approach combining hydrological modeling, atmospheric-model output and remote-sensing data. The J2000g hydrological model was adapted and extended according to the specific characteristics of closed-lake basins on the TP and driven with High Asia Refined analysis (HAR) data at 10 km resolution for the period 2001-2010. Differences in the mean annual water balances among the four basins are primarily related to higher precipitation totals and attributed runoff generation in the Nam Co and Tangra Yumco basins. Precipitation and associated runoff are the main driving forces for inter-annual lake variations. The glacier-meltwater contribution to the total basin runoff volume (between 14 and 30% averaged over the 10-year period) plays a less important role compared to runoff generation from rainfall and snowmelt in non-glacierized land areas. Nevertheless, using a hypothetical ice-free scenario in the hydrological model, we indicate that ice-melt water constitutes an important water-supply component for Mapam Yumco and Paiku Co, in order to maintain a state close to equilibrium, whereas the water balance in the Nam Co and Tangra Yumco basins remains positive under ice-free conditions. These results highlight the benefits of linking hydrological modeling with atmospheric-model output and satellite-derived data, and the presented approach can be readily transferred to other data-scarce closed lake basins, opening new directions of research. Future work should go towards a better assessment of the model-chain uncertainties, especially in this region where observation data are scarce.
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