Abstract. Climate change modifies the water and energy fluxes between the atmosphere and the surface in mountainous regions. This is particularly true over the Qinghai-Tibet Plateau (QTP), a major headwater region of the world, which has shown substantial hydrological changes over the last decades. Among them, the rapid lake level variations observed throughout the plateau remain puzzling and much is still to be understood regarding the spatial distribution of lake level trends (increase/decrease) and paces. The ground across the QTP hosts either permafrost or seasonally frozen ground and both are affected by climate change. In this environment, the ground thermal regime influences liquid water availability, evaporation and runoff. Therefore, climate-driven modifications of the ground thermal regime may contribute to lake level variations. For now, this hypothesis has been overlooked by modelers because of the scarcity of field data and the difficulty to account for the spatial variability of the climate and its influence on the ground thermo-hydrological regime in a numerical framework. This study focuses on the cryo-hydrology of the catchment of Lake Paiku (Southern Tibet) for the 1980–2019 period. We use TopoSCALE and TopoSUB to downscale ERA5 data and capture the spatial variability of the climate in our forcing data. We use a distributed setup of the CryoGrid community model (version 1.0) to quantify thermo-hydrological changes in the ground during the period. Forcing data and simulation outputs are validated with weather station data, surface temperature logger data and the lake level variations. We show that both seasonal frozen ground and permafrost have warmed (1.7 °C per century 2 m deep), increasing the availability of liquid water in the ground and the duration of seasonal thaw. Both phenomena promote evaporation and runoff but ground warming drives a strong increase in subsurface runoff, so that the runoff/(evaporation + runoff) ratio increases over time. Summer evaporation is an important energy sink and we find active layer deepening only where evaporation is limited. The presence of permafrost is found to promote evaporation at the expense of runoff, consistent with recent studies. Yet, this relationship seems to be climate dependent and we show that a colder and wetter climate produces the opposite effect. This ambivalent influence of permafrost may help to understand the contrasting lake level variations observed between the south and north of the QTP, opening new perspectives for future investigations.

We present a novel hybrid framework that incorporates information from the process-based global hydrological model (GHM) PCR-GLOBWB, to reduce prediction errors in streamflow simulations. In addition to catchment attributes and meteorological data, our methodology employs simulated streamflow and state variables from PCR-GLOBWB as predictors of observed river discharge. These outputs are used in a random forest, trained on a global database of streamflow measurements, to improve estimates of simulated river discharge across the globe. PCR-GLOBWB was run for the years 1979–2019 at 30 arcmin and its inputs and outputs were upscaled from daily to monthly time steps. A single random forest model was trained with these state variables, meteorological data and catchment attributes, as predictors of observed streamflow from 2,286 stations worldwide. Model performance was evaluated using Kling–Gupta efficiency (KGE). Results based on cross-validation show that the model is capable of discerning between a variety of hydroclimatic conditions and river flow dynamics, improving KGE of PCR-GLOBWB simulations at more than 80% of testing locations and increasing median KGE from −0.02 in uncalibrated runs to 0.52 after post-processing. Performance boosts are usually independent of the availability of streamflow data, making our method a potential candidate in addressing prediction in poorly gauged and ungauged basins.

<p>Hydrological models include errors when reproducing real-world observations, due to uncertainties in their components that inevitably propagate to the simulated variable. A large body of research in streamflow prediction blends statistical learning into the hydrological sciences, modelling river discharge using meteorological variables and catchment attributes as predictors of observed streamflow.</p><p>We developed a novel hybrid framework that integrates information from the process-based global hydrological model PCR-GLOBWB to reduce prediction errors in streamflow simulations. Our statistical methodology employs simulated streamflow and state variables from PCR-GLOBWB as additional predictors of observed river discharge. These model outputs provide supplemental information that is effectively used in a random forest, trained on a global database of streamflow measurements, to improve estimates of simulated river discharge across the globe. PCR-GLOBWB was run for the years 1979-2019 at 30arcmin and daily resolution, and the simulated state variables were then aggregated to monthly time steps. A single random forest model was trained with these state variables, meteorological data and catchment attributes, as predictors of observed streamflow from 2286 stations worldwide.</p><p>Results based on cross-validation show that the model is capable of discerning between a variety of hydro-climatic conditions and river flow dynamics, improving KGE of PCR-GLOBWB simulations at more than 80% of testing locations and increasing median KGE from -0.02 in uncalibrated runs to 0.52 after post-processing. Performance boosts are usually independent of availability of streamflow data at a particular station, thus making our method a potential candidate in addressing prediction in poorly gauged and ungauged basins.</p><p>Further research is still needed to test the potential influence of additional predictors describing catchment and time-series behaviour. Cluster analysis is required to understand why the post-processing framework still performs poorly at some stations. For prediction purposes, future efforts should also be directed at testing the model at higher spatial resolutions globally, and at finer temporal resolutions.</p>