This study compares two prominent hydrometeorological datasets across 47 shared watersheds in the United States to assess their compatibility. While daily temperature and precipitation data showed notable discrepancies, agreement improved at monthly, seasonal, and annual scales. The variability between the datasets is comparable to the inherent variability within each individual dataset, and our findings validate the results of previous research employing either dataset. 

In the boreal Krycklan Catchment, 40 years of warming has notably altered hydrological patterns, with increases in winter runoff and decreases in summer flows. Winter climate indices effectively predicted minimum winter flows, while summer runoff variability was influenced by temperature extremes across seasons. Isotope data revealed a growing contribution of precipitation to winter runoff, indicating potential challenges for catchment water storage under continued warming.

Modeling reservoir outflow and storage is challenging due to limited publicly available data and human decision-making. For 100 reservoirs in the US, we examined how calibrating reservoir algorithms against outflow and storage-related variables affects performance. We found that calibration notably improves storage simulations, while outflow simulations are more influenced by the quality of inflow data. We recommend using remotely sensed storage anomalies to calibrate reservoir algorithms.

The fate of liquid water from melting snow in winter and spring is difficult to understand in the mountains. This work uses a multi-instrumental network to accurately monitor the dynamics of snowmelt and infiltration at different depths in the ground and at different altitudes. The results show that melting snow quickly infiltrates into the upper layers of the soil but is also quickly transferred through the soil along the slopes towards the river.

To address challenges related to unreliable hydrological simulations, we present an enhanced hydrological simulation with a refined climate model and a more comprehensive hydrological model. The model with the two parts outperforms that without, especially in migrating bias in peak flow and dry-season flow. Our findings highlight the enhanced hydrological simulation capability, with the refined climate and lake module contributing 24 % and 76 % improvement, respectively.