The Global Surface Water Explorer (GSWE) was combined with bathymetric and historical meteorological data to quantify water balance during the period 1984–2020 in the Primera de Palos freshwater lagoon (Southwest Spain). The results showed that the natural hydrological regime of the lagoon was modified from 1995 by water inputs from irrigation returns. Thanks to a water balance approach, these irrigation returns were quantified as the closure term of the water balance.

Hydrological models projecting the impact of changing climate carry a lot of uncertainty. Thus, these models usually have a multitude of simulations using different future climate data. This study used the subjective opinion of experts to assess which climate and hydrological models are the most likely to correctly predict climate impacts, thereby easing the computational burden. The experts could select more likely hydrological models, while the climate models were deemed equally probable.

This work presents the reconstruction of 19 years of daily, monthly, and annual global radiation maps in Sierra Nevada (Spain) derived using daily historical records from weather stations in the area and a modeling scheme that captures the topographic effects that constitute the main sources of the spatial and temporal variability of solar radiation. The generated datasets are valuable in different fields, such as hydrology, ecology, or energy production systems downstream.

This work presents the Guadalfeo Monitoring Network in the Sierra Nevada (Spain), a snow monitoring network in the Guadalfeo Experimental Catchment, a semiarid area in southern Europe representative of snowpacks with highly variable dynamics on both annual and seasonal scales and significant topographic gradients. The network includes weather stations and time-lapse cameras to capture the variability of the ablation phases on different spatial scales.

We investigate the seasonal memory properties of a large sample of European rivers in terms of high and low flows. We compute seasonal correlations between peak and low flows and average flows in the previous seasons and explore the links with various physiographic and hydro-climatic catchment descriptors. Our findings suggest that there is a traceable physical basis for river memory which in turn can be employed to reduce uncertainty and improve probabilistic predictions of floods and droughts.