Safeguarding groundwater, a vital resource in adaptation to global change, yet increasingly under pressure, requires robust knowledge and capacity. While strong foundations in hydrogeology remain indispensable, there is a clear need for joint, interdisciplinary and applied groundwater education at the postgraduate level to address the complex and multifaceted nature of groundwater science. Building on these arguments, the Join...

Storm damage in forests of Central Europe causes severe economic losses and alters these ecosystems significantly. There are many approaches to analyze past damage and to predict the future damage, often relying on data from field surveys. With the current data abundance there is potential in using existing datasets to develop transferable methodologies for modeling storm damage. We hypothesized that the commonly available dat...

The Arabian Peninsula (AP) is an arid region characterised by scarce precipitation with high temporal and spatial variability. Considerable research has predominantly focused on investigating precipitation in the AP from an Eulerian perspective. However, our understanding of the development and lifecycle of precipitation systems from a Lagrangian viewpoint over the AP remains limited. This study seeks to fill the gap by implem...

Hydrological models serve as valuable instruments for assessing the impact of climate change on water resources and agriculture as well as for developing adaptation measures. In Yemen, climate change and variability are imposing a significant impact on the most important sectors such as agriculture and economy. The current study evaluates the influence of future climate on hydrology and water balance components in Yemen’s high...

The sampling adjustment factor (SAF) can correct the underestimation of fixed time interval maxima (F-Maxima) relative to maxima of moving time windows (M-Maxima) as a direct consequence of temporal discretization of time series. Radar data can help to understand the significance of using moving window aggregation rather than fixed window. Here, we investigate SAFs for two gridded radar quantitative precipitation estimates fro...

The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational Scanning (DMS) sets continue to expand our understanding of the mutational landscape of single proteins, the results continue to challenge analyses. Protein Language Models (pLMs) use the latest deep learning (DL) algorithms to leverage growing datab...

Understanding the critical soil moisture (SM) threshold (θ crit ) of plant water stress and land surface energy partitioning is a basis to evaluate drought impacts and improve models for predicting future ecosystem condition and climate. Quantifying the θ crit across biomes and climates is challenging because observations of surface energy fluxes and SM remain sparse. Here, we used the latest database of eddy covariance measur...

When measuring the energy balance at the earth’s surface using the Eddy covariance technique, the obtained budgets seldom produce a closed energy balance. The measurements often miss some of the energy fluxes. A possible reason is the neglect of non-turbulent surface fluxes of latent heat and sensible heat, i.e. advective fluxes of these quantities. We present estimates of advective latent and sensible heat fluxes for three di...

The algorithm for and results of a newly developed multivariate non-parametric model, the Euclidean distance model (EDM), for the hourly disaggregation of daily climate data are presented here. The EDM is a resampling method based on the assumption that the day to be disaggregated has already occurred once in the past. The Euclidean distance (ED) serves as a measure of similarity to select the most similar day from historical...

Despite intense research on climate change (CC), regional studies for Central America, which is considered a CC hot spot, remain scarce. The information provided by general circulation models (GCMs) is too coarse to accurately reproduce local‐scale climatic features, which are needed for impact assessment. Thus, downscaling techniques are employed to address this scale mismatch. Costa Rica is the present case study, for which...

This study investigates the trends of precipitation and temperature extremes for the historical observations (1961–1990) and future period (2061–2090) in the Jhelum River Basin. Future trends are estimated by using ensemble mean of three general circulation models under RCP4.5 and RCP8.5. Therefore, statistical downscaling model has been used to downscale the future precipitation and temperature. A total of 15 precipitation an...

East Africa is one of the most vulnerable regions of Africa to extreme weather and climate events. Regional and local information on climate extremes is critical for monitoring and managing the impacts and developing sustainable adaptation measures. However, this type of information is not readily available at the necessary spatial resolution. Therefore, here we test trends and variability of temperature (1979–2010) and precip...

This study investigates trends and the effects of the interannual and intraseasonal climate variability on the extreme weather of Brazil's capital city Distrito Federal (DF). This area is highly vulnerable to climate variability, having suffered from droughts and floods that affected the drinking water supply and agriculture. We perform trend analysis of 12 rainfall‐related indices from 13 ground observation stations and asses...

Grazing intensity effects on the evapotranspiration ( ET ) partitioning into evaporation ( E ) and transpiration ( T ) were investigated using eddy covariance measurement data during the growing season (May to September) from 2005 to 2008 over typical steppes in Inner Mongolia, China. Four grazing intensities were examined ( UG79 , ungrazed since 1979; WG , winter grazed; CG , continuously grazed; HG , heavily grazed). Soil wa...