Vegetation affects surface runoff in several ways. Surface roughness is increased by stems and leaves, roots and their remnants enhance infiltration into the soil, and through evaporation, the soil water content changes at the beginning of a rainfall event. Our study investigates, how a simulation model is able to react to these effects. While roughness and infiltration are well covered, initial soil moisture is still an unsolved problem.

Our study compares neural network models for predicting discharge in ungauged basins. We evaluated convolutional neural networks (CNNs), long short-term memory (LSTM) and gated recurrent units (GRUs) using 28 years of weather data. CNNs showed the best accuracy, while GRUs were faster and nearly as accurate. Adding static features improved all models. The research enhances flood forecasting and water management in regions lacking direct measurements, offering efficient and accurate predictive tools.

Our research delves into how vegetation affects water flow over land during heavy rain and how accurately we can predict it. We tested various ways of estimating surface roughness in hydrological models, using data from simulated rainfall on hillslopes in Germany. Using advanced modeling techniques, we found that certain approaches, those considering vegetation, perform better in simulating overland flow dynamics.

Agricultural landscapes act as sinks or sources of the greenhouse gases (GHGs) CO₂, CH₄, or N₂O. Various physicochemical and biological processes control the fluxes of these GHGs between ecosystems and the atmosphere. Therefore, fluxes depend on environmental conditions such as soil moisture, soil temperature, or soil parameters, which result in large spatial and temporal variations of GHG fluxes. Here, we describe an example of how this variation may be studied and analyzed.

Despite occupying <1 %; of the globe, streams are significant sources of greenhouse gas (GHG) emissions. In this study, we determined anthropogenic effects on GHG emissions from streams. We found that anthropogenic-influenced streams had up to 20 times more annual GHG emissions than natural ones and were also responsible for seasonal peaks. Anthropogenic influences also altered declining GHG flux trends with stream size, with potential impacts on stream-size-based spatial upscaling techniques.