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Agricultural intensification vs. climate change: what drives long-term changes in sediment load?

This study explored the quantitative contribution of agricultural intensification and climate change to the sediment load of a small agricultural watershed. Rather than a change in climatic conditions, changes in the land structure notably altered sediment concentrations under high-flow conditions, thereby contributing most to the increase in annual sediment loads. More consideration of land structure improvement is required when combating the transfer of soil from land to water.

The influence of vegetation water dynamics on the ASCAT backscatter–incidence angle relationship in the Amazon

This study investigates spatial and temporal patterns in the incidence angle dependence of backscatter from the ASCAT C-band scatterometer and relates those to precipitation, humidity, and radiation data and GRACE equivalent water thickness in ecoregions in the Amazon. The results show that the ASCAT data record offers a unique perspective on vegetation water dynamics exhibiting sensitivity to moisture availability and demand and phenological change at interannual, seasonal, and diurnal scales.

HESS Opinions: Chemical transport modeling in subsurface hydrological systems – space, time, and the “holy grail” of “upscaling”

Extensive efforts have focused on quantifying conservative chemical transport in geological formations. We assert that an explicit accounting of temporal information, under uncertainty, in addition to spatial information, is fundamental to an effective modelling formulation. We further assert that efforts to apply chemical transport equations at large length scales, based on measurements and model parameter values relevant to significantly smaller length scales, are an unattainable “holy grail”.

Uncertainty estimation with deep learning for rainfall–runoff modeling

This contribution evaluates distributional run-off predictions from deep-learning-based approaches. We propose a benchmarking setup and establish four strong baselines. The results show that accurate, precise, and reliable uncertainty estimation can be achieved with deep learning.

Towards hybrid modeling of the global hydrological cycle

We present a physics-aware machine learning model of the global hydrological cycle. As the model uses neural networks under the hood, the simulations of the water cycle are learned from data, and yet they are informed and constrained by physical knowledge. The simulated patterns lie within the range of existing hydrological models and are plausible. The hybrid modeling approach has the potential to tackle key environmental questions from a novel perspective.

Evaporation enhancement drives the European water-budget deficit during multi-year droughts

Droughts are a creeping disaster, meaning that their onset, duration and recovery are challenging to monitor and forecast. Here, we provide further evidence of an additional challenge of droughts, i.e. the fact that the deficit in water supply during droughts is generally much more than expected based on the observed decline in precipitation. At a European scale we explain this with enhanced evapotranspiration, sustained by higher atmospheric demand for moisture during such dry periods.

Future water temperature of rivers in Switzerland under climate change investigated with physics-based models

This study presents an extensive study of climate change impacts on river temperature in Switzerland. Results show that, even for low-emission scenarios, water temperature increase will lead to adverse effects for both ecosystems and socio-economic sectors throughout the 21st century. For high-emission scenarios, the effect will worsen. This study also shows that water seasonal warming will be different between the Alpine regions and the lowlands. Finally, efficiency of models is assessed.

Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+

We present an approach on how to incorporate crop phenology in a regional hydrological model using decision tables and global datasets of rain-fed and irrigated cropland with the associated cropping calendar and management practices. Results indicate improved temporal patterns of leaf area index (LAI) and evapotranspiration (ET) simulations in comparison with remote sensing data. In addition, the improvement of the cropping season also helps to improve soil erosion estimates in cultivated areas.

Assessing the dependence structure between oceanographic, fluvial, and pluvial flooding drivers along the United States coastline

We analyse dependences between different flooding drivers around the USA coastline, where the Gulf of Mexico and the southeastern and southwestern coasts are regions of high dependence between flooding drivers. Dependence is higher during the tropical season in the Gulf and at some locations on the East Coast but higher during the extratropical season on the West Coast. The analysis gives new insights on locations, driver combinations, and the time of the year when compound flooding is likely.

Feedback mechanisms between precipitation and dissolution reactions across randomly heterogeneous conductivity fields

The interplay between dissolution, precipitation and transport is widely encountered in porous media, from CO2 storage to cave formation in carbonate rocks. We show that dissolution occurs along preferential flow paths with high hydraulic conductivity, while precipitation occurs at locations close to yet separated from these flow paths, thus further funneling the flow and changing the probability density function of the transport, as measured on the altered conductivity field at various times.

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