Mapping the spatial transferability of knowledge-guided machine learning: Application to the prediction of drain flow fraction.

Machine learning (ML) methods continue to gain traction in hydrological sciences for predicting variables at large scales. Yet, the spatial transferability of these ML methods remains a critical yet underexamined aspect. We present a metamodel approach to obtain large-scale estimates of drain fraction at 10 m spatial resolution, using a ML algorithm (Gradient Boost Decision Tree).

Spatio-temporal analysis of climate and irrigated vegetation cover changes and their role in lake water level depletion using a pixel-based approach and canonical correlation analysis.

Lake Urmia, located in northwest Iran, was among the world's largest hypersaline lakes but has now experienced a 7 m decrease in water level, from 1278 m to 1271 over 1996 to 2019. There is doubt as to whether the pixel-based analysis (PBA) approach's answer to the lake's drying is a natural process or a result of human intervention. Here, a non-parametric Mann-Kendall trend test was applied to a 21-year record (2000-2020) of satellite data products, i.

Developing a pan-European high-resolution groundwater recharge map - Combining satellite data and national survey data using machine learning.

Groundwater recharge quantification is essential for sustainable groundwater resources management, but typically limited to local and regional scale estimates. A high-resolution (1 km × 1 km) dataset consisting of long-term average actual evapotranspiration, effective precipitation, a groundwater recharge coefficient, and the resulting groundwater recharge map has been created for all of Europe using a variety of pan-European and seven national gridded datasets. As an initial step, the approach developed for continental scale mapping consists of a merged estimate of actual evapotranspiration originating from satellite data and the vegetation controlled Budyko approach to subsequently estimate effective precipitation.

Multi-Constrained Catchment Scale Optimization of Groundwater Abstraction Using Linear Programming.

Due to increasing water demands globally, freshwater ecosystems are under constant pressure. Groundwater resources, as the main source of accessible freshwater, are crucially important for irrigation worldwide. Over-abstraction of groundwater leads to declines in groundwater levels; consequently, the groundwater inflow to streams decreases.

Citizen science: A new perspective to advance spatial pattern evaluation in hydrology.

Citizen science opens new pathways that can complement traditional scientific practice. Intuition and reasoning often make humans more effective than computer algorithms in various realms of problem solving. In particular, a simple visual comparison of spatial patterns is a task where humans are often considered to be more reliable than computer algorithms.