This work presents a global groundwater modeling framework at 5-arcminute resolution, developed through an offline coupling of the H08 water resource model and MODFLOW6. The model includes a single-layer aquifer and is designed to capture long-term mean groundwater dynamics under varying climate types. The manuscript describes the model structure, input datasets, and evaluation against available observations.

This study demonstrates a new method using SMAP soil moisture products to identify irrigation effects, tested to be valid in an example region in California's Central Valley and showed great potential for application in arid/ semi-arid regions. The approach offers a simple, straightforward approach to monitoring irrigation signals without additional in-situ data or model tuning, providing a useful tool to extract irrigation water use data in observation-scarce regions.

Global water models contribute to the evaluation of important natural and societal issues but are – as all models – simplified representation of reality. So, there are many ways to calculate the water fluxes and storages. This paper presents a visualization of 16 global water models using a standardized visualization and the pathway towards this common understanding. Next to academic education purposes, we envisage that these diagrams will help researchers, model developers, and data users.

Reservoirs play important roles in hydrology and water resources management globally and are incorporated into many Global Hydrological Models. Their simulations are, however, poorly validated due to the lack of available long-term in-situ observation data globally. Here we investigated the applicability of the latest satellite-based reservoir storage estimations in the contiguous US. We found that those products are useful for validating reservoir storage simulations when they are normalized.

Simultaneously simulating food production and the requirements and availability of water resources in a spatially explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water nexus studies in the future.