The review highlights the need to integrate urban land surface and hydrological models to better predict and manage compound climate events in cities. We find that inadequate representation of water surfaces, hydraulic systems and detailed building representations are key areas for improvement in future models. Coupled models show promise but face challenges at regional and neighbourhood scales. Interdisciplinary communication is crucial to enhance urban hydrometeorological simulations.

Stream water temperature (SWT) is a key indicator of water quality with implications for public use and health, ecosystem function, and aquatic life survival. Advances in modeling have helped improve our understanding of SWT dynamics, but challenges remain. Recently, machine learning (ML) has been used in SWT modeling, but questions remain about how the use of ML improves insight into SWT causes and effects. This work reviews ML-SWT modeling studies (and metrics) and considers future directions.

Our global review of 130 000 peer-reviewed articles reveals that drought forecasting and plant genetics dominate drought research priorities. Using topic modelling, we traced evolving themes from 1901 to 2022, highlighting a shift from ecology to cutting-edge technologies. By applying unsupervised machine learning, we offer insights into how integrated approaches matter, guiding future priorities to strengthen drought resilience and to safeguard water resources.

We assess 63 gridded ground (G), satellite (S), and reanalysis (R) climate datasets. Higher-density station data and less-hilly terrain improved climate data. In mountainous and humid regions, dataset types performed similarly; however, R outperformed G when underlying data had low station density. G outperformed S or R datasets, although better streamflow modeling did not always follow. Hydrologic analyses need datasets that better represent climate variable dependencies and complex topography.

The concept of the root zone is widely used but lacks a precise definition. Its importance in Earth system science is not well elaborated upon. Here, we clarified its definition with several similar terms to bridge the multi-disciplinary gap. We underscore the key role of the root zone in the Earth system, which links the biosphere, hydrosphere, lithosphere, atmosphere, and anthroposphere. To better represent the root zone, we advocate for a paradigm shift towards ecosystem-centred modelling.