We developed a new model to better understand how water moves in a lake basin. Our model improves upon previous methods by accurately capturing the complexity of water movement, both on the surface and subsurface. Our model, tested using data from China's Qinghai Lake, accurately replicates complex water movements and identifies contributing factors of the lake's water balance. The findings provide a robust tool for predicting hydrological processes, aiding water resource planning.

This study explores the regional streamflow variability in Peninsular India. This variability is governed by monsoons, mountainous systems, and geologic gradients. A linkage between these influencing factors and streamflow variability is established using a Wegenerian approach and flow duration curves.

We introduce an applied approach to evapotranspiration (ET) instruction. In a laboratory experiment, students calculate ET using water balance and energy balance approaches for five urban land surface covers (gravel, soil, grass, asphalt, and mulch). The experiment is paired with an urban heat tour facilitated by thermal infrared cameras. The activities are adaptable for various contexts, ranging from undergraduate lab classes to demonstrations for pre-university classrooms and the public.

Satellite data help estimate groundwater depletion, but earlier assessments missed mass loss from river sediment. In the Ganges–Brahmaputra–Meghna (GBM) river system, sediment accounts for 4 % of the depletion. Correcting for sediment in the GBM mountains reduces estimated depletion by 14 %. It's important to note that the Himalayas' uplift may offset some sediment-induced mass loss. This understanding is vital for accurate water storage trend assessments and sustainable groundwater management.

Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.

Elasticity refers to how much the amount of water in a river changes with precipitation. We usually calculate this using average streamflow values; however, the amount of water within rivers is also dependent on stored water sources. Here, we look at how elasticity varies across the streamflow distribution and show that not only do low and high streamflows respond differently to precipitation change, but also these differences vary with water storage availability.

Contamination from fuel constituents poses a major threat to groundwater. However, studies devoted to identification of the driving parameters for fuel derivative transport in soils are scarce, and none have dealt with heterogeneous layered media. Here, we performed global sensitivity analysis (GSA) on a model of benzene transport to groundwater. The results identified the parameters controlling benzene transport in soils and showed that GSA is as an important tool for transport model analysis.

This opinion paper reflects on the risks of overusing groundwater savings to supply permanent water use requirements. Using novel data recently developed for Chile, we reveal how groundwater is being overused, causing ecological and socioeconomic impacts and concealing a Day Zero scenario. Our argument underscores the need for reformed water allocation rules and sustainable management, shifting from a perception of groundwater as an unlimited source to a finite and vital one.

We present a new method based on network science for unsupervised classification of large datasets and apply it to classify 9067 US catchments and 274 biophysical traits at multiple scales. We find that our trait-based approach produces catchment classes with distinct streamflow behavior and that spatial patterns emerge amongst pristine and human-impacted catchments. This method can be widely used beyond hydrology to identify patterns, reduce trait redundancy, and select representative sites.

Understanding the spatial and temporal distribution of surface water is crucial for effective water resource management, maintaining ecosystem health and assessing flood risks. This study examined permanent and seasonal rivers and lakes globally over 38 years, uncovering a statistically significant expansion in seasonal extent captured in the new SARL database. The findings offer valuable resources for assessing the impact of changing river and lake extents on ecosystems and human livelihoods.

Input data, model and calibration strategy can affect the accuracy of flood event simulation and prediction. Satellite-based precipitation with different spatiotemporal resolutions is an important input source. Data-driven models are sometimes proven to be more accurate than hydrological models. Event-based calibration and conventional strategy are two options adopted for flood simulation. This study targets the three concerns for accurate flood event simulation and prediction.

River flow data are often provided as mean daily flows (MDF), in which a lot of information is lost about the actual maximum flow or instantaneous peak flows (IPF) within a day. We investigate the error of using MDF instead of IPF and identify means to predict IPF when only MDF data are available. We find that the average ratio of daily flood peaks and volumes is a good predictor, which is easily and universally applicable and requires a minimum amount of data.

Stable isotopes of water (described as d-excess) in mountain snowpack can be used to infer proportions of high-elevation snowmelt in stream water. In a Colorado River headwater catchment, nearly half of the water during peak streamflow is derived from melted snow at elevations greater than 3200 m. High-elevation snowpack contributions were higher for years with lower snowpack and warmer spring temperatures. Thus, we suggest that d-excess could serve to assess high-elevation snowpack changes.

We assessed 13 global models using GRACE satellite data over 29 river basins. Simulated seasonal water storage cycles showed discrepancies compared to GRACE. The models overestimated seasonal amplitude in boreal basins and showed underestimation in tropical, arid, and temperate zones, with phase differences of 2–3 months compared to GRACE in cold basins and of 1 month in temperate, arid, and semi-arid basins. Seasonal amplitude and phase differences provide insights for model improvement.

Meandering rivers are an integral part of many landscapes around the world. Here we used a new modeling approach to look at how the slope of riverbanks influences water flow and solute transport from a meandering river channel through its bank and into/out of the connected groundwater compartment (aquifer). We found that the bank slope can be a significant factor to be considered, especially when bank slope angles are small, and riverbank and aquifer conditions only allow for slow water flow.

Global groundwater recharge studies collate recharge values estimated using different methods that apply to different timescales. We develop a recharge prediction model, based solely on chloride, to produce a recharge map for Australia. We reveal that climate and vegetation have the most significant influence on recharge variability in Australia. Our recharge rates were lower than other models due to the long timescale of chloride in groundwater. Our method can similarly be applied globally.