Many communities in the Sahel rely solely on groundwater. We develop a machine learning technique to map areas of groundwater potential. Algorithms are trained to detect areas where there is a confluence of factors that facilitate groundwater occurrence. Our contribution focuses on using variable scaling to minimize expert bias and on testing our results beyond standard metrics. This approach is illustrated through its application to two administrative regions of Mali.

This study aims at designing water supply strategies that are robust against climate, social, and land use changes in a sub-Saharan river basin. We found that robustness analysis supports the discovery of policies enhancing the resilience of water resources systems, benefiting the agricultural, energy, and urban sectors. We show how energy sustainability is affected by water availability, while urban and irrigation resilience also depends on infrastructural interventions and land use changes.

Hourly streamflow ensemble forecasts with the CSSPv2 land surface model and ECMWF meteorological forecasts reduce both the probabilistic and deterministic forecast error compared with the ensemble streamflow prediction approach during the first week. The deterministic forecast error can be further reduced in the first 72 h when combined with the long short-term memory (LSTM) deep learning method. The forecast skill for LSTM using only historical observations drops sharply after the first 24 h.

We evaluate Arctic river discharge using in situ observations and state-of-the-art reanalyses, inter alia the most recent Global Flood Awareness System (GloFAS) river discharge reanalysis version 3.1. Furthermore, we combine reanalysis data, in situ observations, ocean reanalyses, and satellite data and use a Lagrangian optimization scheme to close the Arctic's volume budget on annual and seasonal scales, resulting in one reliable and up-to-date estimate of every volume budget term.

A comprehensive assessment of snowmelt is missing for China. Trends and variability in snowmelt in China under climate change are investigated using historical precipitation and temperature data (1951–2017) and projection scenarios (2006–2099). The snowmelt and snowmelt runoff ratio show significant spatial and temporal variability in China. The spatial variability in snowmelt changes may lead to regional differences in the impact of snowmelt on the water supply.

At night or during cold periods, the shallow littoral region of lakes cools faster than their deeper interior. This induces a cold downslope current that carries littoral waters offshore. From a 1-year-long database collected in a small temperate lake, we resolve the seasonality of this current and report its frequent occurrence from summer to winter. This study contributes to a better quantification of lateral exchange in lakes, with implications for the transport of dissolved compounds.

Applying dimensional analysis to the partitioning of water and soil on terrestrial landscapes reveals their dominant environmental controls. We discuss how the dryness index and the storage index affect the long-term rainfall partitioning, the key nonlinear control of the dryness index in global datasets of weathering rates, and the existence of new macroscopic relations among average variables in landscape evolution statistics with tantalizing analogies with turbulent fluctuations.

We develop an empirical approach to attribute declining streamflow in the Upper Jhelum watershed, a key subwatershed of the transboundary Indus basin. We find that a loss of streamflow since the year 2000 resulted primarily due to interactions among vegetation and groundwater in response to climate rather than local changes in land use, revealing the climate sensitivity of this Himalayan watershed.

In the paper the evaporation front is considered the interface that separates the wet part of a porous medium from its dry surroundings, and its exact definition in time and space is given. Subsequently, the law of the front's motion is derived. The general problem governing completely the front's motion is formulated and, for a special case, solved numerically. It is shown that the solution makes it possible to locate the rate of vaporization in time and space.

In this study, we evaluate the past, present and future quantity of groundwater potentially available for drinking purposes in the metropolitan area of Turin, north-western Italy. In order to effectively manage water resources, a knowledge of the water cycle components is necessary, including precipitation, evapotranspiration and subsurface reservoirs. All these components have been carefully evaluated in this paper, using observational datasets and modelling approaches.

This paper has developed a tractable approach for characterizing watershed heterogeneity and its relationship with key functions such as ecosystem sensitivity to droughts and nitrogen export. We have applied clustering methods to classify hillslopes into watershed zones that have distinct distributions of bedrock-to-canopy properties as well as key functions. This is a powerful approach for guiding watershed experiments and sampling as well as informing hydrological and biogeochemical models.

Evaporation from open water is a well-studied problem in hydrology. Robert E. Horton, unknown to most investigators on the subject, studied it in great detail by conducting experiments and relating them to physical laws. His work furthered known theories of lake evaporation but was not recognized. This is unfortunate because it performs better than five variously complex methods across scales (local to continental; 30 min–2 months) and seems quite relevant for climate-change-era problems.

Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to the lack of observations of such rare events. Here we develop a new approach, pooling reforecast ensemble members from the European Flood Awareness System to increase the sample size available to estimate the frequency of extreme flood events. We demonstrate that such ensemble pooling produces more robust estimates than observation-based estimates.

We present results of a hydrologic study of groundwater recharge near the city of Arequipa, Peru. There are a number of springs below a high-elevation salar that show some chemical evidence of connectivity to the salar basin, possibly facilitated by faults in region. These results suggest that this salar basin is not a strictly terminal lake but that some interbasin groundwater flow exists. In addition, a high-elevation forest ecosystem seems important for groundwater recharge as well.

We performed a machine learning approach to regionalize the parameters of a China-wide hydrological model by linking six model parameters with 10 physical attributes (terrain and soil properties). The results show the superiority of machine-learning-based regionalization approach compared with the traditional linear regression method in ungauged regions. We also obtained the relative importance of attributes against model parameters.

Most models that simulate water and carbon exchanges with the atmosphere rely on information about vegetation, but optimality models predict vegetation properties based on general principles. Here, we use the Vegetation Optimality Model (VOM) to predict vegetation behaviour at five savanna sites. The VOM overpredicted vegetation cover and carbon uptake during the wet seasons but also performed similarly to conventional models, showing that vegetation optimality is a promising approach.