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To overcome the computational cost of numerical models, we propose a deep learning approach inspired by hydraulic models that can simulate the spatio-temporal evolution of floods. We show that the model can rapidly predict dike-breach floods over different topographies and breach locations, with limited use of ground-truth data.

Using 3D numerical heat transpot models, this study quantifies the potential of low-temperature Aquifer Thermal Energy Storage (ATES) in an urban setting in Southwest Germany. Comparing the determined potential with existing heating and cooling demands shows substantial heating and cooling supply rates that could be achieved by a widespread application of ATES systems. The study also highlights possible greenhouse gas emission savings compared to conventional heating and cooling technologies.

The peatland dissolved organic carbon (DOC) lost through aquatic exports can offset a significant proportion of the ecosystem carbon balance. Hence, we propose a new approach to better estimate the DOC exports, based on the specific contribution of a boreal peatland (Canada) during periods of high flow. In addition, we studied the relations between DOC concentrations and stream discharge in order to better understand the DOC export mechanisms under contrasted hydrometeorological conditions.

Meandering rivers are an integral part of many landscapes around the world. In our study, 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 connect groundwater compartment (aquifer). We found that 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.

We evaluated the potential use of SEAS5 seasonal forecasting for Mainland Southeast Asia. The temperature and precipitation skills are tested. We subsequently implemented a hydrological model VIC to produce seasonal streamflow forecasts. SEAS5 is skilful to forecast temperature and rainfall as well as streamflow, but with strong seasonal and regional dependence. Overall, SEAS5 and hydrological forecasts show skill that can potentially be used for hydrological/agricultural management.

Recent studies suggest that the velocities of water running off landscapes in the Canadian Prairies may be much smaller than are generally assumed. Analyses of historical flows for 23 basins in central Alberta, showed that many of the rivers responded more slowly, and that the flows are much slower, than would be estimated from equations developed elsewhere. The effects of slow flow velocities on the development of hydrological models of the region are discussed, as are the possible causes.

We present the results from using soil water content measurements from 13 European forest sites in a state-of-the-art land surface model. We use data assimilation to perform the combination of observation and modeled soil water content and show the improvements in the representation of soil water content. However, we also look at the impact on evapotranspiration and see no corresponding improvements.

"Elasticity" refers to how much the amount of water in a river fluctuates with precipitation. We usually calculate this using the average streamflow values, however, river catchments interact with runoff in ways which differ in drier and wetter periods. Here, we look at how elasticity varies across the streamflow distribution and show that not only does low and high streamflow respond differently to precipitation change, but that these differences vary with water storage availability.

Components of the hydrological cycle exhibit a “memory” in their behaviour which quantifies how long a variable would stay at high/low values. Being able to model and understand what affects it is vital for an accurate representation of the hydrological elements. In the current work, it is found that rainfall affects the fractal behaviour of groundwater levels, which implies that changes to rainfall due to climate change will change the periods of flood and drought in groundwater-fed catchments.

This study proposes a new method for predicting extreme flood levels in rivers like the Meuse. The current has shown to be unreliable as it did not predict a recent flood. We have developed a model that includes information from experts and combines this with measurements. We found that this approach gives more accurate predictions, particularly for extreme events. The research is important for predictions of extreme flood levels that are necessary for protecting communities against floods.

In coastal areas, groundwater managers require information on the risk of well salinization associated with various pumping scenarios. We developed a modeling approach to identify the optimal tradeoff between groundwater pumping and probability of salinization, considering model parameter and historical observation uncertainty as well as uncertainty in sea level and recharge projections. The workflow can be implemented in a wide range of coastal settings.

Among the 8 considered products, GLDAS_CLSM product performs best. All RZSM products overestimate the in situ measurements which attributes to a wet bias of air temperature, precipitation amount and frequency except the underestimation of SMOS L4 RZSM related to the underestimation of SMOS L3 SSM. The higher R between SMPA L4 and MERRA-2 was attributed to they both use CLSM and meteorological forcing from GEOS-5 where precipitation was corrected with CPCU precipitation product.

Based on the quantitative relationship between vapor and water in soil, we propose a determination and calculation method for soil water content. This method significantly improves the measurement accuracy of soil water content, eliminates the effect of the soil properties on soil water content measuring, and has excellent potential for application in the field soil and even rock water content measurement.

We developed a new tile drainage module for Cold Regions Hydrological Model (CRHM) to simulate tile discharge and soil water level. The effect of capillary rise and seasonal soil water fluctuations were considered in our simulations. A novel aspect of this module is the use of field capacity and its corresponding pressure head to provide an estimate of drainable water and thickness of the capillary fringe, rather than a detailed soil retention curve that may not always be available.

Gravimetric satellite data can be used to estimate groundwater depletion. The impact of mass loss by river sediment transport on such estimates has not been considered in the past. We derive this impact for the sediment-rich Ganges-Brahmaputra-Meghna (GBM) river system, where it accounts for 4-2 % of the overall mass decrease currently attributed to groundwater depletion. In the GBM mountain regions, correction for sediment transport reduces the estimated groundwater depletion by 14 %.

In some watersheds, the severity of the dry season has a large impact on aquatic ecosystems. In this study, we design a way to predict, about 5 months in advance, how severe the dry season will be in a rural watershed in northern California. This early warning can support seasonal adaptive management. To predict these two values, we assess data about snow, rain, groundwater, and river flows. We find that maximum snowpack and total wet season rainfall best predict dry season severity.

River plastic emissions into the ocean are of global concern, but the transfer dynamics between freshwater and the marine environment remain poorly understood. We developed a simple Eulerian approach to estimate the net and total plastic transport in tidal rivers. Applied to the Saigon river, Vietnam, we found that net plastic transport amounted to only a third of total transport, highlighting the need to better understand and integrate tidal dynamics in plastic transport and emissions models.

In deltas, salt water sometimes intrudes far inland and causes problems with freshwater availability. We created a model to forecast salt concentrations at a critical location in the Rhine-Meuse delta in the Netherlands. It requires a rather small number of data to make a prediction and runs fast. It predicts the occurrence of salt concentration peaks well, but underestimates the highest peaks. Its speed gives water managers more time to reduce the problems caused by salt intrusion.

The inconsistent changes in temperature and precipitation induced by forest cover change are very likely to affect drought condition. We use a set of statistical models to explore the relationship between forest cover change and drought change in different time scales and climate zones. We find that the influence of forest cover on droughts varies under different precipitation and temperature quantiles. Forest cover also could modulate the impacts of precipitation and temperature on drought.

KarstMod consists in a useful tool for the assessment of karst groundwater variability and sensitivity to anthropogenic pressures (e.g. groundwater abstraction). This tools is devoted to promote good practices in hydrological modeling for learning and occasional users. KarstMod requires no programming skills and offers a user friendly interface allowing any user to easily handle hydrological modeling.

We studied drought in a dataset of possible future river flows and groundwater levels in the UK and found different outcomes for these two sources of water. Throughout the UK, river flows are likely to be lower in future with droughts more prolonged and severe. However, whilst these changes are also found in some boreholes, in others higher levels and less severe drought are indicated for the future. This has implications for the future balance between surface and below ground water.

In this research, the runoff and sediment reduction benefits of hillsides mixed grasslands were examined. The cultivated grasslands effectively increased runoff and decreased sediment along ages. And we found that the runoff was the dominant factor affecting soil erosion modulus in alpine hillsides.

We present an open-source platform, in response to NASA Open-Source Science Initiative, for accessing, and presenting quantitative remote sensing earth observation, and climate data. With our platform scientists, stakeholders, and concerned citizens can engage in the exploration, modeling and understanding of data. We envisioned this platform to lower the technical barriers and simplify the process of accessing and leveraging additional modeling frameworks for data.

In our study, we tested the utility and skill of one state-of-the-art forecasting product for the prediction of regional crop production using a land surface model. Our results illustrate the potential value and skill of combining seasonal forecasts with modelling applications in generating variables of interest for stakeholders such as annual crop productivity for specific cash crops and regions. In addition, this study provides useful insights for future technical model improvements.

Climate models are central to climate change impact studies. Some models project a future deemed too hot by many. We looked at how including hot models may skew the result of impact studies. Applied to hydrology, this study shows that hot models do not systematically produce hydrological outliers.

Seven instrumented lysimeters are used to assess the simulation of the soil water dynamic in one land surface model. Four water potential and hydraulic conductivity closed-form equations including one mixed form are evaluated. One form is more relevant to simulate drainage especially during intense drainage events. Soil profile heterogeneity of one parameter of the closed-form equations is shown to be important.

An efficient regional flood frequency model relates drainage area to discharge, with a major assumption of similar basin conditions. In a landscape with variable glacial deposits and land use, we characterize varying hydrological function using 28 explanatory variables. We demonstrate that (1) a heterogeneous landscape requires objective model selection criteria to optimize the “fit” of flow data, and (2) incorporating land use as a predictor variable improves the drainage area-discharge model.

To avoid overloaded monitoring cost from redundant measurements, this study proposed a nonparametric data-worth analysis framework to assess the worth of future soil moisture data regarding the model-free unsaturated flow models before data gathering. Results indicated that: (1) the method can quantify the data-worth of alternative monitoring schemes to obtain the optimal one; (2) high-quality and representative ‘small’ data could be a better choice than unfiltered ‘big’ data.

We develop an ecohydrological classification for regions with limited hydrological records. It provides causal links of landscape features and their water requirement. The classification is the essential framework for modelling the impact of future coal resource developments via water on the features. A rule set combines diverse data with prioritisation, which results in a transparent, repeatable, and adjustable approach. We show examples of linking ecohydrology with environmental impacts.

Normally the lighter Oxygen and Hydrogen isotopes are preferably evaporated from a water body, which becomes enriched in heavy isotopes. But we observed that, in a water body subject to prolonged evaporation, some periods of heavy isotope depletion instead of enrichment happened. Furthermore, the usual models that describe the isotopy of evaporating waters may be in error if the atmospheric conditions of temperature and relative humidity are time-averaged instead of evaporation flux-weighted.