Abstracted/indexed

We compare two approaches for coupling a 2D groundwater model with multiple 1D models for the unsaturated zone. One is non-iterative and very fast. The other one is iterative and involves a new way of treating the specific yield, which is crucial for obtaining a consistent solution in both model compartments. Tested on different scenarios, this new method turns out to be slower than the non-iterative approach, but more accurate and still very efficient compared to fully integrated 3D model runs.

We use statistical methods and data assimilation method with physical model to verify that prolonged drought can induce non-stationarity in the control catchment rainfall-runoff relationship, which causes three inconsistent results at the Red Hill paired-catchment site. The findings are fundamental to correctly use long-term historical data and effectively assess ecohydrological impacts of vegetation change given that extreme climate events are projected to occur more frequently in the future.

Satellite data was used to disentangle water and heat stress alleviation due to irrigation. Our findings are: 1. Irrigation induced cooling was captured by satellite LST but air temperature failed. 2. Irrigation extended maize growing season duration, especially during grain filling. 3. Water and heat stress alleviation constitute 65 % and 35 % of irrigation benefit. 4. Crop model simulating canopy temperature better captures irrigation benefit.

Through the lens of recent developments in hydrological modelling and data assimilation, we hourly update the soil moisture with ensemble data assimilation and sparse 2-metre-temperature observations in a coupled limited area model system. In idealized experiments, we improve the soil moisture analysis by coupled data assimilation across the atmosphere-land interface. We conclude that we can merge the separated assimilation cycles for the atmosphere and land surface into one single cycle.

Increased nitrate concentrations in surface waters can compromise river ecosystem health. As riverine nitrate uptake is hard to measure, we explore how low frequency nitrate concentration and discharge observations (that are widely available) can help to identify (in)efficient uptake in river networks. We find that channel geometry and water velocity rather than the biological uptake capacity dominate the nitrate-discharge pattern at the outlet. The former can be used to predict uptake.

Extracting water from plant stems can introduce analytical errors in isotope analyses. We demonstrate that sensitivities to suspected errors can be evaluated and that conclusions drawn from extracted plant water isotope ratios are neither generally valid nor generally invalid. Ultimately, imperfect measurements of plant and soil water isotope ratios can continue to support useful inferences if study designs are appropriately matched to their likely biases and uncertainties.

Accurate estimation of terrestrial water storage (TWS) is essential for reliable water resource assessments. TWS can be estimated from the CABLE land surface model, but the spatial resolution is limited to 0.5°. This study reconfigures CABLE parameterization to improves TWS spatial details from 0.5° to 0.05°. The GRACE satellite data are assimilated into CABLE to improve TWS accuracy. Our workflow relies only on publicly accessible, allowing reproduction of 0.05° TWS in any region.

In this study we developed machine learning approaches for daily river water temperature prediction, using different data preprocessing methods, 6 model types, a range of different data inputs and 10 study catchments. By comparing to current state of the art models, we could show a significant improvement of prediction performance of the tested approaches. Furthermore, we could gain insight into the relationships between model types, input data and predicted stream water temperature.

Plants affect the global water and carbon cycles by modifying their water use and carbon intake in response to soil moisture. Global climate models represent this response with either simple empirical models or complex physical models. We reveal that the latter improves predictions in plants with large flow resistance; however, adding dependence on atmospheric moisture demand to the former matches performance of the latter, leading to a new tool for improving carbon and water cycle predictions.

In this study, we assessed the sustainability of water resources in a Wadi region with the help of a hydrologic model. Our assessment showed that the increases in groundwater withdrawal and consumption exacerbate the negative impact of climate change on groundwater sustainability and hydrologic regime alteration. These alterations have severe consequences for a downstream wetland and its ecosystem. The approach may be applicable in other wadi regions with different climate and water use system.

Riverine water quality can change markedly at one particular location. This study developed predictive models to represent the temporal variation of stream water quality across the Great Barrier Reef catchments, Australia. The model structures were informed by a data-driven approach, which is useful to identify important factors determining temporal changes in water quality and, in turn, provide critical information for developing management strategies.

This paper is the result of new research of ancient and early modern sources about the developments of the concept of hydrological cycle and of hydrology in general. It shows that the flooding of the Nile was the first geophysical problem formulated in scientific terms in the cradle of natural philosophy and science in the 6th century BC. Aristotle was able to find the correct solution to the problem, which he tested through what it appears to be the first scientific expedition in history.

A statistical model that can account for complex systems is used to identify the main factors affecting wheat yield in Germany. In many parts of Germany, the main adverse effect is caused by too much soil water in spring. Yield losses in northeastern Germany are related to soil moisture drought. Meteorological effects such as heat do not seem to play a prominent role. Furthermore, the model is able to explain both exceptionally high yields (2014) and yields in very high loss years (2003, 2018).

We used a tracer-aided ecohydrological model to quantify water flux-storage-age interactions for three urban vegetation types: Trees, shrub and grass. The model results showed that evapotranspiration increased in the order shrub < grass < trees during one growing season. Additionally, we could show how “infiltration hot-spots” created by runoff from sealed surfaces unto vegetated surfaces can enhance both evapotranspiration and groundwater recharge.

Freezing-induced groundwater migration and water table decline are widely observed, but quantitative understanding of these processes is lacking. By considering atmospheric conditions and occurrence of lateral groundwater inflow, a model coupling soil water and groundwater well reproduced field observed wintertime variations of soil water content and groundwater level. The model results led to a clear understanding of the balance of water budget during the freezing-thawing cycle.

Response surfaces are increasingly used to identify the hydro-climatic conditions leading to a water resources system's failure. Partitioning the surface usually requires performance thresholds that are not necessarily crisp. We propose a methodology that combines the inherent uncertainty of response surfaces with the ambiguity of performance thresholds. The proposed methodology is illustrated with a multireservoir system in Canada for which some performance thresholds are imprecise.

At our study site during the covered period, differences in mobile and bulk soil water supported the two water worlds (TWW) hypothesis. Isotopic offset occurred at the interface between the soil and Salix matsudana roots and can be attributed to combined effects of ecohydrological separation (as in the TWW hypothesis) and isotopic fractionation. We used root water as a medium to connect the soil and plant xylem, extending our knowledge of isotopic signals in the soil-root-xylem continuum.

Soil moisture plays a critical role in the water and energy cycles of the earth system, for which a long-term soil moisture product with high quality is urgently needed. In-situ observations are generally treated as the true value to systematically evaluate five soil moisture products including one remote sensing product and four reanalysis datasets during 1981–2013. This long-term inter-comparison study provides clues for SM product enhancement and further hydrological applications.

Using partial year streamflow records a regime and change classification were produced for ~400 watersheds in the Nelson and Mackenzie River basins, and trends in water storage and vegetation were detected from satellite imagery. Three areas show consistent changes; north of 60° [increased streamflow and basin greenness], in the western Boreal Plains [decreased streamflow and basin greenness], and across the Prairies [three different patterns of increased streamflow and basin wetness].

This study pioneers in the integration of a spatially explicit soil erosion model with plot and watershed scale characterization and high resolution drone imagery to assess the effect of spatial resolution DTM on discharge and soil loss. Results showed reduction of slope due to resampling-down of DTM. High resolution translates into higher slope, denser fluvial system, and extremer values of soil loss reducing time of concentration and increasing soil loss at the outlet. Best resolution was 4 m.

Agricultural droughts occur when the water content of the soil diminishes to such a level that vegetation is negatively impacted. Here we show that, although they are classified as the same type of drought, substantial differences between soil moisture and vegetation droughts exist. This duality is not included in the term agricultural drought, and thus is a potential issue in drought research. We argue that a distinction should be made between soil moisture and vegetation drought events.

Design flood estimation is a fundamental task in hydrology. We propose a machine learning based approach to estimate design floods anywhere on the global river network. This approach shows considerable improvement over the index-flood based method; and the average bias in estimation is less than 18 % for 10, 20, 50 and 100-year design floods. This approach is a valid method to estimate design floods globally, improving our prediction of the flood hazard, especially in ungauged areas.

Use of scalar features calculated from images of the North Platte River State Line Weir captured with a ground-based camera for a documentary water imaging project to build machine learning models to fill year-long gaps in stream stage and discharge data. Predictions, after filtering for noise with a Kalman filter were shown to perform as well or better than comparative studies. An image feature development process is proposed and tested. Selection of appropriate training data is addressed.

The stationary assumption in hydrology has become obsolete because of climate changes. In that context, it is crucial to assess the performance of a hydrologic model over a wide range of climates and their corresponding hydrologic conditions. We propose a calibration/validation protocol based on the differential split-sample test and numerous, contrasted, climate sequences identified through a Hidden Markov Model (HMM) classification. The proposed protocol is tested on the Senegal River.

The hydrometeorological skills of two new nowcasting systems for forecasting Mediterranean intense rainfall events and floods are investigated. The results reveal that up to 1 h 15/1 h 30 of forecast, the performance of the nowcasting system blending numerical weather prediction and extrapolation of radar estimation is higher than the numerical weather model. For lead times up to 3 h their skills are equivalent in general. Using these nowcasting systems for flash-flood forecasting is also promising.