Dynamic coupling of hydrological and atmospheric models to examine feedback effects

Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analyzed and the result of a research experiment is presented using model weighting with the participation of 6 climate model experts and 6 hydrological model experts. For the experiment, seven climate models are a-priori selected from a larger Euro-CORDEX ensemble of climate models and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two individual elicitations of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological impact modellers in general are more open for assigning weights to different models in a multi model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models, and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only reestablish a uniform weight between climate models.

Added value of distribution in rainfall-runoff models for the Meuse basin

Seasonal hydrological forecasts are of critical importance for the effective management of water resources, particularly in complex and vulnerable basins such as the Mediterranean area which have both a deficitary water regimen and a high anthropogenic pressure. Nevertheless, inaccuracies in meteorological inputs can propagate through hydrological models, amplifying uncertainties in flow predictions. It is imperative to rectify these forecasts, whether meteorological or hydrological, to enhance prediction reliability and provide robust data for informed decision-making. This study proposes an advanced framework for seasonal hydrological forecasting that integrates raw and corrected weather forecasts with distributed hydrological modeling and sophisticated post-processing techniques to enhance flow prediction accuracy in the Júcar River in Spain as a representative case study of the Mediterranean area.The catchment hydrological model was implemented using the model TETIS v9.1 wich was calibrated and validated using observed records from 1981 to 2019 at seven control points. To do this, a split sample test was conducted using the period 2009–2019 for calibration, and the rest of the time series for validation (1981–2008). The process involved refining parameter maps to ensure good or acceptable performance at all control points. Meteorological data were sourced from the W5E5 dataset, downscaled to a 0.09° resolution using ERA5-Land. This improved the spatial and temporal resolution of the hydrological model. Once we have established an acceptable hydrological model,, the seasonal forecast hindcasts were evaluated using meteorological inputs from global forecasting systems, including ECMWF-SEAS5, CMCC_SPSv35, DWD_GCFS21, and MeteoFrance System8. To address uncertainties in the meteorological forecasts and their propagation to hydrological outputs, two complementary correction strategies were implemented. First, artificial intelligence(fuzzy logic) was applied to correct meteorological inputs before integration into the hydrological model, assuming errors originate solely from meteorological data and treating the hydrological model as a “perfect” simulator. Second, a hydrological error model was developed to identify and adjust discrepancies between simulated and observed flows, addressing systematic biases and errors in the hydrological simulation.The results demonstrated that forecasts based on corrected meteorological inputs exhibited significant accuracy improvements compared to those using unprocessed inputs. The hydrological error model further enhanced prediction reliability by mitigating systematic biases. These findings underscore the effectiveness of combining meteorological forecasts with AI-driven corrections to address uncertainties, thereby improving the robustness of seasonal hydrological predictions. This study highlights the potential for integrating advanced correction techniques into seasonal hydrological forecasting frameworks, offering a replicable methodology for other basins with similar complexities. The proposed framework enhances both the reliability and applicability of forecasts, ensuring their relevance for effective decision-making in complex hydrological systems as the Mediterranean area. The improved accuracy of these forecasts provides a sound scientific support for adaptive water resource management, particularly in the face of increasing climatic variability and environmental changes.Acknowledgments: This study was funded by the Colombian Ministry of Science, Technology, and Innovation (MINCIENCIAS) through the Call for Doctorates Abroad 885-2; by the Valencian Regional Government through the WATER4CAST 2.0 (CIPROM/2023/5) research project; and Spanish Ministry of Science and Innovation through the research project TETISPREDICT (PID2022-141631OB-I00). 

Water Accounting Plus for Water Resources Reporting and River Basin Planning

Hydrological modeling and the hydrological response to land-use/land-cover changes induced by human activities have gained enormous research interest over the last few decades. The study presented here analyzes the spatial and qualitative changes in the rainfall–runoff that have resulted from the land-cover changes between 1985–2014 in the Godavari River Basin using the Hydrologic Engineering Centre-Hydrologic Modeling System(HEC-HMS) model and remote sensing—GIS (geographic information system) techniques. The purpose of this paper is to analyze the dynamics of land-use/land-cover (LULC) changes for the years 1985, 1995, 2005, and 2014 for the Godavari Basin. The findings reveal an increase of 0.64% of built-up land, a decrease of 0.92% in shrubland, and an increase of 0.56% in waterbodies between 1985–2014. The LULC change detection results between the years 1985–2014 indicated a drastic change in the cropland, forest, built-up land, and water bodies among all of the other classes. The urbanization and agricultural activities are the major reasons for the increase of cropland, built-up land, and water bodies, at the expense of decreases in shrubland and forest. The study had an overall classification accuracy of 92% and an overall Kappa coefficient of 0.9. The HEC-HMS model is used to simulate the hydrology of the Godavari Basin. The analyses carried out were mainly focussed on the impact of LULC changes on the streamflow pattern. The surface runoff was simulated for the year 2014 to quantify the changes that have taken place due to changes in LULC. The observed and the simulated peak streamflow was found to be the same i.e., 56,780 m3/s on 9 September 2014. In the validation part, the linear regression method was used to correlate the observed and simulated streamflow data at the prominent gauge station of the Badrachalam outlet for the Godavari River Basin and give a correlation coefficient value of 0.83. It was found that the HEC-HMS model is compatible and works better for the rainfall–runoff modeling, as it takes into account the various parameters that are influencing the process. The hydrological modeling that was carried out using the HEC-HMS model has brought out the significant impact of LULCC on rainfall–runoff at the Pranhita sub-basinscale, indicating the model’s ability to successfully accommodate all of the environmental and landscape variables. The study indicates that deforestation at the cost of urbanization and cropland expansions leads to decreases in the overall evapotranspiration (ET) and infiltration, with an increase in runoff. The results of the study show that the integration of remote sensing, GIS, and the hydrological model (HEC-HMS) can solve hydrological problems in a river basin.

Assessment of land use and climate change effects on land subsidence using a hydrological model and radar technique

We investigate the ability of hydrological multimodel ensemble predictions to enhance the skill of streamflow forecasts at short‐ to medium‐range timescales. To generate the multimodel ensembles, we implement a new statistical postprocessor, namely, quantile regression‐Bayesian model averaging (QR‐BMA). Quantile regression‐Bayesian model averaging uses quantile regression to bias correct the ensemble streamflow forecasts from the individual models and Bayesian model averaging to optimally combine their probability density functions. Additionally, we use an information‐theoretic measure, namely, conditional mutual information, to quantify the skill enhancements from the multimodel forecasts. We generate ensemble streamflow forecasts at lead times from 1 to 7 days using three hydrological models: (i) Antecedent Precipitation Index‐Continuous, (ii) Hydrology Laboratory‐Research Distributed Hydrologic Model, and (iii) Weather Research and Forecasting Hydrological modeling system. As forcing to the hydrological models, we use weather ensemble forecasts from the National Centers for Environmental Prediction 11‐member Global Ensemble Forecast System Reforecast version 2. The forecasting experiments are performed for four nested basins of the North Branch Susquehanna River, USA. We find that after bias correcting the streamflow forecasts from each model, their skill performance becomes comparable. We find that the multimodel ensemble forecasts have higher skill than the best single‐model forecasts. Furthermore, the skill enhancements obtained by the multimodel ensemble forecasts are found to be dominated by model diversity, rather than by increased ensemble size alone. This result, obtained using conditional mutual information, indicates that each hydrological model contributes additional information to enhance forecast skill. Overall, our results highlight benefits of hydrological multimodel forecasting for improving streamflow predictions.

Assessment of hydrological parameter uncertainty versus climate projection spread on urban streamflow and floods

Hydrological and climatic uncertainties associated with modeling the impact of climate change on water resources of small Mediterranean coastal rivers

<p>The explosion in the number of hydrologic models and technological advances in cloud infrastructure have combined to create new opportunities in operationalization of hydrologic science and models over the past decade. Colloquially, operationalization has been used to refer to deployments of previously existing model codes, themselves realizations of existing hydrologic science and conceptualizations, run in an unsupervised manner (e.g., automatically) driven by climate input variables that are contemporary (now-casting) or projected (forecasting). With advances in computational infrastructure and power, it has become possible to read, run, and visualize output from automated, operational models across continental domains. In the United States, recent endeavors include U.S. Geological Survey’s integrated water availability assessments, an operational configuration of the Precipitation Runoff Modeling System in the National Hydrologic Model Infrastructure; the National Water Model, an operational configuration of WRF-Hydro for flood forecasting; and several more nascent efforts. While these efforts show significant technological advances in the communication of results of hydrologic models, we ask how they have contributed to advances towards expanding knowledge of the hydrologic sciences more generally. Operational configurations of continental-domain models build upon advances of catchment-scale hydrology generally focused on addressing a single management scenario. The extent to which these model configurations have the fidelity to address a wider range of management scenarios and the translation across spatial and temporal scales is not straightforward. In addition, continental-domain operational deployments allow for the visualization of large-scale hydrologic events (e.g., droughts and floods), but perpetuate problems with communication of accuracy and uncertainty at management-relevant scales. Here we explore how these technological advances can be leveraged to advance the hydrologic science that underlies our models.</p>

Hydrological modelling of a small watershed using MIKE SHE for irrigation planning

Many climate change studies have shown that streamflow has been changing across the US over the last century. However, there are fewer studies examining trends in hydrologic variables such as soil moisture and snow, which tend to be poorly observed in some regions. Hydrologic models can be applied to understand changes in processes with limited spatial or temporal observational records. However, the application of hydrologic models to long time periods can be complicated by human influences on the hydrologic cycle. In this study, we explore hydrologic changes and the applicability of a commonly used hydrologic modelling system for climate change analysis in a heavily managed landscape. Historical streamflow observations and model‐derived time series of subsurface storage, frozen ground, and snow cover were analysed for 10 Iowa watersheds. The modelled data were generated using precipitation and temperature data spanning 1948–2003 as inputs to common hydrologic prediction models. Time series were tested for trends and significance using the Mann‐Kendall (MK) test with the Trend‐Free Pre‐Whitening procedure at a p = 0.1 significance level. Plots of 10‐year running averages were also examined. Significantly increasing trends in mean daily discharge and the number of high‐flow days were found for several watersheds. Increasing monthly subsurface water content was observed throughout the year in most basins, and was greatest during the winter. The number of modelled frozen‐ground days declined at all sites. Three different model calibration periods were tested and found to reproduce the general upward trend observed in the daily mean discharge. However, the model results varied from the observed in both magnitude and the number of sites identified to have MK trends. Additional functionality that considers land management and land cover change may make the models applied here more accurate for studying climate change impacts in regions such as Iowa. Copyright © 2011 Royal Meteorological Society

Increased urbanization, coupled with the projected impacts of climatic change, mandates further evaluation of the impact of urban development on water flow paths to guide sustainable land‐use planning. Though the general urbanization impacts of increased storm runoff peaks and reduced baseflows are well known; how the complex, non‐stationary interaction of the dominant water fluxes within dynamic urban water stores sustain streamflow regimes over longer periods of time is less well quantified. In particular, there is a challenge in how hydrological modelling should integrate the juxtaposition of rapid and slower flow pathways of the urban ‘karst’ landscape and different approaches need evaluation. In this context, we utilized hydrological and water stable isotope datasets within a modelling framework that combined the commonly used Hydrologic Engineering Center Hydrological Modelling System (HEC‐HMS) urban runoff model along with a simple hydrological tracer module and transit time modelling to evaluate the spatial and temporal variation of water flow paths and ages within a heavily urbanized 217 km2 catchment in Berlin, Germany. Deeper groundwater was the primary flow component in the upper reaches of the catchment within fewer urbanized regions, while the addition of wastewater effluent in the mid‐reaches of the catchment was the dominant water supply to sustain baseflow in the lower main stem stream, with additional direct storm runoff and shallow subsurface contributions in the more urbanized lower reaches. Water ages from each modelling approach mirrored flow contributions and water age mixing potential in subsurface storage; with older average water and lower young water contributions in less urbanized sub‐catchments and younger average water and higher young water contributions in more urbanized regions. The results from the first step towards more integrated tracer‐aided hydrologic modelling tools for similar peri‐urban catchments, given the potential limitations of simpler model frameworks. The results have broader implications for assessing the uncertainty in evaluating urban impacts on hydrological function under environmental change.

Across the world, large-scale irrigation projects (LSIPs) have revolutionized agriculture by ensuring food security and effective water resource management. Climate change including an increase in extreme weather events and altered rainfall patterns, has become a major challenge for the sustainability of agriculture. Thus, this paper deals with the prospects and concerns that revolve around LSIPs and urges the development of climate-resilient strategies. Sustainable water management strategies, water pricing, participatory irrigation management, technological inclusions like AI-, remote sensing- and IoT-based irrigation ensure precise and efficient water use. Hydrological models such as hydrologic engineering center-hydrologic modeling system (HEC-HMS), soil and water assessment tool (SWAT) and others help in understanding water resource dynamics. Case studies on China’s Three Gorges Dam, Egypt’s Aswan High Dam, Australia’s Murrumbidgee irrigation area and the Colorado river basin projects in the USA and Mexico reflect towering engineering feats and remarkable socio-economic transformations. While these infrastructures have advanced flood control, irrigation and hydropower generation, they have also highlighted the need for balanced development approaches that prioritize environmental integrity and social well-being. Thus, drawing from past lessons, integrating various adaptive management, community engagement and modern technologies and hydrological models are essential factors for sustaining agricultural productivity, water security and rural development in this rapidly changing and growing world.

<p>Over the last 40 years remote sensing has significantly changed the way we observe and predict the Earth system, particularly in the oceanographic and meteorological sciences. Today, every General Circulation model (GCM) relies upon advanced and well-established data assimilation (DA) techniques, and Land Surface Models (LSMs) – which are integral components of GCM – have been increasingly using DA to constrain the LSM model predictions with available remote sensing data of hydrological, carbon and energy cycles.</p> <p>Despite this, the use of DA into hydrological models (HMs) is still operationally limited and the reasons for that lie in 1) the considerable variability between different HMs, with much uncertainty in their respective representations of processes (often conceptual) and their sensitivity to changes in key variables, 2) the contrast between the scale of application of HMs (often smaller than LSMs) and the coarse-scale information provided by remote sensing along with their associated accuracy and, 3) the variety of the data assimilation setups, specificity of the study areas, and pre-processing used by a plethora of studies on the topic which provide a blurred picture on the real benefit of DA of relevant hydrological variables into HMs (e.g. soil moisture, precipitation, snow, terrestrial water storage anomalies).</p> <p>The recent exponential grown of high-resolution remote sensing data (the European Union's Earth observation Programme Copernicus with the constellation of the Sentinel satellites is a notable example) has potentially opened new opportunities for improving our HMs also for small scale applications.  However, their usefulness is still limited by our ability to analyse and integrate efficiently a large volume of observations with current hydrologic models. In other words, most of the issues mentioned above have been not overcome with a consequent under-exploitation of potentially useful information to constrain HMs.</p> <p>This contribution aims to summarize the main challenges and opportunities of DA into HMs from a hydrological perspective in light of the availaiblity of new and more skillful Earth observations. It identifies and explains critical challenges by using published literature by the author on European catchments as well as on-going studies, and offers insights for a productive research based on new available models and observations as to build a comprehensive hydrologic data assimilation framework that is a critical component of future hydrologic observation and modelling systems.</p>