Hydrological Model Research Articles

On Canopy Rainfall Interception Modeling in a Eucalyptus Plantation

The interaction between the forest canopy and precipitation is a fundamental process for understanding the hydrological cycle in forests. Physical models have been applied to estimate canopy water interception, and their efficiency has been tested based on metrics used to assess hydrological models. For eucalyptus plantations in Brazil, more studies are needed on the canopy rainfall interception model. Thus, we calibrated the Gash model using two complete hydrological years of observation in a eucalyptus plantation in southeastern Brazil. The model’s parametrization was conducted using 17 trees individually in different planting spacings (3 m × 2 m, 3 m × 3 m, and 3 m × 5 m). The average values of the model’s parameters were taken to represent the forest, and the average parameters for each planting spacing were used to assess the model’s performance according to the planting spacings. We used NSE, KGE, and Pbias statistical metrics to assess the model’s performance. For individual trees and rainfall events, the model showed an average NSE and Pbias of 0.59 and 18.2%, respectively, meaning a “satisfactory” performance for eight trees and “poor” performance for nine trees. When the model was averaged for the entire forest and individual rainfall events were considered, the metrics were improved, being 0.643 for NSE and 8.2% for Pbias, indicating a “good” model performance, which was strengthened by an average KGE of 0.746. Regarding the model for the planting spacings, the best results were found for the 3.0 m × 2.0 m spacing (“a good performance”). For the other spacings, Pbias was higher than 15%, leading to inferior performance, but with the NSE and KGE compatible with “good” performance. The practical implications of our findings are significant, as they can be used to enhance the accuracy of models for a better understanding of the hydrological cycle in eucalyptus forests in Brazil, thereby contributing to more effective forest management and conservation.

Enhancing Surface Water Monitoring through Multi-Satellite Data-Fusion of Landsat-8/9, Sentinel-2, and Sentinel-1 SAR

Long revisit intervals and cloud susceptibility have restricted the applicability of earth observation satellites in surface water studies. Integrating multiple satellites offers potential for more frequent observations, yet combining different satellite sources, particularly optical and SAR satellites, presents complexities. This research explores the data-fusion potential and limitations of Landsat-8/9 Operational Land Imager (OLI), Sentinel-2 Multispectral Instrument (MSI), and Sentinel-1 Synthetic Aperture (SAR) satellites to enhance surface water monitoring. By focusing on segmented surface water images, we demonstrate that combining optical and SAR data is generally effective and straightforward using a simple statistical thresholding algorithm. Kappa coefficients(κ) ranging from 0.80 to 0.95 indicate very strong harmony for integration across reservoirs, lakes, and river environments. In vegetative environments, integration with S1SAR shows weak harmony, with κ values ranging from 0.27 to 0.45, indicating the need for further studies. Global revisit interval maps reveal significant improvement in median revisit intervals from 15.87 to 22.81 days using L8/9 alone, to 4.51 to 7.77 days after incorporating S2, and further to 3.48 to 4.62 days after adding S1SAR. Even during wet season months, multi-satellite fusion maintained the median revisit intervals to less than a week. Maximizing all available open-source earth observation satellites is integral for advancing studies requiring more frequent surface water observations, such as flood, inundation, and hydrological modeling.

Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability have minor effects on the hydrological response in the Neckar River basin, Germany

Abstract. Climatic variability can considerably affect catchment-scale root zone storage capacity (Sumax), which is a critical factor regulating latent heat fluxes and thus the moisture exchange between land and atmosphere as well as the hydrological response and biogeochemical processes in terrestrial hydrological systems. However, direct quantification of changes in Sumax over long time periods and the mechanistic drivers thereof at the catchment scale are missing so far. As a consequence, it remains unclear how climatic variability, such as precipitation regime or canopy water demand, affects Sumax and how fluctuations in Sumax may influence the partitioning of water fluxes and therefore also affect the hydrological response at the catchment scale. Based on long-term daily hydrological records (1953–2022) in the upper Neckar River basin in Germany, we found that variability in hydro-climatic conditions, with an aridity index IA (i.e. EP/P) ranging between ∼ 0.9 and 1.1 over multiple consecutive 20-year periods, was accompanied by deviations ΔIE between −0.02 and 0.01 from the expected IE inferred from the long-term parametric Budyko curve. Similarly, fluctuations in Sumax, ranging between ∼ 95 and 115 mm or ∼ 20 %, were observed over the same time period. While uncorrelated with long-term mean precipitation and potential evaporation, it was shown that the magnitude of Sumax is controlled by the ratio of winter precipitation to summer precipitation (p < 0.05). In other words, Sumax in the study region does not depend on the overall wetness condition as for example expressed by IA, but rather on how water supply by precipitation is distributed over the year. However, fluctuations in Sumax were found to be uncorrelated with observed changes in ΔIE. Consequently, replacing a long-term average, time-invariant estimate of Sumax with a time-variable, dynamically changing formulation of that parameter in a hydrological model did not result in an improved representation of the long-term partitioning of water fluxes, as expressed by IE (and fluctuations ΔIE thereof), or in an improved representation of the shorter-term response dynamics. Overall, this study provides quantitative mechanistic evidence that Sumax changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water (and thus latent heat) fluxes as expressed by deviations ΔIE from the parametric Budyko curve over multiple time periods with different climatic conditions. Similarly, it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment. This further suggests that accounting for the temporal evolution of Sumax with a time-variable formulation of that parameter in a hydrological model does not improve its ability to reproduce the hydrological response and may therefore be of minor importance for predicting the effects of a changing climate on the hydrological response in the study region over the next decades to come.

Using exploratory modeling to challenge narratives of risk governance in Mexico City.

Achieving more sustainable adaptation to social-environmental change demands the transformation of the narratives that provide the rationale for risk governance. These narratives often reflect long-standing beliefs about social and political relationships, ascribe actions and responsibilities, and specify solutions to risk. When such solutions are implemented through material investments in landscapes, these narratives become embedded in physical infrastructure with long legacies. Dominant narratives can mask a range of divergent problem framings. By masking alternatives, narratives can contribute to the persistence of unsustainable governance trajectories. Decision-support tools have begun to represent narratives as drivers of system dynamics; making narratives visible can reveal opportunities for more sustainable governance. We present the results of the project "The Dynamics of Multi-Scalar Adaptation in the Megalopolis", a dynamic, exploratory model of socio-hydrological risks in Mexico City that was designed to both endogenize and simultaneously challenge the dominant narratives that characterize water-risk governance in the city. Qualitative data characterize dominant narratives at city and borough scales. An agent-based model, informed by multicriteria decision analysis and coupled with hydrological, urbanization, and climatic model inputs, permitted the development of exploratory governance scenarios designed to challenge dominant narratives. Scenarios revealed how dominant narratives may contribute to the persistence of vulnerability "hotspots" in the city, despite stated goals of equity and vulnerability alleviation. Participatory workshops with representatives of the city government illustrate how making such narratives visible through exploratory modeling can lead to a questioning of prior assumptions and causal relations, recognition of a need for intersectoral collaboration, and insights into potential management strategies.

Altitude characteristics in the response of rain-on-snow flood risk to future climate change in a high-latitude water tower

Global warming is profoundly impacting snowmelt runoff processes in seasonal freeze-thaw zones, thereby altering the risk of rain-on-snow (ROS) floods. These changes not only affect the frequency of floods but also alter the allocation of water resources, which has implications for agriculture and other key economic sectors. While these risks present a significant threat to our lives and economies, the risk of ROS floods triggered by climate change has not received the attention it deserves. Therefore, we chose Changbai Mountain, a water tower in a high-latitude cold zone, as a typical study area. The semi-distributed hydrological model SWAT is coupled with CMIP6 meteorological data, and four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) are selected after bias correction, thus quantifying the impacts of climate change on hydrological processes in the Changbai Mountain region as well as future evolution of the ROS flood risk. The results indicate that: (1) Under future climate change scenarios, snowmelt in most areas of the Changbai Mountains decreases. The annual average snowmelt under SSP126, SSP245, SSP370, and SSP585 is projected to be 148.65 mm, 135.63 mm, 123.44 mm, and 116.5 mm, respectively. The onset of snowmelt is projected to advance in the future. Specifically, in the Songhua River (SR) and Yalu River (YR) regions, the start of snowmelt is expected to advance by 1–11 days. Spatially, significant reductions in snowmelt were observed in both the central part of the watershed and the lower reaches of the river under SSP585 scenario. (2) In 2021–2060, the frequency of ROS floods decreases sequentially for different scenarios, with SSP 126 > SSP 245 > SSP 370 > SSP 585. The frequency increments of ROS floods in the source area for the four scenarios were 0.12 days/year, 0.1 d/yr, 0.13 days/year, and 0.15 days/year, respectively. The frequency of high-elevation ROS events increases in the YR in the low emission scenario. Conversely, in high emission scenarios, YR high-elevation ROS events will only increase in 2061–2100. This phenomenon is more pronounced in the Tumen River (TR), where floods become more frequent with increasing elevation.

Enhanced runoff simulation by precise capture of snowmelt variation signals with satellite-based snow products in a high-elevation basin

Hydrological models stand as a pivotal instrument for runoff simulation, while encountering notable uncertainties in output due to intricate terrain conditions and limited ground-based observations, especially in high-elevation basins. Leveraging satellite-based images presents a promising avenue for deciphering the hydrological model’s state variables. In pursuit of refining runoff simulation, this study developed a two-step enhancement strategy, which involved (1) calibrating snow-related parameters in the hydrological model using remotely sensed snow cover area (SCA) and snow water equivalent (SWE) and (2) capturing snowmelt runoff through the hydrological model and image-based products. Coupled with the Variable Infiltration Capacity (VIC) model, we adopted this strategy as a case study in the Dadu River Basin, China. The results indicated (1) the daily Nash-Sutcliffe Efficiency (NSE) of runoff simulation reached 0.84 by the enhancement strategy, markedly surpassing the strategy reliant on soil parameters with a single calibrated reference (daily NSE of 0.66), and exhibited comparability to the strategy incorporating snow parameters but calibrated solely based on observed discharge (daily NSE of 0.83). (2) the enhancement strategy demonstrated hydrological consistency with snowmelt information derived from imagery. Specifically, multi-year average contributions of model’s and image-based snowmelt calculations were 31.8% and 33.4%, respectively. Additionally, simulated dates of snow accumulation and ablation appeared an average deviation of approximately one week compared to the imagery results. This study elucidates a potential methodological approach for offering valuable insights into hydrological processes within analogous high-elevation basins globally.

Superiority of artificial neural networks over conventional hydrological models in simulating urban catchment runoff

ABSTRACT The synergistic impacts of climate change and urbanisation have amplified the recurrence and austerity of intense rainfall events, exacerbating persistent flooding risk in urban environments. The intricate topography and inherent non-linearity of urban hydrological processes limit the predictive accuracy of conventional models, leading to significant discrepancies in flow estimation. Recent advancements in artificial neural network (ANNs) have demonstrated remarkable progress in mitigating most limitations, specifically in simulating complex, non-linear relationships, without an intricate comprehension of the underlying physical processes. This paper proposes a deep learning ANN-based flow estimation model for enhanced precision simulation of streamflow in urban catchments, with the research's distinctive contribution involving rigorous comparative evaluation of the developed model against the established Australian hydrological model, RORB. Gardiners Creek catchment, an urban catchment situated in East Melbourne was designated as the study area, with the model being calibrated upon historical storm incidences. The findings reveal that the ANN model substantially outperforms RORB, as evidenced by superior correlation, prediction efficiency, and lower generalisation error. This underscores the ANN's adeptness in accurately replicating non-linear-catchment responses to storm events, marking a substantial advancement over conventional modelling practices and indicating its transformative potential for enhancing flood prediction precision and revolutionising current estimation practices.

Spatio-Temporal Distribution of Water Availability in Marshyangdi Basin: Hydrological Model Development

This Study has developed a hydrological model for the Marshyangdi river basin in Western Nepal, showing considerable potential for developing water resources and overall national prosperity. The model is also used to characterise hydrology and assess the availability of water resources across different spatial and temporal dimensions, hence improving our comprehension of water availability and its possible applications. The newly developed hydrological model in the Soil and Water Assessment Tool (SWAT) replicates the mean flows, hydrological pattern, and flow duration curve at the basin and 54 sub-basin outputs. Additionally, the model undergoes calibration and validation, employing Sufi-2 to fine-tune hydrological parameters. Based on the data generated by the model, the annual average precipitation (P) in the Marshyangdi basin is projected to be 1843 mm. Approximately 17.88% of the average yearly rainfall is lost via evapotranspiration; however, this varies significantly across different locations and time periods. Contrasting the two, the mountains have a higher moisture level than the Himalayan regions. The predicted average annual flow volume at the basin outflow is 9467.423 million cubic metres (MCM). Examining water shortages, finding regions with excess water, and developing solutions to address the competing needs of agriculture, industry, urban areas, and ecosystems are advantageous. Therefore, this method may improve the effectiveness of strategic planning and the long-term management of water resources.

Evaluation of distributed and semi-distributed hydrological models in complex River Basin system, Nepal

Hydrological information is essential for planning water resource projects and for simulating hydrological models to calibrate and generate streamflow data. This study evaluates the performance of distributed and semi-distributed hydrological models in the Tamakoshi River Basin (TRB), Nepal. The models assessed include the Spatial Process in Hydrology (SPHY), Hydrologiska Byråns Vattenbalansavdelning (HBV), and Hydrologic Engineering Centre Hydrologic Modeling System (HEC-HMS). The models were evaluated using metrics such as Nash-Sutcliffe Efficiency (NSE) ranging from 0.62 to 0.77, Coefficient of Determination (R2) from 0.76 to 0.77, and Percentage Bias (P Bias) from 26.16 % to −3.54 % during daily calibration. All three models were successfully calibrated and validated on a daily and monthly basis using observed discharge data at the Busti station in the TRB. The SPHY model performed better during the calibration period compared to the validation period, while the HBV and HEC-HMS models showed better performance during validation than calibration. Among the statistical indicators, the coefficient of determination closely matched, but NSE and P Bias showed some deviations. This comparative analysis of calibrated and validated models is valuable for filling gaps in hydrological data records and for selecting appropriate hydrological models for planning, managing, and developing water resource projects in the TRB.

A comprehensive method for error separation in hydrological modelling

AbstractEvaluation metrics play a pivotal role in the calibration process of hydrological models, serving as objective functions that directly influence the final values of model parameters and significantly affect users' perceptions of model performance. However, the choice and interpretation of evaluation metrics are subjective; therefore, this study provides a more objective framework for assessing model performance. This paper initially explored the applicability of various commonly used evaluation metrics, providing an overview of their limitations. Following this, we decomposed errors by analysing their physical significance and geometric representation in scatter plots, categorizing them into systematic and unsystematic errors. Through the decomposition and derivation of the Nash–Sutcliffe efficiency (NSE) formula, we established the quantitative relationship among various evaluation metrics. The soil and water assessment tool (SWAT) model was utilized to simulate monthly runoff in the Baishan basin (China), for the period 1994–2017, with NSE serving as the objective function for calibration. Our findings are consistent with previous studies, indicating that the model tends to slightly underestimate high flows while significantly overestimating low flows. Further analysis through error decomposition and the examination of relationships among various evaluation metrics revealed that unsystematic errors were dominant during the spring snowmelt runoff period, while systematic errors prevailed in the dry season. By evaluating the runoff series based on the magnitude of runoff or by categorizing it according to seasons and months, a more stringent assessment of the model's performance was achieved. These findings not only highlight the necessity for careful selection of evaluation metrics but also underscore the significance of our methodological advancements in enhancing hydrological model precision and reliability.

Snow simulation for the rangeland hydrology and erosion model

In the western US, most rangelands receive snowfall. Yet, a commonly used tool to assess rangeland’s vulnerability to erosion, the USDA’s Rangeland Hydrology and Erosion Model (RHEM) is run using long-term simulated climate inputs that assumes that all precipitation occurs as rainfall. This can be problematic for areas that receive heavy snowfall or substantial rain-on-snow events. In this research, we have developed an efficient snow module for RHEM, called RHEM-Snow, which partitions precipitation between rainfall and snowfall, simulates snowpack accumulation and ablation, and passes net water input (consisting of rainfall, snowmelt, or both) to RHEM. In some areas, the inclusion of the snow module can reduce annual overland flow runoff and erosion estimates by more than 20 % of the total annual overland flow runoff and erosion produced without the snow module (or by as much as 10–50 mm/year for overland flow runoff or >100 kg/ha-yr for erosion). The reclassification of precipitation events from rainfall in RHEM to snowfall in RHEM-Snow tends to reduce overland flow runoff and erosion, but this reduction can be partially counterbalanced by increases from snowmelt and rain-on-snow. However, hydrologic responses to rain-on-snow events can either be enhanced or muted depending on the characteristics of the storm and the snowpack, as sometimes the snowpack can absorb the precipitation inputs, and sometimes snowmelt enhances the precipitation inputs. Because of this mixed impact, the average difference in erosion caused by rain on snow events is relatively small compared to corresponding events where only the liquid phase is considered. Further study is needed of the complex erosion processes under snowpack and frozen soil/variable saturation conditions. Overall, RHEM-Snow provides more realistic timing and magnitude of overland flow runoff and erosion in cold environments, better satisfying the conditions for RHEM applications.

Forecasting of sub-surface flow velocity through a temporarily open/close estuary under closed condition using machine learning

TOCE or temporarily open/closed estuary is a complex estuary system created by interaction of longshore transport and river outflows. Under the closed condition when an estuarine sandbar is formed, river flow is impeded creating backwater rise that could result in floods. At present, there are no hydrological models that could cater for river flows under closed estuary conditions. This paper proposes the application of a machine learning (ML) approach to model flow velocity in an estuarine sandbar which is crucial for flow estimations. A TOCE located at Mengabang Telipot, Terengganu, Malaysia was chosen for model development where river water and groundwater levels (inside the sandbar) were measured and tidal levels from an existing station were used. Average flow velocities were calculated using Darcy’s equation to represent observed data. Multilayer perceptron (MLP) neural networks were trained by the Levenberg-Marquardt algorithm and developed concerning the parameters above to predict groundwater average velocity. In this study, the optimal model was a neural network with four neurons in the hidden layer and the degree of influence of each parameter was determined by the results of the sensitivity analysis, where the R2 was obtained to be about 97–99% when predicted data was compared to observed data. Hence, the results indicated that neural networks were able to predict groundwater mean velocity quite well. The developed model was further verified using data from another TOCE from a different time. The results were very good having a R2 of 0.990. Hence, it was concluded that the ML approach was able to model flow in a TOCE and has great potential application in such an environment seeing that it is simpler than complex physics-based models.

Evaluating input data sources for isotope‐enabled rainfall‐runoff models

AbstractIsotope‐enabled models provide a means to generate robust hydrological simulations. However, daily isotope‐enabled rainfall‐runoff models applied to larger spatial scales (>100 km2) require more input data than conventional non‐isotope models in the form of precipitation isotope time series, which are difficult to generate even with point station measurements. Spatially distributed isotope data can be circumvented by isotope‐enabled climate models. Here, we evaluate the hydrological simulations of the J2000‐isotope enabled hydrological model driven with data from corrected and un‐corrected isotope‐enabled global and regional climate models (isotope‐enabled global spectral model [IsoGSM] and isotope‐enabled regional spectral model [IsoRSM], respectively) compared with 1 year of measured reference station and a yearly average precipitation isotope input for a pilot site, the data‐scarce sub‐humid Eerste River catchment in South Africa. The models driven by all input products performed well for upstream and downstream discharge gauges with Nash Sutcliffe efficiency (NSE) from 0.58 to 0.85 and LogNSE of 0.66 to 0.93. The simulated δ2H stream isotopes using the reference J2000‐iso and J2000‐isoRSM were good for the main river with a stream Kling Gupta efficiency (KGE) of between 0.4–0.9 and the top 100 Monte Carlo simulations varying by around 5‰ for δ2H. For smaller tributaries the model was unable to capture the measured stream isotopes due to biased precipitation isotope inputs. Adjusting the J2000‐iso with a bias corrected IsoRSM improved the stream and groundwater isotope simulation and outperformed the model driven by an average yearly precipitation isotope input. Differences in simulated hydrological processes were only evident between the models when evaluating percolation with unrealistic simulations for the standard J2000 model. While the regional climate model is computationally more intensive than its global counterpart, it provided better stream isotope simulations and improvements to simulated percolation. Our results indicate that isotope‐enabled climate models can provide useful input data in data scarce regions for hydrological models, where improved water management to address climate change impacts is needed.

A multi-variable calibration framework at the grid scale for integrating streamflow with evapotranspiration data to improve the simulation of distributed hydrological model

Study regionThe Ganjiang River Basin, China Study focusParameter calibration is crucial for the accurate and reliable operation of hydrological models. Traditional methods face challenges in calibrating spatially heterogeneous parameters of distributed hydrological models, and existing multi-variable calibration strategies often fall short in comprehensively improving model performance, particularly in streamflow simulations. To address these challenges, this study proposes a multi-objective calibration framework that integrates observed streamflow data and satellite-based evapotranspiration (ET) data. The spatiotemporal information of the merged ET is utilized to calibrate six hydrological parameters of the Variable Infiltration Capacity (VIC) model at the grid scale, enhancing hydrological simulations for the Ganjiang River basin. New hydrological insightsCompared to the benchmark scheme based solely on streamflow, the proposed calibration framework improves simulations of area-average ET at the sub-basin scale and soil moisture content in the Ganjiang River basin, without compromising the accuracy of daily streamflow simulations. Additionally, notable enhancements are observed in monthly streamflow simulations. This study provides a promising and comprehensive calibration framework using satellite-based data to constrain parameters and enhance the performance of distributed hydrological models.

Water budgets in an arid and alpine permafrost basin: Observations from the High Mountain Asia

Ground freeze‒thaw processes have significant impacts on infiltration, runoff and evapotranspiration. However, there are still critical knowledge gaps in understanding of hydrological processes in permafrost regions, especially of the interactions among permafrost, ecology, and hydrology. In this study, an alpine permafrost basin on the northeastern Qinghai‒Tibet Plateau was selected to conduct hydrological and meteorological observations. We analyzed the annual variations in runoff, precipitation, evapotranspiration, and changes in water storage, as well as the mechanisms for runoff generation in the basin from May 2014 to December 2015. The annual flow curve in the basin exhibited peaks both in spring and autumn floods. The high ratio of evapotranspiration to annual precipitation (>1.0) in the investigated wetland is mainly due to the considerably underestimated ‘observed’ precipitation caused by the wind-induced instrumental error and the neglect of snow sublimation. The stream flow from early May to late October probably came from the lateral discharge of subsurface flow in alpine wetlands. This study can provide data support and validation for hydrological model simulation and prediction, as well as water resource assessment, in the upper Yellow River Basin, especially for the headwater area. The results also provide case support for permafrost hydrology modeling in ungauged or poorly gauged watersheds in the High Mountain Asia.

Evaluating the influence of hydrologic signatures on hydrological modeling using remotely sensed surrogate river discharge

In recent studies, a surrogate river discharge model (SRM) has been proposed that uses remote sensing data, termed henceforth as the Surrogate River discharge (SR), instead of river discharge in hydrological model calibration. SR is calculated from the relative values of target and reference reflectance obtained from satellite observations and thus does not have a defined unit. Hence, to ensure physical consistency, the model calibration mandates the integration of a hydrological feature to translate the non-dimensional SR to a quantified river discharge estimate. In this context, an estimate of the mean catchment river discharge (QM) is proposed as a suitable signature. This variable is derived from the Budyko relationship, which only uses two climate average data: precipitation and potential evapotranspiration. This allows the SRM calibration to depend primarily on remotely sensed data and climate data. However, the model’s sensitivity to the hydrologic signature approximation is generally unknown and should be characterized for improved probabilistic predictions in ungauged basins. Therefore, this study evaluates the impact of the hydrologic signature on SRM. The study first identifies the sensitivity of the SRM to the QM accuracy. Subsequently, the SRM integrated with the Budyko relationship (SRMB) was compared to the Australian Landscape Water Balance model (AWRA-L) using real catchment data on 49 selected catchments of various sizes, topographies, and climates to assess its uncertainty. Then the investigation is subsequently broadened to include 384 catchments in Australia to analyze the sensitivity of SRM with respect to classified SR quality, as assessed through a range of hydrological signature estimation methods. To summarize, while the efficacy of SRM is contingent upon SR quality and mean flow accuracy, incorporating the Budyko framework yields effective mean flow estimations, bolstering prediction reliability. Given its efficient calibration, SRM demonstrates potential for broader application in hydrological studies.

Estimating the Ebro river discharge at 1 km/daily resolution using indirect satellite observations

Estimating river discharge Q at global scale from satellite observations is not yet fully satisfactory in part because of limited space/time resolution. Furthermore, on highly anthropized basins, it is essential to anchor the analysis to reliable Q measurements. Gauge networks are however very sparse and limited in time, and SWOT (Surface Water Ocean Topography) river discharge estimates at global scale are not yet available. The method proposed here is able to obtain continuous daily Q estimates at 1 km/daily resolution, using indirect satellite data and ground-based estimates. We focus here on the Ebro. Over such an anthropized basin (e.g. change of land use, irrigation), the exploitation of 205 available gauges at their nominal resolution (i.e., daily point measurements) is a necessity. The hydrological Continuum model is used to help interpolate spatially and temporally the observations into our optimal interpolation scheme. The proposed Q-mapping is similar to an assimilation scheme were Earth observations (precipitation, evapotranspiration and total water storage change) and model simulations are constrained by in situ gauge measurements. The Q estimates are evaluated using a rigorous leave-one-out experiment, showing a good agreement with the in situ data: a correlation of 0.72 (median), and a 75th percentile of Nash-Sutcliffe Efficiency up to 0.62. Our spatio-temporal continuous Q estimates at high spatial/temporal resolution can describe complex continental water dynamics, including extreme events. SWOT estimates will soon be available, at the global scale but with irregular space/time sampling: our method should help exploit them to obtain a regular space-temporal description of the water cycle at high resolution.

Predictor Importance for Hydrological Fluxes of Global Hydrological and Land Surface Models

Predictor Importance for Hydrological Fluxes of Global Hydrological and Land Surface Models

Assessing the suitability of groundwater resources based on farmers’ predicted intentions and human–water feedbacks

In this study, a behavioral −hydrological simulation modeling framework is developed to explore the functioning of human-water systems. The main objective is to identify the psycho-social factors affecting water conservation intentions among farmers, and then realize the interactions and bidirectional feedback between humans and water resources. For this purpose, the agent-based modeling (ABM) and extended theory of planned behavior (TPB) is recruited to reflect on the agents’ behaviors. As the first step, the framework of the extended TPB together with the agent behavior was designed via field researches through the completion of 169 questionnaires upon proportional-to-size (PPS) random sampling. The partial least squares-structural equation modeling (PLS-SEM) is then implemented, at the second step, to analyze the data and obtain the weights of the extended TPB constructs. The results reveal that the constructs supplemented to the TPB, including moral norms (MNs) and perceived risk (PR), could have significant effects on water conservation intentions and behaviors among farmers. At the last step, the farmers and their decision-making as the dynamic elements by obtaining feedbacks of water resources were modeled using the ABM. To evaluate the proposed framework, the recorded data of groundwater fluctuations and cultivated areas in the Lenjanat plain is utilized and compared with the socio-hydrological simulation modeling outcomes using the Nash-Sutcliffe efficiency (NSE) coefficient. Then, five scenarios are defined to shed light on the changes in the agent behavior on the aquifer suitability, including water consumption rate and groundwater levels in the aquifer. The results correspondingly demonstrated that merging the second and third scenarios in an effort to raise the prices of crops with low water need along with the increase in utility costs (here, water and electricity) could lead to a 75% increase of farmers’ income and a 21.5% reduction in the surface and groundwater uses in this region, implying the highest level of water conservation among other scenarios.

Enhancing flood risk mitigation by advanced data-driven approach

Flood events in the Sefidrud River basin have historically caused significant damage to infrastructure, agriculture, and human settlements, highlighting the urgent need for improved flood prediction capabilities. Traditional hydrological models have shown limitations in capturing the complex, non-linear relationships inherent in flood dynamics. This study addresses these challenges by leveraging advanced machine learning techniques to develop more accurate and reliable flood estimation models for the region. The study applied Random Forest (RF), Bagging, SMOreg, Multilayer Perceptron (MLP), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models using historical hydrological data spanning 50 years. The methods involved splitting the data into training (50–70 %) and validation sets, processed using WEKA 3.9 software. The evaluation revealed that the nonlinear ensemble RF model achieved the highest accuracy with a correlation of 0.868 and an root mean squared error (RMSE) of 0.104. Both RF and MLP significantly outperformed the linear SMOreg approach, demonstrating the suitability of modern machine learning techniques. Additionally, the ANFIS model achieved an exceptional R-squared accuracy of 0.99. The findings underscore the potential of data-driven models for accurate flood estimating, providing a valuable benchmark for algorithm selection in flood risk management.