Hydrological Model Research Articles

Quantization‐Based Latin Hypercube Sampling for Dependent Inputs With an Application to Sensitivity Analysis of Environmental Models

ABSTRACTNumerical models are essential for comprehending intricate physical phenomena in different domains. To handle their complexity, sensitivity analysis, particularly screening is crucial for identifying influential input parameters. Kernel‐based methods, such as the Hilbert‐Schmidt Independence Criterion (HSIC), are valuable for analyzing dependencies between inputs and outputs. Implementing HSIC requires data from the original model, which leads to the need of efficient sampling strategies to limit the number of costly numerical simulations. While, for independent input variables, existing sampling methods like Latin Hypercube Sampling (LHS) are effective in estimating HSIC with reduced variance, incorporating dependence is challenging. This article introduces a novel LHS variant, quantization‐based LHS (QLHS), which leverages Voronoi vector quantization to address dependent inputs. The method provides good coverage of the range of variations in the input variables. The article outlines expectation estimators based on QLHS in various dependency settings, demonstrating their unbiasedness. The method is applied to several models of growing complexities, first on simple examples to illustrate the theory, then on more complex environmental hydrological models, when the dependence is known or not, and with more and more interactive processes and factors. The last application is on the digital twin of a French vineyard catchment (Beaujolais region) to design a vegetative filter strip and reduce water, sediment, and pesticide transfers from the fields to the river. QLHS is used to compute HSIC measures and independence tests, demonstrating its usefulness, especially in the context of complex models.

Assessment of Climate Change Impact on Flood Hazard Zones

AbstractThere have been many destructive pluvial and fluvial floods in Poland and the projection of increasing flood hazards in the future is a reason of considerable concern. The maps of river hazard zones are changing over time, and understanding these changes is of primary importance for flood risk reduction and climate change adaptation. This article aims to assess the impact of climate change on the spatial extent and depth classes of flood hazard zones for a selected reach of the River Warta in the western part of Poland. To this end, we integrated the Soil & Water Assessment Tool (SWAT) hydrological model of the Warta River Basin with the 1D hydraulic model HEC-RAS of the selected reach. The climate change effect was quantified based on the coupled model simulations forced with bias-corrected projections from the EURO-CORDEX project. Flood hazard maps were developed for two townships along the River Warta (Oborniki and Wronki), three greenhouse gas concentration scenarios (one for the baseline scenario in the reference period, 1971–2000; one for RCP 4.5 and one for RCP 8.5, for the time horizon 2021–2050) and for three return periods (10-, 100- and 500-year floods). Based on the ensemble mean, the increase in the flooded area projected in the future is more pronounced for RCP8.5 than for RCP4.5. This unique combination of software and data enabled the transformation of climate change impact into the land surface part of the hydrological cycle and assessment of changes in flood hazard and opens the way to assess the potential increases in the economic losses in the future.

A practitioner-oriented regional hydrology data product for use in site-specific hydraulic applications

In the 465,000 km2 Canadian Prairies ecozone, robust hydrological input data for hydraulic model applications are uncommon because of the sparse monitoring network and the intermittently connected stream network. New hydrological datasets can offer a valuable advancement for making water management decisions and designing infrastructure in this water stressed region. The Prairie Hydrology Design and Analysis Product (PHyDAP) was created to address existing limitations, and provides a comprehensive regional dataset for use in hydraulic modelling applications. PHyDAP is a collection of outputs from a physically based hydrological modelling framework, run for periods ranging from 38 to 150 years, according to three climate forcing datasets. The dataset includes vertical and lateral fluxes (rainfall, snowmelt, upland runoff, and open water evaporation) and basin streamflow, at hourly or 3-hourly intervals for the >4000 small basins of approximately 100 km2 that span the region. This contribution describes the motivation for this work and methodology used to derive the data product, summarizes the data and its accessibility, and provides an overview of potential use cases.

Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems.

Integrated Hydrological and Hydraulic Modeling for River Freezing Simulation: Impacts of a Changing Climate on the Freeze-Up of the Exploits River in Newfoundland

Integrated Hydrological and Hydraulic Modeling for River Freezing Simulation: Impacts of a Changing Climate on the Freeze-Up of the Exploits River in Newfoundland

Modelling fields of hydrological cycle characteristics in the Nizhnekamskoye Reservoir watershed of the Volga River basin under climate change

The assessment of trends in averaged over the watershed area hydroclimatic characteristics and changes in the fields of the hydrological cycle components in the XXI century in large river basin of the Nizhnekamskoye reservoir was carried out on the basis of a model approach combining regional space distributed hydrological model and GCMs. It is shown that a slight decrease by 8% is expected in the area-averaged specific runoff by the end of the century according to the RCP8.5 scenario. However, in various parts of the watershed both a slight increase by 3-6% and a significant decrease in the mean annual runoff by 22% can occur, depending on physiographic conditions. Moreover, climatic changes can increase contrasts in the ecological status of the territory: warm regions in the steppe zone become even hotter and more arid, and the moisture availability of wetter territories in the center and east of the basin increases even more.

Hydrological Modeling to Unravel the Spatiotemporal Heterogeneity and Attribution of Baseflow in the Yangtze River Source Area, China

Revealing the spatiotemporal variation in baseflow and its underlying mechanisms is critical for preserving the health and ecological functions of alpine rivers, but this has rarely been conducted in the source region of the Yangtze River (SRYR). Our study employed the Soil and Water Assessment Tool (SWAT) model coupled with two-parameter digital filtering and geostatistical approaches to obtain a visual representation of the spatiotemporal heterogeneity characteristics of the baseflow and baseflow index (BFI) in the SRYR. The SWAT model and multiple linear regression model (MLR) were used to quantitatively estimate the contribution of climate change and human activities to baseflow and BFI changes. The results underscore the robust applicability of the SWAT model within the SRYR. Temporally, the precipitation, temperature, and baseflow exhibited significant upward trends, and the baseflow and BFI showed contrasting intra-annual distribution patterns, which were unimodal and bimodal distribution, respectively. Spatially, the baseflow increased from northwest to southeast, and from the watershed perspective, the Tongtian River exhibited higher baseflow values compared to other regions of the SRYR. The baseflow and BFI values of the Dangqu River were greater than those of other tributaries. More than 50% of the entire basin had an annual BFI value greater than 0.7, which indicates that baseflow was the major contributor to runoff generation. Moreover, the contributions of climate change and human activities to baseflow variability were 122% and −22%, and to BFI variability, 60% and 40%. Specifically, precipitation contributed 116% and 60% to the baseflow and BFI variations, while the temperature exhibited contributions of 6% and 8%, respectively. Overall, it was concluded that the spatiotemporal distributions of baseflow and the BFI are controlled by various factors, and climate change is the main factor of baseflow variation. Our study offers valuable insights for the management and quantitative assessment of groundwater resources within the SRYR amidst climate change.

Integrating Machine Learning, Land Cover, and Hydrological Modeling to Contribute Parameters for Climate Impacts on Water Resource Management

The purpose of this study is to establish basic policies for managing the impacts of climate change on water resources using the integration of machine learning and land cover modeling. We predicted future changes in land cover within the water management and assessed its vulnerability to climate change. After confirming this vulnerability, we considered measures to improve climate resilience and presented future water resource parameters. We reviewed the finances available to promote climate projects, noting the major river management funds. The future project will serve as a stepping stone to promote climate resilience projects addressing water resource challenges exacerbated by future climate change. The study examined the results of analyzing changes in land cover maps due to climate change and assessed vulnerability in water management areas until 2050. According to the analysis results, the regulations for our study areas were set lower than those for other water management zones, resulting in a high rate of urbanization. Therefore, the climate resilience project in the water management area should be implemented first, despite the need for a long-term view in adapting to climate change.

Assessment of surface irrigation potential and crop water demand in Daramalo Wereda, southern Ethiopia

AbstractAssessing available water and land resources as well as their potential for surface irrigation use is vital for sustainable agricultural development and planning. Agricultural irrigation also plays a crucial role in ensuring food security and reducing poverty in Ethiopia. However, it is dominated by rain‐fed agriculture, with rainfall distribution and intensity vary spatially and temporally. Enhancing agricultural water management and irrigation has the potential to increase agricultural productivity. The objective of the study was to evaluate surface water potential and the irrigation water demand of Zage River sub‐watershed, which were estimated using hydrological model of Soil and Water Assessment Tool (SWAT) and CROPWAT8.0 software, respectively. Slope, soil type, drainage densities, land use/cover, and river proximity were the factors utilized to determine irrigation land suitability. A weighted overlay was completed, with 17.32% of irrigable lands found to be highly suitable, 41.29% moderately suitable, 37.61% marginally suitable, and 3.78% not suitable. The SWAT model result gave very good model performance indicator values of NE = 0.773 and R2 = 0.872 for calibration and NE = 0.771 & R2 = 0.945 for validation. The SWAT model result showed a total of 33.248 million m3 of surface runoff generated annually. Compared with irrigation water demand of 21.068 million m3, this suggests the watershed has high surface water potential for surface irrigation for selected crop types of the watershed. The findings of this research are relevant to improving local water management policies and practices and enable stakeholders to make decisions that enhance agricultural productivity and sustainable agricultural practices and help ensure the food security of local farmers.

Sustainable groundwater regulation in typical irrigation areas of inland river basins based on ecological indicators and the MIKE-SHE hydrological model

Study regionThe study area is located in the Nukus irrigation area on the lower reaches of the Amudarya River in Uzbekistan. Study focusThe extensive agricultural land development has introduced a substantial amount of water resources into the irrigation area, leading to a significant rise in regional groundwater levels. Quantifying regional groundwater resource issues and achieving sustainable groundwater management under conditions of high spatiotemporal heterogeneity are key scientific issues for regional ecological protection. Thus, this study used the concept of ecological water level to quantify groundwater issues, used the MIKE-SHE hydrological model to conduct scenario simulations, formulated and subsequently evaluated a groundwater regulation plan with spatiotemporal attributes. New hydrological insights for the regionRegional sustainable development groundwater level should be within the range of 1.78–2.78 m. During the irrigation and non-irrigation periods, the groundwater levels in approximately 84.3 % and 72.2 % of the study area respectively exceeded the upper and lower ecological water level limits. Under the background of gradually increasing groundwater fluctuations, it will pose a serious threat to regional ecology. Therefore, this paper established a monthly-scale groundwater zoning control plan, which can make the area of regional groundwater within the ecological water level reach 87.53 %. All research not only contributes to the protection of the local ecological environment but also provides valuable insights for water resources management in other regions.

Coupling SWAT and Transformer Models for Enhanced Monthly Streamflow Prediction

The establishment of an accurate and reliable predictive model is essential for water resources planning and management. Standalone models, such as physics-based hydrological models or data-driven hydrological models, have their specific applications, strengths, and limitations. In this study, a hybrid model (namely SWAT-Transformer) was developed by coupling the physics-based Soil and Water Assessment Tool (SWAT) with the data-driven Transformer to enhance monthly streamflow prediction accuracy. SWAT is first constructed and calibrated, and then its outputs are used as part of the inputs to Transformer. By correcting the prediction errors of SWAT using Transformer, the two models are effectively coupled. Monthly runoff data at Yan’an and Ganguyi stations on Yan River, a first-order tributary of the Yellow River Basin, were used to evaluate the proposed model’s performance. The results indicated that SWAT performed well in predicting high flows but poorly in low flows. In contrast, Transformer was able to capture low-flow period information more accurately and outperformed SWAT overall. SWAT-Transformer could correct the errors of SWAT predictions and overcome the limitations of a single model. By integrating SWAT’s detailed physical process portrayal with Transformer’s powerful time-series analysis, the coupled model significantly improved streamflow prediction accuracy. The proposed models offer more accurate and reliable predictions for optimal water resource management, which is crucial for sustainable economic and societal development.

Evaluation of flood metrics across the Mississippi-Atchafalaya River Basin and their relation to flood damages.

Societal risks from flooding are evident at a range of spatial scales and climate change will exacerbate these risks in the future. Assessing flood risks across broad geographical regions is a challenge, and often done using streamflow time-series records or hydrologic models. In this study, we used a national-scale hydrological model to identify, assess, and map 16 different streamflow metrics that could be used to describe flood risks across 34,987 HUC12 subwatersheds within the Mississippi-Atchafalaya River Basin (MARB). A clear spatial difference was observed among two different classes of metrics. Watersheds in the eastern half of the MARB exhibited higher overall flows as characterized by the mean, median, and maximum daily values, whereas western MARB watersheds were associated with flood indicative of high extreme flows such as skewness, standardized streamflow index and top days. Total agricultural and building losses within HUC12 watersheds were related to flood metrics and those focused on higher overall flows were more correlated to expected annual losses (EAL) than extreme value metrics. Results from this study are useful for identifying continental scale patterns of flood risks within the MARB and should be considered a launching point from which to improve the connections between watershed scale risks and the potential use of natural infrastructure practices to reduce these risks.

Innovating on Hydrological Modelling Calibration Towards a Realistic Simulation of Climate Change Impacts on Water Resources

Innovating on Hydrological Modelling Calibration Towards a Realistic Simulation of Climate Change Impacts on Water Resources

Effect of calculation unit division in distributed hydrological models on the analysis of hydrological effects of land use change

Numerous papers have utilized distributed hydrological models to assess the hydrological effects of land use changes. However, can these models reflect actual land use changes? This study focuses on the effect of calculation unit division in distributed hydrological models on the analysis of hydrological effects of land use change. The Soil and Water Assessment Tool (SWAT) model is tested here, using the Dongjiang headwater region in southern China as the study area. Thirteen calculation unit division schemes are developed to analyze the effects of land use generalization caused by calculation unit division on runoff and sediment load simulations by SWAT. Two indices, including Land Use Identification Accuracy (LUIA) and Land Use Change Identification Accuracy (LUCIA) are employed to quantify the model’s precision in describing land use and changes under various scenarios. In addition, this study examines how differences in land use descriptions, resulting from calculation unit division, influence the hydrological effect of land use changes in the SWAT model. The findings indicate that: (1) Calculation unit division leads to the generalization of land uses identified by the SWAT model. As the sub-watershed Drainage Area Threshold (DAT) increases, the small land use types tend to aggregate into the larger land use types. (2) Land use generalization, resulting from calculation unit division, significantly affects runoff and sediment load simulations. An increase in DAT induces a significant rise in simulated runoff and sediment load (P<0.001). (3) Calculation unit division substantially impacts the model’s description of land use. LUIA and LUCIA show a significant downward trend as DAT increases. (4) Calculation unit division significantly alters the analysis results of hydrological effects of land use changes. Different calculation unit division schemes yield different analysis results of hydrological effects of land use changes. These results demonstrate that the division of calculation units in the SWAT model leads to land use generalization, which impacts the model’s description of land use changes and subsequently affects the assessment of the hydrological effects of land use changes. The research results imply that when analyzing the hydrological effects of land use change, a more refined calculation unit division scheme should be adopted as far as possible to improve the accuracy of model’s description of land use change and reduce the uncertainty of model simulation results.

Runoff Estimation in Kajurli Watershed Using SCS-CN and GIS Techniques

The study focuses on estimating surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method, combined with Remote Sensing (RS) and Geographic Information System (GIS) techniques, for the Kajurli Watershed in Ratnagiri District, Maharashtra. Surface runoff is a key factor influencing agricultural productivity, soil erosion, and water management at the watershed level. The SCS-CN method, a widely used tool for runoff estimation, utilizes rainfall, land use, and soil type data. By integrating RS and GIS, the study achieves a more accurate assessment of land use and soil characteristics, which are critical inputs for runoff calculations. The Kajurli Watershed falls under hydrological soil group B, known for moderate runoff potential. The study uses 33 years of historical rainfall data (1990-2022) to compute annual runoff using the SCS-CN method. Key parameters such as Curve Numbers (CN) are derived based on land use, hydrological soil groups, and antecedent moisture conditions (AMC). The average annual rainfall in the watershed is found to be 3392.8 mm, with 39.69% of this rainfall contributing to surface runoff. The integration of RS and GIS simplifies spatial analysis, enabling accurate and efficient runoff estimation. The study highlights the sensitivity of the SCS-CN method to CN values, emphasizing the importance of precise land use and soil data for reliable hydrological modelling. These findings offer crucial insights for water resource management, flood control, and agricultural planning in the region.

Comparative analysis of lumped and semi-distributed hydrological models in humid Mediterranean environments

ABSTRACT This study conducts a comparative analysis of four hydrological models—GR4J, HBV-light, HEC-HMS, and WEAP—to evaluate their effectiveness in simulating surface water responses in humid Mediterranean climates. The Longaví basin, located in the Maule River basin of central Chile, was selected for its characteristic climate. The models used lumped and semi-distributed approaches to simulate the basin’s hydrological cycle. The study area was divided into three zones, considering climate, soil properties, and altitude. Results showed that GR4J, HEC-HMS, and WEAP performed well during the winter months (June to October), while HBV-light was more effective in summer (November to May). WEAP model, with its extensive parameterization, effectively captured streamflow variability during the rainy season. The Differential Split Sample Test indicated that GR4J and WEAP offered the best fit for analysing climatic variability. These models are highly recommended for climate change impact studies, given their strong performance in capturing seasonal hydrologic responses across similar regions.

Estimating soil moisture from environmental gamma radiation monitoring data

AbstractSoil moisture (SM) information is invaluable for a wide range of applications, including weather forecasting, hydrological and land surface modeling, and agricultural production. However, there is still a lack of sensing information that adequately represents root‐zone SM for longer periods and larger spatial scales. One option for root‐zone SM observation is terrestrial gamma radiation (TGR), as it is inversely related to SM. Hence, the near real‐time data of more than 5000 environmental gamma radiation (EGR) monitoring stations archived by the EUropean Radiological Data Exchange Platform (EURDEP) is a potential source to develop a root‐zone SM product for Europe without extra investments in SM sensors. This study aims to investigate to what extent the EURDEP data can be used for SM estimation. For this, two EGR monitoring stations were equipped with in situ SM sensors to measure reference SM. The terrestrial component of EGR was extracted after eliminating the contributions of rain washout and secondary cosmic radiation, and used to obtain a functional relationship with SM. We predicted the weekly volumetric SM with a root mean square error of 7%–9% from TGR measurements. Nevertheless, we believe that this technique, due to its greater penetration depth and long data legacy, can provide useful data complementary to satellite‐based remote sensing techniques to estimate root‐zone SM at the continental scale.

Evaluation of Groundwater Resources in the Middle and Lower Reaches of Songhua River Based on SWAT Model

The SWAT model primarily investigates sources of water pollution and conducts ecological assessments of surface water in contemporary hydrology and water resources research. To date, there have been limited accomplishments in the study of groundwater resources in China. The MODFLOW model currently primarily simulates groundwater levels and the migration of water quality, depending on the hydrological surface water data in the relevant area. This study aims to investigate the groundwater distribution characteristics of the middle and lower reaches of the Songhua River, a significant agricultural and grain production region in China. The research focuses on the middle and lower reaches of the Songhua River basin in Northeast China and employed the SWAT distributed hydrological model to simulate runoff. The monthly recorded runoff at Tongjiang Station in Jiamusi City was utilized to calibrate the model parameters. Consequently, the MODFLOW model was introduced to compare and assess the simulation outcomes of the SWAT model, ultimately ascertaining the distribution characteristics of shallow groundwater, groundwater recharge, recoverable volume, and groundwater levels in the Songhua River Basin. The findings indicate that: (1) The SWAT model demonstrates efficacy in the study region, achieving R2 and NS values of 0.81 and 0.76, respectively, thereby fulfilling the fundamental criteria for scientific research. The MODFLOW model exhibits strong performance in the study region, achieving a periodic R2 of 0.98 and a verification R2 of 0.97, with the discrepancy between simulated and actual groundwater levels confined to 0.6 m, thereby satisfying the criteria for scientific research. (2) In 2011, 2014, and 2016, the groundwater recharge in the middle and lower sections of the Songhua River was 24.33 × 108 m3, 30.79 × 108 m3, and 32.25 × 108 m3, respectively, aligning closely with the SWAT simulation results, while the average annual groundwater level depth was 8.17 m. (3) In the research area, groundwater recharging occurs primarily by atmospheric precipitation, while drainage predominantly transpires via groundwater as base flow, constituting 81.46%. Secondly, the recharge of shallow groundwater to deep aquifers is around 7.14%, with a minimal share attributed to vadose zone loss, constituting merely 2.1%. (4) From 2010 to 2016, the average groundwater runoff modulus of the middle and lower reaches of the Songhua River basin was 1.005 L/(s·km²), with a total recharge of 216.58 × 108 m3 and a total recoverable amount of 105.11 × 108 m3. The mean yearly supply was 25.11 × 108 m3. The total groundwater recharge was 26.54 × 108 m3 in the driest year (2011) and 33.25 × 108 m3 in the year of most ample water (2016).

Development of runoff generation models in the former USSR and Russia: a historical overview

The paper presents a historical overview of the development of hydrological models in the former USSR and Russia since the 1930s. An overwhelming number of hydrological papers were published in Russian, and they are not sufficiently familiar to the world hydrological community. The purpose of the review is to fill the gap and present the main achievements, some of which were pioneering for their time. The period under consideration is divided into three sub-periods. Pre-computer 1930-1950s were associated, first of all, with the development of models of unsteady water flow in river channels and linear flood-routing models; in 1960-1980s, many theoretical and practical foundations for modelling the processes of the hydrological cycle and the formation of river runoff were developed; and the Russian era following 1991, when several notable advancements in the considered field were also made.

Field assessment of engineered bioretention as microplastics sink through site characterization and hydrologic modeling

Green stormwater infrastructure (GSI) practices like bioretention are considered a sink for microplastics washed in from urbanized land uses-land covers, and thereby regulating the environmental dispersion of microplastics. However, the capacity of GSI in microplastic sequestration remain unclear. This work investigated the spatial distributions of microplastics within bioretention cells and their soils, concentration in the GSI groundwater monitoring well, and the overall potential of GSI as a sink for microplastics. The average microplastic concentration in GSI soils (2718 - 3833 MPs/kg dry soil) is 7−10 times higher than background soils (262 - 520 MPs/kg dry soil). The most abundant microplastics observed were polypropylene (57 %), polyethylene (17 %), and rubber/bituminous (16 %), indicating the major sources of microplastics derived from post-consumer plastics and tire wear. Soils closest to the GSI stormwater inlet sequestered the most particles, and the microplastic concentration decreased with soil depth. Our hydrologic modeling, based on measured soil and hydraulic parameters, suggested that runoff overtopping the freeboard of the bioretention cell – and potentially dispersing microplastics into the surrounding environment – was unlikely. Microplastics in the GSI groundwater monitoring well was minimal at 11 MPs/L. These results suggest that GSI can capture and sequester microplastics to mitigate microplastic pollution in the environment.