Hydrological Parameters Research Articles

Advancing SWAT Model Calibration: A U-NSGA-III-Based Framework for Multi-Objective Optimization

In recent years, remote sensing data have revealed considerable potential in unraveling crucial information regarding water balance dynamics due to their unique spatiotemporal distribution characteristics, thereby advancing multi-objective optimization algorithms in hydrological model parameter calibration. However, existing optimization frameworks based on the Soil and Water Assessment Tool (SWAT) primarily focus on single-objective or multiple-objective (i.e., two or three objective functions), lacking an open, efficient, and flexible framework to integrate many-objective (i.e., four or more objective functions) optimization algorithms to satisfy the growing demands of complex hydrological systems. This study addresses this gap by designing and implementing a multi-objective optimization framework, Py-SWAT-U-NSGA-III, which integrates the Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III). Built on the SWAT model, this framework supports a broad range of optimization problems, from single- to many-objective. Developed within a Python environment, the SWAT model modules are integrated with the Pymoo library to construct a U-NSGA-III algorithm-based optimization framework. This framework accommodates various calibration schemes, including multi-site, multi-variable, and multi-objective functions. Additionally, it incorporates sensitivity analysis and post-processing modules to shed insights into model behavior and evaluate optimization results. The framework supports multi-core parallel processing to enhance efficiency. The framework was tested in the Meijiang River Basin in southern China, using daily streamflow data and Penman–Monteith–Leuning Version 2 (PML-V2(China)) remote sensing evapotranspiration (ET) data for sensitivity analysis and parallel efficiency evaluation. Three case studies demonstrated its effectiveness in optimizing complex hydrological models, with multi-core processing achieving a speedup of up to 8.95 despite I/O bottlenecks. Py-SWAT-U-NSGA-III provides an open, efficient, and flexible tool for the hydrological community that strives to facilitate the application and advancement of multi-objective optimization in hydrological modeling.

Projection of land use and land cover changes based on land change modeler and integrating both land use land cover and climate change on the hydrological response of Big Creek Lake Watershed, South Alabama

Changing land use/land cover (LULC) and climate substantially affect the hydrological components of a watershed. This study explored the future impact of the hydrological responses due to the changing LULC and climate on the Big Creek Lake watershed in Alabama, USA, from 2021 to 2050 using the Soil and Water Assessment Tool (SWAT). Five climate model datasets were used under the moderate scenario (Representative Concentrative Pathways 4.5) and the extreme scenario (Representative Concentrative Pathways 8.5), and the datasets were downscaled and bias-corrected. In addition, changing the LULC of five categories was predicted by Cellular Automata Markov (CA- Markov). With these data combined with the elevation (Digital Elevation Model), soils, and weather data, the SWAT model was calibrated and validated for the studied watershed to quantify how climate change will affect streamflow, nitrogen, and phosphorus. Our results indicate streamflow will increase due to the 50-acre increase in urban LULC. As streamflow increases, the percolation, surface runoff, lateral flow, groundwater flow, and water yield will also increase because the streamflow impacts these hydrological components. Moreover, the increase rate in streamflow is the same for all the components for January, February, and March. Therefore, there is a strong correlation between these months. On the contrary, evaporation will be high in May, June, and July because of the increasing temperature and streamflow. However, the changes in the water hydrological parameters and total nitrogen and phosphorus will be more intense in RCP8.5 than in RCP4.5.

Response time of fast flowing hydrologic pathways controls sediment hysteresis in a low-gradient watershed, as evidenced from tracer results and machine learning models

Hydrologic controls on the timing of sediment transport and sediment hysteresis patterns remain an open area of investigation in hydrology, especially for low-gradient watersheds with substantial instream sediment deposition. Sediment hysteresis, which describes the mismatch between hydrograph peak and sedigraph peak, aids with elucidation of the mechanisms of sediment transport in watersheds. Most frequently, the controls of hysteresis are attributed to the proximity of sediment sources to monitoring locations in a watershed. However, this assumption, while widely applied, is infrequently verified. We investigated the controls of sediment hysteresis in a low gradient system located in the Bluegrass Region of central Kentucky, USA. Turbidity and conductivity sensors installed at the basin outlet provided data to quantify sediment hysteresis and separate hydrologic flow pathways (i.e., by describing the source of water delivered to the watershed’s outlet) using a tracer-based approach. Predictive hydrologic parameters, including hydrologic pathways, event magnitude, and antecedent conditions, were estimated and grouped based on hydrologic similitude. Thereafter, we identified parameters required to predict sediment hysteresis using a tailored ensemble feature selection approach coupled with three machine learning algorithms—Random Forest, K-Nearest Neighbors, and Gradient Boosted Trees. Results from the analysis of 68 storm events occurring over a two-year period showed that clockwise events accounted for 85 % of the total sediment yield despite comprising only 53 % of the events. The hysteresis index (HI) can be predicted (r = 0.8, RMSE = 0.12) using three, out of the thirty-nine hydrologic parameters considered. The most important predictors of HI reflect the volume of event rainfall and the relative proportions of new water (i.e., water derived from precipitation during the storm event) and old water (i.e., water previously stored in the watershed) comprising the hydrograph. Further analyses reveal that new water timing—which changes with the rainfall volume—and sediment timing are closely linked, suggesting that variations in the hysteresis patterns are controlled by changes in the response time of fast flowing water pathways. This implies that hydrologic pathways, as opposed to sediment proximity to the watershed outlet, control sediment hysteresis in this watershed. These results have important implications for better understanding the mechanisms controlling sediment transport at the watershed scale.

Decision Support Framework for Water Quality Management in Reservoirs Integrating Artificial Intelligence and Statistical Approaches

Planning, managing and optimising surface water quality is a complex and multifaceted process, influenced by the effects of both climate uncertainties and anthropogenic activities. Developing an innovative and robust decision support framework (DSF) is essential for effective and efficient water quality management, so it can provide essential information on water quality and assist policy makers and water resource managers to identify potential causes of water quality deterioration. This framework is crucial for implementing actions such as infrastructure development, legislative compliance and environmental initiatives. Recent advancements in computational domains have created opportunities for employing artificial intelligence (AI), advanced statistics and mathematical methods for use in improved water quality management. This study proposed a comprehensive conceptual DSF to minimise the adverse effects of extreme weather events and climate change on water quality. The framework utilises machine learning (ML), deep learning (DL), geographical information system (GIS) and advanced statistical and mathematical techniques for water quality management. The foundation of this framework is the outcomes from our three studies, where we examined the application of ML and DL models for predicting water quality index (WQI) in reservoirs, utilising statistical and mathematical methods to find the seasonal trend of rainfall and water quality, exploring the potential connection between streamflow, rainfall and water quality, and employing GIS to show the spatial and temporal variability of hydrological parameters and WQI. Three potable water supply reservoirs in the Toowoomba region of Australia were taken as the study area for practical implementation of the proposed DSF. This framework can serve as a comprehensive mechanism to identify distinct seasonal characteristics and understand correlations between rainfall, streamflow and water quality. This will enable policy makers and water resource managers to enhance their decision making processes by selecting the management priorities to safeguard water quality in the face of future climate variability, including prolonged droughts and flooding.

Quantitative and Weight Indicators of African Catfish Eggs and Larvae Development under Exposure to the Low-Power Ultrasound

Introduction. The growth of the world population and changing climate conditions on the planet induce the search for the innovative methods and technologies capable to increase the productivity and efficiency of the agricultural sector, and in particular — industrial fish-farming. In recent years, ultrasound has become one of such methods, widely used in many industries due to its unique properties and capabilities. In fish-farming, the use of ultrasound can significantly improve the processes of fish breeding by increasing the growth rate, improving digestion and overall health of fish, however, the issue of biostimulation of eggs and larvae with low-power ultrasound remains unexplored. The aim of this work is to study the biostimulation of eggs and larvae of African (clarias) catfish with ultrasound in aquariums as an advanced method of improving the growth and survival of this biological object at the early stages of its development, as well as a potential method for preventing the infectious and invasive diseases.Materials and Methods. The object of this study is an African clarias catfish, also known as the marbled clarias catfish (Clarias gariepinus). During the experiment, carried out at the fish-farm the “Marbled Catfish” Fish Farm” LLC (Lipetsk) from March to September 2023, 4 groups were formed — three experimental and one control. The eggs and larvae of the African catfish were exposed to the low-power ultrasound, in the experimental groups the exposure lasted for 30 s, 60 s and 120 s, respectively; the control group didn’t undergo any ultrasonic treatment. Biostimulation was carried out with a low-power submersible source of ultrasonic waves (0.243 W/cm2) and was performed 6 times. Sorting was carried out on the 15-th day from the start of incubation. In total, four series of experiment were carried out.Results. The first, second and third groups in all series of the experiment, according to sorting results, contained the largesized larvae of the African catfish in a percentage ratio of 44 to 46%; the percentage of the large-sized larvae in the control group was 19%. In terms of average-size, sorting gave the following result: in 1–3 experimental groups — from 52 to 54% of the total number of larvae; in the control one — 72%. For small-sized larvae, the following values were obtained: in groups 1–3 — from 2 to 3%, in the control group — 9% of the total number of larvae in the groups and series, respectively.Discussion and Conclusion. The growth and development of African catfish eggs and larvae are greatly influenced by the hydrological parameters of water: temperature, oxygenation, illumination, pH, hardness, content of hydrocarbonates, phosphates, nitrates and other chemical elements. In addition, fertilization of eggs may occur unevenly due to non-uniform mixing of eggs and milt, quality and maturity of eggs during fertilization, which can result in different quantitates of the catfish larvae output. The exposure of eggs and larvae to the ultrasound in the experiment resulted in an increase of a number of large-sized larvae, which is favourable for obtaining the fish seed material. Timely sorting of fish seed material before transferring an African catfish to the closed water supply systems reduces the cannibalism during further cultivation. The use of ultrasound in fish-farming requires further study to identify the optimal frequency of treatment and the effect on the commercial fish immunity and growth rate, which will foster the satisfaction of the growing needs of the population in the high-quality products of fishing industry.

Integrated geophysical and GIS approaches for groundwater potential assessment: a case study of Aladja, Delta State, Nigeria

ABSTRACT This study explores the groundwater potential in Aladja, Udu, Local Government Area of Delta State, Nigeria, using an integrated approach combining electrical resistivity techniques with Remote Sensing (RS) and Geographic Information System (GIS) methods. The primary aim is to delineate aquifers and comprehensively assess groundwater resources. Methods include Vertical Electrical Sounding (VES) to gather resistivity data, RS and GIS for analyzing geological and hydrological parameters such as lineament density, drainage density, slope, soil type, and land use. The resistivity values ranged from 147 to 460.50 Ωm, with aquifer thicknesses varying between 6.50 and 36.30 m. The RS and GIS analysis indicated high groundwater potential in regions characterized by significant lineament density and moderate slopes. The study highlights the complementarity of VES and RS/GIS in groundwater exploration, revealing substantial groundwater resources in the northwestern regions of the study area. This approach underscores the importance of integrating these methods for a more accurate assessment of groundwater potential. The novelty of this study lies in validating RS and GIS findings with VES data, ensuring reliable groundwater potential mapping and effective resource management. This integrated methodology offers a robust framework for sustainable groundwater resource assessment and management in similar geological settings.

Integrating hydrological parameters in wildfire risk assessment: a machine learning approach for mapping wildfire probability

Abstract The increasing occurrence of catastrophic wildfire across the globe threatens public health, community safety, ecosystem functioning, and biodiversity resilience. Wildfire risk is closely connected to shifting climatic trends and their impacts on fuel availability and flammability. Although previous research has explored the connection between meteorological conditions and wildfire probabilities, there remains a substantial gap in understanding the influence of hydrologic drivers, such as groundwater recharge, on wildfire dynamics. Both short- and long-term variations in these variables are crucial in shaping fuel conditions, and significant changes can create environments more prone to severe wildfires. This study focuses on Santa Barbara County to examine the connection between wildfire probability and various environmental factors, including meteorological and hydrological data from 1994 to 2021, topography, vegetation, and proximity to road. Using a random forest (RF) machine learning model and fine-scale data (270 m resolution) we achieved high predictive accuracy in identifying wildfire probability. Our findings confirm the important roles of short-term meteorological conditions, such as mean precipitation 12 months and relative humidity 1 month before a wildfire event, in predicting wildfire occurrence. In addition, our results emphasize the critical contribution of long-term hydrological components, such as mean deviation from the historical normal in actual evapotranspiration and recharge in the years preceding the fire, in influencing wildfire probability. Partial dependence plots from our RF model revealed that both positive and negative deviations of these hydrological variables can increase the likelihood of wildfire by controlling fuel water availability and productivity. These findings are particularly relevant given the increasing extreme weather patterns in southern California, significantly affecting water availability and fuel conditions. This study provides valuable insights into the complex interactions between wildfire occurrence and hydrometeorological conditions. Additionally, the resulting wildfire probability map, can aid in identifying high-risk areas, contributing to enhanced mitigation planning and prevention strategies.

Applying Low-Impact Development Techniques for Improved Water Management in Urban Areas

Worldwide, the increase in impervious surfaces due to urbanization has led to significant water cycle issues such as groundwater depletion, urban heat islands, and flooding. To address these challenges, Low-Impact Development (LID) techniques are increasingly being applied in stormwater management. This study focuses on Ulsan, designated as a water cycle model city in South Korea, with a particular emphasis on the highly urbanized Okgyo drainage watershed. Using the Stormwater Management Model (SWMM) version 5.1, long-term runoff simulations were conducted to evaluate the effects of LID implementation on water cycle change rates and recovery rates. The model incorporates detailed hydrological and hydraulic parameters, including inflow, runoff, infiltration, and evapotranspiration for six subcatchments within the watershed. The SWMM was calibrated and validated using 30 years of historical rainfall data (1987–2016) from the Ulsan weather station. Calibration and validation processes used the NRCS-CN (Curve Number) method to ensure accuracy in simulating runoff patterns and water balance. The study specifically evaluated the effectiveness of two LID techniques: bioretention and permeable pavements. Three scenarios were modeled: bioretention applied to 5% of the area, permeable pavements applied to 5% of the area, and a combined application of both techniques. The results showed that the combined scenario provided the best outcome, with a 7.80% reduction in surface runoff and a 14.56% improvement in water cycle health. The LID application scenario confirmed the potential to achieve the water cycle management target of handling 25.5 mm of rainfall. These findings demonstrate that the introduction of LID techniques in public spaces can significantly enhance water management. This research provides insights into effective water cycle management methods tailored to specific urban land uses, laying a foundation for future urban planning and sustainable development.

Flash floods on the northern coast of the Black Sea: Formation and characteristics

Flash floods are one of the most dangerous hydrometeorological events in the world. The current study investigates flash floods on the northern Black Sea Coast. The data about stochastic and relatively stable factors of flash flood formation (such as hydrological, meteorological, lithological, geomorphological, and anthropogenic parameters) were collected for 22 events. The main trigger of flash floods is heavy rainfall of high intensity in the region but in some cases flash flood occurrence is connected with combinations of several “non-critical” factors. The small watershed area (≤ 351 km2) of river basins experiencing flash floodspromotes very rapid flow concentration. Analysis of extreme precipitation demonstrates significant increasing trends in river basins on the Crimean Peninsula and decreasing a maximum precipitation amount in 5 days (r5d) and 1 day (r1d) in river basins in the Caucasus Black Sea Coast in the 21st century as determined by processing of Integrated Multi-satellite Retrievals for Global precipitation measurement (IMEGR) satellite data. At the same time land network data indicates increasing r5d at the Anapa and r1d at the Tuapse meteorological stations in 1961–2020. More frequent occurrence of flash floods has been suggested in the area due to statistical analysis of the longest precipitation ranges. The main reason for significant social and economic damage is uncontrolled human activity in flooded areas on the northern Black Sea Coast.

Decadal Trends and Predictive Insights into Aquatic Ecosystem Health in the Namhan River, South Korea: Longitudinal Analysis of Hydro-Environmental Factors and Stream Health Indices (2008–2022)

The main focus of this study is to explore the complex interactions between aquatic ecosystem health (AEH) and time-varying hydro-environmental factors. We measure AEH in the Namhan River of South Korea using stream health index (SHI) scores for the trophic diatom index (TDI), benthic macroinvertebrate index (BMI), and fish assessment index (FAI) for 2008 to 2022. Key hydro-environmental variables associated with AEH, including their time-lagged responses, are identified. Water quality parameters such as chemical oxygen demand (COD) are significantly and negatively correlated with TDI (), BMI (), and FAI (), highlighting the critical role of water quality in the sensitivities of diatoms, macroinvertebrates, and fish health. The apparent response lag of aquatic ecosystems to hydrological parameters, such as a one-year-lagged correlation of BMI with mean flow () and annual flow anomalies (), suggests their potential use as predictors in AEH models. A longitudinal analysis of SHIs over a decade showed consistent changes in AEH from upstream to downstream, with notable trends in the deterioration and improvement of SHIs in different areas. The upstream TDI and BMI deteriorated, while the upstream FAI improved. A multiple-linear-regression–based predictive model demonstrated efficacy in predicting AEH under various hydro-environmental conditions, achieving R2 values up to 0.85 for BMI and also has the potential to simulate the impacts of anticipated climatic and hydrological changes.

Investment cost analysis in small hydropower plants in terms of efficiency and a case study in the Euphrates and Tigris basins

Flow rate is the basic parameter for calculating the energy capacity and investment costs of small hydropower plants. Because energy costs are directly related to energy capacity. While the energy capacity increases, the investment cost of the hydropower plant increases also. However, if the energy capacity is high, the energy production will be high and the investment cost will be amortized in a short time. In this study, small rivers in the Euphrates and Tigris Basins with previously determined hydropower capacities were selected, and the investment costs of small hydroelectric power plants to be designed on these rivers were analysed. For this analysis, 10 streams were selected from the Euphrates and Tigris Basins, and only Baskoy and Celebiyan streams have flow observation stations. The flow values of the remaining 8 streams were found by regression analysis using the basin and flow values of Baskoy and Celebiyan streams. Therefore, cost–benefit analyses were carried out separately for these flows in this study. In this study, because it includes the most effective hydrological parameters as well as many technical and economical parameters, the SMART Mini Hydro Tool Program was used.

Comparative analysis of FAO dual Kc, Priestley‐Taylor and flux variance similarity methods in partitioning evapotranspiration from flood‐irrigated rice fields

AbstractAccurate quantification of evapotranspiration (ET) and its bifurcation into productive transpiration (T) and unproductive evaporation (E) are essential for the successful implementation of sustainable irrigation practices. This study compares the performances of three distinct methods for partitioning ET into E and T fluxes from flood‐irrigated rice fields. These methods include: i) the FAO dual Kc‐ETo model, which utilizes Penman–Monteith (PM) estimates of reference ET and crop‐specific scaling factors; ii) the Priestley‐Taylor (PT) method, which relies on radiation‐driven energy balance at the canopy surface; and iii) the flux variance similarity (FVS) method, which utilizes high‐frequency eddy covariance (EC) measurements of carbon and water vapour fluxes. Meteorological, phenological and hydrological parameters were monitored over two winter seasons in a paddy field, replicating site‐specific management strategies. The cumulative ETs obtained from the PM, PT and FVS methods were 445 mm, 300 mm and 356 mm, respectively, for season 1 and 428 mm, 321 mm and 375 mm, respectively, for season 2. The accuracy of the ET estimation and partition methods was assessed against independent measurements of ET and E using a lysimeter and a microlysimeter. The results demonstrated that the EC‐based FVS method offers superior accuracy in both quantifying (R2 = 0.67, RMSE = 0.70 mm) and partitioning (R2 = 0.51, RMSE = 0.76 mm) ET fluxes, followed by the PM‐based FAO dual Kc‐ETo and PT methods. Evaporation accounted for 29 ± 5% of consumptive water use, highlighting an opportunity for the implementation of water‐saving strategies, particularly during vegetative and transplantation stages. The findings of the path analysis indicate that vegetative and radiation factors influence ET variation, while meteorological and soil factors significantly impact T variation.

Identification of Flood-Prone Areas Using Geo-Informatics: A Case Study of Erbil City, Kurdistan Region, Iraq

Global flood events, intensified by an intricate interplay of natural dynamics and human activities, pose severe environmental and safety threats, driving the demand for advanced vulnerability assessments. This research innovates with a holistic geo-informatics methodology, pinpointing flood-prone regions in Erbil City, Kurdistan Region, Iraq. It harnesses the Analytic Hierarchy Process and Geographic Information System synergies, enriched with insights from multispectral satellite observations, to conduct an exhaustive analysis of key flood susceptibility indicators. These encompass geological, hydrological, meteorological, and anthropogenic parameters, integrated into a robust Geographic Information System analytical matrix. Through strategic factor analysis and prioritization, the study distills a detailed flood susceptibility cartogram, classifying regional vulnerabilities into distinct risk echelons: high (encompassing 25%), moderate (38.1%), low (28.4%), and very low (8.5%). The final flood map highlights an acute susceptibility epicenter in the northeast, contrasting with the minimal threat southwest. This demarcation reaffirms the Analytic Hierarchy Process-Geographic Information System fusion's precision in translating multifaceted data into strategic intelligence, proving instrumental for scholars, administrative frameworks, and developmental strategists. The insights garnered are pivotal in sculpting informed urban development and flood counteractive blueprints, instrumental in mitigating flood repercussions and preserving ecological and human integrities.

Deep Learning for Water Quality Prediction—A Case Study of the Huangyang Reservoir

Water quality prediction is a fundamental prerequisite for effective water resource management and pollution prevention. Accurate predictions of water quality information can provide essential technical support and strategic planning for the protection of water resources. This study aims to enhance the accuracy of water quality prediction, considering the temporal characteristics, variability, and complex nature of water quality data. We utilized the LTSF-Linear model to predict water quality at the Huangyang Reservoir. Comparative analysis with three other models (ARIMA, LSTM, and Informer) revealed that the Linear model outperforms them, achieving reductions of 8.55% and 10.51% in mean square error (MSE) and mean absolute error (MAE), respectively. This research introduces a novel method and framework for predicting hydrological parameters relevant to water quality in the Huangyang Reservoir. These findings offer a valuable new approach and reference for enhancing the intelligent and sustainable management of the reservoir.

Long-Term Dynamics of Ecological and Hydrological Parameters of the Functioning of Sturgeon Spawning Grounds in the Middle Reaches of the Ural River

Long-Term Dynamics of Ecological and Hydrological Parameters of the Functioning of Sturgeon Spawning Grounds in the Middle Reaches of the Ural River

Dynamics of phytoplankton communities in the Baltic Sea: insights from a multi-dimensional analysis of pigment and spectral data—part I, pigment dataset

This study aims to investigate the seasonal and spatial distribution of surface phytoplankton communities in the Baltic Sea, using pigment analysis and hydrological parameters. Data were collected during six oceanographic campaigns between 2005 and 2008, including high-performance liquid chromatography (HPLC) pigment characterization and hydrological measurements. The first part of this comprehensive study was focused on the HPLC phytoplankton pigment dataset in relation to hydrological conditions. The research highlighted the importance of high-quality input data for accurate taxonomic analysis. Several unsupervised machine learning approaches, such as hierarchical cluster analysis (HCA), principal component analysis (PCA), and network-based community detection analysis (NCA), were used to analyze the data and identify phytoplankton communities based on biomarker pigments. Five main phytoplankton communities were identified: diatoms, dinoflagellates, cryptophytes, green algae, and cyanobacteria. The results evidenced distinct seasonal patterns, with diatom blooms dominating in spring, cyanobacterial blooms in mid-summer, and haptophyte and dinoflagellate peaks occurring in late summer and autumn. While PCA and NCA provided consistent insights into community structure, HCA offered less clarity in distinguishing between groups. The results of the statistical analysis were then compared with those of traditional approaches such as CHEMTAX and region-specific bio-optical algorithms, providing new perspectives on the taxonomic composition of phytoplankton groups. This study provides valuable insights into phytoplankton dynamics in the Baltic Sea and the effectiveness of different analytical approaches in understanding community structure, providing metrics that can enhance current and future advancements in remote sensing, including support for hyperspectral ocean color remote sensors.

Exploring the Effects of Fissures on Hydraulic Parameters in Subsurface Flows from the Perspective of Energy Changes

Reynolds number (Re), pore water pressure (P), and water flow shear force (τ) are primary indicators reflecting the characteristics of subsurface flow. Exploring the calculation of these parameters will facilitate the understanding of the hydrodynamic characteristics in different subsurface flows and quantify their differences. Hence, we conducted a study to monitor soil water content, matrix potential, and pore water pressure in two typical soil profiles (with and without fissures). The distribution of Re, P, and τ in both matrix flow (MF) and preferential flow (PF) were calculated with an improved calculation method, focusing on their energy changes. Results showed that these hydrologic parameters are quite different between MF and PF. Re values in MF remained below 0.1, indicating lower water flow velocities, while the Re values ranged from 0.8 to 2 in PF, indicating higher flow velocities. The P values in PF was tens to hundreds of times higher than that in MF, which is mainly due to the rapid accumulation and leakage of water within soil fissures. Additionally, the larger hydraulic radius and gradient in PF also resulted in higher τ values in PF (2~6 N m−2) than in MF (0~1.5 N m−2). In PF, the pressure potential was the significant factor for τ, while τ in MF was dominated by the matrix potential and varies with the magnitude of the matrix potential gradient. This study suggests that Re, P, and τ could be considered as the major indexes to reflect dynamic characteristics of subsurface flow.

Simulation and Analysis of Water Quality Improvement Measures for Plain River Networks Based on Infoworks ICM Model: Case Study of Baoying County, China

The water environment of plain river networks can be self-cleaning to a certain extent, but if the wastewater load exceeds a certain threshold, it can disturb the natural balance and cause water pollution. This underlines the importance of water pollution control measures. However, the development of water pollution control measures requires a large number of hydrological and hydrodynamic parameters and the establishment of corresponding relationships through modelling. Therefore, this study mainly used the Infoworks ICM model to construct a detailed hydrological–hydrodynamic water environment analysis model for the Yundong area of Baoying County, Yangzhou City, China, screened the main pollution source areas and pollution time periods of the typical rivers in the study area, and proposed effective improvement measures according to the actual situation of the study area. The results show that after the synergistic effect of multiple measures, the water quality can reach the Class III standard (GB3838-2002). This study can provide a reference for the water environment management and improvement of the plain river network and has good application prospects.

River quality management: Integrating uncertainty, failure probability, and assimilation capacity

Managing river water quality is challenging due to uncertainties in hydraulic and hydrologic parameters. This study integrates the symmetric exponential function (SEF) approach for solving the advection-dispersion equation with the Monte Carlo method in MATLAB. This combination allows us to explore the river's assimilation capacity and the failure probability (Pf) of maintaining desired water quality standards. Here, Pf represents the likelihood of pollutant concentrations exceeding acceptable limits under varying river conditions. A key contribution of this study is the introduction of a novel equation, developed using the Genetic Programming (GP) soft computing tool, to calculate assimilation capacity considering the failure probability of water quality provision. This equation provides a valuable tool for risk assessment in water resource management by quantifying pollutant assimilation dynamics. Its robustness is validated through high Coefficient of Determination (R2) and Overall Index (OI) values near 1, along with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The study identifies critical river characteristics, such as flow velocity and pollutant load, significantly influencing the reliability index (β). By outlining how adjustments in these parameters can achieve a target reliability index (β=4.526), our study offers a practical approach to safeguarding river ecosystems. For example, increasing flow velocity by 76 % can shift the river from a safe state (Pf=3×10−5) to a hazardous state (Pf=1), while a 44 % decrease in velocity allows for 57 % more pollutant assimilation. These findings highlight the importance of flow control as a cost-effective strategy for mitigating high pollutant concentrations and ensuring sustainable water quality management. By integrating numerical approaches with reliability sampling methods and soft computing techniques, this study enhances understanding of river system dynamics and supports informed decision-making for protecting water resources.

Hydrological modeling using HEC-HMS model, case of Tikur Wuha River Basin, Rift Valley River Basin, Ethiopia

Modeling rainfall-runoff is widely recognized as one of the most complex types of hydrological modeling, primarily because it involves the integration of a diverse array of watershed characteristics. Due to its ability to emulate the hydrological behavior of a watershed, the modeling of rainfall-runoff plays a crucial role in predicting the runoff generated at the watershed's outlet. The present study aimed to simulate runoff by utilizing HECHMS in the Tikur Wuha River watershed situated in the Rift Valley Basin of Ethiopia. To achieve this goal, tools such as HEC-GeoHMS and ArcGIS were employed to establish the necessary input parameters for HECHMS. Various methods were implemented at different stages of the modeling process, including SCS-CN for estimating precipitation loss, SCS-UH for transforming excess rainfall, Muskingum for flood routing, and the monthly constant method for modeling base flow. The process of calibration and validation entailed the use of daily observed flow data from the periods (1990 to 2009) and (2010 to 2015) correspondingly. Nash Sutcliff Efficiency (NSE) and coefficient of determination (R2) were employed as metrics to evaluate the model's performance. The findings showed that the model exhibited high performance in both calibration and validation stages, producing values of (NSE = 0.83, R2 = 0.91) and (NSE = 0.84, R2 = 0.86) respectively. Furthermore, the Percent Bias (PBIAS) values in calibration and validation remained within acceptable ranges, registering at 2.69 % and 4.67 % respectively. After the calibration and validation of the model, the estimated peak flood discharge simulated by the model (206.3m3/s) was compared with the observed stream flow (197.1m3/s), indicating a significant similarity between the model's output and the observed data. Consequently, it can be inferred that the model exhibits a high capability in replicating hydrological parameters effectively for the Tikur Wuha watershed and other watersheds sharing similar hydrological characteristics.