Hydrologic Soil Group Research Articles

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.

Calculating Surface Runoff by Scs-Cn Model in the Sanjai River Basin, Jharkhand

Surface runoff prediction is a very intricate, evolving, and exponential phenomenon controlled by numerous interconnected components. Regulated by numerous types of related factors, surface runoff production is a highly complex, dynamic, and non-linear phenomena. Advancement and long-term control of water resources depend on run-off forecast. For runoff forecasts, many techniques and models are at hand; Among strategies, “Soil Conservation Services curve number(SCS- CN)” approach stands out. In order to determine potential outflow from a drainage system or region, the SCS implemented the Curve Number index. It is possible to figure out the curve number of a drainage basin by taking into account the antecedent moisture conditions(AMC), the soil, land surface use and land cover (LULC). Considered also is the Hydrological Soil Group (HSG). Four kinds of soils are offered at HSG: “A, B, C, and D”. Whereas soil type D stands for low penetration rates and greater surface flow capacity, soil category A comprises higher penetration rates below the surface and less runoff potentiality. The study's research site was the Sanjai river basin in Jharkhand. Arc Gis 10.1 and Erdas 14 software helped produce thematic layers like soil maps, LULC maps, and so on. The whole catchment was divided into sub-watersheds in order to more accurately measure runoff. The findings demonstrate that the SCS-CN simulation performs productive runoff estimates. The research demonstrates that more sustainable water use results from suitable surface runoff analysis.

Analytical derivation of optimal irrigation water depth for efficient irrigation scheduling.

Optimal irrigation water depth is a crucial parameter in irrigation engineering, often referred to as root zone depth. It is typically assumed to lie between 1 and 1.5m below the ground surface, depending on the crop and soil types as well as the practitioner's skill and experience. This approach can lead to inefficient irrigation scheduling. Coupling Richards' equation with the Soil Conservation Service Curve Number (SCS-CN) concept and using the three-phase diagram of soil column widely used in geotechnical engineering, this paper suggests an analytical expression for optimal irrigation water depth providing the maximum storage capacity of a soil depending on its hydraulic/storage properties. The results for winter wheat crop in different hydrologic soil groups show that the use of the proposed concept can lead to savings of 71.79% and 57.69% of irrigation water in sandy soils (HSG-A) compared to that used in traditional irrigation considering lump-sum 1.5m and 1m optimal irrigation water depths, respectively. In the case of silty loam soils (HSG-C), these savings can assume 52.42% and 28.62%, respectively. The proposed relation can also be of great help in volumetric assessment of field capacity, moisture content, maximum water storage capacity (of different agricultural soils), and avoiding the issue of waterlogging that may arise from over-irrigation and thus is useful in efficient irrigation scheduling as well as in sustainable agricultural water management.

Developing flood mapping procedure through optimized machine learning techniques. Case study: Prahova river basin, Romania

Study regionPrahova river basin located in the central-southern region of Romania. Study focusThis study aims to assess the susceptibility to flooding by using state-of-the-art machine learning and optimization procedures. To achieve this goal, we employed ten flood-related variables as independent variables in our machine learning models. These variables include slope angle, convergence index, distance from the river, elevation, plan curvature, hydrological soil group, lithology, topographic wetness index, rainfall, and land use. We used 158 flood locations as dependent variables in the training of four hybrid models: Deep Learning Neural Network-Statistical Index (DLNN-SI), Particle Swarm Optimization-Deep Learning Neural Network-Statistical Index (PSO-DLNN-SI), Support Vector Machine-Statistical Index (SVM-SI), and Particle Swarm Optimization-Support Vector Machine-Statistical Index (PSO-SVM-SI). Utilizing the Statistical Index method, we calculated coefficients for each flood predictor class or category. New hydrological insights for the regionThe PSO-DLNN-SI model demonstrated the best performance, achieving an AUC-ROC curve of 0.952. It's worth noting that the application of the PSO algorithm significantly enhanced the model's performance. Additionally, it's crucial to highlight that approximately 25 % of the study region exhibits a high to very high susceptibility to flood events. Taking into account the very precise results of the models applied in the present study, we can state that from a hydrological point of view, the current research contributes to a better understanding of the intensity with which floods can affect the different areas of the Prahova river basin.

Rainfall-Runoff Estimation in the North-Eastern Region of Bangladesh Using SCS-CN Method

Measuring discharge in a developing nation like Bangladesh is very important for flood prediction, land management, and sustainable development. Rainfall-induced runoff is a part of the cycle of hydrology and is required for efficient water resource planning. The most significant debatable procedure in hydrology is calculating and determining catchment surface runoff. The study aims to estimate runoff using the GIS-based Soil Conservation Service-Curve Number method (SCS – CN) method, where this hydrological model has a physical foundation and spatial distribution, and the curve number plays a key role in this model's runoff computation. The Hydrologic Soil Group (HSG) present in the study area and the land use pattern are used to find the Curve Number (CN). The calculated weighted CN for Antecedent Moisture Condition (AMC) I, II, and III for the study area was roughly 74.78, 87.12, and 94.06 respectively with the incorporation of Land use and land cover (LULC) and HSG. For total average rainfall of 35163.8mm, the SCS-CN approach determined the total average runoff 16077.55 mm for the period of 2009-2018. With a correlation coefficient of 0.966, the average rainfall and SCS-CN discharge have a significant linear relationship. The runoff regulates the amount of water that enters stream systems and returns extra precipitation to the ocean benefitting the hydrological cycle. With this runoff estimation, the quality and amount of water resources can be better understood, managed, and tracked. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 12(2), 2023, P 83-96

Integrating deep learning, satellite image processing, and spatial-temporal analysis for urban flood prediction

Urban flooding is escalating worldwide due to the increasing impervious surfaces from urban developments and frequency of extreme rainfall events by climate change. Traditional flood extent prediction and mapping methods based on physical-based hydrological principles often face limitations due to model complexity and computational burden. In response to these challenges, there has been a notable shift toward satellite image processing and Artificial Intelligence (AI) based approaches, such as Deep Learning (DL) models, including architectures like Convolutional Neural Networks (CNN). The objective of this research is to predict near real-time (NRT) flood extents within urban areas. This research integrated CNN (U-Net) with Sentinel-1 satellite imagery, Digital Elevation Model (DEM), hydrologic soil group (HSG), imperviousness, and rainfall data to create a flood extent prediction model. To detect flooded areas, a binary raster map was created using calibrated backscatter values derived from the VV (vertical transmit and vertical receive) polarization mode of Sentinel-1 imagery, which was highlighted as having a significant impact on backscatter behavior and prediction results. Application of the model was demonstrated in urban areas of Miami-Dade County, Florida. The results demonstrated the capability of the model to provide rapid and accurate flood extent predictions at a spatial resolution of 10 m, with an overall accuracy of 97.05 %, F-1 Score of 92.49 %, and AUC of 93 % in the study area. The U-Net model’s flood predictions were compared with historical floodplain data and then using GIS overlay analysis, resulting in a Ground Truth Index of 84.05 % that shows the accuracy of the model in identifying flooded areas. The research incorporated crucial flood-influencing data (including rainfall) to the flood extent prediction models and expanded the focus models beyond major rainfall events only to encompass a wider range of flood events. The presented NRT flood extent mapping model has a broad range of applicability, including, but not limited to, the continuous monitoring of flood events and their potential impacts on civil infrastructure assets (e.g., construction, operation, and maintenance of roads and bridges), early warning systems for timely evacuation and preparedness measures, and insurance risk assessment.

Surface Runoff Estimation Using SCS-CN Method for Kurumballi Sub-watershed in Shivamogga District, Karnataka, India

SCS-curve number (CN) is one of the most well-liked and commonly applied methods for estimating surface runoff. The present study aims to calculate surface runoff using SCS-CN watershed-based calculation and geospatial technology in the Kurumballi sub-watershed Shivamogga District of Karnataka, India. The study area covers about an area of 47.67 sq. km. The union of land use/land cover classification with hydrological soil groups (HSG) yields the runoff estimation by the SCS-CN curve approach. This method calculates the runoff volume from the land surface flows into the river or streams. Moreover, the study area’s delineation of runoff potential zones was done using the thematic integration method. Different thematic layers were used, including lithology, geomorphology, soil, slope, land use and land cover, drainage, surface water bodies, groundwater contour, and isohyetal maps. Furthermore, associating it with the SCS-CN technique, the total surface runoff volume of the study area was estimated. The total surface runoff volume in the study area is 21065849.7 m3. To this study, thematic integration with the SCS-CN approach to estimate runoff for watersheds is valuable for improving water management and soil conservation.

DEVELOPMENT AND ANALYSIS OF A GIS-BASED CURVE NUMBER MAP OF THE PHILIPPINES

Stormwater management requires quantitative methods to determine objective flood risk by estimating how much rainfall becomes runoff. With the absence of a locally generated runoff coefficient database in the Philippines, the standard model for surface runoff estimation is yet to be implemented using homegrown datasets. Traditionally, the empirical method is adapted from the National Resources Conservation Service (NRCS). The method quantifies rainfall-runoff relationships underscoring the combined effects of ground cover, soil hydraulic conductivity, and antecedent runoff condition (ARC) on runoff potential via a hydrologic parameter called the Curve Number (CN). Using geographic information system (GIS) tools, the Philippine CN map is developed by preprocessing and intersecting land use and land cover (LULC) and hydrologic soil group (HSG) with the CN look-up table. It was revealed that Group C soils dominate the Philippines. New data products—three raster CN maps at 25m spatial resolution—indicated the prevalence of medium to high runoff potential. National curve numbers were 61, 78, and 89 for dry, average, and wet ARCs, respectively. The outputs of this study provide access to spatially varied local runoff potential data in GIS format thus allowing direct GIS use for the swift simulation of flood damage mitigation models, among others.

An Advanced Empirical NRCS-CN Model Estimation for Ungauged Catchment Insufficient Data

The calculation of runoff is continuously considered a difficult analysis to define specific prediction methods in ungauged catchments. This leads to the implementation of an observed model to determine curve number (CN) and estimate peak discharge from ungauged basins. Therefore, this study aimed to modify an experimental NRCS model through fieldwork and conduct sensitivity analysis for relevant modeling procedures. In the analysis, the assessment of CN required land use, soil, infiltration in situ measurement, and Hydrological Soil Group (HSG) parameters for the catchment area. The determination of infiltration rate was also initially carried out using the Horton method (double-ring infiltrometer), accompanied by the evaluation of CN through soil classes and land use parameters. Based on infiltration rate and soil classification, HSG was significantly defined for the catchment area. The results showed that the analytical parameters in rainfall-runoff modeling included the Composite Curve Number (SCS-CN ( ⁄S) with a ratio value of 0.2. This was accompanied by the potential retention maximum (S) of 264.37 mm, with the initial proportional abstraction ( ) being 52.87 at an assumed preliminary coefficient of 0.2. Therefore, the CN composite estimation was 65.5, and the correlation between P and Q was evaluated using graph analysis. The trial CN ranging from 39-74 were also significantly considered to optimize the development models of HEC HMS for the best performance, proving that the improvement of CN was interrelated with discharge.

Estimation of water inflows to reservoirs of irrigation dams in the Guinea Savannah ecological zone of Ghana

ABSTRACT The study was carried out in the northern Ghana to estimate water inflows to nine reservoirs of irrigation dams for a period of 20 years (1999–2018) using the USDA Natural Resources Conservation Service Curve Number model. The key input parameters of the model were hydrologic soil group, rainfall amount, landuse/landcover, weighted curve number, antecedent moisture condition, and potential maximum soil moisture retention. The annual rainfall within the reservoir catchments was found to vary between 617.20 and 1,382.30 mm. The estimated annual runoff depths ranged from 54.10 to 125.55 mm. A high degree of positive correlation was found between rainfall and runoff depths with a coefficient of determination (R2) ranging from 79.80 to 90.70%. Hydrologic soil groups accounted for about 67.2 and 62.5% of the variation in runoff depth and percentage runoff, respectively. The estimated annual water inflows to the reservoirs ranged from 145,206 ± 125,814 m3/y at Gambibgo to 47,074,634 ± 4,395,860 m3/y at Tono. The variation in the annual water inflows and annual storage capacity recharge among the various reservoirs was noted to be influenced by reservoir size, catchment size, rainfall, and soil characteristics in the reservoir catchments.

Combining geospatial information and SCS-CN for surface runoff estimation in Rib watershed, upper Blue Nile Basin, Ethiopia

Surface runoff is the most significant environmental concern in the Rib Watershed. Hence, this study estimates runoff by combining geospatial information and the Soil Conservation Service Curve Number (SCS-CN) model in the watershed. Rainfall, land use, land cover, hydrologic soil group, maximum soil water retention, and CN values were processed using Arc GIS and ERDAS Imagine software accordingly. The model was validated using the coefficient of determination (R 2) and Nash-Sutcliffe efficiency (NSE). The R 2 (0.9861, 0.9508, and 0.9136) and NSE (0.7, 0.68, and 0.6) values for the periods (2018, 2020, and 2022), respectively, confirmed the good performance of the model. The result also showed runoff ranges from 497 mm/year to 1,258 mm/year. Therefore, the highest runoff is observed in most areas of Farta and Debre Tabor and in some parts of Lay Gayint, Fogera, and Kemkem districts. Consequently, this may result in a loss of soil moisture, a decline of surface and ground water, low crop yield and animal fodder, and unproductivity of the land. This, in turn, affects food security and the livelihoods of the community at large in the region. Therefore, well-planned watershed management practices should be put into practice by prioritizing runoff hotspot sites in the catchment.

Sensitivity analysis on stormwater management response to land cover dynamics and urban expansion of developing City in Lake Hawassa watershed, Ethiopia

AbstractHawassa is a rapidly developing city in Lake Hawassa watershed of Ethiopia. Analyzing the effect of land cover dynamics on surface runoff remains imperative to adaptive urban stormwater management. This study quantified spatial variation of land cover and sensitivity of stormwater management response. Historical 30 years of daily annual rainfall, three satellite imageries, DEM, and hydrological soil group data were analyzed. A statistical‐based combined approach of geospatial techniques and Soil Conservation Service‐Curve Number (SCS‐CN) model was employed. CN and surface runoff depth for the delineated urban watersheds were determined. The result revealed that the built‐up area increased by 30.9 km2, where the rate varies spatially. The variation of impervious land cover explains 58.6% of change in CN with coefficient of 0.352. While CN is inversely correlated with agricultural and vegetation land cover variations. The finding suggests CN explains 96.78% of the change in surface runoff with a significant correlation coefficient of 3.91. The proposed integrated model approach justifies the potential to reorganize the relationship between the spatial effect of land cover variation on surface runoff at the urban watersheds. Thus, suitable local‐specific solutions can be devised for effective management of flood risk and optimize the drainage system of urban areas.

Efficiency Evaluation of Surface Water Collection Infrastructure during Floods: Information Analysis and Zoning Management

Objective: Flood events, occurring as natural and unexpected phenomena, have become increasingly prevalent in recent decades. Assessing the probability of flood risk and creating flood zoning maps in vulnerable areas, particularly urban regions, are crucial for mitigating flood damages and managing such events. This research aims to determine the flood-prone zones along the Darakeh River, located in Tehran, the capital city of Iran, by integrating the hydraulic model HEC-RAS with Geographic Information System (GIS). Methods: After establishing the physiographic characteristics of the watershed, including area, perimeter, elevation, slope, and time of concentration, influential factors on flood occurrences such as permeability, hydrological soil groups, runoff potential, curve number, land use, and vegetation cover were identified. This information, along with other hydraulic data and flow specifications such as boundary conditions, roughness coefficients, and design discharge, was incorporated into the HEC-RAS model. The model was executed for different return periods of 2, 5, 10, 25, 50, and 100 years. Subsequently, the hydraulic model outputs were transferred to the GIS model to obtain the flood-prone zones along the Darakeh River for the aforementioned return periods. Results: As observed, in the middle section of the Khoshke Channel, which has a lower gradient, the floodplain exhibits a wider extent. Furthermore, along the course of the river and after passing through a stretch of the Khoshke Channel with a reduced gradient, the floodplain's area increases up to the location of the western flood channel. Conclusion: It is concluded that areas with lower river slopes exhibited larger flood-prone zones, highlighting the necessity of increased attention to prevent significant damages in urban areas during flood events.

Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas

This study explores and compares the predictive capabilities of various ensemble algorithms, including SVM, KNN, RF, XGBoost, ANN, DT, and LR, for assessing flood susceptibility (FS) in the Houz plain of the Moroccan High Atlas. The inventory map of past flooding was prepared using binary data from 2012 events, where “1” indicates a flood-prone area and “0” a non-flood-prone or extremely low area, with 762 indicating flood-prone areas. 15 different categorical factors were determined and selected based on importance and multicollinearity tests, including slope, elevation, Normalized Difference Vegetation Index, Terrain Ruggedness Index, Stream Power Index, Land Use and Land Cover, curvature plane, curvature profile, aspect, flow accumulation, Topographic Position Index, soil type, Hydrologic Soil Group, distance from river and rainfall. Predicted FS maps for the Tensift watershed show that, only 10.75% of the mean surface area was predicted as very high risk, and 19% and 38% were estimated as low and very low risk, respectively. Similarly, the Haouz plain, exhibited an average surface area of 21.76% for very-high-risk zones, and 18.88% and 18.18% for low- and very-low-risk zones respectively. The applied algorithms met validation standards, with an average area under the curve of 0.93 and 0.91 for the learning and validation stages, respectively. Model performance analysis identified the XGBoost model as the best algorithm for flood zone mapping. This study provides effective decision-support tools for land-use planning and flood risk reduction, across globe at semi-arid regions.

Mapping SCS hydrological soil groups over South Africa at terrain unit spatial resolution

Given the many users in South Africa of the SCS (Soil Conservation Service) hydrograph-generating technique, which requires key soils information, this paper presents a methodology to develop a detailed map (at the spatial resolution of South Africa's 27 491 terrain units) of the SCS runoff-related soil groups. In order to achieve this objective, background is first provided on the soils context within the SCS technique, as well as on the approaches to defining SCS hydrological soil groups, by assigning SCS soil groups to South African soil series. Steps when mapping SCS soil groups across South Africa at terrain unit spatial resolution are then outlined. This is followed by the presentation and interpretation of the detailed terrain unit level map of SCS soil groups for South Africa. The detailed new map illustrates the finer detail across contributing catchments countrywide now obtained from the terrain unit approach, compared to the previous map of SCS soil groups based only on the coarser soil land types.

MaxEnt machine learning model predicts high groundwater potential areas in a fractured volcanic aquifer system

Groundwater constitutes a vital resource for public water supply, and thus, it is imperative to recognize the areas of highest potential for increasing availability. The present study employs the MaxEnt model to discern the most favorable areas for locating high-yield wells in Caxias do Sul, Rio Grande do Sul, southern Brazil, where the Serra Geral Aquifer System, a fractured volcanic aquifer, emerges. This aquifer system is characterized by its heterogeneous, discontinuous, and highly anisotropic nature. A dataset comprising 83 wells with high flow rates (≥10 m³/h) was selected from the municipal registry of deep tubular wells, along with 14 factors that influence groundwater occurrence (specific capacity, transmissivity, altitude, slope, horizontal curvature, vertical curvature, relief dissection index, drainage density, distance to drainage, topographic wetness index, distance to lineament, lineament density, precipitation, and soil hydrological group). The model output was a Groundwater Potential Map, which stochastically expresses the probability of obtaining flow rates ≥10 m³/h. The map was validated through cross-validation, resulting in an average accuracy of 65.14%, and by the Receiver Operating Characteristic analysis, resulting in an Area Under the Curve value of 0.911, indicating satisfactory validation. While the MaxEnt model is widely used in ecology to model species distribution, its application in groundwater prediction remains limited, particularly in fractured aquifers associated with volcanic rocks. Apart from optimizing the use of groundwater resources, this study also enhances the understanding of natural phenomena in this type of aquifer.

Characterization of landuse and landcover dynamics and their impact on runoff generation patterns in dam catchments of Northern Ghana

Landuse alteration is a critical factor that exerts influence over hydrological mechanisms and the oversight of water resources across the globe. Northern Ghana is characterized by erratic rainfall, leading to the intense construction of small dams and dugouts. This study investigated historical landuse/cover (LULC) changes and physical factors associated with surface runoff generation within eight (8) sub-catchments namely; Guno, Gbahali, Sambu, Duago, Denugo, Kwisini, Sandu, and Nyeko, with areas of 29.5, 110, 38.5, 106.04, 67.81, 2.59, 10.56 and 120.65 km2, respectively, located in four (4) regions of northern Ghana. For LULC change detection, historical satellite imagery and geospatial data were used with the aid of remote sensing and applying supervised maximum likelihood classification to Landsat images of 1995, 2005, 2015, and 2023. Runoff factors and depths were identified based on LULC classes, hydrologic soil groups, and precipitation conditions prevailing in the areas, based on the curve number (CN) method. With regards to LULC, the results from the overall analysis gave an insight into the rampant conversion, where grassland/mix vegetation witnessed significant declines, with decreases ranging from 150 to 395.25% in different catchments. Agricultural and arable lands emerged as the dominant landuse category, indicating a remarkable expansion of 11.61–51.17 km2, from 1995 to 2023 respectively, followed by built-up areas. While grassland decreased in all catchments over the same period, in some places, open tree (savannah woodland) increased, which confirmed the implementation of the afforestation policy. The CN values offered essential insights into the hydrological characteristics of each catchment. In addition to the impact of LULC and watershed characteristics on runoff, the observed variations in runoff patterns can be attributed to interannual variability in total annual precipitation which is critical driver of runoff. The findings hold significant importance for developing effective strategies and integrated watershed management policies to contribute to better water resource management and inform sustainable landuse planning in the face of changing environmental conditions.

An evaluation on the parameters used in flood susceptibility analysis

Taşkınlar her geçen gün artan büyüklük ve sıklıklarına bağlı olarak dünyada ve ülkemizde önemi giderek artan afetlerden birisidir. Bu çalışmadaki temel amaç, taşkın duyarlılık ile ilgili uluslararası ve ulusal literatürün değerlendirilmesi ve duyarlılık çalışmalarına yeni bir yaklaşım olarak sel ve taşkınların meydana geldiği yerleşmelerin su toplama havzaları temelli taşkın duyarlılık parametrelerinin belirlenmesini gerçekleştirmektir. Bu kapsamda çalışmada tarihsel taşkın envanterine bağlı olarak Bursa ili sınırları içerisinde vadi tabanı ve akarsu kenarında sel ve taşkınların yaşandığı yerleşmelerin havzalarına bağlı olarak taşkın duyarlılık analizi parametreleri belirlenmiştir. Çalışmada kullanılan temel altlık veriler, Bursa iline ait 5m çözünürlüklü Sayısal Yükseklik Modeli (SYM), 1956-2022 yılları arasını kapsayan envanter verileri, litoloji, hidrolojik toprak grupları (HTG) ve yağış (WorldClim) verileridir. Bursa il sınırları içerisinde meydana gelen tarihsel sel ve taşkın envanterine bağlı olarak 28 yerleşme ve bu yerleşmelerin su toplama havzaları belirlenmiş ve bu havzalara sel ve taşkının oluşmasında hazırlayıcı 12 parametre uygulanmıştır. Taşkın hazırlayıcı parametreler sınıflandırma aşamasında 0-1 arasında normalize edilerek ortaya çıkan sonuca göre taşkın duyarlılık için parametre katsayıları oluşturulmuştur. Sonuç olarak envantere göre maksimum etkiye sahip parametreler; çatallanma oranı (R_b), drenaj yoğunluğu (D_d), akım toplanma zamanı (T_c), eğim, topografik nemlilik indeksi, akarsu güç indeksi, hidrolojik toprak grupları, olarak belirlenmiştir. Bu çalışma ile taşkın duyarlılık analizinde önceki çalışmalardan farklı olarak envantere bağlı ve yerleşim temelli havzalardan taşkın duyarlılık parametreleri belirlenmiştir.

Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality in Data-Scarce Regions.

This research delves into the efficacy of machine learning models in predicting water quality parameters within a catchment area, focusing on unraveling the significance of individual input variables. In order to manage water quality, it is necessary to determine the relationship between the physical attributes of the catchment, such as geological permeability and hydrologic soil groups, and in-stream water quality parameters. Water quality data were acquired from the Iran Water Resource Management Company (WRMC) through monthly sampling. For statistical analysis, the study utilized 5-year means (1998-2002) of water quality data. A total of 88 final stations were included in the analysis. Using machine learning methods, the paper gives relations for 11 in-stream water quality parameters: Sodium Adsorption Ratio (SAR), Na+, Mg2+, Ca2+, SO42-, Cl-, HCO3-, K+, pH, conductivity (EC), and Total Dissolved Solids (TDS). To comprehensively evaluate model performance, the study employs diverse metrics, including Pearson's Linear Correlation Coefficient (R) and the mean absolute percentage error (MAPE). Notably, the Random Forest (RF) model emerges as the standout model across various water parameters. Integrating research outcomes enables targeted strategies for fostering environmental sustainability, contributing to the broader goal of cultivating resilient water ecosystems. As a practical pathway toward achieving a delicate balance between human activities and environmental preservation, this research actively contributes to sustainable water ecosystems.

CN-N: A Python-based ArcGIS Tool for Generating SCS Curve Number and Manning’s Roughness

Water resources engineers and geospatial analysts often face the challenge of spatially estimating parameters such as the Soil Conservation Service (SCS) Curve Number (CN) and Manning’s roughness number (n), which are critical for predicting runoff and streamflow in hydrologic studies. Addressing the above challenge, this paper presents an innovative ArcMap tool developed using Python. This tool streamlines the SCS-CN and Manning’s n spatial calculations and is designed to handle large datasets, even at the scale of the entire US. Additionally, it offers the unique capability of geoprocessing mixed soil types and seamlessly integrating data if the watershed spans over different states. Our tool automates the integration of land cover data, hydrologic soil group data, and hydrologic boundaries. The tool reads watershed boundaries and uses the National Land Cover Database (NLCD) and the Gridded Soil Survey Geographic Database (gSSURGO) to develop SCS-CN and Manning’s n spatial layers. The tool also offers users the unique flexibility to add any desired values for CN or Manning’s n in the form of a so-called lookup table, which is a great help with the iterative process of calibrating hydrologic or hydraulic models. Our tool addressed one of the major limitations of its predecessors, acknowledging the existence of mixed hydrologic soil groups, e.g., B/C or C/D, and allowing for user adjustments to address hydrologic or hydraulic models’ calibration needs. The tool was developed with a flexible framework to incorporate additional spatial parameters soon, such as the spatial green-ampt parameters. With a user-friendly interface and integration capabilities, the tool is invaluable for hydrologic and hydraulic studies at local, regional, and global scales.