Service Curve Research Articles

Assessment of LULC change and its impact on Surface Runoff using SCS-CN method for Noakhali Region, Bangladesh

Land use and land cover (LULC) change detection and its subsequent impact on hydrology are of significant importance in the field of urban planning and the sustainable management of water and land resources. The primary objective of this research work is to illustrate LULC map in a specific study area, which encompasses approximately 316.7 km² in the Noakhali region. Additionally, this study tries to examine the enduring impacts of LULC changes on surface runoff within the specified area. In order to achieve this goal, the analysis of LULC change trends in the region was conducted using Landsat OLI/TM data from 2000 to 2020. The analysis focused on five distinct classes: built-up land, vacant land, farmland, vegetation, and waterbodies. The research employs the Soil Conservation Service Curve Number (SCS-CN) model to evaluate the influence of LULC changes on surface runoff. Notably, the findings indicate a substantial increase in built-up areas from 1.05% in 2000 to 11.21% in 2020. This urban expansion is strongly correlated with a notable rise in surface runoff, as evidenced by a high correlation coefficient (R2 = 0.98) between urban expansion and modelled runoff depth. The observed increase in impervious urban areas contributes to a 2.65% rise in average runoff from 2000 to 2020, equivalent to an 8.96 mm increment in average runoff. The study underscores the profound implications for managing urban storm floods and designing urban areas, especially in rapidly expanding cities globally, with a focus on developing nations. The insights gained offer valuable information for informed decision-making in sustainable urban development and water resource management, not only in the Noakhali region but also beyond.

An optimization model for the impervious surface spatial layout considering differences in hydrological unit conditions for urban waterlogging prevention in urban renewal

The expansion of impervious surfaces during urbanization, as well as the irrationality of their spatial layout, is one of the main causes of urban waterlogging. Optimizing the spatial layout of impervious surfaces through urban renewal is important for urban waterlogging prevention. The differences in hydrological conditions among different hydrological units affect the optimization efficiency of the spatial layout of impervious surfaces in different hydrological units. Therefore, this study proposes an effective optimization model for impervious surface spatial layout, employing a hybrid approach that couples the Soil Conservation Service Curve Number (SCS-CN) model, nondominated sorting genetic algorithm 2 (NSGA2), and multiple linear programming (MLP) algorithm. This approach is applied in a case study of Guangzhou, China. The results show that (1) compared to the MLP-SCS model, the enhanced model not only provides reduced runoff coefficients but also achieves a better spatial balance of the retention water volume. (2) Compared to the conditions before optimization, medium- and high-density impervious surfaces significantly change after optimization, and the connectivity between patches is reduced. The optimized layout alters the original gradient connection method, preventing the aggregation of runoff from high-density impervious surfaces and thereby reducing the risk of urban waterlogging during heavy rain. This study can provide a reference for optimizing the spatial layout of impervious surfaces aimed at urban waterlogging prevention in urban renewal planning.

An Evaluation of the Impact of Urban Growth on Runoff in Abeokuta, Southwestern Nigeria

Abeokuta is one of the urban areas in Nigeria with high cases of runoff fatalities in recent times, indicating the need for a proper understanding of prominent runoff-generating mechanisms, as well as the causative factors. Consequently, this paper, which is focused on flood-prone settlements, is aimed at providing information on the impact of change in urban land use on runoff in the study area. The specific objectives were to determine the change in average runoff in terms of the Soil Conservation Service Curve Number (SCN-CN), and to assess the impact of urban growth on runoff in the area. The SCN-CN was derived from the 30m spatial Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). At the same time, land cover change was estimated using Landsat TM+ from 2000 and Landsat 8 OLI from 2018. Data were analysed using the Hydrologic Engineering Centre’s Hydrologic Modelling System (HEC-HMS) and ArcGIS (version 10.1). The results showed a 14% increase (from 39% in 2000 to 53% in 2018) in urban areas of selected catchments; and a relative increase in average CN from 76.9 units in 2000 to 79.9 units in 2018, suggesting an increase in runoff potential relative to the increase in urban/impermeable space in the catchment. Annual discharge depth increased from 891.84mm to 956.9mm, while peak discharge increased from 161.9m3 /s to 196.2m3 /s. Runoff in the study area tends to exhibit spatial variability that is similar to the pattern exhibited by built-up areas across the study area, suggesting that the development of built-up areas can explain runoff exacerbation in part of the area. The use of SCN-CN and satellite images makes the approach reproducible, and the mixed methods of geographic information system and hydrological model revealed the spatial variability typically hidden in stand-alone hydrological models. The paper recommends further studies with the use of less coarse datasets, as well as the implementation of policies that focus on sustainable urban growth in the region, and other cities.

Estimation of Natural Groundwater Recharge in Altun Kopri Basin NE Iraq

The main objectives of the current study are to classify and assess the climate, estimate the actual evapotranspiration and runoff, determine the soil water budget, and estimate groundwater recharge. The climate of the study area is classified as Humid to Moist and Moist to Subarid. Evapotranspiration was determined by using the Thornthwaite method, with its annual value equal to 1342.68 mm. The Runoff was estimated by using the simple and widely used method of Soil Conservation Service Curve Number, the total runoff was 127.6 mm, which represents 36.44% of total rainfall precipitation. The groundwater recharge was estimated according to the modified Thornthwaite-Mather soil water budget, which equals 58.6 mm and represents 16.74 % of the total annual precipitation. The quantity of renewal groundwater is about 47.88 MCM.

The Alteration of Flood Peak Discharge by Land Cover Change in Prek Thnot Watershed, Kampong Speu Provine, Cambodia

This study analyses the alterations of peak catchment runoff from Prek Thnot watershed using the Soil Conservation Service Curve Number (SCS-CN) methodology. Two major studies using different approaches have previously been conducted in the catchment, albeit producing inconsistent results. The Snowy Mountain Hydro-electric Authority published the first study in 1967 and found that the peak surface flow runoff over a 10-year return period was 710 m3/s. The second study was conducted by the Japanese International Cooperation Agency (JICA) between 1991 and 2006 and reported a peak catchment discharge of 1,380 m3/s. To date, there has been no discussion comparing the two studies to determine why the discharge during the later study was double that of the first. As the annual rainfall during the two study periods was similar, it is suggested that this discrepancy may be attributed to physical changes in the watershed, such as changes to forest cover. We deployed the Hydrologic Engineering Centre-Hydrologic Modeling System to simulate the peak runoff of the catchment. Our result was very similar to the JICA finding. Our data suggested that the increase in catchment discharge may be attributed to the conversion of 26% of forest cover in the catchment to agricultural land since the early 2000s. This information is useful for flood management practices, particularly with respect to managing catchment runoff in the context of rapid deforestation and the hydrological impacts of climate change within the watershed. The paper analyzes the impact of land-use changes on catchment runoff and provides valuable insights for flood management practices in the context of climate change and deforestation.

Improved Network-Calculus Nodal Delay-Bounds in Time-Sensitive Networks

In time-sensitive networks, bounds on worst-case delays are typically obtained by using network calculus and assuming that flows are constrained by bit-level arrival curves. However, in IEEE TSN or IETF DetNet, source flows are constrained on the number of packets rather than bits. A common approach to obtain a delay bound is to derive a bit-level arrival curve from a packet-level arrival curve. However, such a method is not tight: we show that better bounds can be obtained by directly exploiting the arrival curves expressed at the packet level. Our analysis method also obtains better bounds when flows are constrained with g-regulation, such as the recently proposed Length-Rate Quotient rule. It can also be used to generalize some recently proposed network-calculus delay-bounds for a service curve element with known transmission rate.

Impacts of hydrometeorological factors on discharge simulation in the North West Himalayas: a SUFI-2 algorithm-driven investigation using the SWAT model.

The Soil and Water Assessment Tool (SWAT) is a computational hydrological model extensively utilised for developing sustainable strategies and viable approaches for prudent management of water resources. The central emphasis of this study is on the utilisation of SWAT model along with SWAT-CUP (SWAT calibration toolbox) to simulate streamflow in the upper Jhelum basin, the North West Himalayas, for a period of 20years from 2000 to 2019. The global sensitivity analysis algorithm, Sequential Uncertainty Fitting 2 (SUFI-2) of SWAT-CUP, is used for sensitivity and uncertainty analysis. The optimised parameter set estimated by SUFI-2 constitutes 11 parameters that are found to be sensitive with soil conservation service (SCS) curve number (CN) being the most influential parameter followed by snowmelt base temperature. Autocorrelation analysis using the autocorrelation function was conducted on the temperature and precipitation time series data, followed by a pre-whitening procedure to remove any autocorrelation effects. Subsequently, the modified Mann-Kendall (MMK) test was applied to examine trends in the annual temperature and precipitation data. The results indicated statistically significant positive trends in both datasets on an annual scale. The results for the calibration period (2003-2014) for monthly simulation displayed good model performance at three gauging stations, Rambiara, Sangam and Ram Munshi Bagh with R2 values of 0.83, 0.847, 0.829, P factor values of 0.73, 0.76, 0.75 and R factor values of 0.61, 0.58, 0.63, respectively. The validation results for monthly simulation for the 2015-2019 period showed good model agreement with R2 values of 0.817, 0.853, and 0.836, P factor values of 0.76, 0.8, and 0.75 and R factor values of 0.62, 0.53, and 0.65, respectively. The study concludes that the SWAT hydrological model can perform satisfactorily in high mountainous catchments and can be employed to analyse the impact of land use-land cover changes and the effect of climate variation on streamflow dynamics.

Identification of potential zones on the estimation of direct runoff and soil erosion for an ungauged watershed based on remote sensing and GIS techniques

An investigation of soil and water resources is essential to determine the future scenario of water management and water resources to attain food and water security. The improper management of watersheds results in a huge amount of sediment loss and surface runoff. Therefore, the present study was carried out to estimate the surface runoff and soil erosion using the Soil Conservation Service Curve Number (SCS-CN) method and RUSLE approach, respectively. These have been estimated using geospatial technologies for the ungauged Mandri river watershed from the Kanker district of Chhattisgarh State in India. The runoff potential zones, which are defined by the area's impermeable surfaces for a given quantity of precipitation were identified based on curve numbers at the sub-watershed levels. The land use data were collected from LISS IV images of 2009. The results showed that the average volume of runoff generated throughout the 16 years (2000-2015) was 14.37 million cubic meters (mM3). While average annual soil loss was found to be 17.23 tons/ha/year. Most of the eroded area was found to be around the major stream in a drainage system of Mandri River and on higher slopes of the terrain in the watershed. This study revealed that surface runoff and soil erosion are primary issues, which adversely affected the soil and water resources in this watershed. Therefore, suitable water harvesting sites and structures can be constructed based on the potential runoff zone and severity of soil erosion to conserve the soil and water in the watershed.

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.

Integrating geographic data and the SCS-CN method with LSTM networks for enhanced runoff forecasting in a complex mountain basin

IntroductionIn complex mountain basins, hydrological forecasting poses a formidable challenge due to the intricacies of runoff generation processes and the limitations of available data. This study explores the enhancement of short-term runoff forecasting models through the utilization of long short-term memory (LSTM) networks.MethodsTo achieve this, we employed feature engineering (FE) strategies, focusing on geographic data and the Soil Conservation Service Curve Number (SCS-CN) method. Our investigation was conducted in a 3,390 km2 basin, employing the GSMaP-NRT satellite precipitation product (SPP) to develop forecasting models with lead times of 1, 6, and 11 h. These lead times were selected to address the needs of near-real-time forecasting, flash flood prediction, and basin concentration time assessment, respectively.Results and discussionOur findings demonstrate an improvement in the efficiency of LSTM forecasting models across all lead times, as indicated by Nash-Sutcliffe efficiency values of 0.93 (1 h), 0.77 (6 h), and 0.67 (11 h). Notably, these results are on par with studies relying on ground-based precipitation data. This methodology not only showcases the potential for advanced data-driven runoff models but also underscores the importance of incorporating available geographic information into precipitation-ungauged hydrological systems. The insights derived from this study offer valuable tools for hydrologists and researchers seeking to enhance the accuracy of hydrological forecasting in complex mountain basins.

Exploring the impact of climate change on flood risk at cultural heritage sites using a GIS-based SCS-CN method: A case study of Shanxi province, China

Cultural heritage sites in China are facing an increasing risk of flooding caused in part by global climate change. This paper aims to provide a new method used to predict flood risk of cultural heritage sites while considering the impact of climate change. Using a GIS-based Soil Conservation Service Curve Number (SCS–CN) method, we assessed the risk of flooding at cultural heritage sites in Shanxi Province by the end of 21st century under three different climate change scenarios, namely SSP119, SSP245, and SSP585. The results show that 268 sites are currently at risk from a 100-year storm event. By 2099, about 100 additional cultural heritage sites are projected to be threatened by flooding. The cultural heritage sites protected at the city and county level are generally more vulnerable to climate change. In addition, those along the Fen River in Taiyuan City, the Qin and Dan rivers in Jincheng City, and the upstream region of the Zhuozhang River in Changzhi City also have relatively high flood risks. These findings can provide insights for climate change adaptive action and cultural heritage conversation in Shanxi Province.

Improving Runoff Prediction Accuracy in a Mountainous Watershed Using a Remote Sensing-Based Approach

Due to the limited number and sparse distribution of meteorological and hydrometric stations in most watersheds, the runoff estimation based on these stations may not be accurate. However, the accurate determination of the Antecedent Soil Moisture (ASM) in watersheds can improve the accuracy of runoff forecasting. The objective of this study is to utilize the ASM derived from satellite imagery to enhance the accuracy of runoff estimation in a mountainous watershed. In this study, a range of Remote Sensing (RS) data, including surface biophysical and topographic features, climate data, hydrometric station flow data, and a ground-based measured SM database for the Balikhli-Chay watershed in Iran, were utilized. The Soil Conservation Service Curve Number (SCS-CN) method was employed to estimate runoff. Two approaches were used for estimating the ASM: (1) using the precipitation data recorded in ground stations, and (2) using the SM data obtained from satellite images. The accuracy of runoff estimation was then calculated for these two scenarios and compared. The mean Nash–Sutcliffe statistic was found to be 0.63 in the first scenario and 0.74 in the second scenario. The inclusion of ASM derived from the satellite imagery in the precipitation–runoff model resulted in a 51% increase in the accuracy of runoff estimation compared to using precipitation data recorded in ground stations. These findings have significant implications for improving the accuracy of ASM and runoff modeling in various applications.

On the relation between antecedent basin conditions and runoff coefficient for European floods

The event runoff coefficient (i.e. the ratio between event runoff and precipitation that originated the runoff) is a key factor for understanding basin response to precipitation events. Runoff coefficient depends on precipitation intensity and duration but also on specific basin geohydrology attributes (including soil type, geology, land cover, topography) and last but not least, antecedent (or pre-storm) conditions (i.e., the amount of water stored in the different hydrological compartments, like the river, groundwater, soil and snowpack). The relation between runoff coefficient and basin pre-storm conditions is critical for flood forecasting, yet, the understanding of where, when and how much basin pre-storm conditions control runoff coefficients is still an open question.Here, we tested the control of basin pre-storm conditions on runoff coefficient for 60620 flood events across 284 basins in Europe. To do so, we derived basin pre-storm conditions from different proxies, namely: antecedent precipitation; surface and root zone soil moisture from hydrological models, reanalyses and land surface models also ingesting satellite observations; pre-storm river discharge, and pre-storm total water storage anomalies. We evaluated the coupling strength between runoff coefficient and pre-storm conditions proxies in relation to five classes of European basins, defined based on land use and soil type (as indexed by the Soil Conservation Service curve number CN), topography, hydrology and long-term climate and tested their ability to explain stormflow volume variability.We found that precipitation explains relatively well the stormflow volumes for both small and large events but not very well the peak discharge, especially for large floods. The runoff coefficient of events shows different distributions for the five different classes and correlates well with deep soil storages (such as root-zone soil moisture and pre-storm total water storage anomalies), pre-storm river discharge, and pre-storm snow water equivalent. Overall, these correlations depend on the class. Poor correlations are found against antecedent precipitation index despite its wide use in the hydrological community. Seasonal and interannual climate variability exert a key role on the coupling strength between runoff coefficient and pre-storm conditions by inducing sharp changes in the correlation with season and climate.These results increase our understanding of the coupling between pre-storm conditions and runoff coefficients. This will aid flood forecasting, hydrological and land surface model calibration, and data assimilation. Furthermore, these findings can help us to better interpret future flood projections in Europe based on expected changes in long and short-term climatic drivers.

Ascertainment of Hydropower Potential Sites Using Location Search Algorithm in Hunza River Basin, Pakistan

The recent energy shortfall in Pakistan has prompted the need for the development of hydropower projects to cope with the energy and monetary crisis. Hydropower in the northern areas is available yet has not been explored too much. Focusing on the sustainable development goal (SDG) “Ensure access to affordable, reliable, sustainable and modern energy”, thirteen proposed sites were selected from upstream to downstream of the Hunza River for analysis. The head on all the proposed sites was determined by taking the elevation difference between the proposed turbine and the intake at all sites. The discharge on all proposed ungauged sites was determined using ArcGIS for watershed delineation and the area ratio method along with Soil Conservation Service–Curve Number (SCS-CN) by using gauged data of Hunza River provided by Water and Power Development Authority (WAPDA) Pakistan at Daniyor bridge Gilgit, Shimshal and the Passo tributaries of Hunza River. The Location Search Algorithm (LSA) approach used a multi-criteria decision-making tool (MDM) to make a decision matrix considering the location and constraint criteria and then normalizing the decision matrix using benefit and cost criteria, the relative weights were assigned to all criteria using a rank sum weighted method and the sites were ranked on the basis of the final score. The results revealed that Hunza River has a significant hydropower potential and based on the final score in the LSA approach, proposed site 13, site 4 and site 9 were concluded as the most promising sites among proposed alternatives. The proposed methodology could be an encouraging step for decision makers for future hydropower development in Pakistan.

Spatial Estimation of Runoff Using SCS-CN Embedded in GIS Environment: A Case Study of Nalgonda District of Telangana, India

The main aim of the study was to calculate runoff using the Soil Conservation Service Curve Number (SCS-CN) approach in the Nalgonda district of Telangana, India. The data required for the study was a soil map and a land use land cover map for the calculation of the curve number. The curve number ranges from 1-100 for different land use and soil condition. CN I = 62.57, CN II = 79.22, and CN III = 89.93 are the hydrological soil groups of the AMC conditions for dry, normal, and wet conditions. Rainfall is the most important factor influencing the runoff. The daily rainfall data from 2011 to 2020 were collected from the NASA POWER data viewer website based on latitude and longitude. The highest rainfall was observed during 2013 and 2020 with 987.05mm and 1136.62mm respectively. The runoff was also observed highest in the years 2013 and 2020 with 89.24mm and 89.60mm respectively. The year 2015 is absorbed as a drought year by the Telangana state government with rainfall of 451.68mm and the runoff was observed the lowest at 0.22mm. The total average annual volume of runoff calculated for the Nalgonda district is 207773154.2 m3 from the study area of 7148077000 m2. From 2011 to 2020, the observed percentage of rainfall converting to runoff in the study area was 24.22%. According to this study, it has been concluded that an increase in rainfall results in an increase in runoff. The information is useful for the water resource Engineers to plan the water harvesting methods to increase groundwater resources.

Consideration of vegetation interception of rainfall within the SCS-CN model: Application to the west bank of Dianchi Lake

Study regionThe west bank of Dianchi Lake, China. Study focusThe Soil Conservation Service Curve Number (SCS-CN) model is widely used to simulate rainfall runoff of mountain floods. The determination of the CN parameter within the SCS-CN model is key to accurately simulating runoff. CN is closely related to regional land use, soil type, antecedent moisture conditions and slope. Therefore, this study established an optimized SCS-CN model by comprehensively considering the above factors, particularly the interception of rainfall by vegetation. New hydrological insights for the region(1) The original CN value resulted in underestimation of surface runoff in the study area [Nash-Sutcliffe Efficiency Coefficient (NSE) = 0.2260], indicating that regional differences reduce the applicability of the original CN value; (2) the optimized model considering the slope, antecedent rainfall, and other single or combined factors generated simulations of runoff that were closer to observations (NSE = 0.6811); the accuracy of the CN value can be effectively improved by combining various factors; however, since the CN had not yet reached the optimal value, simulations remained inaccurate; (3) consideration of interception of rainfall by vegetation in the model greatly improved the accuracy of simulated runoff [NSE = 0.8620]; indicating that the impact of vegetation interception of rainfall on surface runoff cannot be ignored. The results demonstrated that the SCS-CN model can provide more accurate simulations of mountain flood runoff in the study area after considering multiple factors, such as vegetation interception of rainfall.

Scenario of Water Sensitive Urban Design for Cimahi City, Indonesia

Cimahi City as a part of the Upper Citarum Sub-Watershed has problems with flooding, land subsidence, and clean water crisis caused by urban development. Currently, the multidisciplinary approach to urban water management has been expanded to include the hydrological cycle in it; one of the concepts that has been developed is water-sensitive urban design (WSUD). The purpose of this study is to measure changes in surface runoff as a result of changes in infiltration and runoff processes in the water cycle, calculate planned flood volume, determine the potential location and area of five WSUD elements, and formulate and measure the capability of WSUD scenarios in Cimahi City. This study uses the algebraic average, soil conservation service curve figures (SCS-CN), log-Pearson-III distribution, planned flood volume, and geospatial analysis. Based on the results of the SCS-CN method in Cimahi City in 3 time spans (2013, 2016, and 2019), there was an increase in the CN index and a decrease in the value of surface drift (S) that reflected the potential for runoff water produced. However, at the next stage of analysis, the SCS-CN method showed a decrease in surface runoff; this was due to the decreased rainfall from 2013 to 2016, then from 2016 to 2019 in Cimahi City. The geospatial analysis resulted in the potential location and area of five WSUD elements in Cimahi City. Furthermore, after testing the effectiveness of five WSUD elements and formulating a WSUD scenario, three WSUD elements were obtained as a part of the WSUD scenario in this study, namely sediment basin, retarding basin with, and stormwater harvesting. Last, the measurement of the WSUD scenario capability shows that the WSUD scenario reduces the planned flood volume by 84%.

Simulation of the impact of land use change on surface run-off in Karst Leang Lonrong Sub-Watershed

Estimation of surface run-off in karst watersheds has not been widely carried out and the estimation method is generally developed for non-karst watersheds. This study aims to analyze the accuracy of estimation of river discharge at the outlet of the Karst Biringere Sub-watershed using the modified Soil Conservation Service Curve Number (SCS-CN) method and analyzed the impact of land use change on river discharge at the outlet of the Karst Biringere Sub-watershed. Modification of the SCS-CN method in estimating direct flow at the outlet of the Beringere Sub-watershed is influenced by the similarity of rainfall and direct flow fluctuation of river discharge. The modified SCS-CN method provides satisfactory direct flow estimation when rainfall with observed direct flow of river discharge forms a linear relationship with a strong correlation. Changes in land use with reduced forest cover into mining areas result in increased run-off and reduced storage, the larger the forest land becomes mining areas, the greater the increase in run-off and reduction in storage. Reclamation of the former mining area to be a forest reduces run-off and increases water storage.

The Efficiency of SCS-CN, HEC-1, HEC-HMS, TR55, RATIONAL, and SNYDER UNIT Hydrograph Models for Determining Peak Flood Discharge in the Upper Part of Lesser Zab Basin, Kurdistan Region, Iraq

The Unit Hydrograph (UH) and Soil Conservation Service Curve Number (SCS-CN) are prevalent methods for estimating peak flow and peak time within the hydrological river basins. Different types of data, such as gauging data, morphometric analysis, and Land Use-Land Cover (LULC), are used to derive UH for the Kanarwe River Basin (KRB), which is an off-the-Lesser Zab River Basin (LZRB). Different hydrograph models, including HEC-1 (Hydrological Engineering Centre), TR55 (Technical release 55), HEC-HMS (Hydrological Engineering Centre-Hydrological Modeling System), Rational method, and Snyder unit hydrograph, have been applied and correlated with field data. Metrological data, geological setting, and land cover were integrated into the Geographic Information Systems (GIS) and Watershed Modelling System (WMS 11.1). The peak time (Tp) and peak flow (Qp) were estimated based on the five applied models. The results for models are (Qp = 739.93 m3/sec, Tp = 20 hr), (Qp 181.4 m3/sec, Tp = 14 hr), (Qp = 800 m3/sec, Tp = 12 hr, (Qp = 341.13 m3/sec, Tp = 11.65 hr), (Qp = 443 m3/sec, Tp = 19.9 hr), (Qp = 243 m3/sec) for HEC-1, TR55, HEC-HMS, Rational method, Synder unit hydrograph, and observed data respectively. The observed model (field data) peak time and peak flow value best agreed with the peak time and peak flow value of TR55, Snyder, and Rational models. Our finding confirmed that the geomorphoclimatic unit hydrograph, such as (Snyder) is highly efficient and more realistic for estimating peak time and peak flow for large basins than other models because it relates to basin characteristics.

Use of Nonofficial Intermittent Waterfall Occurrence Data for the Validation of an Infiltration Model for Volcanic Jeju Island, Korea

This study attempts to validate an infiltration model, the Soil Conservation Service–Curve Number (SCS–CN) method, using the nonofficial intermittent occurrence data of Eongtto Falls on Jeju Island, Korea. Simply due to the limited official continuous runoff data concerning Jeju Island, the validation of a newly set SCS-CN method for Jeju Island was practically impossible. Instead, this study tries to use nonofficial data for this purpose. This study focuses on the intermittent occurrence of Eongtto Falls, which is one of the most famous tourist attractions on the island. Various records of Eongtto Falls can be collected from newspapers, personal homepages, and various social networking services. The SCS-CN method is, in this study, used to check if effective rainfall occurs or not. In fact, this approach is quite effective on Jeju Island, as most streams are fully dry during non-rain periods. Evaluation of the SCS-CN method is based on the analysis of a contingency table, which measures the consistency of the occurrence of effective rainfall events and waterfall records. Additionally, to quantify the results of the contingency table, some measures such as accuracy, hit ratio, and false alarm ratio are used. This analysis is carried out using all the rainfall events from 2011 to 2019, and the derived results confirm that the newly set SCS-CN method is far better than the conventional one used thus far.