Development of the 2D/1D Coupled S-RAT Model for Flooding Simulation Dual Drainage in watershed

The urban underground pipe network plays a vital role in the city development, the construction of unreasonable urban pipe networks restricts the city development, even has a disastrous effect on the city. However, it is not desirable to overstate the investment of the urban underground pipe network excessively. It is an important subject of the city construction that how to establish reasonable urban pipe networks to meet service conditions of underground pipe networks. Because the construction of underground drainage pipe networks of most of the major cities in our country mainly comes from the 50s of last century and there are more pipe network system with the development of the city, it is the main content of our research that how to reform and construct them under the condition of existing underground pipe networks.

Urban drainage systems are an essential component of urban infrastructure that plays a critical role in managing stormwater and preventing flooding in urban areas. With the changing climate and urbanization, the challenges faced by urban drainage systems are becoming increasingly complex. Additionally, aging infrastructure further exacerbates the problem, creating anurban sewage line that must be made more resilient. Redundancy is just a fundamental characteristic of such a robust urban drainage network. Redundancy in urban drainage systems can help to ensure that the system continues to function during extreme weather events or emergencies, reducing the likelihood of flooding and damage. However, the exact locations where redundancy should be increased and its contribution to resilience are not well understood. In recent years, several studies have focused on developing frameworks for optimising urban drainage structures which account pipeline redundancy.One similar research presented a paradigm for constructing the ideal network layout for urban drainage infrastructure, which considers pipeline redundancy under consideration. The original architecture and structure of the urban drainage network was developed using emperor penguin optimizer algorithms and graph theory in the research. Complicated system modelling was done to find extra water pathways or redundancy which might well be implemented to boost resistance. The suggested approach has been utilised to the test region in Dongying City, Shandong Province, China, and its findings revealed even under rainfall above the design specification, the entire overflow capacity of such urban drainage network including pipeline redundancy significantly decreased about 20-30%, compared to the network without pipeline redundancies. The interest in creating optimization algorithms had also increased recently that can be used to design and manage urban infrastructure systems. One such algorithm is the Emperor Penguin Optimization (EPO) algorithm. EPO was a recently developed swarm-based optimization An algorithm which simulates Emperor penguins behaviour in their search for food in Antarctica. The algorithm has shown promising results in solving complex optimization problems in different fields, including engineering, computer science, and management. The EPO algorithm's key features include an emperor search strategy, local search, and randomization, enabling that to efficiently and successfully examine the search process. The algorithm's emperor penguin search strategy enables it to dynamically adjust the search parameters based on the problem's characteristics and progress. The local search feature allows it to escape local optima and explore the search space further. Finally, the randomization feature adds stochasticity to the search process, helping to ensure that the algorithm can avoid getting stuck in a sub-optimal solution. In this article, we aim to explore the potential of EPO as a tool for optimizing Urban drainage solutions which take into account pipeline redundancy. We will start by reviewing the existing Literature upon that optimization in urban drainage facilities, particularly the application of particle swarm optimization, genetic algorithms, and ant colony enhancement. The EPO algorithm will next be described in full including its working principle, key features, and the steps involved in applying it to an optimization problem. Finally, we will present a case study that applies the EPO technique was developed to optimise the network model of such an urban drainage infrastructure taking into account pipeline redundancies, and compare the results with other optimization algorithms. Through this, we aim to demonstrate the potential of EPO as a powerful tool for designing and managing urban infrastructure systems, particularly for enhancing the resilience of urban drainage systems. The study will provide valuable insights into the optimal design of urban drainage technologies which take into account pipeline redundancy, helping policymakers and urban planners make informed decisions about improvingthe adaptability of urban drainage networks. The findings can contribute to the development of sustainable and resilient urban infrastructure systems, which are essential for ensuring the well-being and prosperity of urban residents.

This research aims to determine the runoff depth using the Soil Conservation Service Curve Number (SCS-CN) method with Geographic Information Technique (GIS). A rainfall-runoff model is a mathematical model describing the rainfall-runoff relations of a catchment area, drainage basin or watershed. Remote sensing technology can augment the conventional methods to a great extent in rainfall-runoff studies. The role of remote sensing in runoff calculation is generally to provide a source of input data or as an aid for estimating equation coefficients and model parameters. The study was carried out in the Gomal River watershed about 540 km catchment areas, Latitude: 36.5155556°, Longitude: 43.5144444°. The area within the boundary of the Kurdistan region starts from north of Shahia to south west of Dohuk City. A SCS-CN method was applied for estimating the runoff depth in the semi-arid Gomal watershed. Hydrologic stream flow, soil group, slopes and land use maps were generated in a GIS environment. The curve number method was used to estimate the runoff depth for selected storm events in the watershed. Effect of slope on CN values and runoff depth was determined by using the WMS 7.1 program. The max rainfall depth with different return period was calculated and the mean annual rainfall depth for the year 1947 to 2005 of Mosul metrological station was used to calculate the runoff depth of the catchment area. The results of the WMS 7.1 program showed that the CN curve number for the area is about 80. The average annual runoff depth is equal to 311.14 mm

The Soil Conservation Service curve number (CN) method is widely used for predicting direct runoff from rainfall. However, despite the extent of cultivation on hillslope areas, very few attempts have been made to incorporate a slope factor into the CN method. The objectives of this study were (1) to evaluate existing approaches integrating slope in the CN method, and (2) to develop an equation incorporating a slope factor into the CN method for application in the steep slope areas of the Loess Plateau of China. The dataset consisted of 11 years of rainfall and runoff measurements from two experimental sites with slopes ranging from 14 to 140%. The results indicated that the standard CN method underestimated large runoff events and overestimated small events. For our experimental conditions, the optimized and non‐optimized forms of the slope‐modified CN method of the Erosion Productivity Impact Calculator model improved runoff prediction for steep slopes, but large runoff events were still underestimated and small ones overpredicted. Based on relationships between slope and the observed and theoretical CN values, an equation was developed that better predicted runoff depths with an R2 of 0·822 and a linear regression slope of 0·807. This slope‐adjusted CN equation appears to be the most appropriate for runoff prediction in the steep areas of the Loess Plateau of China. Copyright © 2005 John Wiley & Sons, Ltd.

ABSTRACTThe Natural Resources Conservation Service (NRCS)‐curve number (CN) method was originally proposed to predict runoff on small and midsize catchments, but it has also been used at the scale of erosion plots. In this case, uncertainties exist with reference to the factors, for example, scale effects, affecting the experimental CN values. In this study, the reliability of the CN method in reproducing plot runoff is analysed by using data collected at the Sparacia erosion plots (Sicily, Southern Italy), which are characterised by different sizes and steepness. This investigation aimed to test the possibility of using simulated runoff within universal soil loss equation (USLE)‐type models, including runoff as a term in the erosivity factor. This analysis pointed out that the experimentally determined value of the initial abstraction ratio of the CN method was very low (0.0001). For each plot type (i.e., fixed length and steepness), the calibration was performed for 18 combinations of three rainfall ranges (all data, rainfall depth less than the median, and exceeding the median), two calibration approaches (least‐squares and median value) and three datasets (all data, interrill, and rill). The best CN model fit was systematically produced for data with rainfall depth less than the median. The least‐squares calibration approach generally performed slightly better than the median value one. Results showed that the CN method can be considered effective only for events producing rills. The CN values generally increased with plot steepness and decreased as plot length increased. For each plot type, CN tendentially increased for increasing soil moisture before the rainfall event, but moisture and rainfall depth were able to explain a minor part (from 19.5% to 41%) of CN variance. Finally, the USLE‐MB that incorporates runoff simulated by the CN method was found to satisfactorily predict (relative standard error = 0.69, Nash and Sutcliffe Efficiency Index = 0.54) event soil loss caused by simultaneous interrill and rill erosion due to the higher rainfall depths recorded at the Sparacia station.

Rainfall-runoff models compared for tile-drained agricultural fields in the Western Lake Erie Basin, Ohio

Runoff is one of the significant hydrologic variables used in generally of the water resources applications. The Soil Conservation Service–Curve Number (SCS–CN) method is adopted for the evaluation of surface runoff in the Karur District, Tamil Nadu, India using multispectral remote sensing data, rainfall data and curve number approach. The weighted curve number is determined based on antecedent moisture condition (AMC)-II with an integration of Hydrologic Soil Groups(HSGs) and land use/ land cover categories. The daily runoff was estimated for rainfall Period September 2018 to November 2018. The results of the present study shows that the runoff depth for the study area are ranging in between 106.57 and 713.65 mm and runoff volume are ranging in between 2.75 and 126.21 mcm. In the present study, the methodology for determination of runoff for study area using remote sensing, GIS and SCS–CN method was described. Keywords: Surface runoff estimation, sub-watershed, Remote Sensing, GIS, Curve number.

ABSTRACT: The U.S. Department of Agriculture Curve Number (CN) method is one of the most common and widely used techniques for estimating surface runoff and has been incorporated into a number of popular hydrologic models. The CN method has traditionally been applied using compositing techniques in which the area weighted average of all curve numbers is calculated for a watershed or a small number of sub‐watersheds. CN compositing was originally developed as a time saving procedure, reducing the number of runoff calculations required. However, with the proliferation of high speed computers and geographic information systems, it is now feasible to use distributed CNs when applying the CN method. To determine the effect of using composited versus distributed CNs on runoff estimates, two simulations of idealized watersheds were developed to compare runoff depths using composite and distributed CNs. The results of these simulations were compared to the results of similar analyses performed on an urbanizing watershed located in central Indiana and show that runoff depth estimates using distributed CNs are as much as 100 percent higher than when composited CNs are used. Underestimation of runoff due to CN compositing is a result of the curvilinear relationship between CN and runoff depth and is most severe for wide CN ranges, low CN values, and low precipitation depths. For larger design storms, however, the difference in runoff computed using composite and distributed CNs is minimal.

Surface runoff is a crucial hydrological variable in the analysis of water infrastructure planning. A reliable method for predicting surface runoff resulting from rainfall in an ungauged watershed is the SCS CN method. This research aims to represent the effectiveness of the Curve Number (CN) method in calculating peak discharge in the Manikin Watershed. The data used for this analysis includes rainfall data from three rain stations, each with a 25-year dataset, water level data of 11 years, digital elevation model (DEM) data, land use maps, and hydrogeological maps. The SCS curve number method is the most commonly used method for the estimation of peak discharge in a watershed. The calculated flood discharge values for the Manikin River Basin, with return periods of 5, 10, 20, 25, 50, 100, 500, and 1000 years, are as follows: 60.32 m3/s, 84.74 m3/s, 111.98 m3/s, 121.41 m3/s, 153.25 m3/s, 188.79 m3/s, 287.24 m3/s, and 337.30 m3/s, respectively. The reliability testing of the method in the Manikin Watershed was determined by a Nash–Sutcliffe efficiencies (NSE) value of 0.93 and root mean square error (RMSE) value of 19.70. As a result, the Curve Number Method proves to be highly reliable in representing the peak discharge in the Manikin Watershed.

In the 1950s, the United States Department of Agriculture (USDA)–Soil Conservation Service (SCS) developed the empirical runoff curve number (CN) method, providing modelers, planners, and designers a tool for estimating runoff from rainfall accounting for losses such as evaporation, transpiration, infiltration, and surface storage. SCS originally developed the CN method to analyze primarily agricultural watersheds for watershed protection and flood prevention operations Act of 1954 (PL-83-566) projects. Today modelers world-wide use the CN method, or methods based on it, to model agricultural and urban hydrology. For more than 80 years, SCS, and its successor, the Natural Resources Conservation Service (NRCS), worked and continue to work with farmers; ranchers, State, Tribal, and local governments; and other Federal agencies to maintain healthy and productive working landscapes. In 2015, NRCS partnered with the American Society of Civil Engineers (ASCE) to develop updates to four chapters for the NRCS National Engineering Handbook, Part 630, Hydrology (NEH-630), to incorporate the latest research recommendations to revise the Ia/S (initial abstraction/maximum potential storage) ratio. This represents the first significant update to the CN method since its original development. The Curve Number (CN) Task Committee, under the ASCE–Environmental and Water Resources Institute’s Watershed Management Technical Committee was instrumental in this effort. Through the internal review process, NRCS received over 1,000 individual comments and identified items needing further review. NRCS continues to work to incorporate the internal review comments and additional update recommendations from the CN Task Committee. This paper discusses the purpose of the NEH-630, the process of developing the NEH updates, and actions taken to finalize the NEH-630 CN chapters.

The application of hydrologic modeling tools to represent urban watersheds is widespread, and calculation of infiltration losses is an essential component of these models. The curve number (CN) method is widely used in such models and is implemented in US EPA’s Storm Water Management Model (SWMM 5). SWMM 5 models can be created either using CN values computed only for the pervious fraction of subcatchments, or using the entire subcatchment area, but choice is not clearly understood. The present work evaluates the differences between these approaches in CN computation within SWMM through a comparison with field data collected in an urban watershed in Alabama and with WinTR-55. Four approaches to computing CN were considered in which the impervious fractions varied according to a threshold CN value. Results indicated that a Fully Composite approach, which computed CN from all subcatchment areas, yielded the best results for the sub-watershed with higher average CN. It was also observed that results from the approaches using CN Cut-off values of 90 and 93 were better for subcatchments with lower average CN. The comparison between SWMM 5 and WinTR-55 indicated that SWMM 5 hydrographs had larger peak flow rates, but these differences decreased with larger intensity rain events. Research findings are useful to hydrologic modelers, and in particular for setting up SWMM 5 models using CN method.

Real-time urban drainage system modelling using weather radar rainfall data

Warner, Richard C., Carmen T. Agouridis, Page T. Vingralek, and Alex W. Fogle, 2010. Reclaimed Mineland Curve Number Response to Temporal Distribution of Rainfall. Journal of the American Water Resources Association (JAWRA) 46(4): 724‐732. DOI: 10.1111/j.1752‐1688.2010.00444.xAbstract: The curve number (CN) method is a common technique to estimate runoff volume, and it is widely used in coal mining operations such as those in the Appalachian region of Kentucky. However, very little CN data are available for watersheds disturbed by surface mining and then reclaimed using traditional techniques. Furthermore, as the CN method does not readily account for variations in infiltration rates due to varying rainfall distributions, the selection of a single CN value to encompass all temporal rainfall distributions could lead engineers to substantially under‐ or over‐size water detention structures used in mining operations or other land uses such as development. Using rainfall and runoff data from a surface coal mine located in the Cumberland Plateau of eastern Kentucky, CNs were computed for conventionally reclaimed lands. The effects of temporal rainfall distributions on CNs was also examined by classifying storms as intense, steady, multi‐interval intense, or multi‐interval steady. Results indicate that CNs for such reclaimed lands ranged from 62 to 94 with a mean value of 85. Temporal rainfall distributions were also shown to significantly affect CN values with intense storms having significantly higher CNs than multi‐interval storms. These results indicate that a period of recovery is present between rainfall bursts of a multi‐interval storm that allows depressional storage and infiltration rates to rebound.

Abstract. The curve number (CN) method was developed more than half a century ago and is still used in many watershed and water-quality models to estimate direct runoff from a rainfall event. Despite its popularity, the method is plagued by a conceptual problem where CN is assumed to be constant for a given set of watershed conditions, but many field observations show that CN decreases with event rainfall (P). Recent studies indicate that heterogeneity within the watershed is the cause of this behavior, but the governing mechanism remains poorly understood. This study shows that heterogeneity in initial abstraction, Ia, can be used to explain how CN varies with P. By conventional definition, Ia is equal to the cumulative rainfall before the onset of runoff and is assumed to be constant for a given set of watershed conditions. Our analysis shows that the total storage in Ia (IaT) is constant, but the effective Ia varies with P, and is equal to the filled portion ofIaT, which we call IaF. CN calculated using IaF varies with P similar to published field observations. This motivated modifications to the CN method, called variable Ia models (VIMs), which replace Ia with IaF. VIMs were evaluated against conventional models CM0.2 (λ = 0.2) and CMλ (calibrated λ) in their ability to predict runoff data generated using a distributed parameter CN model. The performance of CM0.2 was the poorest, whereas those of the VIMs were the best in predicting overall runoff and watershed heterogeneity. VIMs also predicted the runoff from smaller events better than the CMs and eliminated the false prediction of zero-runoffs, which is a common shortcoming of the CMs. We conclude that including variable Ia accounts for heterogeneity and improves the performance of the CN method while retaining its simplicity.

A popular way for describing the link between storm rainfall depth and direct runoff is the curve number (CN) method. It is a straightforward approach that has been extensively studied and widely adopted. However, there has been less focus on the impact of slope and the initial abstraction ratio, which is a crucial factor for accurately estimating direct runoff when utilizing the soil conservation service- Curve Number (SCS-CN) method. The initial abstraction ratio is typically assumed to be 0.20, as initially proposed by the method's developers. In this study, we analyzed daily rainfall data from seventeen watersheds in different physiographic locations in the Kurdistan region of Iraq, recorded between 2022 and 2023. Our aim was to assess the effect of slope adjusted curve number and modified the initial abstraction ratio (0.1) on estimation of direct runoff. The results demonstrated that adjusting the CN for slope and using a modified initial abstraction ratio increased the estimated runoff compared to the original method (without adjustment for slope and initial abstraction ratio=0.2). Therefore, when applying the SCS-CN method, it is crucial to correct the CN for slope in steeper areas and consider the initial abstraction ratio rather than relying on the suggested value of 0.2. this study highlights the importance of considering local conditions and estimating the initial abstraction ratio based on specific watershed characteristics to enhance the accuracy of direct runoff estimation using the CN method.