Soil Conservation Service Runoff Curve Research Articles

Overcoming equifinality: time-varying analysis of sensitivity and identifiability of SWAT runoff and sediment parameters in an arid and semiarid watershed.

The phenomenon of "equifinality for different parameters" limits the link between parameters and catchment characteristics; however, solving the equifinality problem is a major challenge in the development, generalization, and application of a model. This study focused on the Yanhe River Watershed to investigate the time-varying characteristics of sensitivity and identifiability of SWAT (Soil and Water Assessment Tool) runoff and sediment parameters based on the Sobol' and generalized likelihood uncertainty estimation methods. The results indicate that (i) the nondominated sorting genetic algorithm-II has good adaptability and reliability in parameter calibration of the SWAT model in the Yanhe River Watershed. The evaluation indicators (Nash-Sutcliffe efficiency, R2, and percent bias) of monthly runoff and sediment in the Ganguyi hydrological station were all satisfactory per the SWAT model during the calibration and validation periods. (ii) The interaction between runoff and sediment parameters is a crucial reason for parameter sensitivity, which has obvious time-varying characteristics and is largely dependent on precipitation in the Yanhe River Watershed. Temporal and spatial variability of precipitation should be considered in the detailed analysis of parameter identifiability, and watershed managers should not ignore changes in the runoff process when regulating sediment. (iii) Only a relatively small number of parameters can be identified in the runoff and sediment simulation process of the Yanhe River Watershed, such as CN2 (initial soil conservation service runoff curve number for moisture condition II), CH_K2 (effective hydraulic conductivity in main channel alluvium), ALPHA_BF (baseflow alpha factor), USLE_C (cover and management factor), USLE_P (support practice factor), and USLE_K (soil erodibility factor), due to high surface runoff, reduced lag time, reduced low flows, increased peak flows, and channel erosion, respectively. More importantly, there is a strong positive correlation between parameter identifiability and parameter sensitivity. Both are effective methods of parameter diagnosis, but the identifiability of parameters is not equivalent to its sensitivity. Our results strongly suggest that a detailed parameter sensitivity and identifiability analysis is a critical step in improving hydrological model performance to reduce the risk of "equifinality for different parameters" while articulating all relevant hydrological processes.

A Bayesian-based multilevel factorial analysis method for analyzing parameter uncertainty of hydrological model

In this study, a Bayesian-based multilevel factorial analysis (BMFA) method is developed to assess parameter uncertainties and their effects on hydrological model responses. In BMFA, Differential Evolution Adaptive Metropolis (DREAM) algorithm is employed to approximate the posterior distributions of model parameters with Bayesian inference; factorial analysis (FA) technique is used for measuring the specific variations of hydrological responses in terms of posterior distributions to investigate the individual and interactive effects of parameters on model outputs. BMFA is then applied to a case study of the Jinghe River watershed in the Loess Plateau of China to display its validity and applicability. The uncertainties of four sensitive parameters, including soil conservation service runoff curve number to moisture condition II (CN2), soil hydraulic conductivity (SOL_K), plant available water capacity (SOL_AWC), and soil depth (SOL_Z), are investigated. Results reveal that (i) CN2 has positive effect on peak flow, implying that the concentrated rainfall during rainy season can cause infiltration-excess surface flow, which is an considerable contributor to peak flow in this watershed; (ii) SOL_K has positive effect on average flow, implying that the widely distributed cambisols can lead to medium percolation capacity; (iii) the interaction between SOL_AWC and SOL_Z has noticeable effect on the peak flow and their effects are dependent upon each other, which discloses that soil depth can significant influence the processes of plant uptake of soil water in this watershed. Based on the above findings, the significant parameters and the relationship among uncertain parameters can be specified, such that hydrological model’s capability for simulating/predicting water resources of the Jinghe River watershed can be improved.

Evaluation of the Langmuir model in the Soil and Water Assessment Tool for a high soil phosphorus condition

Phosphorus adsorption by a water treatment residual was tested through Langmuir and linear sorption isotherms and applied in the Soil and Water Assessment Tool (SWAT). This study uses laboratory and greenhouse experimental Phosphorus data to evaluate the performance of a modified version of SWAT for high P concentration simulation development. A combination of vegetative filter strips (VFS) and water treatment residuals (WTR) were used to reduce soluble P runoff concentration. To effectively simulate the concentration measured in the experiments, a Langmuir model was incorporated into SWAT. The effective depth of surface runoff and soil interaction over a small subwatershed (0.07 km2) was based on an experimentally determined WTR rate of 64 Mg ha−1. A continuous flow method for rapid measurement of soil hydraulic properties was used to determine soil water contents and hydraulic conductivities. A parameter sensitivity analysis to model output indicated that the Soil Conservation Service runoff curve number for moisture soil condition II was the most responsive to change for this subwatershed. The SWAT model yielded significantly different soluble P and P leached amounts once the Langmuir model was included as an option to the linear P sorption model. With this new adaptation, SWAT was able to simulate higher P concentrations as validated by laboratory and greenhouse experimental data. The laboratory and greenhouse assessment of the WTR provided insight into the data required to evaluate the incorporation of the Langmuir model into a watershed scale tool. The choice of P model simulation (between the Freundlich option already in SWAT and the Langmuir method) was included in a sensitivity analysis performed to define the model sensitivity to selected parameters. This study provides one case of higher P conditions that SWAT was able to more adequately simulate with the Langmuir model incorporated.

Bayesian 모형을 이용한 단일사상 강우-유출 모형의 불확실성 분석

본 연구에서는 국내외에서 범용되고 있는 단일강우사상 모형인 미육군공병단의 HEC-1 모형을 이용하여 대청댐 유역의 실측 강우-유출 사상을 중심으로 강우-유출 모의를 수행하였으며, 매개변수 검정에는 실제 대청댐의 시간당 유입량을 기준으로 검정을 실시하였다. HEC-1 모형에는 매개변수를 자동으로 최적화시키는 프로그램이 내장되어 있으나 본 연구의 대상유역과 같이 다수의 소유역이 있는 경우, 매개변수 추정시 매개변수 중 일부는 수렴되지 못하고 발산하는 문제가 있었으며, 첨두유량의 추정능력 역시 저하되는 문제를 보였다. 따라서 이러한 HEC-1 모형의 매개변수의 불확실성을 고려하기 위한 방안으로 Bayesian 모형을 HEC-1모형에 연동시켜 활용하였으며, 기존 HEC-1 강우-유출 모형에 적용할 수 있는 매개변수 최적화 및 불확실성 정량화를 위해 HEC-1 강우-유출 모형 매개변수는 SCS 1개, Clark 단위도 2개를 Bayesian MCMC 기법을 적용하여 매개변수간 조건부확률로 모의발생을 한 후, Bayesian 모형으로부터 각 매개변수의 사후분포(posterior distribution)를 추정하여 사후분포의 추정이 매개변수의 불확실성 정량화를 수행하였다. 본 연구를 통해 제안된 BHEC-1 모형을 대상으로 대청댐 유역에 실측 강우-유출사상에 대해서 모형의 적합성을 평가한 결과, 7개 유역의 21개의 매개변수가 해의 발산 없이 안정된 매개변수 추정이 가능하였다. 한편, Bayesian 모형을 근간으로 하기 때문에 최종결과로서 매개변수들의 사후분포(posterior)의 추정이 가능하여 향후 홍수빈도곡선 유도, 댐 위험도분석과 기후변화 문제와 같은 다양한 수문학적 문제의 연구에 적용 가능할 것으로 전망된다. The study applies a hydrologic simulation model, HEC-1 developed by Hydrologic Engineering Center to Daecheong dam watershed for modeling hourly inflows of Daecheong dam. Although the HEC-1 model provides an automatic optimization technique for some of the parameters, the built-in optimization model is not sufficient in estimating reliable parameters. In particular, the optimization model often fails to estimate the parameters when a large number of parameters exist. In this regard, a main objective of this study is to develop Bayesian Markov Chain Monte Carlo simulation based HEC-1 model (BHEC-1). The Clark IUH method for transformation of precipitation excess to runoff and the soil conservation service runoff curve method for abstractions were used in Bayesian Monte Carlo simulation. Simulations of runoff at the Daecheong station in the HEC-1 model under Bayesian optimization scheme allow the posterior probability distributions of the hydrograph thus providing uncertainties in rainfall-runoff process. The proposed model showed a powerful performance in terms of estimating model parameters and deriving full uncertainties so that the model can be applied to various hydrologic problems such as frequency curve derivation, dam risk analysis and climate change study.

Hydrographs and Estimates of Scour Depth Excess for Pier Scour Prediction

A practical procedure for using the Scour Rate in Cohesive Soils (SRICOS) method to predict bridge pier scour depth in ungauged streams is presented. The procedure is illustrated by the Interstate 90 westbound bridge over Split Rock Creek in South Dakota. Instead of generating a continuous hydrograph for the design service life of a bridge, the simplified approach considered a series of maximum annual floods. The parameters of peak flow distribution were calculated with estimates of peak flow magnitude obtained from regional regression equations. Flood duration was estimated with a rainfall atlas and a Soil Conservation Service runoff curve number based on a design storm. A set of equally probable future hydrographs was generated and used with the SRICOS method to compute the final scour depth. The predicted final scour depth followed the normal distribution. The risk values associated with different design scour depths were calculated with the mean and standard deviation of the normal distribution. The scour predictions were consistent with a bridge founded on highly erosion-resistant cohesive soils. The cumulative effect of scour from the small floods in a long sequence of floods could be represented by an equivalent time; the scour depth produced by the whole sequence could be modeled by using only the large floods if the equivalent time were small compared with the combined duration of the large floods.

A first-order analysis of the climate change effect on flood frequencies in a subalpine watershed by means of a hydrological rainfall–runoff model

Scientific evidence indicates that a global climate change due to human activity is possible within a century. It is expected that doubling of CO 2 and increasing the amount of other greenhouse gases in the atmosphere would result in more severe weather, among other consequences. The purpose of the paper is to examine how flood frequencies and magnitudes in a mid-size subalpine watershed on the eastern slopes of the Rocky Mountains in Alberta, Canada, would change under 2×CO 2 conditions. Given the poor spatial resolution of present general circulation models (GCM) and their uncertain performance at the regional scale, a first-order analysis is carried on, in which only rainfall intensity changes are considered to have the most significant impact on future floods. Estimates of storm rainfall increases are based on the literature survey, GCM projections for the study area, and transposition of southern climatic conditions. Two scenarios of likely most severe changes were selected: first, a 25% increase in the mean and standard deviation of Gumbel distribution of rainfall depth for storm durations from 6 to 48 h; second, a 50% increase in the standard deviation only. The HEC-1 watershed model and the soil conservation service runoff curve method for abstractions were used in Monte Carlo simulation. Comparison of Monte Carlo derived flood frequency curves for the two scenarios with the present day curve shows that scenario 1 is more critical in terms of flood flow increases than scenario 2. Under scenario 1, the mean annual flood on the study watershed would increase by almost 80% and the 100-year flood would increase by 41%.

An Alternate Method of Finding the USDA Soil Conservation Service Runoff Curve Number for a Small Watershed

Currently, one of the most widely-used methods for estimating losses for a watershed is the Soil Conservation Service (SCS) runoff curve number method (Chow et…

Heuristic Knowledge-based Tool for Rainfall Synthesis, Runoff Estimation and Hydrograph Generation

Knowledge-based engineering has emerged as a potential technique for incorporating human expertise and some degree of intelligent judgment into decision-supporting procedures. A knowledge-based expert system (KBES) methodology to estimate the volume and hydrograph of direct surface runoff from rain events using the Soil Conservation Service Runoff Curve Number method was developed for hydrologic modeling and decision support systems. The KBES approach was designed to determine runoff curve numbers with limited available information on watershed characteristics, to generate rainfall intensity of the geographic location for desired durations and return periods, and to use the curve number and rainfall intensity estimates to calculate runoff volume, peak runoff and time-to-peak for design purposes. Rainfall intensity estimation is based on four regional partial-duration series parameters corresponding to a given geographic location. Currently, a regional parameter database for 12 midwestern states (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin) is compiled into the KBES for the rainfall synthesis.

Derivation of unit and flood hydrographs using a gis

A geographic information system (GIS) supporting a flood hydrograph prediction software package is described. The hydrograph prediction method is based on the convolution of excess rainfall with a synthetic unit hydrograph, derived by the Soil Conservation Service runoff curve number and a regional dimensionless unit hydrograph method, respectively. The GIS uses a raster method to store the following data: land use and land cover, soil type, rainfall intensity-frequency-duration statistics, runoff curve numbers (CN), regional dimensionless unit hydrograph, and regional lag-time relationship. The GIS has also the capability of computing a number of watershed and hydrologic parameters required for predictions, such as a watershed average rainfall and CN value, area, centroid, stream length etc. Most of the data for such computations are input from a digitizer. Substantial time and cost savings are possible once the data base has been created. Application of the system is illustrated by an example predicting flood frequency curves for selected watersheds in Alberta's Rocky Mountain foothills, Canada.

Hydrologic modelling for runoff determination

The quantitative assessment of runoff from a catchment is necessitated when a structure in the vicinity of a watercourse has to be designed. In the present study the USDA Soil Conservation Service runoff curve numbers, modified for Indian conditions, are established for the Kaliaghai river basin from a digitized land‐use or land‐cover map derived from IRS‐IA LISS‐II data. The software ‘HYDMOD’ was developed to translate the curve number into volume of runoff. The linear regression model was developed to correlate the observed and estimated runoff. Average percentage deviation between the observed and estimated runoff was found to be in the order of 5.9.

A geographic information system for prediction of design flood hydrographs

A geographic information system (GIS) supporting a flood hydrograph prediction software package is described. The hydrograph prediction method is based on the convolution of excess rainfall with a synthetic unit hydrograph, derived by the Soil Conservation Service runoff curve number and a regional dimensionless unit hydrograph method, respectively. The GIS uses a raster method to store the following data: land use and land cover, soil type, rainfall intensity–frequency–duration statistics, runoff curve numbers (CN), regional dimensionless unit hydrograph, and regional lag-time relationship. The GIS has also the capability of computing a number of watershed and hydrologic parameters required for predictions, such as a watershed average rainfall and CN value, area, centroid, stream length, etc. Most of the data for such computations are input from a digitizer. Substantial time and cost savings are possible once the data base has been created. Application of the system is illustrated by an example of predicting flood frequency curves for selected watersheds in Alberta's Rocky Mountain foothills. Key words: geographic information system, flood hydrograph, curve number, hydrologic simulation, flood frequency.

RUNOFF VOLUME ESTIMATION USING GIS TECHNIQUES1

ABSTRACT: Rainfall runoff of six watersheds was modeled via the Soil Conservation Service runoff curve number model in two ways: conventionally (manually) and via a geographic information system (GIS). Input data (elevation, soils, and landcover) were digital for the latter method. In contrast to previous studies, the GIS was ised for all phases of the modeling process, including watershed delineation and routing of runoff. A comparison between the two methods was consistent with results reported by others and indicates that the use of a GIS is an acceptable alternative to the conventional method for watersheds lacking relatively flat terrain. Given this limitation, the GIS method may prove advantageous over manual methods when study areas are large or numerous, runoff is modeled repetitively, alternative landcover scenarios are explored, or a digital database already exists for the study area.

Rainfall retention probabilities computed for different cropping-tillage systems

Rainfall and runoff event data from several different cropping and tillage systems on three field-sized watersheds in the Southern Piedmont of the U.S.A. are used to estimate empirical probability distributions of the USDA Soil Conservation Service runoff curve number. Long-term rainfall records from a nearby gauge are also employed to obtain probabilities for rainfall event occurrence and depth. These probability distributions of runoff curve number and rainfall occurrence and depth are incorporated in a recursive computer procedure to compute estimated probability distributions of annual rainfall retention for the different cropping-tillage systems. Comparison of these estimated rainfall retention probability distributions shows a reduction in the risk for low rainfall retention with the installation of conservation tillage systems.

Estimating Soil Loss Probabilities for Southern Piedmont Cropping-Tillage Systems

ABSTRACT -Rainfall depth, runoff volume, peak runoff rate, and soil loss event data from three field-sized Piedmont watersheds under several different cropping-tillage systems are used to obtain estimated probability distributions of the Soil Conservation Service runoff curve number and the Universal Soil Loss equation combined C-P factor. In addition, long-term rainfall records from a nearby gage are employed to obtain probabilities of rainfall event occurrence and depth. These probability distributions of runoff and soil-loss model parameters and rainfall occurrence and depth are incorporated in a recursive computer procedure to compute estimated soil loss probability distributions for the different cropping-tillage systems. Comparison of these estimated probability distributions reveals cropping-tillage effects on soil loss probabilities and shows a definite reduction in soil loss risk for conservation tillage in the Southern Piedmont.