پديد آورندگان :
بهيان مطلق، سودابه دانشگاه شهركرد، شهركرد، ايران , پژوهش، مهدي دانشگاه شهركرد، شهركرد، ايران , عبدالهي، خدايار دانشگاه شهركرد، شهركرد، ايران
كليدواژه :
مقياس محلي , بزرگمقياس , مدل هيدرولوژيكي , SC , HEC-HMS
چكيده فارسي :
استفاده از مدل‌هاي هيدرولوژيكي يكي از روش‌هاي رايج در تجزيه‌وتحليل كمي حوضه‌هاي آبخيز است. پيشرفت‌هاي اخير در زمينه‌سازي¬هاي بارش –رواناب كه انعطاف بيشتري در حل مسائل و پديده‌هاي هيدرولوژيكي دارند، آن‌ها را جايگزين مناسبي به‌جاي روابط تجربي كرده است. به‌طور مرسوم در پژوهش‌هاي صورت گرفته پيشين مبتني بر بهينه‌سازي مدل در همان مقياس محلي موردبررسي قرارگرفته كه فاقد عموميت مكاني است. هدف اين مطالعه ارائه پارامترهايي با اندازه معين است تا در سطح عمومي‌تري براي تمام زيرحوضهها پاسخ قابل قبولي را ارائه دهد. بدين منظور پارامترهايي از مدل هيدرو گراف سيلاب كه در هيدروگراف سيلاب نقش دارند، در مقياس زيرحوضه‌اي و بزرگمقياس مورد مقايسه قرارگرفته است. در اين مطالعه از مدلHEC-HMSبراي مدل‌سازي رواناب زير حوضه‌ها و ميزان رواناب خروجي از حوضه استفاده‌شده است؛ براي ساختار مدل از عوامل شماره منحني SCS براي روش تلفات و هيدروگراف واحد SCS جهت روش انتقال استفاده شد. به‌منظور واسنجي ابتدا بهينه‌سازي پارامترها در هريك از زيرحوضهها به‌صورت جداگانه انجام گرفت و سپس مقدار عمومي پارامترها با به دست آوردن اندازه ثابتي از پارامترهاي حساس كه در همه زير حوضه‌ها پاسخ قابل قبولي ارائه دهند، انجام شد. يافته‌هاي تحقيق نشان مي‌دهد كه گرچه كارايي مدل در واسنجي با پارامترهاي محلي برتر از واسنجي با استفاده از پارامترهاي بزرگمقياس است، ولي ايستايي پارامترهاي عمومي بهتر است. به‌منظور ارزيابي كارايي مدل از شاخص Nash-sutcliffeاستفاده گرديد كه اين شاخص براي كاليبراسيون محلي 85/0 و كاليبراسيون بزرگمقياس 65/0 به دست آمد كه در دامنه مطلوبي براي شبيه‌سازي قرار دارد
چكيده لاتين :
. Introduction Application of hydrological models is one of the common methods for quantitative analysis of watersheds. A class of these models is used to simulate rainfall-runoff processes. A hydrological rainfall-runoff model deals with integration of time series data, operational parameters, local variables and physical governing system laws in order to simulate runoff and other processes of the catchment. The flood hydrograph is an important graphical representation in hydrological analysis. The shape of hydrograph is a direct response to management strategies basin. In fact, watershed management is not possible unless hydrological characteristics of the basin could be fully understood. HEC-HMS model is a rainfall - runoff model with either a simple lump or a quasi-distributed structure. The model was developed by Hydrologic Engineering Center of US Army Corps of Engineers. One important capability of this model is the possibility of its integration with ArcGIS. The HEC-GEOHMS extension automatically calculates most of the geospatial data that needs to be calculated manually. Its user-friendly interface offers several tools/icons such as the basin, reach, junction, etc. to enter the data. The component-based set up of the model makes it possible to modify the structure of the modeling system by the user. Different options for calculating the loss method, transmission, and streaming flow are available in HEC-HMS model. Each element has its own parameters or inputs. This flexible system provides an opportunity to apply the model to the regions with limited access to data. For a similar reason, models like HEC-HMS are useful tools to investigate the effects of changes in some of the spatial and temporal variables. In this study, the effects of the spatial scale on the optimization of HEC-HMS have been investigated. The modeling parameter sets obtained by large scale setting generally outperform local scale hydrologic parameterization. Most research in the field of calibration of hydrological models has focused on the local parameters but if the at larger scales, it can be regionalized to other places. In such procedures, the sensitivity analysis is initially used to identify the most important parameters. The aim of this study was to compare local and large scale calibration. 2. Material and Methods Kohsukhteh watershed is geographically located between 50 °40' to 51 ° 20 ' East longitude and 31° 20' North and 32 ° latitude. The study area covers an area of 2783 square kilometers. The minimum and maximum elevation values in this basin are 1705 and 3398 meters respectively. The average slope of the area is 19%. The mean recorded annual precipitation (Shahrekord synoptic station) is 320 mm. Data preparation for HEC-GEOHMS was the first stage of modeling. Different physical characteristics, base maps and time series data of the basin were introduced to the model. The model set up was implemented by setting Loss, Transform and Base-flow methods for each sub-basin. The necessary steps for meteorological model and control specifications were taken as well. The 6-hour rainfall and 6-hour discharge time series were primarily used for the analyses. The model was parameterized at both local and large scale levels. Parameters related to Loss method, Transfer method and Routing were selected on the basis of model performance. In order to identify the most sensitive parameters, sensitivity analysis was performed. Given that the accuracy of the input parameters is an indicator of the efficiency of modeling and the results of the final simulation, the results depend on this stage. After identifying the sensitive parameters, they were selected to optimize the model locally and in a large scale. Despite the uncertainties and errors of the models, no computer model can expect a complete and accurate prediction. Therefore, they should be calibrated and validated. To evaluate the performance of models with different conditions, six events were chosen for the calibration and two for validation of the model. 3. Results and Discussion Sensitivity analysis on the parameters of the model showed that Lag time parameter and initial abstraction had the highest sensitivity. Calibrated values of the model parameters were obtained for both calibration and validation events. The results of calibration in six events and the observed and simulated runoff volume in the basin were compared. Two events were selected to validate the calibrated model. Runoff discharge and volume for the basin were simulated. The values obtained by simulation were compared against the recorded observational data at the hydrometric stations were compared. After calibration and optimization of the model at local scale, the results showed that both estimated peak discharge and the time to peak flow, was performed better than obtained values for the basin scale simulation. The inter-comparison among the events also show some differences in model performance. Event December 25, 2012 showed the highest accuracy and March 30, 2012 was simulated with the least accuracy. The difference may be due to the change in soil permeability during winter time. In the case of large scale model, the Event December 25, 2012 was simulated with high efficiency. Lag time, initial abstraction, curve number and impervious ratio were identified as the most important factors for calibration. The obtained Nash-Sutcliffe for local calibration was 0.85 while it dropped to 0.65 for large scale calibration. 4. Conclusion Modeling is an indirect method that is much faster than field methods. In order to achieve accurate results in modeling, we need to estimate the model parameters as well as the time and place of variables. Calibration refers to the process of comparing of the measurements against the estimated values. The use of local scale calibration versus large scale calibration has a higher apparent accuracy, and this is in line with Samaniego, Kumar and Attinger (2010) and Hundecha and Bárdossy. (2004). However, the commutated value for Sutcliffe model efficiency is less variable in the case of local calibration. It was demonstrated that the simulated peak runoff was closer to observations in local calibration compared to the large scale calibration. A similar result was found with the simulated runoff volume using local calibration. Although, both calibration settings provided an acceptable response to the estimation of runoff; the obtained parameters at basin scale show less spatial sensitivities. This makes it possible to generalize the calibration results to nearby areas with limited data. The results show that the large-scale parameterization actually has less spatial dependency and therefore may provide more reliable results. This finding is in line with reported advantages of large-scale parameter estimations, in term of saving the time (Troy, 2008; Pokhrel, 2008; Beven, 2001). Sensitivity analysis also showed that parameters such as imperviousness and initial abstraction had a high sensitivity in the study
