JWSS - Journal of Water and Soil Science

---

An extensive data collection on precipitation and runoff is required for development and implementation of soil and water projects. The unit hydrograph (UH) is an appropriate base for deriving flood hydrographs and therefore provides comprehensive information for planners and managers. However, UH derivation is not easy job for whole watersheds. The development of UH by using easily accessible rainfall data is then necessary. Besides that, the validity evaluation of different statistical modeling methods in hydrology and UH development has been rarely taken into account. Towards the attempt, the present study was planned to compare the efficiency of different modeling procedures in hydrograph and 2-h representative UH relationship in Kasilian watershed with concentration time of some 10h. The study took place by using 23 storm events occurred during four seasons within 33 years and applying two and multivariable regression models and 36 variables. According to the results, the median of estimated errors in estimation of 2-h UH dependent variables for verification stage varied from 37 to 88%. The results verified the better performance of cubic and linear bivariate models and logarithm-transformed data in multivariable model as well. The efficiency of multivariable models decreased when they were subjected to principle component analysis. The performance of backward method was frequently proved for estimation of dependent variables based on evaluation criteria, whereas the forward was found to be more efficient for time-dependent factors estimation.

---

Accurate estimation of runoff for a watershed is a very important issue in water resources management. In this study, the monthly runoff was estimated using the rainfall information and conditional probability distribution model based on the principle of maximum entropy. The information of monthly rainfall and runoff data of Kasilian River basin from 1960 to 2006 were used for the development of model. The model parameters were estimated using the prior information of the watershed such as mean of rainfall, runoff and their covariance. Using the developed model, monthly runoff was estimated for different values of runoff coefficient, , return period, , at different probability levels of rainfall for the basin under study. Results showed that the developed model estimates runoff for all return periods satisfactorily if the runoff coefficient value is taken 0.6. Also, it is observed that at a particular probability level and runoff coefficient, the estimated runoff decreases as return period increases. However, the rate of change of runoff decreases slightly as return period increases.

---

Rainfall-runoff modeling and prediction of river discharge is one important parameter in flood control and management, hydraulic structure design, and drought management. The goal of this study is simulating the daily discharge in Kasilian watershed by using WetSpa model and adaptive neuro-fuzzy inference system (ANFIS). The WetSpa model is a distributed hydrological and physically based model, which is able to predict flood on the watershed scale with various time intervals. The ANFIS is a black box model which has attracted the attention of many researchers. The digital maps of topography, land use, and soil type are 3 base maps used in the model for the prediction of daily discharge while intelligent models use available hydrometric and meteorological stations' data. The results of WetSpa model showed that this model can simulate the river base flow with Nash- Sutcliff criteria of 64 percent in the validation period, but shows less accuracy with flooding discharges. The reason for this result can be the small and short Travel time noted. This model can simulate the water balance in Kasilian watershed as well. The sensitivity analysis showed that groundwater flow recession and rainfall degree-day parameters have the highest and lowest effect on the results, respectively. Also, ANFIS with the inputs of rainfall 1-day lag and evaporation 1-day lag, with Nash-Sutcliff criteria of 80, was superior to WetSpa model with Nash-Sutcliff criteria of 24 percent in the validation period.