Application of snowmelt runoff model(SRM) in mountainous watersheds:A review

The snowmelt runoff model （SRM） has been widely used in simulation and forecast of streamflow in snow-dominated mountainous basins around the world. This paper presents an overall review of worldwide applications of SRM in mountainous watersheds, particularly jn data-sparse watersheds of northwestern China. Issues related to proper selection of input climate variables and parameters, and determination of the snow cover area （SCA）using remote sensing data in snowmelt runoff modeling are discussed through extensive review of literature. Preliminary applications of SRM in northwestern China have shown that the model accuracies are relatively acceptable although most of the watersheds lack measured hydro-meteorological data. Future research could explore the feasibility of modeling snowmelt runoff in data-sparse mountainous watersheds in northwestern China by utilizing snow and glacier cover remote sensing data, geographic information system （GIS） tools, field measurements, and innovative ways of model parameterization.

Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River,China

This paper presents the application of autoregressive integrated moving average （ARIMA）, seasonal ARIMA （SARIMA）, and Jordan-Elman artificial neural networks （ANN） models in forecasting the monthly streamflow of the Kizil River in Xinjiang, China. Two different types of monthly streamflow data （original and deseasonalized data） were used to develop time series and Jordan-Elman ANN models using previous flow conditions as predictors. The one-month-ahead forecasting performances of all models for the testing period （1998-2005） were compared using the average monthly flow data from the Kalabeili gaging station on the Kizil River. The Jordan-Elman ANN models, using previous flow conditions as inputs, resulted in no significant improvement over time series models in one-month-ahead forecasting. The results suggest that the simple time series models （ARIMA and SARIMA） can be used in one-month-ahead streamflow forecasting at the study site with a simple and explicit model structure and a model performance similar to the Jordan-Elman ANN models.

Integration of aspect and slope in snowmelt runoff modeling in a mountain watershed

This study assessed the performances of the traditional temperature-index snowmelt runoff model(SRM) and an SRM model with a finer zonation based on aspect and slope(SRM + AS model) in a data-scarce mountain watershed in the Urumqi River Basin,in Northwest China.The proposed SRM + AS model was used to estimate the melt rate with the degree-day factor(DDF) through the division of watershed elevation zones based on aspect and slope.The simulation results of the SRM + AS model were compared with those of the traditional SRM model to identify the improvements of the SRM + AS model's performance with consideration of topographic features of the watershed.The results show that the performance of the SRM + AS model has improved slightly compared to that of the SRM model.The coefficients of determination increased from 0.73,0.69,and 0.79 with the SRM model to 0.76,0.76,and 0.81 with the SRM + AS model during the simulation and validation periods in 2005,2006,and 2007,respectively.The proposed SRM + AS model that considers aspect and slope can improve the accuracy of snowmelt runoff simulation compared to the traditional SRM model in mountain watersheds in arid regions by proper parameterization,careful input data selection,and data preparation.