Application of SEBAL and Markov Models for Future Stream Flow Simulation Through Remote Sensing

Watershed hydrology, including the volumes of stream flow is widely considered to be influenced by global climate change. Traditional studies using the (GWLF) model to estimate stream flows have relied on evapotranspiration cover coefficient (Kc) obtained from published references. Other factors, such as future land-use status and evapotranspiration (ET) change, are usually not considered. This study aims to improve on traditional studies by including remote sensing techniques to estimate the Kc, as well as integrating the SEBAL model, the CGCM1 model, and the Markov model to predict land-use and ET changes. The chosen study area was in the north of Taiwan. The processes include land-use classification using hybrid approach and Landsat-5 TM images, a comparison of stream flow simulations using the GWLF model with two Kc values derived from remote sensing and traditional methods, and finally the prediction of future land-use and Kc parameters for assessing the effect of land-use change and ET change. The results indicated that the study area was classified into seven land-use types with 89.09% classification accuracy. The stream flows simulated by two estimated Kcs were different, and the simulated stream flows using the remote sensing approach presented more accurate hydrological characteristics than a traditional approach. In addition, the consideration of land-use change and ET change indeed affected the predicted stream flows under climate change conditions. These results imply that the integration of remote sensing, the SEBAL model, the CGCM1 model, and the Markov model is a feasible scheme to predict future land-use, ET change, and stream flow. Therefore, these models will improve future studies of predictions in water resource management and global environmental change.