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Abstract: To deal with water resources crisis caused by climate change and intensified human activities, the Hetao Irrigation District of Inner Mongolia in North China has been experiencing rehabilitation for water-saving since 1999. This has significantly changed its regional hydrological cycle and thus water use patterns over different land use types. In this paper, Surface Energy Balance Algorithm for Land (SEBAL) model fed with MODIS data was applied in Hetao area from 2000 to 2010 to examine the spatial and temporal patterns of evapotranspiration (ET). The SEBAL estimated ET agreed well with that from other methods in the study area. The results indicate that inter-annual variability in ET over agricultural land, water body, woodland and irrigated grassland are primarily explained by the variation of reference ET. So it is with the seasonal variability in agricultural land ET on monthly basis. However, the inter-annual variations of ET over sandy land, Gobi desert and mountain areas are mainly controlled by precipitation. Over the study period, a reduction in river water diversion for irrigation has not reduced the agricultural land ET, indicating no significant impact on agricultural production. But ET over the non-irrigated grassland tends to decrease, which was likely caused by declining groundwater table in recent years. [Copyright &y& Elsevier]

• A new ET model (SW_OPT) integrates the SW and the Ts-VI model using PSO. • E, T and ET from SW_OPT were validated in a typical irrigated area of China. • SW_OPT calculates ET and its components with high accuracy. • SW_OPT is significantly more accurate compared to SW_Ori. • SW_OPT has low sensitivity to most of input and empirical parameters. Daily actual evapotranspiration (ET) and its components – soil evaporation (E) and vegetation transpiration (T) – play a key role in water resource management of irrigated areas. Nevertheless, due to large uncertainties in the parameterization of the resistances in irrigated areas, traditional ET models do not always provide accurate ET estimates, especially for its components. This uncertainty is mainly due to the difficulty of determining the empirical parameters accurately of soil and canopy resistances which is debated for a long time and the error in input variables. This paper proposed an optimized Shuttleworth-Wallace model (SW) using the particle swarm optimization (PSO) algorithm to integrate the original SW with the surface temperature-vegetation index (Ts-VI) triangle models (named Shuttleworth & Wallace_Temperature Vegetation Index model, SW_TVI).The performance of the SW_TVI model was significantly improved by optimizing the soil and canopy resistances in the original SW model using discontinuous regional ET estimates from the Ts-VI model under clear-sky conditions. Compared with the original SW model, the root mean squared error (RMSE) and mean absolute deviation (MAD) of the SW_TVI model were reduced by more than 30%, against in situ measurements in the Heihe River Basin. The Bias of the T/ET ratio was also significantly reduced from -22.3% for the original SW model to -5.5% for the SW_TVI model. The annual contributions of E and T to ET were about 20% and 80%, respectively, and had a strong seasonal variation in the typical irrigated area of China. In summary, the SW_TVI model shows three outstanding advantages: (1) it estimates daily continuous E, T, and total ET with high accuracy; (2) it is very robust and insensitive or slightly sensitive to most input variables and empirical parameters; (3) it is independent from ground data. This new SW_TVI model will benefit water resources management in irrigated areas, particularly in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]