Evapotranspiration (ET) is one of the most important factors controlling hydrologic, agricultural, and weather cycles. It also converts a large portion of rainfall into vapor, being known as the largest water flux from the earth into the atmosphere. Since ET is affected by many factors, such as land surface characteristics and climatic conditions, it undergoes considerable spatiotemporal variations, particularly at the watershed scale. Hence, to obtain a more accurate estimation of ET, it is required to identify homogenous and uniform regions, each represented by a meteorological station. In this study, three scenarios were proposed in order to identify homogenous regions to estimate ET based on METRIC method, and the scenarios were tested in Sefidrood Watershed in the north of Iran. The first scenario included only vegetation factor with one representative station for the entire case study watershed and ignored diverse conditions affecting ET across the watershed. The second scenario incorporated not only the vegetation factor but also the altitudinal variations of the watershed. In the second scenario, the watershed was divided into two distinct altitudinal sections, each with a representative station with a specific influenced area, with ET being estimated separately for each section. Finally, the third scenario incorporated the altitudinal and climatic variations. The results indicated that the second scenario performed better than two other scenarios in ET estimation. In other words, altitude and vegetation strongly influenced spatial and temporal distributions of ET, leading to considerable variations of it in the watershed.

Improving precipitation accuracy over a watershed is one of the highest priorities in water resources studies and management. Several global precipitation datasets are available for estimating precipitation over any region in the world. However, local or regional application of these datasets should account for and correct potential errors in the original products. This article presents a novel spatiotemporal calibration framework to improve the accuracy (bias and correlation) of global precipitation datasets in regional applications. The proposed methodology consists of two steps. First, gridded global precipitation datasets are regressed pointwise against rain gauge data. This yields downscaled and bias-corrected precipitation values at the point scale. Second, the resulting point-scale regression parameters are used to build a geostatistical model that predicts the regression parameters across the region of interest, allowing for bias-correcting the precipitation datasets at the regional scale. The framework is applied to the Urmia Lake Watershed in northwestern Iran. Eight global high-resolution monthly precipitation datasets (CHIRPS, ERA-5, IMERG, PERSIANN, PERSIANN-CCS, PERSIANN-CDR, TRMM and Terra) are evaluated and three downscaling approaches including linear, Q-Q and Linear Scaling (LS) regression methods are used to calibrate the precipitation datasets based on a regional network of rain gauge observations. Ordinary kriging is subsequently used to predict the regression parameters at ungauged locations. Out of all combinations (i.e., eight datasets and three methods), downscaled IMERG using linear and Q-Q regression methods showed the best performance in estimating the spatiotemporal variations of monthly precipitation across the watershed of interest. The original IMERG dataset overestimated the monthly precipitation by approximately 20% compared to the precipitation from rain gauges. After applying the proposed methodology in this article, the IMERG bias was reduced by 93%, with an additional 26% decrease in the RMSE.

Accurate estimation of the spatial distribution of precipitation is crucial for hydrologic modeling. To achieve the realistic estimation of precipitation, developing a ground-based observatory system is a costly and time-consuming strategy compared with other solutions such as using a combination of satellite- and ground-based observations. In this paper, to improve the estimation accuracy of spatial precipitation variation, various linear regression methods were used that combine digital elevation model (DEM) data, rain gauge observations, and Tropical Rainfall Measuring Mission (TRMM) products. Specifically, fuzzy cluster-based linear regression (FCLR), local multiple linear regression using historical similarity (LMLR-HS), model tree (MT), and moving least squares (MLS) were used in the proposed methodology based on local data behavior. The results were compared with those obtained from multiple linear regression (MLR) methods including simple multiple linear regression (SMLR), robust multiple linear regression (RMLR), and generalized linear model (GLM) for monthly precipitation estimation. The study area was Namak Lake watershed, one of the largest watersheds in Iran. The results, estimated for wet and dry years (years 1999 and 2003, respectively), show superiority of local linear regression methods over the other linear methods. Based on the statistical metrics used for assessing the quality the results, FCLR and MLS outperformed other tested methods.