Localized linear regression methods for estimating monthly precipitation grids using elevation, rain gauge, and TRMM dat
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ORIGINAL PAPER
Localized linear regression methods for estimating monthly precipitation grids using elevation, rain gauge, and TRMM data Mercedeh Taheri 1 & Neda Dolatabadi 1 & Mohsen Nasseri 1,2
&
Banafshe Zahraie 1 & Yasaman Amini 1 & Gerrit Schoups 2
Received: 17 January 2020 / Accepted: 6 July 2020 # Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract 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.
1 Introduction Precipitation is known as one of the ruling environmental factors of the earth surface processes; hence, determining its spatiotemporal distribution accurately improves our understanding of the hydrologic cycle and the interactions between earth, atmosphere, and water resources (Guo et al. 2015; Immerzeel et al. 2009; Jia et al. 2011). Among hydroclimatological variables, estimation of temporal and spatial distribution of precipitation is among the most challenging. Despite the possibility of using spatiotemporal information from meteorological radars, they cannot be used to extract highly accurate estimation of precipitation required for Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00704-020-03320-2) contains supplementary material, which is available to authorized users. * Mohsen Nasseri [email protected] 1
School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran
2
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, The Netherlands
hydrological modeling (Tao et al. 2014). Therefore, developing novel approaches to obtain accura
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