Statistical downscaling of monthly precipitation using NCEP/NCAR reanalysis data for tahtali river basin in Turkey

Statistical downscaling methods describe a statistical relationship between large-scale atmospheric variables such as temperature, humidity, precipitation, etc., and local-scale meteorological variables like precipitation. This study examines the potential predictor variables selected from the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis data set for downscaling monthly precipitation in Tahtali watershed in Turkey. An approach based on the assessment of all possible regression types was used to select the predictors among the NCEP reanalysis data set, and artificial neural network (ANN)-based downscaling models were designed separately for each station in the basin. The results of the study showed that precipitation, surface and sea level pressures, air temperatures at surface, 850-, 500-, and 200-hPa pressure levels, and geopotential heights at 850- and 200-hPa pressure levels are the most explanatory NCEP/NCAR parameters for the study area. It was concluded that ANN-based downscaling models can be implemented to downscale coarse-scale atmospheric parameters to monthly precipitation at station scale by using the above parameters as inputs in the study area. © 2011 ASCE

Temperature and precipitation projections under AR4 scenarios: The case of kucuk menderes basin, Turkey

###EgeUn###In the study, downscaling models based on artificial neural networks were established for monthly average and maximum temperature and monthly total precipitation projections of Seferihisar, Selcuk and Odemis meteorological stations in the basin. In the models, NCEP/NCAR re-analysis variables were used as predictors. The downscaling models calibrated with the optimum predictors convert the coarse resolution results of both reference period (20C3M; 1981-2010) and future period (A2, A1B and B1; 2021-2100) scenarios of ECHAM5 climate model to the station scale temperature and rainfall forecasts. Corrections of biases in the forecasts are achieved by using cumulative distribution functions. According to the A2, A1B and B1 scenarios, the mean of monthly average temperatures of 2021-2100 period could increase by 3.2, 3.5 and 2.8oC, respectively and the mean of monthly maximum temperatures of 2021-2100 period could increase by 1.6, 2.1 and 1.1o C, respectively, the mean of annual total precipitation could decrease by 31.6, 42.9 and 30.2%, respectively over study region. Under these possible impacts, it is expected that the average net irrigation water demand and soil salinity will increase, water supply will decrease. Under these stressed conditions, it has to be changed cropping pattern of the basin. © 2019, Scibulcom Ltd. All rights Reserved.Firat University Scientific Research Projects Management UnitAcknowledgements. This work was supported by Ege University Scientific Research Projects -

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