Assessment of climate change impacts on rainfall using large scale climate variables and downscaling models – A case study

Abstract

Many of the applied techniques in water resources management can be directly or indirectly influenced by hydro-climatology predictions. In recent decades, utilizing the large scale climate variables as predictors of hydrological phenomena and downscaling numerical weather ensemble forecasts has revolutionized the long-lead predictions. In this study, two types of rainfall prediction models are developed to predict the rainfall of the Zayandehrood dam basin located in the central part of Iran. The first seasonal model is based on large scale climate signals data around the world. In order to determine the inputs of the seasonal rainfall prediction model, the correlation coefficient analysis and the new Gamma Test (GT) method are utilized. Comparison of modelling results shows that the Gamma test method improves the Nash-Sutcliffe efficiency coefficient of modelling performance as 8% and 10% for dry and wet seasons, respectively. In this study, Support Vector Machine (SVM) model for predicting rainfall in the region has been used and its results are compared with the benchmark models such as K-nearest neighbours (KNN) and Artificial Neural Network (ANN). The results show better performance of the SVM model at testing stage. In the second model, statistical downscaling model (SDSM) as a popular downscaling tool has been used. In this model, using the outputs from GCM, the rainfall of Zayandehrood dam is projected under two climate change scenarios. Most effective variables have been identified among 26 predictor variables. Comparison of the results of the two models shows that the developed SVM model has lesser errors in monthly rainfall estimation. The results show that the rainfall in the future wet periods are more than historical values and it is lower than historical values in the dry periods. The highest monthly uncertainty of future rainfall occurs in March and the lowest in July.

Assessment of Climate Change for Precipitation and Temperature Using Statistical Downscaling Methods in Upper Godavari River Basin, India

“…SDSM as a statistical tool was adopted due to several advantages such as low cost and user friendly over dynamical methods. There are many studies which have used SDMS in climate change impact assessments [14]- [16]. Regression method establishes a linear or nonlinear regression between predictands and predictors.…”

Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan

“…Several regression-based statistical methods have been developed and applied, such as, principal component analysis, artificial neural networks, multiple linear regression, and canonical correlation analysis (Mahmood and Babel 2013). However, the relationship between predictor and predictand is often very complex in nature, and linear regression based methods cannot work very well Ahmadi et al 2014). This is especially true for an arid region, where the relation between local rainfall and ocean-atmospheric circulation parameters are not explicitly understood.…”

Projected climate changes in four different regions in Colombia

“…The results presented in Table 2 show an increase of both maximum and minimum temperature over the next decades as well as a decrease in relative humidity with a slight change of precipitation which will most likely decrease for most of the considered stations-especially in the last decades of the XXI century. In contrast to the case of temperature, difficulties to perform accurately a downscaling of daily precipitation agrees with the results of other studies (Huang et al 2011;Nguyen et al 2006;i.a., González-Rojí et al 2019;Saraf and Regulwar 2016;Ahmadi et al 2014;Saddique et al 2019;Hussain et al 2017;Cavazos and Hewitson 2005;Fiseha et al 2012;Osma et al 2015), also in these studies a low correlation in a regional scale between daily precipitation and different set of predictors was found, this creates a difficulty to adjust the model and calibrate it more accurately. That can be seen in this study in the Figs.…”