Precipitation projection using a CMIP5 GCM ensemble model: a regional investigation of Syria

Abstract

The possible changes in precipitation of Syrian due to climate change are projected in this study. The symmetrical uncertainty (SU) and multi-criteria decision-analysis (MCDA) methods are used to identify the best general circulation models (GCMs) for precipitation projections. The effectiveness of four bias correction methods, linear scaling (LS), power transformation (PT), general quantile mapping (GEQM), and gamma quantile mapping (GAQM) is assessed in downscaling GCM simulated precipitation. A random forest (RF) model is performed to generate the multi model ensemble (MME) of precipitation projections for four representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.5. The results showed that the best suited GCMs for climate projection of Syria are HadGEM2-AO, CSIRO-Mk3-6-0, NorESM1-M, and CESM1-CAM5. The LS demonstrated the highest capability for precipitation downscaling. Annual changes in precipitation is projected to decrease by −30 to −85.2% for RCPs 4.5, 6.0, and 8.5, while by < 0.0 to −30% for RCP 2.6. The precipitation is projected to decrease in the entire country for RCP 6.0, while increase in some parts for other RCPs during wet season. The dry season of precipitation is simulated to decrease by −12 to −93%, which indicated a drier climate for the country in the future.

Spatial Shift of Aridity and Its Impact on Land Use of Syria

“…It also resulted in the exodus of people from the afflicted villages to the cities [85]. With the projected decrease in annual precipitation by −30 to −85.2% for Syria [86], there may be increased aridity, losses of land productivity, increases in crop water demand and increasing frequency of droughts can increase huge economic loss and more conflicts in Syria.…”

An Evaluation of Statistical Downscaling Techniques for Simulating Daily Rainfall Occurrences in the Upper Ping River Basin

“…Since the statistical characteristics of the raw historical GCM data differ from the reanalysis data, bias correction of GCM outputs is essential. Multiplicative factors, change factors, delta change approach and quantile mapping are a few examples of simple approaches often used to bias correct the raw GCM output and also as downscaling add on [29,[33][34][35][36]. These approaches have some limitations as well, for example, adopting observed distribution and capturing average statistics of historical time series without focusing on the extremes [37][38][39].…”

“…Although it is an effective technique to remove historical biases relative to observed data, it can artificially corrupt the projection of future trends [28]. Furthermore, Homsi et al [29] found that linear scaling (LS) demonstrated the highest capability for precipitation downscaling comparing to three bias correction methods comprising power transformation (PT), general quantile mapping (GEQM) and gamma quantile mapping (GAQM). Saengsawang et al [28] predicted future rainfall under the Representative Concentration Pathway scenarios (RCP2.6, RCP4.5 and RCP8.5) in the UPRB using regression-based downscaling to relate between rainfall depth and climate variables instead of applying raw GCM rainfall data as applied in the aforementioned studies.…”

Trends of Runoff Variation and Effects of Main Causal Factors in Mun River, Thailand During 1980–2018

“…A statistically significant decreasing trend was detected based on runoff data from 925 seagoing rivers including the Mekong River from 1948 to 2004, and yet the impact of human activities on global river runoff was found far less than the effect of climate change [11]. In the prediction of future river runoff variation in Thailand and Malaysia, machine learning models and distributed hydrological models coupled with future climate scenarios are employed and the results show that the variation of forest area induced by land use change is a very influential factor [12][13][14].…”