ChinaXiv合作期刊 Prediction of meteorological drought in arid and semi-arid regions using PDSI and SDSM: a case study in Fars Province, Iran Sheida DEHGHAN1, Nasrin SALEHNIA2, Nasrin SAYARI1*, Bahram BAKHTIARI1 1 Department of Water Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman 7616914111, Iran; 2 Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 9177949207, Iran Abstract: Drought is one of the most significant environmental disasters, especially in arid and semi-arid regions. Drought indices as a tool for management practices seeking to deal with the drought phenomenon are widely used around the world. One of these indicators is the Palmer drought severity index (PDSI), which is used in many parts of the world to assess the drought situation and continuation. In this study, the drought state of Fars Province in Iran was evaluated by using the PDSI over 1995–2014 according to meteorological data from six weather stations in the province. A statistical downscaling model (SDSM) was used to apply the output results of the general circulation model in Fars Province. To implement data processing and prediction of climate data, a statistical period 1995–2014 was considered as the monitoring period, and a statistical period 2019–2048 was for the prediction period. The results revealed that there is a good agreement between the simulated precipitation (R2>0.63; R2, determination coefficient; MAE<0.52; MAE, mean absolute error; RMSE<0.56; RMSE, Root Mean Squared Error) and temperature (R2>0.95, MAE<1.74, and RMSE<1.78) with the observed data from the stations. The results of the drought monitoring model presented that dry periods would increase over the next three decades as compared to the historical data. The studies showed the highest drought in the meteorological stations Abadeh and Lar during the prediction period under two future scenarios representative concentration pathways (RCP4.5 and RCP8.5). According to the results of the validation periods and efficiency criteria, we suggest that the SDSM is a proper tool for predicting drought in arid and semi-arid regions. chinaXiv:202006.00236v1 Keywords: PDSI; SDSM; RCP4.5; RCP8.5; climate change; extreme drought 1 Introduction One of the critical subjects facing the world is climate change since it is predicted to alter climate patterns and increase the frequency of extreme weather events (Palmer and Raisanen, 2002; Hayes et al., 2004; IPCC, 2012). Over the last few years, the frequency of droughts caused by global warming-related climate change has increased and along with an increase in the intensity of these events (IPCC, 2013; Yu et al., 2013; Salehnia et al., 2017a). Therefore, it is crucial to establish appropriate expectations of future drought impacts of severe droughts caused by the *Corresponding author: Nasrin SAYARI (Email: [email protected]) Received 2019-02-14; revised 2019-11-11; accepted 2020-01-25 © Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020 http://jal.xjegi.com; www.springer.com/40333 ChinaXiv合作期刊 Sheida DEHGHAN et al.: Prediction of meteorological drought in semi-arid and arid regions… climate change. This study investigates the impact of climate change on drought over a long-term scale, which is necessary to diminish vulnerability and establish suitable innovation strategies for drought mitigation and preparedness. Drought is a significant natural stochastic hazard that arises from a considerable deficiency in precipitation (Gao and Zhang, 2016), which can have devastating impacts on the regional agriculture, water resources and environment (Sternberg, 2011; Escalante-Sandoval and Nuñez-Garcia, 2017, Salehnia et al., 2017b), causing extensive damage and affect a large number of people. Droughts and floods are extreme climate events that percentage-wise are likely to change more rapidly than the mean climate (Trenberth et al., 2003). With increasing temperature and changing distribution of precipitation, the drought risk is expected to rise further (Sillmann et al., 2013). The most important index in the meteorological drought is the Palmer drought severity index (PDSI), which was developed by Palmer in 1965. The PDSI can be used to measure the cumulative variation compared to local mean conditions in atmospheric moisture supply and demand at the ground surface; and to simulate the moisture content of the soil in monthly scale and compare its anomalies in regions under different climatic conditions (Szép et al., 2005). The PDSI might be the most extensively used regional drought index for observing droughts (Alley, 1984). However, the PSDI introduced as a meteorological drought index based on soil moisture content and meteorological variables. It counts several conditions, such as precipitation, evapotranspiration and soil moisture (Alley, 1984). The PDSI can be used to determine the beginning, ending and severity of the drought periods, and has been normalized to allow comparisons across space and time. The PDSI is traditionally calculated using a two-layer bucket type model to obtain water balance components, which does not consider the impacts of spatial heterogeneity of soil, vegetation cover and topography on watershed hydrological processes, etc. (Jin et al., 2016). In previous studies, the PDSI was estimated mostly based on the meteorological station monitoring at point scale and having the restriction of collecting long-term serial soil moisture and actual evapotranspiration at a large scale. Additionally, the PDSI in the previous studies could not precisely reflect the regional differentiation of drought. Moreover, the PDSI uses a simplified model of potential evaporation, which only responds to the changes in temperature and thus responds incorrectly to global warming in recent decades (Sheffield et al., 2012). These indices have been used extensively to detect long-term drought trends under global warming in some areas around the world. According to the findings, drought phenomena have been increasing around the world because of climate change in the past few decades (Dai, 2011, 2013). A global climate model (GCM) is generally used for predicting long-term drought by projecting chinaXiv:202006.00236v1 meteorological and hydrological data and applying different future climate scenarios (Hessami et al., 2007). Considerable studies are published based on climate change models and future climate scenarios presenting significant changes in occurrence and duration of severe drought (Dai, 2013; Bak and Labedzki, 2014; Dubrovský et al., 2014; Touma et al., 2015). The fifth evaluation report carried out by the Intergovernmental Panel for Climate Change (IPCC) introduced the representative concentration pathway (RCP) to achieve much more accurate forecasting of future climate (Moss et al., 2010). The objective of this study was to find out the trend of drought under climate change conditions using the PDSI in Fars Province of Iran under two future scenarios RCP4.5 and RCP8.5. It would be in help for understanding and predicting drought trends in the arid and semi-arid regions around the world. 2 Study area and methods 2.1 Study area The study area, Fars Province (27°03ʹ–31°40ʹN, 50°36ʹ–55°33ʹE), is located in the southwest of Iran, covering an area of 1.33×1 05 km2 (Fig. 1). The study area can be divided into three categories according to its climatic characteristics: the north and northwest part with cold winter ChinaXiv合作期刊 JOURNAL OF ARID LAND and mild summer, the south and southeast part with cold winter and hot summer, and the central area with rainy, mild winter and hot, dry summer (Rahimi et al., 2013). Fig. 1 Location of the study area, Fars Province of Iran (a). The north and northwest part has cold winter and mild summer; the central area has rainy, mild winter and hot, dry summer; and the south and southeast part has cold winter and hot summer (b). N, north; NW, northwest; S, south, SE, southeast. Historical daily weather data of six different locations across the study area were collected from the meteorological stations, Shiraz, Fasa, Abadeh, Darab, Lar and Eghlid (Fig. 1). Physiographic details of the six stations are presented in Table 1. Table 1 Characteristics of the six meteorological stations in Fars Province of Iran during 1995–2014 Meteorological Elevation Mean annual temperature Total daily precipitation Location Climate station (m) (°C) (mm) Shiraz 29°32′N, 52°36′E 1484 18.6 6370.1 Semi-arid Fasa 28°58′N, 53°41′E 1288 19.4 5429.3 Semi-arid Abadeh 31°11′N, 52°40′E 2030 14.4 2649.4 Arid Darab 28°47′N, 54°17′E 1098 22.1 4983.8 Arid Lar 27°42′N, 54°17′E 792 23.9 3533.2 Arid Eghlid 30°54′N, 52°38′E 2300 13.0 6325.9 Arid chinaXiv:202006.00236v1 2.2 Data The observed data covered the period 1995–2014, which was used as the baseline period. Daily meteorological variables, including daily average temperature and total daily precipitation, were used to calculate the drought indices. In this study, a range of future climate change scenarios were presented by the IPCC. The representative concentration pathways form a set of greenhouse gas concentration and emission pathways that are designed to support researches on impacts and potential policy responses to climate change (Moss et al., 2010). The data, which included two types of daily predictors needed for this research, were obtained from the Canadian Institute for Climate Studies (CICS) website (http://www.cics.uvic.ca/scenar ios/sdsm/select.cgi), including 26 predictors of the national center of environmental prediction (NCEP) and 26 predictors of Canadian Earth System Model (CanESM2) under RCP4.5 and RCP8.5 scenarios for the period 2019–2048. The CanESM2 under RCP output data was input to the calibrated statistical downscaling model (SDSM) for each meteorological station to reproduce future daily temperature and precipitation values. For better analyzing, the boxplot diagram is applied in the result section.