Statistical Downscaling Model Model Research Articles

Future Evolutions of Precipitation and Temperature Using the Statistical Downscaling Model (SDSM), Case of the Guir and the Ziz Watershed, Morocco

The current study is essential for obtaining an accurate representation of weather conditions in the Ziz and Guir watersheds, characterized by an arid climate. This study combined climate data from the ERA5 model with data from observation stations in order to evaluate the ERA5 model in Morocco’s arid environment and increase the temporal and geographical coverage of climate data. From the data collected, precipitation, minimum and maximum temperatures were predicted under the RCP4.5 and RCP8.5 scenarios by applying the SDSM model in the two watersheds for the 2025 and 2100 periods. These forecasts contribute to the development of adaptation strategies in the face of climate change by giving precise indications of future trends and providing local communities with tools for enhancing their resilience capacity. At all climatic stations, the temperature changes predicted under these scenarios showed a marked positive trend for both minimum and maximum temperatures. By the end of the century, minimum temperatures may increase by 1.84 °C and 2.39 °C under the RCP4.5 and RCP8.5 scenarios, respectively. Similarly, maximum temperatures may increase by 1.78 °C and 2.9 °C under the RCP4.5 and RCP8.5 scenarios, respectively. In addition, the precipitation forecast under the RCP 4.5 scenario showed a significant negative trend at the Ait Haddou station, while under the RCP 8.5 scenario, significant negative trends were predicted for the Sidi Hamza, Ait Haddou, Tit N’Aissa, and Bouanane stations.

Prediction of groundwater level using GMDH artificial neural network based on climate change scenarios

One of the main challenges regarding the prediction of groundwater resource changes is the climate change phenomenon and its impacts on quantitative variations of such resources. Groundwater resources are treated as one of the main strategic resources of any region. Given the climate change phenomenon and its impacts on hydrological parameters, it is necessary to evaluate and predict future changes to achieve an appropriate plan to maintain and preserve water resources. In this regard, the present study is put forward by utilizing the Statistical Down-Scaling Model (SDSM) to forecast the main climate variables (i.e., temperature and precipitation) based on new Rcp scenarios for greenhouse gas emissions within a period from 2020 to 2060. The results obtained from the prediction of climate parameters indicate different values in each emission scenario, so the limit, minimum and maximum values occur in the Rcp8.5, Rcp2.6 and Rcp4.5 scenarios, respectively. Also, a model is developed by utilizing the GMDH artificial neural network technique. The developed model predicts the average groundwater level based on the climate variables in such a way that by implementing the climate parameters forecasted by the SDSM model, the groundwater level within a time period from 2020 to 2060 is predicted. The results obtained from the verification and validation of the model imply its proper performance and reasonable accuracy in predicating groundwater level based on the climate variables. The findings derived from the present paper indicate that compared to the years prior to the prediction period, the groundwater level of the Sahneh Plain has dramatically dropped so that based on the Rcp scenarios, the groundwater level values are in their lowest state within the period from 2046 to 2056. The findings of this paper can be used by managers and decision makers as a layout for evaluating climate change effects in the Sahneh Plain.

EVALUATION OF SDSM MODELS FOR CLIMATE PREDICTIONS IN BANGLADESH

As one of the poorest countries in the world, agriculture is Bangladesh’s economic pillar, leading to Bangladesh’s economy becoming vulnerable to global warming. The generation of high-resolution climate predictions in Bangladesh can help to reduce the huge damage and losses inflicted by disasters linked to climate. The statistical downscaling model (SDSM) is the most widely used software on robust climate downscaling and prediction analysis. In this study, by using the SDSM model, we established the statistical relationship between observed climate data in Bangladesh and the large-scale low-resolution NCEP data and used three statistical indicators to evaluate the prediction performance of the SDSM software. Our results show that the SDSM software is more suitable for forecasting humidity/temperature in Bangladesh than rainfall.

IMPACTS OF SNOWMELT AND CLIMATE CHANGE ON HIMALAYAN RIVERS: THE CASE OF DATA-SCARCE DUDHKOSHI RIVER IN EASTERN NEPAL

Water from snow-melt is crucial to provide ecosystem services in downstream of the Himalayas. To study the fate of snow hydrology, an integrated modeling system has been developed coupling Statistical Downscaling Model (SDSM) outputs with Snowmelt Runoff Model (SRM) in the Dudhkoshi Basin, Nepal. The SRM model is well-calibrated in 2011 and validated in 2012 and 2014 using MODIS satellite data. The annual average observed and simulated discharges for the calibration year are 177.89 m3 /s and 181.47 m3 /s respectively. To assess future climate projections for the periods 2020s, 2050s, and 2080s, the SDSM model is used for downscaling precipitation, maximum temperature, and minimum temperature from the Canadian GCM model (CanESM2) under three different scenarios RCP2.6, RCP4.5 and RCP8.5. All considered scenarios are significant in predicting increasing trends of maximumminimum temperature and precipitation and the storehouse of freshwater in the mountains is expected to deplete rapidly if global warming continues.

Prediction of Future Climate Change for Rainfall in the Upper Kurau River Basin, Perak Using Statistical Downscaling Model (SDSM)

Climate change is considered to be one of the biggest threats faced by nature and humanity today. The goal of this study is to predict future climate change for rainfall in the Upper Kurau Basin. In this research, the applicability of statistical downscaling model (SDSM) in downscaling rainfall in the Upper Kurau River basin, Perak, Malaysia was investigated. The investigation includes calibration of the SDSM model by using large-scale atmospheric variables encompassing the National Centers for Environmental Prediction (NCEP) reanalysis data. Rainfall data were derived for three 30-year time slices, 2020s, 2050s and 2080s, with A2 and B2 scenarios. A2 is considered among the “worst” case scenarios, projecting high emissions for the future. Unlikely, B2 projected a lower emission for the future and it is considered as “environmental” case scenarios. Results from simulation showed that during the calibration and validation stage, the SDSM model was well acceptable in regards to its performance in downscaling of daily and annual rainfalls. Under both scenarios A2 and B2, during the prediction period of 2010–2099, changes of annual mean rainfall in the Upper Kurau River basin would present a trend of increased rainfall in 2020s; insignificant changes in the 2050s; and a surplus of rainfall in the 2080s, as compared to the mean values of the base period. Annual mean rainfall would increase by about 33.7% under scenario A2 and increase by 27.9% under scenario B2 in the 2080s. Most of the areas of the Upper Kurau River Basin were dominated by increasing trend of rainfall and will become wetter in the future.

Projected climate changes in four different regions in Colombia

BackgroundConsidering the lack of research over this region the Statistical Downscaling Model (SDSM) was used as a tool for downscaling meteorological data statistically over four representative regions in the eastern side of Colombia. Data from the two Global Climate Models CanESM2 and IPSL-CM5A-MR, which are part of the CMIP5-project have been used to project future maximum and minimum temperature, precipitation and relative humidity for the periods 2021–2050 and 2071–2100. For both models, the Representative Concentration Pathways RCP2.6 and RCP8.5 were considered, representing two different possible future emission trajectories and radiative forcings. Predictor variables from the National Centre for Environmental Prediction (NCEP-DOE 2) reanalysis dataset, together with analyzed correlation coefficient (R) and root mean square error (RMSE) were used as performance indicators during the calibration and validation process.ResultsResults indicate that Maximum and minimum temperature is projected to increase for both Global Climate Models and both Representative Concentration Pathways; relative humidity shows a decreasing trend for all scenarios and all regions; and precipitation shows a slight decrease over three regions and an increase over the warmest region. As expected, the results of the simulation for the period 2071–2100 show a more drastic change when compared to the baseline period of observations.ConclusionsThe SDSM model proves to be efficient in the downscaling of maximum/minimum temperature as well as relative humidity over the studied regions; while showing a lower performance for precipitation, agreeing with the results for other statistical downscaling studies. The results of the projections offer good information for the evaluation of possible future-case scenarios and decision-making management.

The potential of canonical correlation analysis in multivariable screening of climate model

The statistical downscaling model (SDSM) been used to analyse the potential changes of local climate trend in the long term. The difficulty of the SDSM model in selecting the best predictors group which having good association to the local climate. Even the SDSM provides screening process to analyse the predictor-rainfall relationship, however it has limited ability in analysing multiple variables from 26 predictors with 10 rainfall stations around Kedah state, Malaysia. In this regard, the Canonical Correlation Analysis (CCA) been used to analyse the multi predictor-rainfall relationships. The concept of canonical coefficient is sufficient to show the capability and reliability of the predictors based on the percentages of variance that can explained in the dependent variable using the independent variable. There were 10 predictors’ group have been developed and one predictor’s group was built based on the CCA result. The performances of these predictors groups were tested using statistical analyses. Results revealed that the predictors group selected by the CCA method has produced smaller values of MAE and MSE for all stations except at station of Ladang Tanjung Pauh. The box plot’s results, which generated from one hundred simulated samples, indicated that the performance of CCA method was remarkable. The presence of discrepancies in the HadCM3-A2 and HadCM3-B2 scenario simulations were relatively small and considered acceptable.

Applicability Analysis of SDSM Technology to Climate Simulation in Xingtai City, China

With global warming, it is having a significant impact on the variation of precipitation and temperature. Both of them bring extreme discomfort to human’s life and social development. The city of Xingtai in China, have suffered from many high temperatures and floods, which are resulted from climate change. Therefore, the purpose of this paper is to study the influence of climate change (precipitation and temperature) on water resources in Xingtai city by using the Statistical Downscaling Model (SDSM). SDSM is a coupled downscaling method based on multivariate regression and weather generator, which can effectively solve the spatial scale mismatch problem of small-scale hydrological response and large-scale climate information. Compared with other statistical methods, SDSM can be operated more simply and easily, and its results would be much better. The results show that: (1) the performances of calibration SDSM model are basically acceptable; (2) SDSM can better simulate the trend of precipitation and temperature; (3) The determination coefficients (R2) of measured and simulated values about minimum temperature, maximum temperature, average temperature and precipitation in the verification period can be above 95%, 94%, 93% and 64% respectively.

Modeling Climate Change Projections for Ferozpur Sub-catchment of Jhelum Sub-basin of Kashmir Valley

Aims: The study aimed at modeling the climate change projections for Ferozpur subcatchment of Jhelum sub-basin of Kashmir Valley using the SDSM model.
 Study Design: The study was carried out in three different time slices viz Baseline (1985-2015), Mid-century (2030-2059) and End-century (2070-2099).
 Place and Duration of Study: Division of Agricultural Engineering, SKUAST-K, Shalimar between August 2015 and July 2016.
 Methodology: Statistical downscaling model (SDSM) was applied in downscaling weather files (Tmax, Tminand precipitation). The study includes the calibration of the SDSM model by using Observed daily climate data (Tmax, Tmin and precipitation) of thirty one years and large scale atmospheric variables encompassing National Centers for Environmental Prediction (NCEP) reanalysis data, the validation of the model, and the outputs of downscaled scenario A2 of the Global Climate Model (GCM) data of Hadley Centre Coupled Model, Version 3 (HadCM3) model for the future. Daily Climate (Tmax, Tmin and precipitation) scenarios were generated from 1961 to 2099 under A2 defined by Intergovernmental Panel on Climate Change (IPCC).
 Results: The results showed that temperature and precipitation would increase by 0.29°C, 255.38 mm (30.97%) in MC (Mid-century) (2030-2059); and 0.67oC and 233.28 mm (28.29%) during EC (End-century) (2070-2099), respectively.
 Conclusion: The climate projections for 21st century under A2 scenario indicated that both mean annual temperature and precipitation are showing an increasing trend.

Comparison of two statistical climate downscaling models: a case study in the Beijing region, China

Climate change can have a significant impact on the hydrological cycle. This article focuses on the comparison of the statistical down scaling model (SDSM) and the automated statistical downscaling model (ASD), which are applied to global climate model (GCM) predictions for the Beijing region. Through the analysis of the evaluation indices in the calibration and validation periods, the results show that both downscaling models simulate the temperature and evapotranspiration well, but the simulation of precipitation is not as good as that of other climate factors. The overall performance of ASD model is slightly superior to that of SDSM model, especially in the process of predictor's selection. The future climate change downscaled by the two models shows an analogous trend as well. The temperature and evapotranspiration show a general increasing trend. The precipitation shows a different trend with an increasing trend in the south and a decreasing trend in the north.

Comparison of two statistical climate downscaling models: a case study in the Beijing region, China

Climate change can have a significant impact on the hydrological cycle. This article focuses on the comparison of the statistical down scaling model (SDSM) and the automated statistical downscaling model (ASD), which are applied to global climate model (GCM) predictions for the Beijing region. Through the analysis of the evaluation indices in the calibration and validation periods, the results show that both downscaling models simulate the temperature and evapotranspiration well, but the simulation of precipitation is not as good as that of other climate factors. The overall performance of ASD model is slightly superior to that of SDSM model, especially in the process of predictor's selection. The future climate change downscaled by the two models shows an analogous trend as well. The temperature and evapotranspiration show a general increasing trend. The precipitation shows a different trend with an increasing trend in the south and a decreasing trend in the north.

Application of physical scaling towards downscaling climate model precipitation data

Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2–4 day), and long (more than 5-day) precipitation events is projected.

Evaluation of Climate Change Impact on Runoff: A Case Study

Background/Objectives: In this study we have investigating Evaluation of Climate Change Impact on runoff. Methods/Statistical Analysis: In this study, outputs of HADCM3 (Hadley Center Coupled Model, version 3) GCM (General Circulation Model) under A2 scenario is used to investigate the climate change impact on runoff of Gaveh Rood catchment in western Iran. The SDSM (Statistical Down Scaling Model) is employed to statistically downscale climate variables of temperature and precipitation. A runoff-rainfall Hydrological Model, named HYMOD is used for developing runoff time series under climate change impacts regarding the downscaled climate variables. Findings: The performance of calibrated SDSM model shows its well performance in rainfall and temperature downscaling in the study area. The downscaling results show considerable changes in temperature and precipitation in future under climate change impacts. In the period of 2020-2099, the mean temperature and precipitation is increased by 5-8.5% and 3-5%, respectively, relative to the observed period of 1989-2000. The calibration and validation results of the developed HYMOD show that it the mean runoff decreased by 7-16% (11.5%, on average) in the predicted period relative to the observed period. Applications/Improvement: These changes highly affect the water resources systems and special attention should be given to them in future planning of water resources.

Downscaling approach to develop future sub-daily IDF relations for Canberra Airport Region, Australia

Abstract. Downscaling of climate projections is the most adopted method to assess the impacts of climate change at regional and local scale. In the last decade, downscaling techniques which provide reasonable improvement to resolution of General Circulation Models' (GCMs) output are developed in notable manner. Most of these techniques are limited to spatial downscaling of GCMs' output and still there is a high demand to develop temporal downscaling approaches. As the main objective of this study, combined approach of spatial and temporal downscaling is developed to improve the resolution of rainfall predicted by GCMs. Canberra airport region is subjected to this study and the applicability of proposed downscaling approach is evaluated for Sydney, Melbourne, Brisbane, Adelaide, Perth and Darwin regions. Statistical Downscaling Model (SDSM) is used to spatial downscaling and numerical model based on scaling invariant concept is used to temporal downscaling of rainfalls. National Centre of Environmental Prediction (NCEP) data is used in SDSM model calibration and validation. Regression based bias correction function is used to improve the accuracy of downscaled annual maximum rainfalls using HadCM3-A2. By analysing the non-central moments of observed rainfalls, single time regime (from 30 min to 24 h) is identified which exist scaling behaviour and it is used to estimate the sub daily extreme rainfall depths from daily downscaled rainfalls. Finally, as the major output of this study, Intensity Duration Frequency (IDF) relations are developed for the future periods of 2020s, 2050s and 2080s in the context of climate change.

Evaluation of future precipitation scenario using statistical downscaling model over humid, subhumid, and arid region of Nepal—a case study

Statistical downscaling model (SDSM) was applied in downscaling precipitation in the three climatic regions of Nepal. The study includes the calibration of the SDSM model by using large-scale atmospheric variables encompassing National Centers for Environmental Prediction (NCEP) reanalysis data, the validation of the model, and the outputs of downscaled scenarios A2 and B2 of the HadCM3 model for the future. The average R2 value during validation period was 0.84, indicating the good applicability of SDSM for simulating precipitation. Under both scenarios A2 and B2, during the prediction period of 2010–2099, the change of annual mean precipitation in the three climatic regions would present a tendency of surplus of precipitation as compared to the mean values of the base period. On the average for all three climatic regions of Nepal, the annual mean precipitation would increase by about 13.75 % under scenario A2 and increase near about 11.68 % under scenario B2 in the 2050s. The model showed better performance over humid region; moreover, simulated results for the peak monsoon months seem to be overestimated over subhumid and arid regions.

Investigation of Precipitation and Temperature Change Projections in Werii Watershed, Tekeze River Basin, Ethiopia; Application of Climate Downscaling Model

Climate change is an overwhelming global issue. Everything, living or non-living in one or another way, have in contact with climate change. A statistical downscaling model (SDSM) was used to investigate future climate projections based on precipitation and temperature using (Regional climate Model) for A1B and B1 emission scenarios in Werii watershed (1797 Km2). Four meteorological stations, namely; Abyiadi, Adwa, Hawzen and Adigrat were selected based on proximity to the watershed and data availability. For A1B scenario, precipitation is likely to increase in each station by 11%, 34%, 31% and 20%, whereas for B1 scenario, precipitation will increase by 10%, 33%, 33% and 25% at Abyiadi, Adwa, Hawzen and Adigrat stations respectively by 2050. Change in maximum temperature shows increasing at Hawzen for A1B (0.16°C) and B1 (0.2°C) and smaller change at Adwa (0.06°C for A1B and -0.01°C for B1). The maximum temperature is expected to change in the range of -0.01°C to 0.2°C. Similarly, change in minimum temperature is expected to increase positively with maximum at Hawzen station for A1B (0.34°C) and B1 (0.29°C) and smaller change is likely at Adigrat station (0.07°C for A1B and 0.09°C for B1). Generally, future projection shows the change in precipitation and temperature is positive and will show an increasing trend in the period from 2015 to 2050. The REMO model and SDSM have simulated well in Werii watershed. Hence, REMO and SDSM model can be used in similar watersheds in the semi-arid regions.

Assessment of future precipitation indices in the Shikoku region using a statistical downscaling model

In this study, we used the statistical downscaling model (SDSM) to estimate mean and extreme precipitation indices under present and future climate conditions for Shikoku, Japan. Specifically, we considered the following mean and extreme precipitation indices: mean daily precipitation, R10 (number of days with precipitation >10 mm/day), R5d (annual maximum precipitation accumulated over 5 days), maximum dry-spell length (MaDSL), and maximum wet-spell length (MaWSL). Initially, we calibrated the SDSM model using the National Center for environmental prediction (NCEP) reanalysis dataset and daily time series of precipitation for ten locations in Shikoku which were acquired from the surface weather observation point dataset. Subsequently, we used the validated SDSM, using data from NCEP and outputs form general circulation models (GCM), to predict future precipitation indices. Specifically, the HadCM3 GCM was run under the special report on emissions scenarios (SRES) A2 and B2 scenarios, and the CGCM3 GCM was run under the SRES A2 and A1B scenarios. The results showed that: (1) the SDSM can reasonably be used to simulate mean and extreme precipitation indices in the Shikoku region; (2) the values of annual R10 were predicated to decrease in the future in northern Shikoku under all climate scenarios; conversely, the values of annual R10 were predicted to increase in the future in the range of 0–15 % in southern and western Shikoku. The values of annual MaDSL were predicted to increase in northern Shikoku, and the values of annual MaWSL were predicted to decrease in northeastern Shikoku; (3) the spatial variation of precipitation indices indicated the potential for an increased occurrence of drought across northeastern Shikoku and an increased occurrence of flood events in the southwestern part of Shikoku, especially under the A2 and A1B scenarios; (4) characteristics of future precipitation may differ between the northern and southern sides of the Shikoku Mountains. Regional variations in extreme precipitation indices were not notably evident in the B2 scenario compared to the other scenarios.

Uncertainty analysis of statistical downscaling models using general circulation model over an international wetland

Regression-based statistical downscaling model (SDSM) is an appropriate method which broadly uses to resolve the coarse spatial resolution of general circulation models (GCMs). Nevertheless, the assessment of uncertainty propagation linked with climatic variables is essential to any climate change impact study. This study presents a procedure to characterize uncertainty analysis of two GCM models link with Long Ashton Research Station Weather Generator (LARS-WG) and SDSM in one of the most vulnerable international wetland, namely “Shadegan” in an arid region of Southwest Iran. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. Uncertainties were then evaluated from comparing monthly mean dry and wet spell lengths and their 95 % CI in daily precipitation downscaling using 1987–2005 interval. The uncertainty results indicated that the LARS-WG is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % uncertainty bounds while the SDSM model is the least capable in this respect. The results indicated a sequences uncertainty analysis at three different climate stations and produce significantly different climate change responses at 95 % CI. Finally the range of plausible climate change projections suggested a need for the decision makers to augment their long-term wetland management plans to reduce its vulnerability to climate change impacts.

Improved statistical downscaling of daily precipitation using SDSM platform and data‐mining methods

ABSTRACTIn this paper, an extension of the statistical downscaling model (SDSM), namely data‐mining downscaling model (DMDM), has been developed. DMDM has the same platform as the most cited statistical downscaling models, namely SDSM and ASD. Multiple linear regression (MLR), ridge regression (RR), multivariate adaptive regression splines (MARS) and model tree (MT) constitute the mathematical core of DMDM. DMDM uses linear basis functions in MARS and linear regression rules in MT to keep the linear structure of SDSM; therefore, all of the SDSM assumptions are also valid in DMDM. These methods highlight the effect of data partitioning for meteorological predictors in the downscaling procedure. Inputs and output of the presented approaches are the same as SDSM and ASD. In the case study of this research, NCEP/NCAR databases have been used for calibration and validation. According to the inherent linearity of the methods, suitable predictor selection has been done with stepwise regression as a preprocessing stage. The results of DMDM have been compared with observed precipitation in 12 rain gauge stations that are scattered in different basins in Iran and represent different climate regimes. Comparison between the results of SDSM and DMDM has indicated that the presented approach can highly improve downscaling efficiency in terms of reproducing monthly standard deviation and skewness for both calibration and validation datasets. Among the proposed methods in DMDM, the results of the case study have shown that MT has provided better performances both in modelling occurrence and amount of precipitation. Also, MT is potentially recognized as a powerful diagnostic tool that could extract information in key atmospheric drivers affecting local weather. It also has fewer parameters during dry seasons, in which the number of historical precipitation events might not be enough for calibrating SDSM model in many arid and semi‐arid regions.

Downscaling technique uncertainty in assessing hydrological impact of climate change in the Upper Beles River Basin, Ethiopia

We investigate the uncertainty associated with downscaling techniques in climate impact studies, using the Upper Beles River Basin (Upper Blue Nile) in Ethiopia as an example. The main aim of the study is to estimate the two sources of uncertainty in downscaling models: (1) epistemic uncertainty and (2) stochastic uncertainty due to inherent variability. The first aim was achieved by driving a Hydrologic Engineering Centre-Hydrological Modelling System (HEC-HMS) model with downscaled daily precipitation and temperature using three downscaling models: Statistical Downscaling Model (SDSM), the Long Ashton Research Station Weather Generator (LARS-WG) and an artificial neural network (ANN). The second objective was achieved by driving the hydrological model with individual downscaled daily precipitation and temperature ensemble members, generated by using the stochastic component of the SDSM. Results of the study showed that the downscaled precipitation and temperature time series are sensitive to the downscaling techniques. More specifically, the percentage change in mean annual flow ranges from 5% reduction to 18% increase. By analyzing the uncertainty of the SDSM model ensembles, it was found that the percentage change in mean annual flow ranges from 6% increase to 8% decrease. This study demonstrates the need for extreme caution in interpreting and using the output of a single downscaling model.