Drought Classes Research Articles

Regional applicability analysis of four drought indices in Chongzuo of South China

Based on the precipitation anomaly percentage index (Pa), the relative moisture index (MI), the standardized precipitation index (SPI), and the standardized precipitation evapotranspiration index (SPEI), the regional applicability was examined in Chongzuo over the period 1961to 2012. The results showed that: (1) The MI and Pa significantly underestimated severe drought and extreme drought at annul time scale. The monitoring result of annual SPEI and SPI was accurate and more close to the actual drought level. (2) The frequency range of different drought class is close to actual level for the SPI and SPEI at different time scale. The severe and extreme drought seldom occurred using Pa and MI monitoring results, the adaptability and reliability of MI were relatively lower in Chongzuo. (3) The annual MI and Pa showed smaller coverage with the severity of moderate and above drought than statistical results of SPI and SPEI in Chongzuo. (4) The SPI was found more sensitive to precipitation changes than the SPEI at 1-month scale in dry period. SPI and SPEI can be properly used for the monitoring as well.

An analysis of droughts in Iran during 1988–2017

Understanding how drought changes have occurred is imperative to enhance the ability of mitigating drought impacts. This consideration encourages carrying out a comprehensive analysis of drought phenomenon in Iran using the standardized precipitation evapotranspiration index (SPEI) on various time scales based on data recorded at 100 weather stations over the period 1988–2017. We employed a nonparametric distribution i.e. the kernel density estimator instead of the commonly-used three-parameter log-logistic distribution for estimating the cumulative distribution functions of the monthly aggregated water deficits/surpluses, and found that the kernel density estimates frequencies of drought classes more accurately than the three-parameter log-logistic. Thereafter, an analysis was conducted by applying a principal component analysis and a Mann–Kendall test to spatio-temporal variables of the nonparametric SPEI at different time scales. Moreover, a bivariate risk assessment was done by calculating the joint exceedance probability using copula functions which depicts joint behavior of the drought characteristics. The spatial variability of drought in Iran revealed the existence of five coherent regions i.e. the northwestern, the southwestern, the southeastern, the northern and the central/eastern over the country. Temporal analysis indicates significant negative trends of surface water balance in all regions by producing a dry condition throughout the country, especially in two last decades (i.e. 1998–2017). However, the northwestern region is dominated by the highest risk of droughts and experienced droughts with longer durations and higher severities. This study was a forward step in considering an aggregative perspective of meteorological, agricultural and hydrological water deficit.

Modeling and mapping of climatic classification of Pakistan by using remote sensing climate compound index (2000 to 2018)

The entire world is collectively facing the problem of climate change. The deterioration of the earth’s climate change is being noticed and felt most apparently in Southeast Asia and predominantly in Pakistan. Pakistan is a victim of climate change, due to which Pakistan faces several geographical, political, economic and even social problems. The harmful impacts of climate change in the form of smog and abnormal heat waves have claimed the life of many Pakistanis. Climate change has brought disastrous impact on the agrarian economy of Pakistan, which has plunged the country into awful straits. Climatic change is a slow and continuous process. It is needed that climatic changes in an area should be traced out in time to face upcoming climatic challenges. Present research work has traced out such changes and introduced a new climatic classification scheme for the climate of Pakistan, by using remote sensing (RS) and a new climatic compound index that is RSCCI gives a calculated value, which is used to describe the state and the changes in the climatic system of an area. RSCCI is the combination of different indices. On the basis of RSCCI, a climatic index, spatiotemporal investigation is conducted to measure aridity, humidity and semi-aridity all over Pakistan. In order to find out the extent of these climatic conditions, three MODIS dataset images of 250 m resolution were acquired. RS applications are used effectively to assess the changing climatic trends for the period of eighteen years in Pakistan from 2000 to 2018. On the basis of the above-mentioned results, a new climatic classification has been introduced with five major classes, i.e., drought, aridity, humidity, wetlands and cold drought. The area of five classes has been calculated by using RS tools and RSCCI for the years of 2000 and 2018. New climatic classification of Pakistan divides Pakistan into five regions which is based on RSCCI . There is an increase in arid region of 1.84% in Pakistan from RSCCI 2000 to RCSSI 2018. Similarly, there is also increase in an area of wetlands and humid regions of Pakistan, i.e., 1.9% and 9.72%, respectively, from RSCCI 2000 to RCSSI 2018. On the other hand, there is 0.78% reduction of area of cold drought region, 8.49% reduction in moderate drought and 4.19% reduction in an area of intense drought classes from RSCCI 2000 to RCSSI 2018, which is a positive change. The results show dramatic changes which advocate the need of a new climatic classification for Pakistan. This new climate classification of Pakistan is based on 18 years of data only. Dramatic climatic changes could be imagined and predicted within next 30 years in Pakistan.

Detection of drought area using greening index and wetness index in Sukaresmi Subdistrict, Regency Cianjur, West Java

Drought is a natural disaster that reduces the supply of water in the atmosphere or on the ground, causing plants cannot grow normally. Drought is causing the decrease of plantation and harvest area, productivity, and quality of agricultural products. Sukaresmi Subdistrict, Cianjur Regency is depending on agricultural sector and has the risk of loss from drought. This research aims to detect spatial and temporal of drought areas and to analyze drought areas according to the physical condition of the land (altitude, slope, soil type, and land use) in Sukaresmi Subdistrict. This research uses Landsat 8 OLI imagery data from July-September 2013 and June-August 2017 analyzed by Normalization of Differences Vegetation Index (NDVI) and Tasseled Cap Transformation (TCT). Overlay of the moderate, high, and very high drought classes in the same year will generate drought areas that overlap each year. Drought areas during 2013 and 2017 are 726 hectares or 8% of the total Sukaresmi Subdistrict area, dominates the Kawang Luwuk Village and Cibadak Village in the western part of Sukaresmi Subdistrict. Mostly, drought areas located in paddy field land use (322.18 hectares); brown latosol soil type (447.85 hectares); and altitude of 500-1000 msl (624.1 hectares).

Probabilistic hydrological drought index forecasting based on meteorological drought index using Archimedean copulas

Abstract Hydrological drought forecasting is considered a key component in water resources risk management. As sustained meteorological drought may lead to hydrological drought over time, it is conceptually feasible to capitalize on the dependency between the meteorological and hydrological droughts while trying to forecast the latter. As such, copula functions are powerful tools to study the propagation of meteorological droughts into hydrological droughts. In this research, monthly precipitation and discharge time series were used to determine Standardized Precipitation Index (SPI) and Standardized Hydrological Drought Index (SHDI) at different time scales which quantify the state of meteorological and hydrological droughts, respectively. Five Archimedean copula functions were adopted to model the dependence structure between meteorological/hydrological drought indices. The Clayton copula was identified for further investigation based on the p-value. Next, the conditional probability and the matrix of forecasted class transitions were calculated. Results indicated that the next month's SHDI class forecasting is promising with less than 10% error. Moreover, extreme and severe meteorological drought classes lead to hydrological drought condition with a more than 70% probability. Other classes of meteorological drought/wet conditions lead to normal hydrological (drought) condition with less than 50% probability and to wet hydrological condition with over 20% probability.

An ensemble procedure for pattern recognition of regional drought

AbstractDrought being a complex natural hazard is challenging to monitor. Although standardized procedures for drought characterization are commonly used, the uncertainty about accurate drought characterization under various procedures always exists. Therefore, for effective drought mitigation policies, it is necessary to compare the performance of new and the existing drought procedures. In this study, we proposed a new procedure—The Drought Intensity Pattern Determinate (DIPD) to examine and compare the spatial‐temporal intensities of various drought episodes based on Standardized Drought Indices (SDI). The DIPD framework provides a new tool—The Drought Concentration Index (DCI) to understand the annual intensity of drought classes by alluding the observed frequencies of drought episodes. At the preliminary level of assessment, DIPD is employed at 52 meteorological stations of Pakistan, and compared three commonly used standardized drought indices: Standardized Precipitation Index (SPI), Standardized Evapotranspiration Index (SPEI) and Standardized Precipitation Temperature Index (SPTI). Exploratory analysis shows a mixed picture as all the drought indices behaving similar for some patterns of drought classes, while some classes show significant discrepancies. In addition, we assessed and compared the spatial distributions using spatial Poisson lognormal model by assuming SDI as a non‐Gaussian process for individual total classes of observed DCI. Results show that the proposed method has ability to incorporate the inner variability in patterns of drought classes. In summary, the DIPD provided a new regional based comparative framework, which accounts the all possible episodes of drought classes and effect of inner discrepancies among quantitative values of SDI.

Probabilistic Characteristics of Drought Propagation from Meteorological to Hydrological Drought in South Korea

A reliable understanding of propagation from meteorological to hydrological drought is necessary for accurate forecasting of hydrological droughts. Our current understanding of drought propagation is limited because the characteristics of each drought and the lag time between droughts are uneven due to spatial variability in underlying conditions and climatic variables. The objective of this study is to identify the probabilistic relationship between lag time and the occurrence of different classes of hydrological drought in South Korea while considering propagation factors and using a Bayesian network model. The results show that the propagation rate varied from 27% to 60% and the maximum value of the lag time was projected to be 4 to 10 weeks. For moderate intensity of meteorological drought, the occurrence probability of lag time was high and decreased when the intensity changed to severe and extreme. In addition, the probability for each class of hydrological drought intensity varied with space and increased as the intensity of propagated meteorological drought class changed from moderate to extreme. The observed probabilistic characteristics of hydrological drought may be useful in decision-making strategies for mitigating water shortage.

Seasonal drought forecasting in arid regions, using different time series models and RDI index

AbstractConfronting drought and reducing its impacts requires modeling and forecasting of this phenomenon. In this research, the ability of different time series models (the ARIMA models with different structures) were evaluated to model and predict seasonal drought based on the RDI drought index in the south of Iran. For this purpose, the climatic data of 16 synoptic stations from 1980 to 2010 were used. Evaluation of time series models was based on trial and error. Results showed drought classes varied between ‘very wet’ to ‘severely dry’. The more occurrence frequency of ‘severely dry’ class compared to other drought classes represent the necessity of drought assessment and the importance of managing the effects of this phenomenon in the study area. Results showed that the highest severity of drought occurred at Abadeh, Shiraz, Fasa, Sirjan, Kerman, Shahre Babak and Saravan stations. According to selecting the best model fitted to the computed three-month RDI time series, results indicated that the MA model based on the Innovations method resulted in maximum cases with the best performance (37.5% of cases). The AR model based on the Yule–Walker method resulted in minimum cases with the best performance (6.3% of cases) in seasonal drought forecasting.

A comparative study of precipitation-based drought indices with the aim of selecting the best index for drought monitoring in Iran

Drought is a natural disaster that occurs frequently or intermittently and affects all economic, social, and environmental aspects of a society. The present study is an attempt to compare different drought indices and select the best drought index for drought monitoring in Iran. To this end, seven drought indices including SPI, PN, ZSI, DI, CZI, EDI, and MCZI were used for 1-, 3-, 6-, 12-, and 24-month timescales. The monthly precipitation data of 41 synoptic stations in Iran over a period of 28 years (1985–2013) were used in order to achieve the research objectives. After the required databases were obtained, the effective drought index (EDI) was used as a basic indicator and as the basis of calculations in order to choose the best index for drought monitoring. The results showed that the EDI index indicates the general drought classes better than other indices. Of the indices used in the study, three indices namely ZSI, SPI, and CZI showed the highest consistency in their behavior. In addition, the 6-, 12-, and 24-month timescales showed very similar drought results in the abovementioned indices. Moreover, the behavior of DI and PN indices proved to be inconsistent with the behavior of other indices, and these indices proved to be unsuitable for drought monitoring in Iran. The MCZI index, as compared to the EDI index, showed no acceptable performance in drought monitoring. Finally, SPI and EDI indices were detected as the first and second best indices for drought monitoring in Iran.

A Probabilistic Weighted Joint Aggregative Drought Index (PWJADI) criterion for drought monitoring systems

Drought is a complex natural hazard. Its several adverse impacts are prevailing in almost all climatic zones around the world. In this regards, drought monitoring and forecasting play a vital role in making drought mitigation policies. Therefore, several drought monitoring tools based on probabilistic models had been developed for precise and accurate inferences of drought severity and its effects. However, risk of inaccurate determination of drought classes always exists in probabilistic models. To overcome this issue, we proposed a new system based Probabilistic Weighted Joint Aggregative Drought Index (PWJADI) criterion for three multi-scalar drought indices, namely Standardized Precipitation Index (SPI), Standardized Precipitation Temperature Index (SPTI), and Standardized Precipitation Evapotranspiration Index (SPEI) at one-month time scale. By the basic assumption of the Markov chain, the PWJADI is based on the temporal switched weights that are propagated from the transition probability matrix of each temporal classification of drought index. Application of the proposed method is made for three meteorological stations of Pakistan. We found that our proposed model has ability to restructure the drought classes by capturing and bending the information from the historical behaviour of each drought class. Consequently, to make accurate and precise drought mitigation policies, the proposed method may integrate into effective drought monitoring systems.

Selection of appropriate time scale with Boruta algorithm for regional drought monitoring using multi-scaler drought index

Spatial distribution of drought plays key role specifically in hydrological research. Drought is a complex stochastic natural hazard caused by prolonging shortage of rainfall and available water resources. The multi-scalar drought indices (based on probability distribution) are commonly used for making effective drought mitigation policies. In addition, the multi-scalar drought indices have great extent of the inaccurate determination of drought classes due to its probabilistic nature. However, the interpretation and applicability of various time scales are cumbersome for multi-scalar drought in various meteorological stations at a particular region. In this regards, accurate estimation and continuous monitoring of future drought at regional level requires a more appropriate and important time scale with respect to regions under study. In this study, we aimed to investigate the appropriate time scale for multi-scalar drought indices by using geo-reference points of meteorological stations. We used Boruta algorithm with two random forest adapters of machine learning algorithms for regionalized optimization of drought monitoring time scale. We explored the appropriate time scale for the Standardized Precipitation Temperature Index (SPTI) of 52 meteorological stations that are located across Pakistan. Results show that the significant importance of SPTI-1 (1-month time scale) for further spatial and temporal studies. That is, being high ranked and prominence, SPTI-1 has the ability to capture the characteristics of all other time scales that are in some circumstances applicable for drought characterization and classification.

Models validation and forecasting for drought risk reduction in developing countries

Drought risk reduction should be based on effective monitoring and early warning systems affordable by both privileged and vulnerable nations and regions. Drought can be pandemic over months and hence, Standardized Precipitation Index (SPI) at the time scale of one – month was modelled for drought monitoring and real-time forecasting. Developed predictive SPI models were used to forecast droughts in the seven meteorological stations in the region in the year 2009 and the forecast negative SPIs, reflecting droughts, were compared with drought class thresholds to predict and identify drought occurrences at different phases such as emergence watch, warning and emergency. The results showed drought forecasts of emergence phase at most of the stations. This implied that monitoring was necessary so that warning alert could be declared as soon as the drought emergence phase progressed into warning stage. It is recommended that the models should be used to forecast droughts ahead on monthly bases. Also, the simple predictive models should be developed for other less developed regions as the early warning services component of people-centred early warning systems for effective drought risk reduction in developing countries. Key words: Drought risk reduction, hazard risks, standardized precipitation index

A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes

Drought is a complex stochastic natural hazard caused by prolonged shortage of rainfall. Several environmental factors are involved in determining drought classes at the specific monitoring station. Therefore, efficient sequence processing techniques are required to explore and predict the periodic information about the various episodes of drought classes. In this study, we proposed a new weighting scheme to predict the probability of various drought classes under Weighted Markov Chain (WMC) model. We provide a standardized scheme of weights for ordinal sequences of drought classifications by normalizing squared weighted Cohen Kappa. Illustrations of the proposed scheme are given by including temporal ordinal data on drought classes determined by the standardized precipitation temperature index (SPTI). Experimental results show that the proposed weighting scheme for WMC model is sufficiently flexible to address actual changes in drought classifications by restructuring the transient behavior of a Markov chain. In summary, this paper proposes a new weighting scheme to improve the accuracy of the WMC, specifically in the field of hydrology.

IDENTIFIKASI DAN MITIGASI KERENTANAN KEKERINGAN DAS MOYO (Identification and mitigation of drought vulnerability in Moyo Watershed)

Drought identification is needed as a baseline for its mitigation. Several drought identification methods are available, but they need to be tested in dry climates. The purpose of this study was to identify dry area and its mitigation in Moyo Watershed, West Nusa Tenggara Province. Paimin and Syarif methods were used in the study. The results showed that the Paimin method produced a uniform vulnerability map, which was medium vulnerable for the entire watershed. This is because the applied parameters only have one class for the entire watershed. The Syarif method provides more variable results because the parameters produce several classes of vulnerabilities. However, the later method has a weakness in applying the depth of groundwater. Moyo watershed only had one class, whereas the depth of the groundwater level varied from 4 to 11 m in the dry season. The two methods produced the same results and there were classified as “medium susceptible” for Moyo watershed. For dry areas, it is necessary to reclassify the drought class of each parameter. By combining the two methods above, it is expected to obtain more accurate results. To get the Water Use Index parameter efficiently, the number of population and water usage should be more efficient. Extentions to the community about the effectively water usage is important. Adaptation to drought can also be done by making reservoirs and setting cropping patterns.

Application of a hybrid multiscalar indicator in drought identification in Beijing and Guangzhou, China

The Palmer drought severity index (PDSI) is physically based with multivariate concepts, but requires complicated calibration and cannot easily be used for multiscale comparison. Standardized drought indices (SDIs), such as the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), are multiscalar and convenient for spatiotemporal comparison, but they are still challenged by their lack of physical basis. In this study, a hybrid multiscalar indicator, the standardized Palmer drought index (SPDI), was used to examine drought properties of two meteorological stations (the Beijing and Guangzhou stations) in China, which have completely different drought climatologies. The results of our case study show that the SPDI is correlated with the well-established drought indices (SPI, SPEI, and PDSI) and presents generally consistent drought/wetness conditions against multiple indicators and literature records. Relative to the PDSI, the SPDI demonstrates invariable statistical characteristics and better comparable drought/wetness frequencies over time and space. Moreover, characteristics of major drought events (drought class, and onset and end times) indicated by the SPDI are generally comparable to those detected by the PDSI. As a physically-based standardized multiscalar drought indicator, the SPDI can be regarded as an effective development of the Palmer drought indices, providing additional choices and tools for practical drought monitoring and assessment.

Changes in the Carbon and Water Fluxes of Subtropical Forest Ecosystems in South-Western China Related to Drought

Drought impacts carbon and water fluxes of terrestrial ecosystems, which are strongly coupled. However, the magnitudes of response of carbon and water fluxes to drought are dependent on many processes, which are more complex than previously expected. Southern China experienced regional climatic perturbation events in the past decade and a two-year drought in 2009–2010. We used a terrestrial ecosystem model coupled with remotely sensed observations and metrological data to simulate the variations of net primary productivity (NPP), evapotranspiration (ET), and water-use efficiency (WUE) (i.e., NPP/ET) in south-western China during the period 2001–2010. Using the standard precipitation index (SPI) classifying different drought stresses, we also quantified the effect of drought on the ecosystem by comparing changes in modelled estimates of monthly WUE, NPP and ET under normal (i.e., baseline) and drought conditions (i.e., 2009 and 2010). The results indicated that NPP and ET showed synchronized declines in drought periods, with time-lag effects. Furthermore, drought-induced NPP decline was larger than ET reduction. An increasing trend in WUE from the moderate to extreme drought classes occurred not only in baseline conditions but also in drought conditions. Especially in the extreme drought period (January, 2010), WUE for the forest ecosystem typically showed a positive response to drought, indicating a drought-resilient forest ecosystem. Our study has important implications for understanding climate extreme effects on the carbon and water cycle of the forest ecosystem.

Modeling, prediction and trend assessment of drought in Iran using standardized precipitation index

Abstract Drought as an exigent natural phenomenon, with high frequency in arid and semi-arid regions, leads to enormous damage to agriculture, economy, and environment. In this study, the seasonal Standardized Precipitation Index (SPI) drought index and time series models were employed to model and predict seasonal drought using climate data of 38 Iranian synoptic stations during 1967–2014. In order to model and predict seasonal drought ITSM (Interactive Time Series Modeling) statistical software was used. According to the calculated seasonal SPI, within the study area, drought severity classes 4 and 3 had the greatest occurrence frequency, while classes 6 and 7 had the least occurrence frequency. Results indicated that the best fitted models were Moving-Average or MA (5) Innovations and MA (5) Hannan-Rissenen, with 60.53 and 15.79 percentage, respectively. On the other hand, results of the prediction as well, indicated that drought class 4 with the highest percentages, was the most abundant class over the study area and drought class 7 was the least frequent class. According to results of trend analysis, without attention to significance of them, observed seasonal SPI data series (1967–2014), in 84.21% of synoptic stations had a negative trend, but this percentage changes to 86.84% when studying the combination of observed and predicted simultaneously (1967–2019).

Monthly Prediction of Drought Classes Using Log-Linear Models under the Influence of NAO for Early-Warning of Drought and Water Management

Drought class transitions over a sector of Eastern Europe were modeled using log-linear models. These drought class transitions were computed from time series of two widely used multiscale drought indices, the Standardized Preipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI), with temporal scales of 6 and 12 months for 15 points selected from a grid over the Prut basin in Romania over a period of 112 years (1902–2014). The modeling also took into account the impact of North Atlantic Oscillation (NAO), exploring the potential influence of this large-scale atmospheric driver on the climate of the Prut region. To assess the probability of transition among different drought classes we computed their odds and the corresponding confidence intervals. To evaluate the predictive capabilities of the modeling, skill scores were computed and used for comparison against benchmark models, namely using persistence forecasts or modeling without the influence of the NAO index. The main results indicate that the log-linear modeling performs consistently better than the persistence forecast, and the highest improvements obtained in the skill scores with the introduction of the NAO predictor in the modeling are obtained when modeling the extended winter months of the SPEI6 and SPI12. The improvements are however not impressive, ranging between 4.7 and 6.8 for the SPEI6 and between 4.1 and 10.1 for the SPI12, in terms of the Heidke skill score.

Fuzzy rule-based forecast of meteorological drought in western Niger

Understanding the causes of rainfall anomalies in the West African Sahel to effectively predict drought events remains a challenge. The physical mechanisms that influence precipitation in this region are complex, uncertain, and imprecise in nature. Fuzzy logic techniques are renowned to be highly efficient in modeling such dynamics. This paper attempts to forecast meteorological drought in Western Niger using fuzzy rule-based modeling techniques. The 3-month scale standardized precipitation index (SPI-3) of four rainfall stations was used as predictand. Monthly data of southern oscillation index (SOI), South Atlantic sea surface temperature (SST), relative humidity (RH), and Atlantic sea level pressure (SLP), sourced from the National Oceanic and Atmosphere Administration (NOAA), were used as predictors. Fuzzy rules and membership functions were generated using fuzzy c-means clustering approach, expert decision, and literature review. For a minimum lead time of 1 month, the model has a coefficient of determination R2 between 0.80 and 0.88, mean square error (MSE) below 0.17, and Nash-Sutcliffe efficiency (NSE) ranging between 0.79 and 0.87. The empirical frequency distributions of the predicted and the observed drought classes are equal at the 99% of confidence level based on two-sample t test. Results also revealed the discrepancy in the influence of SOI and SLP on drought occurrence at the four stations while the effect of SST and RH are space independent, being both significantly correlated (at α < 0.05 level) to the SPI-3. Moreover, the implemented fuzzy model compared to decision tree-based forecast model shows better forecast skills.

Evaluation of Drought Condition in Arid and Semi- Arid Regions, Using RDI Index

Investigation of drought event has a great importance in the natural resources management and planning water resources management. One strategy to manage drought is to predict drought conditions by probabilistic tools. In this study climate data of 11 synoptic stations in south of Iran during 1980–2014 were used to estimate of seasonal drought based on RDI index. To prediction of drought (from 2015 to 2020) and analysis of changes trend of it, time series model, first-order Markov Chain model and parametric and non- parametric statistical methods were used. Results showed that MA (5), MA (10), AR (12) and AR (15) were the best time series models that fitted in data of all stations. According to results of prediction of drought classes, classes with normal and moderate dry condition had allocated the most frequency of seasonal drought classes from 2015 to 2020 based on time series model and Markov Chain method. Analysis of changes trend of drought classes showed that based on observed data (1980–2014) and predicted data (1980–2020) changes trend of drought classes in all stations had increasing trend based on parametric and non- parametric statistical methods but increasing trend in about 27% of stations include: Bandar Abbas, Bandar Lengeh, Jask and Shiraz had significant level of 5%. Finally result showed that the study area in 2020 compared to 2014 will be drier.