Estimating the Drought-Induced Yield Loss for Winter Wheat in a Semi-Arid Region of the Southern United States Using a Drought Index

The economy of most rural locations in the semi-arid region of Llano Estacado in the southern United States is predominantly based on agriculture, primarily beef and wheat (Triticum aestivum L.) production. This region is prone to drought and is projected to experience a drier climate. Droughts that coincide with the critical phenological phases of a crop can be remarkably costly. Although drought cannot be prevented, its losses can be minimized through mitigation measures if it is predicted in advance. Predicting yield loss from an imminent drought is an important need of stakeholders. One way to fulfill this need is using an agricultural drought index, such as the Agricultural Reference Index for Drought (ARID). Being plant physiology-based, ARID can represent drought-yield relationships accurately. This study developed an ARID-based yield model for predicting the drought-induced yield loss for winter wheat in this region by accounting for its phenological phase-specific sensitivity to water stress. The reasonable values of the drought sensitivity coefficients of the yield model indicated that it could reflect the phenomenon of water stress decreasing the winter wheat yields in this region reasonably. The values of the various metrics used to evaluate the model, including Willmott Index (0.86), Nash-Sutcliffe Index (0.61), and percentage error (26), indicated that the yield model performed fairly well at predicting the drought-induced yield loss for winter wheat. The yield model may be useful for predicting the drought-induced yield loss for winter wheat in the study region and scheduling irrigation allocation based on phenological phase-specific drought sensitivity.

The Research about A Regional Drought Identifying System Based on Dynamic Drought Index

With the data of daily precipitation and daily evaporation,dynamic drought index was calculated and compared with the identification standard of drought grade to qualify the severity of drought.According to the dynamic drought index,a regional drought identifying system was developed for the watershed between the reach of the Yangtze River and Huaihe River in Anhui Province by using VC++ working platform and Access database.This drought identifying system would be very useful to forecast and early warn the happening of drought in this area.

Spatial and temporal variation of drought index in a typical steep alpine terrain in Hengduan Mountains

This study describes the spatial and temporal variation of a drought index and makes inferences regarding the environmental factors that influence this variability in the Hengduan Mountains. A drought index is typically used to determine the moisture conditions and the magnitude of water deficiency in a given area. Based on data from 26 meteorological stations over the period 1960-2012, the spatial and temporal variations of the drought index were analyzed using a thin plate smoothing splines method that considered elevation as a covariate. The drought index was estimated based on the potential evapotranspiration(E0) as defined by the Penman Monteith model modified by FAO(1998). The results of the reported analysis showed that the drought index in the Hengduan Mountains has been decreasing since 1960 at a rate of-0.008/a. This represented a progressive shift from the "sub-humid" class, which typified the wider area in the Hengduan Mountains, toward the "humid" class, which appeared in the Hengduan Mountains areas. The drought index was relatively high in the north and low in the south and the variation of the drought index varied with seasons. The drought index showed increasing trends in summer and autumn and it is greater in autumn than in summer, while it showed a decreasing trend in spring and winter. Drought index is inversely proportional to the soil relative humidity and Normalized Difference Vegetation Index(NDVI).

Assessment of an Evapotranspiration Deficit Drought Index in Relation to Impacts on Ecosystems

Ecosystems have increasingly been subject to the challenge of heavy drought under global warming. To quantitatively evaluate the impacts of drought on ecosystems, it is necessary to develop a drought index that can sensitively depict the response of vegetation to drought evolution at a biological time scale. For the ability of direct connection between climate and ecosystem by deficit of evapotranspiration, in the present study, a drought index was defined based on standardized evapotranspiration deficit (SEDI), according to the difference between actual and potential evapotranspiration, to meet the need for highlighting drought impacts on ecological processes. Comparisons with traditional indices show that SEDI can reasonably detect droughts and climatic dry and wet transitions, especially at a monthly time scale, and can also regenerate long-term trends. Moreover, SEDI can more sensitively capture the biological changes of ecosystems in response to the dynamics of drought intensity, compared with the indices of precipitation and temperature. SEDI is more practical than the precipitation and temperature indices to highlight signals of biological effects in climate droughts. Hence, it has potential for use in assessments of climate change and its impact on ecosystems.

Introducing a drought index to a crop model can help to reduce the gap between the simulated and statistical yield

A well-established and pre-calibrated crop model can normally represent the overall characteristics of crop growth and yield.However,it can hardly include all relevant factors that affect the yield,and usually overestimates the crop yield when extreme weather conditions occur.In this study,the authors first introduced a drought index(the Standardized Precipitation Evapotranspiration Index)into a process-based crop model(the Agro-C model).Then,the authors evaluated the model’s performance in simulating the historical crop yields in a double cropping system in the Huang-Huai-Hai Plain of China,by comparing the model simulations to the statistical records.The results showed that the adjusted Agro-C model significantly improved its performance in simulating the yields of both maize and wheat as affected by drought events,compared with its original version.It can be concluded that incorporating a drought index into a crop model is feasible and can facilitate closing the gap between simulated and statistical yields.

Annual growth of Fagus orientalis is limited by spring drought conditions in Iran’s Golestan Province

Due to the lack of a uniform and accurate defi-nition of‘drought’,several indicators have been introduced based on different variables and methods,and the efficiency of each of these is determined according to their relationship with drought.The relationship between two drought indices,SPI(standardized precipitation index)and SPEI(standard-ized precipitation-evapotranspiration index)in different sea-sons was investigated using annual rings of 15 tree samples to determine the effect of drought on the growth of oriental beech(Fagus orientalis Lipsky)in the Hyrcanian forests of northern Iran.The different evapotranspiration calcula-tion methods were evaluated on SPEI efficiency based on Hargreaves-Samani,Thornthwaite,and Penman-Monteith methods using the step-by-step M5 decision tree regression method.The results show that SPEI based on the Penman-Monteith in a three-month time scale(spring)had similar temporal changes and a better relationship with annual tree rings(R^(2)=0.81)at a 0.05 significant level.Abrupt change and a decreasing trend in the time series of annual tree rings are similar to the variation in the SPEI based on the Penman-Monteith method.Factors affecting evapotranspiration,temperature,wind speed,and sunshine hours(used in the Penman-Monteith method),increased but precipitation decreased.Using non-linear modeling methods,SPEI based on Penman-Monteith best illustrated climate changes affecting tree growth.

Estimating the Yield Loss of Winter Wheat from Drought in the United States Southern Plains Region as Influenced by El Niño-Southern Oscillation (ENSO)

Wheat (Triticum aestivum L.) production is a major economic activity in most regional and rural areas in the Southern Plains, a semi-arid region of the United States. This region is vulnerable to drought and is projected to experience a drier climate in the future. Since the interannual variability in climate in this region is linked to an ocean-atmospheric phenomenon, called El Niño-Southern Oscillation (ENSO), droughts in this region may be associated with ENSO. Droughts that occur during the critical growth phases of wheat can be extremely costly. However, the losses due to an impending drought can be minimized through mitigation measures if it is predicted in advance. Predicting the yield loss from an imminent drought is crucial for stakeholders. One of the reliable ways for such prediction is using a plant physiology-based agricultural drought index, such as Agricultural Reference Index for Drought (ARID). This study developed ENSO phase-specific, ARID-based models for predicting the drought-induced yield loss for winter wheat in this region by accounting for its phenological phase-specific sensitivity to drought. The reasonable values of the drought sensitivity coefficients of the yield model for each ENSO phase (El Niño, La Niña, or Neutral) indicated that the yield models reflected reasonably well the phenomena of water stress decreasing the winter wheat yields in this region during different ENSO phases. The values of various goodness-of-fit measures used, including the Nash-Sutcliffe Index (0.54 to 0.67), the Willmott Index (0.82 to 0.89), and the percentage error (20 to 26), indicated that the yield models performed fairly well at predicting the ENSO phase-specific loss of wheat yields from drought. This yield model may be useful for predicting yield loss from drought and scheduling irrigation allocation based on the phenological phase-specific sensitivity to drought as impacted by ENSO.

Monitoring vegetation drought in the nine major river basins of China based on a new developed Vegetation Drought Condition Index

The effect of global climate change on vegetation growth is variable.Timely and effective monitoring of vegetation drought is crucial for understanding its dynamics and mitigation,and even regional protection of ecological environments.In this study,we constructed a new drought index(i.e.,Vegetation Drought Condition Index(VDCI))based on precipitation,potential evapotranspiration,soil moisture and Normalized Difference Vegetation Index(NDVI)data,to monitor vegetation drought in the nine major river basins(including the Songhua River and Liaohe River Basin,Haihe River Basin,Yellow River Basin,Huaihe River Basin,Yangtze River Basin,Southeast River Basin,Pearl River Basin,Southwest River Basin and Continental River Basin)in China at 1-month–12-month(T1–T12)time scales.We used the Pearson's correlation coefficients to assess the relationships between the drought indices(the developed VDCI and traditional drought indices including the Standardized Precipitation Evapotranspiration Index(SPEI),Standardized Soil Moisture Index(SSMI)and Self-calibrating Palmer Drought Severity Index(scPDSI))and the NDVI at T1–T12 time scales,and to estimate and compare the lag times of vegetation response to drought among different drought indices.The results showed that precipitation and potential evapotranspiration have positive and major influences on vegetation in the nine major river basins at T1–T6 time scales.Soil moisture shows a lower degree of negative influence on vegetation in different river basins at multiple time scales.Potential evapotranspiration shows a higher degree of positive influence on vegetation,and it acts as the primary influencing factor with higher area proportion at multiple time scales in different river basins.The VDCI has a stronger relationship with the NDVI in the Songhua River and Liaohe River Basin,Haihe River Basin,Yellow River Basin,Huaihe River Basin and Yangtze River Basin at T1–T4 time scales.In general,the VDCI is more sensitive(with shorter lag time of vegetation response to drought)than the traditional drought indices(SPEI,scPDSI and SSMI)in monitoring vegetation drought,and thus it could be applied to monitor short-term vegetation drought.The VDCI developed in the study can reveal the law of unclear mechanisms between vegetation and climate,and can be applied in other fields of vegetation drought monitoring with complex mechanisms.

Spatio-Temporal Analysis of Drought in the North-Eastern Coastal Region of Vietnam Using the Standardized Precipitation Index (SPI)

Spatio-temporal analysis of drought provides valuable information for drought management and damage mitigation. In this study, the Standardized Precipitation Index at the time scale of 6 months (SPI-6) is selected to reflect drought conditions in the North-Eastern coastal region of Vietnam. The drought events and their characteristics from 1981 to 2019 are detected at 9 meteorological stations and 10 Chirps rainfall stations. The spatio-temporal variation of drought in the study region is analyzed on the basis of the number, duration, severity, intensity, and peak of the detected drought events at the 19 stations. The results show that from 1981 to 2019 the drought events mainly occurred with 1-season duration and moderate intensity and peak. The number, duration, severity, and peak of the drought events were the greatest in the period 2001-2010 and were the smallest in the period 2011-2019. Among the 19 stations, the drought duration tends to decrease at 11 stations, increase at 7 stations, and has a slight variant at 1 station;the drought severity tends to decrease at 14 stations, increase at 4 stations, and has not a significant trend at 1 station;the drought intensity tends to decrease at 17 stations, increase at 1 station, and has a slight variant at 1 station;and the drought peak tends to decrease at 18 stations and increase at 1 station.

Regional Climate Models in the Simulation of the Drought of the 1970’s and 1980’s Years in Senegal (In West Africa)

West Africa was hit by an unprecedented drought in the 1970’s and 1980’s years, with dramatic consequences for surface and groundwater resources. In the context of climate change, there are many studies for the prediction of the increase in the occurrence of these droughts. To predict this situation in the Senegalese region, it is necessary to use regional climate models, which carrying out the study. This work deals with the interest to examine the capacity of the RCMs (regional climate models) in order to reproduce the deficit on the 1970’s year rainfall in Senegal. In this work, we used daily precipitation data from five (5) regional climate models to characterize the droughts in Senegal by using the SPI (Standardized Precipitation Index) on different time scales (3, 6, 12 and 24 months). For this purpose, the index was calculated over two distinct periods: 1951-1969 and 1970-1990. The results show that the period 1970-1990 was drier than the period 1951-1969. For the zonal average, the results show that the North of Senegal was more affected by this deficit rainfall than the South part. The analysis of the interannual variability of rainfall for some stations in Senegal shows that the drought did not start at the same time throughout the zone.

Spatio-Temporal Agricultural Drought Quantification in a Rainfed Agriculture, Athi-Galana-Sabaki River Basin

This study employs a quantitative approach to comprehensively investigate the full propagation process of agricultural drought, focusing on pigeon peas (the most grown crop in the AGS Basin) planting seasonal variations. The study modelled seasonal variabilities in the seasonal Standardized Precipitation Index (SPI) and Standardized Agricultural Drought Index (SADI). To necessitate comparison, SADI and SPI were Normalized (from −1 to 1) as they had different ranges and hence could not be compared. From the seasonal indices, the pigeon peas planting season (July to September) was singled out as the most important season to study agricultural droughts. The planting season analysis selected all years with severe conditions (2008, 2009, 2010, 2011, 2017 and 2022) for spatial analysis. Spatial analysis revealed that most areas in the upstream part of the Basin and Coastal region in the lowlands experienced severe to extreme agricultural droughts in highlighted drought years. The modelled agricultural drought results were validated using yield data from two stations in the Basin. The results show that the model performed well with a Pearson Coefficient of 0.87 and a Root Mean Square Error of 0.29. This proactive approach aims to ensure food security, especially in scenarios where the Basin anticipates significantly reduced precipitation affecting water available for agriculture, enabling policymakers, water resource managers and agricultural sector stakeholders to equitably allocate resources and mitigate the effects of droughts in the most affected areas to significantly reduce the socioeconomic drought that is amplified by agricultural drought in rainfed agriculture river basins.

Meteorological Drought Detection and Forecast Using Standardized Precipitation Index and Univariate Distribution Models: Case Study of Bamako, Mali

As an extended period of unusually dry weather conditions without sufficient rain, drought poses enormous risk on societies. Characterized by the absence of precipitation for long periods of time, often resulting in water scarcity, droughts are increasingly posing significant environmental challenges. Drought is therefore considered an important element in the management of water resources, especially groundwater resources during drought. This study therefore sought to investigate the rainfall variability and the frequency of drought for the period 1991 to 2020 in Bamako based on monthly rainfall data from Bamako-Senou gauge station. The standardized precipitation index (SPI) for 12-month, 6-month and 3-month timescales and the SPI for annual totals were used to characterized drought in the study area (Bamako). Univariate parametric probability distributions such as Normal, Log-normal, Gumbel type I and Pearson type III (P3) distributions were fitted with drought variables (severity and duration) for future planning and management. Non-parametric test such as Mann-Kendall trend test was also used to detect trend in annual rainfall data. The results showed that based on 12-month SPI, Bamako experienced two (02) extreme droughts one in July 2002 (SPI = -2.2165) and another in June 2015 (SPI = -2.0598 QUOTE SPI=-2.0598 ). Drought years represented 46.67% for the overall periods according to the SPI for annual totals. The result further indicated that based on the goodness of fit test, the P3 distribution represents the best fitted distribution to both drought severity and duration over Bamako. Bamako is expected to experience several severe severities with higher and shorter duration in the future. Severities with 1, 2, 6, and 10-month duration had return periods ranged from 2.4 to 3.8 years, while 5, 10, 20, 25, 50, and 100-year return periods had 18.51, 26.08, 33.25, 35.50, 42.38, and 49.14 severities, respectively, and durations associated to these severities were 19.8, 26.9, 33.5, 35.6, 42, and 48.2 months, respectively.

Index-based assessment of agricultural drought in a semi-arid region of Inner Mongolia, China

Agricultural drought is a type of natural disaster that seriously impacts food security.Because the relationships among short-term rainfall,soil moisture,and crop growth are complex,accurate identification of a drought situation is difficult.In this study,using a conceptual model based on the relationship between water deficit and crop yield reduction,we evaluated the drought process in a typical rainfed agricultural region,Hailar county in Inner Mongolia autonomous region,China.To quantify drought,we used the precipitation-based Standardized Precipitation Index(SPI),the soil moisture-based Crop Moisture Index(CMI),as well as the Normalized Difference Vegetation Index(NDVI).Correlation analysis was conducted to examine the relationships between dekad-scale drought indices during the growing season(May–September)and final yield,according to data collection from 2000 to 2010.The results show that crop yield has positive relationships with CMI from mid-June to mid-July and with the NDVI anomaly throughout July,but no correlation with SPI.Further analysis of the relationship between the two drought indices shows that the NDVI anomaly responds to CMI with a lag of 1 dekad,particularly in July.To examine the feasibility of employing these indices for monitoring the drought process at a dekad time scale,a detailed drought assessment was carried out for selected drought years.The results confirm that the soil moisture-based vegetation indices in the late vegetative to early reproductive growth stages can be used to detect agricultural drought in the study area.Therefore,the framework of the conceptual model developed for drought monitoring can be employed to support drought mitigation in the rainfed agricultural region of Northern China.

A daily drought index based on evapotranspiration and its application in regional drought analyses

With climate warming, frequent drought events have occurred in recent decades, causing huge losses to industrial and agricultural production, and affecting people’s daily lives. The monitoring and forecasting of drought events has drawn increasing attention. However, compared with the various monthly drought indices and their wide application in drought research,daily drought indices, which would be much more suitable for drought monitoring and forecasting, are still scarce. The development of a daily drought index would improve the accuracy of drought monitoring and forecasting, and facilitate the evaluation of existing indices. In this study, we constructed a new daily drought index, the daily evapotranspiration deficit index(DEDI), based on actual and potential evapotranspiration data from the high-resolution ERA5 reanalysis dataset of the European Center for Medium-Range Weather Forecasts. This new index was then applied to analyze the spatial and temporal evolution characteristics of four drought events that occurred in southwest, north, northeast, and eastern northwest China in the spring and summer of 2019. Comparisons with the operationally used Meteorological Drought Composite Index and another commonly used index, the Standardized Precipitation Evapotranspiration Index, indicated that DEDI characterized the spatiotemporal evolution of the four drought events reasonably well and was superior in depicting the onset and cessation of the drought events,as well as multiple drought intensity peaks. Additionally, DEDI considers land surface conditions, such as vegetation coverage,which enables its potential application not only for meteorological purposes but also for agricultural drought warning and monitoring.

Projected Changes of Palmer Drought Severity Index under an RCP8.5 Scenario

The potential change of drought measured by the Palmer Drought Severity Index （PDSI） is projected by using a coupled climate system model under a Representative Pathway 8.5 （RCP8.5） scenario.The PDSI changes calculated by two potential evapotranspiration algorithms are compared.The algorithm of Thomthwaite equation overestimates the impact of surface temperature on evaporation and leads to an unrealistic increasing of drought frequency.The PM algorithm based on the Penman-Monteith equation is physically reasonably and necessary for climate change projections.The Flexible Global Ocean-Atmosphere-Land System model,Spectral Version 2 （FGOALS-s2） projects an increasing trend of drought during 2051-2100 in tropical and subtropical areas of North and South America,North Africa,South Europe,Southeast Asia,and the Australian continent.Both the moderate drought （PDSI ＜-2） and extreme drought （PDSI ＜-4） areas show statistically significant increasing trends under an RCP8.5 scenario.The uncertainty in the model projection is also discussed.

A drought resistance index to select drought resistant plant species based on leaf water potential measurements

The water deficit in arid and semi-arid regions is the primary limiting factor for the development of urban greenery and forestation. In addition, planting the species that consume low levels of water is useful in arid and semi-arid regions that have poor water management measures. Leaf water potential(Ψ) is a physiological parameter that can be used to identify drought resistance in various species. Indeed, Ψ is one of the most important properties of a plant that can be measured using a pressure chamber. Drought avoiding or drought resistant species have a lower Ψ than plants that use normal or high levels of water. To determine drought resistance of species that are suitable for afforestation in arid urban regions, we evaluated twenty woody species in the Isfahan City, central Iran. The experimental design was random split-split plots with five replications. The species were planted outdoor in plastic pots and then subjected to treatments that consisted of two soil types and five drip irrigation regimes. To evaluate the resistance of each species to drought, we used the Ψ and the number of survived plants to obtain the drought resistance index(DRI). Then, cluster analysis, dendrogram, and similarity index were used to group the species using DRI. Result indicates that the evaluated species were classified into five groups:(1) high water consuming species(DRI>–60 MPa);(2) above normal water consuming species(–60 MPa≥DRI>–90 MPa);(3) normal water consuming species(–90 MPa≥DRI>–120 MPa);(4) semi-drought resistant species(–120 MPa≥DRI>–150 MPa);and(5) drought resistant species(DRI≤–150 MPa). According to the DRI, Salix babylonica L., Populus alba L., and P. nigra L. are high water consuming species, Platanus orientalis L. and Albizia julibrissin Benth are normal water consuming species, and Quercus infectoria Oliv. and Olea europaea L. can be considered as drought resistant species.

The concept and statistical method of drought resistance index in crops

There is evindence showing that stress susceptibility index(SSI)(1一Yd/Yp)/(1—(?)d/(?)p)used as a measure of drought resistance of crop on the field is an altered form of droughtresistance coefficient(DRC)(Yd/Yp).The correlative coefficient SSI and DRC is r=-1.Therefore,the SSI doesn’t improve the defect of the DRC.After two years experiments per-formed by using thirty winter wheat varieties as trial materials,the concept of drought resistanceindex in crops was put forward.Its expressing equation is:the yield in drylan×drought resis-tance coefficient/average yield in dryland.It makes the drought resistance coefficient(physicalindex)correlate well with the yield in dryland(agronomy index)and is suitable for breeder.

Using leaf area index(LAI) to assess vegetation response to drought in Yunnan province of China

Climatic extremes such as drought have becoming a severe climate-related problem in many regions all over the world that can induce anomalies in vegetation condition. Growth and CO2 uptake by plants are constrained to a large extent by drought.Therefore, it is important to understand the spatial and temporal responses of vegetation to drought across the various land cover types and different regions. Leaf area index(LAI) derived from Global Land Surface Satellite(GLASS) data was used to evaluate the response of vegetation to drought occurrence across Yunnan Province, China(2001-2010). The meteorological drought was assessed based on Standardized Precipitation Index(SPI)values. Pearson's correlation coefficients between LAI and SPI were examined across several timescales within six sub-regions of the Yunnan. Further, the drought-prone area was identified based on LAI anomaly values. Lag and cumulative effects of lack of precipitation on vegetation were evident, with significant correlations found using 3-, 6-, 9-and 12-month timescale. We found 9-month timescale has higher correlations compared to another timescale.Approximately 29.4% of Yunnan's area was classified as drought-prone area, based on the LAI anomaly values. Most of this drought-prone area was distributed in the mountainous region of Yunnan.From the research, it is evident that GLASS LAI can be effectively used as an indicator for assessing drought conditions and it provide valuable information for drought risk defense and preparedness.

Spatial and Temporal Characteristics of Drought and Flood in Quanzhou Based on Standardized Precipitation Index (SPI) in Recent 55 Years

To analyze the characteristics of drought and flood variations in Quanzhou during recent 55 years, the standardized precipitation index (SPI) and Empirical Orthogonal Function (EOF) and Rotated Empirical Orthogonal Function (REOF) were calculated by using the monthly precipitation data from 6 meteorological bureaus across Quanzhou for 1960-2014. Results showed that: 1) During 1960-2014, the drought and flood showed Periodic variation characteristics in Quanzhou, the specific period of frequent drought was 1963-1972, 1977-1986 and 2009-2011, and the specific period of frequent flood was 1961-1962, 1972-1975, 1990-1992 and 1997-2007;the drought and flood did not have significant tendency of variation in Spring and Summer, and the drought and flood showed a non-significant downward trend in Autumn and Winter. 2) The drought and flood variation had relatively consistent trend in Quanzhou, but there was a big difference on the northwest mountainous area, the middle semi-mountainous area and the southeast coast area in some periods. 3) The precipitation cell and distribution in every season were Nan’an and Anxi, but there was a big difference in rainfall less area: it was Yongchun and Dehua in Spring, Chongwu and Jinjiang in Summer, Chongwu in Autumn, Dehua and Chongwu in Winter.

Evaluation of Spatial-Temporal Variability of Drought Events in Iran Using Palmer Drought Severity Index and Its Principal Factors (through 1951-2005)

Intensity and variability of droughts are considered inIranduring the period 1951 to 2005. Four variables are considered: the Palmer Drought Severity Index (PDSI), the soil moisture, the temperature and the precipitation (products used for the analysis are downloaded from the NCAR website). Link with the climatic indexLa Ninais also considered (NOAA downloadable products is used). The analysis is based on basic statistical approaches (correlation, linear regressions and Principal Component Analysis). The analysis shows that PDSI is highly correlated to the soil moisture and poorly correlated to the other variables—although the temperature in the warm season shows high correlation to the PDSI and that a severe drought was experienced during 1999-2002 inthe country.