Hydrological Drought Index Research Articles

Hydrological Drought Assessment in a Small Lowland Catchment in Croatia

Hydrological drought is critical from both water management and ecological perspectives. Depending on its hydrological and physical features, the resilience level of a catchment to groundwater drought can differ from that of meteorological drought. This study presents a comparison of hydrological and meteorological drought indices based on groundwater levels from 1987 to 2018. A small catchment area in Croatia, consisting of two sub-catchments with a continental climate and minimum land-use changes during the observed period, was studied. The first analysis was made on a comparison of standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI). The results showed their very high correlation. The correlation between the standardized precipitation index (SPI) and standardized groundwater index (SGI) of different time scales (1, 3, 6, 12, 24 and 48 months) showed different values, but had the highest value in the longest time scale, 48 months, for all observation wells. Nevertheless, the behavior of the SPI and groundwater levels (GW) correlation showed results more related to physical catchment characteristics. The results showed that groundwater drought indices, such as SGI, should be applied judiciously because of their sensitivity to geographical, geomorphological, and topographical catchment characteristics, even in small catchment areas.

A New Non-stationary Hydrological Drought Index Encompassing Climate Indices and Modified Reservoir Index as Covariates

A New Non-stationary Hydrological Drought Index Encompassing Climate Indices and Modified Reservoir Index as Covariates

Drought impact links to meteorological drought indicators and predictability in Spain

Abstract. Drought affects many regions worldwide, and future climate projections imply that drought severity and frequency will increase. Hence, the impacts of drought on the environment and society will also increase considerably. Monitoring and early warning systems for drought rely on several indicators; however, assessments of how these indicators are linked to impacts are still lacking. Here, we explore the links between different drought indicators and drought impacts within six sub-regions in Spain. We used impact data from the European Drought Impact Report Inventory database and provide a new case study to evaluate these links. We provide evidence that a region with a small sample size of impact data can still provide useful insights regarding indicator–impact links. As meteorological drought indicators, we use the Standardised Precipitation Index and the Standardised Precipitation Evapotranspiration Index; as agricultural and hydrological drought indicators, we use a Standardised Soil Water Content Index and a Standardised Streamflow Index and a Standardised Reservoir Storage Index. We also explore the links between drought impacts and teleconnection patterns and surface temperature by conducting a correlation analysis, and then we test the predictability of drought impacts using a random forest model. Our results show that meteorological indices are best linked to impact occurrences overall and at long timescales between 15 and 33 months. However, we also find robust links for agricultural and hydrological drought indices, depending on the sub-region. The Arctic Oscillation, Western Mediterranean Oscillation, and the North Atlantic Oscillation at long accumulation periods (15 to 48 months) are top predictors of impacts in the northwestern and northeastern regions, the community of Madrid, and the southern regions of Spain, respectively. We also find links between temperature and drought impacts. The random forest model produces skilful models for most sub-regions. When assessed using a cross-validation analysis, the models in all regions show precision, recall, or R2 values higher than 0.97, 0.62, and 0.68, respectively. Thus, our random forest models are skilful in predicting drought impacts and could potentially be used as part of an early warning system.

Analysis of the Potential Impact of Climate Change on Climatic Droughts, Snow Dynamics, and the Correlation between Them

Climate change is expected to increase the occurrence of droughts, with the hydrology in alpine systems being largely determined by snow dynamics. In this paper, we propose a methodology to assess the impact of climate change on both meteorological and hydrological droughts, taking into account the dynamics of the snow cover area (SCA). We also analyze the correlation between these types of droughts. We generated ensembles of local climate scenarios based on regional climate models (RCMs) representative of potential future conditions. We considered several sources of uncertainty: different historical climate databases, simulations obtained with several RCMs, and some statistical downscaling techniques. We then used a stochastic weather generator (SWG) to generate multiple climatic series preserving the characteristics of the ensemble scenario. These were simulated within a cellular automata (CA) model to generate multiple SCA future series. They were used to calculate multiple series of meteorological drought indices, the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and a novel hydrological drought index (Standardized Snow Cover Index (SSCI)). Linear correlation analysis was applied to both types of drought to analyze how they propagate and the time delay between them. We applied the proposed methodology to the Sierra Nevada (southern Spain), where we estimated a general increase in meteorological and hydrological drought magnitude and duration for the horizon 2071–2100 under the RCP 8.5 emission scenario. The SCA droughts also revealed a significant increase in drought intensity. The meteorological drought propagation to SCA droughts was reflected in an immediate or short time (1 month), obtaining significant correlations in lower accumulation periods of drought indices (3 and 6 months). This allowed us to obtain information about meteorological drought from SCA deficits and vice versa.

Drought identification based on Palmer drought severity index and return period analysis of drought characteristics in Huaibei Plain China

The threshold level method for drought identification is challenging due to the problems of selection of drought index reflecting the drought process associated with water supply and demand as well as the underlying physical meaning of drought thresholds. The frequently used hydrological drought indices (e.g., runoff) are susceptible to being affected by human activities, and drought characteristics are incapable of revealing spatial and temporal comparability. Furthermore, the drought process with the same severity but a longer duration is more likely to be evaluated as a more severe event, which contradicts the actual drought situation. In this study, the Palmer drought severity index (PDSI) method, in which the meteorological factors less influenced by human activities were taken as the input, was adopted to determine the dry/wet states and the PDSI value at each period firstly. The dry/wet states were defined with dry period, wet period, transition period, transition period in dry spell, and transition period in a wet spell. Following that, drought identification criteria were established through the dry/wet states and PDSI value according to the consistency of the identified results and the actual drought situations. Particularly, drought severity and peak intensity were taken as drought characteristics in this paper, and the joint return periods of the characteristics were estimated based on the Gumbel-Hougaard copula function. And eventually, a case study was conducted in Huaibei Plain, China. The results showed that the most severe droughts identified by PDSI had a good consistency with the actual drought situations, drought severity and peak intensity were applicable to reflect the drought impacts. It is worth noting that the implications of the joint return period and the relationships among different types of them. The occurrence probability of a multi-characteristic drought event should be calculated by the integration of joint probability density function over the region corresponding to the event of interest, and the joint frequency of drought characteristics should not be used as the occurrence probability of the drought disaster losses greater (or less) than that caused by the drought with the same characteristics. In addition, drought processes identified by PDSI and standardized precipitation indices (SPI) from monthly and seasonal scales were compared, indicating the drought identified results through PDSI are almost consistent with the actual situations.

Development of a New Multivariate Composite Drought Index for the Blue Nile River Basin

Drought is a slow-onset phenomenon that evolves over a season or even years. Drought affects people more than any other natural disaster due to its widespread and significant negative impacts. Population growth and associated water demand add further stress on water resources, especially in periods of drought. Drought indices represent a single value resulting from processing a considerable amount of data. These indices provide a short message to stakeholders to adapt water resource management strategies. Since drought results from interconnected phenomena, designing a composite drought index that includes several drought indices can accurately capture drought events. Drought assessment over a large-scale basin (e.g., the Blue Nile) is a challenging objective that has not been deeply tackled before except for small portions of the basin. This paper assessed droughts over the whole basin by evaluating meteorological, agricultural, and hydrological drought indices. The calculated drought indices (Standardized Runoff Index (SRI), Standardized Precipitation Index (SPI), and standardized soil moisture index (SSI)) in addition to the development of a new standardized evapotranspiration index (sETI) are jointly integrated into a novel composite drought index for the Blue Nile (BNI). The optimal weights for SPI, SRI, sETI, and SSI were 0.33, 0.26, 0.2, and 0.19, respectively, in the designed BNI.

Nonstationary analysis of hydrological drought index in a coupled human-water system: Application of the GAMLSS with meteorological and anthropogenic covariates in the Wuding River basin, China

Significant nonstationarity of hydrological sequences, driven by the coupled influence of climate change and anthropogenic activities, has challenged traditional hydrological drought analysis under the stationarity assumption in some regions, and has questioned regional hydrological drought analysis under the changing environment. To evaluate hydrological drought characteristics, the novelty of this paper is the proposition of a GAMLSS (Generalized Additive Models for Location, Scale, and Shape)-based nonstationary standardized runoff index (NSRI), considering climate change and anthropogenic influence, for investigating the hydrological drought regime in the Wuding River basin. To that end, nonstationary test methods were performed on hydrological sequences, and data was segmented into three parts according to the results of the Pettitt test. A nonstationary probability distribution was fitted to runoff data with meteorological covariates (precipitation, temperature) and anthropogenic covariates (water consumption for social development demand, and water consumption triggered by water impounding by check dams). After selecting an optimal combination of covariates, the NSRI was calculated utilizing the GAMLSS model. A newly constructed evaluation threshold was proposed, based on the traditional SRI threshold. The performance of SRI and NSRI were compared at the Dingjiagou station in the Wuding River basin. Results of comparison between index identification and recorded drought events demonstrated that the NSRI better performed in drought event identification. Moreover, the NSRI identified more frequent severe droughts and extreme droughts. Therefore, the proposed NSRI provided a more accurate basis for the identification of drought events, which can offer valuable information for drought planning, preparedness, and mitigation.

The use of predefined drought indices for the assessment of groundwater drought episodes in the Baltic States over the period 1989–2018

Study regionThe Baltic region (Estonia, Latvia, Lithuania) located at the Eastern Shore of the Baltic Sea, Northern Europe. Study focusThe spatial and temporal distribution of groundwater drought events over the period 1989–2018 was examined. Standardized Groundwater level Index (SGI) was calculated for six monitoring wells installed into shallow unconfined or semiconfined aquifers evenly distributed across the region. A set of meteorological and hydrological drought indices were used to characterize drought events using data from EAR-5 reanalysis. New hydrological insightsFour major drought events affecting all six monitoring wells were identified: 1992–1994, 1996–1997, 2002–2004 and 2005–2007 with another episode apparently starting in 2018. Meteorological drought indices - Standardize Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Reconnaissance Drought Index (RDI) - showed the highest correlation with groundwater drought events. The ERA-5 parametrization of surface and subsurface runoff used to calculate the hydrological indices - Standardized Runoff Index (SRI) and Standardized Sub-surface Runoff Index (SSRI) - most likely did not adequately represent the hill-slope scale processes affecting the unconfined groundwater dynamics, thus indices SRI and SSRI showed less clear relationship with groundwater drought events. Different patterns of SGI fluctuations and correlation with other drought indices highlight the importance of localized meteorological, land use, geomorphological or geological factors in controlling the groundwater response to the atmospheric forcing.

Surface Water Change Detection via Water Indices and Predictive Modeling Using Remote Sensing Imagery: A Case Study of Nuntasi-Tuzla Lake, Romania

Water body feature extraction using a remote sensing technique represents an important tool in the investigation of water resources and hydrological drought assessment. Nuntasi-Tuzla Lake, a component of the Danube Delta Natural Reserve, is located on the Romanian Black Sea littoral. On account of an event in summer 2020, when the lake surface water decreased significantly, this study aims to identify the variation of the Nuntasi-Tuzla Lake surface water over a long-term period in correlation with human intervention and climate change. To this end, it provides an analysis in the period 1965–2021 via hydrological drought indices and data mining classification. The latter approach is based on several water indices derived from Landsat TM/ETM+/OLI and MODIS full-time series datasets: Normalized Difference Vegetation Index (NDVI), Normalized Difference Vegetation Index (NDVI), Modified NDWI (MNDWI), Weighted Normalized Difference Water Index (WNDWI), and Water Ratio Index (WRI). The experimental results indicate that the proposed classification methods can extract relevant features from waterbodies using remote sensing imagery with a high accuracy. Moreover, the study shows a similarity in the evolution of surface water cover identified with the data mining classification and the drought periods detected in the flow data series for the Nuntasi and Sacele Rivers that supply the Nuntasi-Tuzla Lake. Overall, the results of our investigation show that human intervention and hydrological drought had an extensive impact on the long-term changes in surface water of the Nuntasi-Tuzla Lake.

Establishing a statistical relation between meteorological and hydrological drought indices

Abstract Investigation of the hydrological drought behaviour of the Bharathapuzha river basin of Kerala, India, on account of the meteorological drought condition of the watershed was carried out in this study with the aid of four popular drought indices. Statistical relations were established between the indices using polynomial regression models to help in predictions. A study on the decadal spatial variation of hydrological drought behaviour over the basin was also conducted. The cross-correlation study was also performed to understand the propagation of hydrological drought in the event of meteorological drought. The results reveal a slight decrease in the spatial extent of hydrological drought over time. The correlation between meteorological and hydrological drought indices was found to be more assertive at grid locations G1 and G4. According to the findings, the strongest correlation of 0.8 in the propagation of meteorological to hydrological drought was observed at a lag of 3 months over a 12 -month timescale. Run theory reveals that the river basin is subjected to hydrological drought over 54–65% of the months in the historic period. Regression analysis suggests that the cubic model outperforms the other models.

Long-term drought monitoring of the Zayandehrud River basin (central Iran) using hydroclimatological models and satellite observations

Drought is one of the most complex and threatening disasters, defined as a period of drier-than-normal conditions which results in water-related problems. In addition to climate variation, human activities can cause or intensify drought through groundwater overextraction, unsupervised expansion of agricultural lands, etc. One prime example of such drought has been observed in the Zayandehrud River basin in central Iran. We investigate the long-term (1986 to 2019) drought of the Zayandehrud River basin using meteorological, hydrological, and agricultural drought indices. The main focus of this research is to explore the impact of the irrigated area expansion in the upstream sub-basins on the agricultural drought of the downstream. To this end, different drought indices are employed over the whole basin and its sub-basins. The results indicate that the long-term precipitation index and snow reserves do not show a long-term trend during the study period, while the combined precipitation–evapotranspiration index and groundwater storage decline. The research concludes that the expansion of the agricultural lands in the river upstream, rising air temperature, and increasing population and industrial activities in the basin are most likely the driving forces to intensify the agricultural drought in the midstream and downstream parts of the basin.

Yeşilırmak Havzası için Hidrolojik ve Meteorolojik Kuraklık Tahmini, Türkiye

Drought is the most dangerous natural disaster. It differs from the other disasters in that it occurs insidiously, its effects are revealed gradually, and it persists for a long period. Drought has huge, negative effects on both society and natural ecosystems. In this study, values from the Standardized Precipitation Index (SPI) were used to generate drought estimation models by using Artificial Neural Networks (ANN). In addition, the probability of hydrological drought was determined by using SPI values to predict Streamflow Drought Index (SDI) values with ANN. Also, the SPI and SDI were used as the meteorological and hydrological drought indices, respectively, in conjunction with Feed Forward Neural Networks (FFNN), in ANN models. For this purpose, three rainfall and three flow gauging stations located in the Yesilirmak River Basin of Turkey were selected as the study units. The SPI and SDI values for the stations were calculated in order to create ANN estimation models. Different ANN forecasting models for SPI and SDI were trained and tested. In addition, the effects of the spatial distribution of precipitation on flows were determined by using the Thiessen Method to develop the SDI prediction model. The results generated by the ANN prediction models and resulting values were compared and the performances of the models were analyzed. The combination of ANN and SPI predicted meteorological drought with high accuracy but the combination of ANN and SDI was not as good in predicting hydrological drought.

Assessment and attribution of China’s droughts using an integrated drought index derived from GRACE and GRACE-FO data

Different definitions of drought from multiple perspectives complicate drought studies. Studying droughts from an integrated perspective helps to better understand their evolutions and driving factors, but few studies have taken such a comprehensive approach in China. This study conducted an assessment and attribution analysis of droughts across China from 2002 to 2020 based on an integrated drought index derived from observed terrestrial water storage (TWS) data. First, the latest solution of satellite gravimetry data was employed to analyze spatiotemporal changes in TWS deficits. Second, droughts associated with TWS deficits were detected using the Water Storage Deficit Index (WSDI). Third, an attribution analysis was conducted using the stepwise regression method to determine the driving factors of droughts. The results show that China’s TWS decreases at a significant rate (−1.79 ± 0.43 mm/yr, P < 0.05) from 2002 to 2020. The droughts detected by WSDI can generally combine the durations and intensities of the meteorological, hydrological, agricultural, and socioeconomic drought indices, which provides a comprehensive perspective. The severity and intensity of countrywide drought have been at least doubled, especially in the northern regions (including the Northern Arid and Semiarid region, the Huang-Huai-Hai Plain, and the Loess Plateau). The attribution analysis revealed that both climate change and anthropogenic impacts played a dominant role in driving intensified droughts. Decreasing precipitation explains 20% to nearly 100% of TWS deficits across the Huang-Huai-Hai Plain. Human water use, especially for agriculture, has also exacerbated droughts in the water-scarce northern regions of China. The WSDI was highly correlated with the water supply for human activities in the Northern Arid and Semiarid region and the Loess Plateau. This study shows the need to adopt measures to limit human water use beyond the regional capacity.

Estimating the Aquifer’s Renewable Water to Mitigate the Challenges of Upcoming Megadrought Events

In arid and semi-arid regions of the world, the occurrence of prolonged drought events (megadroughts) associated with climate change can seriously affect the balance between water supply and demand, thereby severely increasing the susceptibility of such regions to adverse impacts. In this study, a simple framework is introduced to estimate renewable water volumes (RW) to mitigate the challenges of megadrought events by managing the groundwater resources. The framework connects a weighted annual hydrological drought index (wSPEI) to RW, based on the short time-scale precipitation volume. The proposed framework, which was in a proof-of-concept case study applied to the Neishaboor watershed in the semi-arid part of Iran, showed that developing the weighted drought index can be valuable to estimate RW. The results suggested that the wSPEI, aggregating hydrological drought index (HSPEI) with the time scale k = 5 days and the regional coefficient s = 1.3 can be used to estimate RW with reasonable accuracy (R2 = 0.73, RMSE = 11.5 mm year−1). This indicates that in the Neishaboor watershed, the best estimation of RW can be determined by precipitation volumes (or the lack thereof) falling over 5-day aggregation periods rather than by any other time scales. The accuracy of the relationship was then investigated by cross validation (leave-one-out method). According to the results, the proposed framework performed fairly well for the estimation of RW, with R2 = 0.75 and RMSE = 12.2 mm year−1 for k = 5 days. The Overall agreement between the wSPEI, the RW derived from water balance calculations, and the estimated RW by the proposed framework was also assessed for a period of 34 years. It showed that the annual RW followed closely the wSPEI, indicating a reasonable relationship between wSPEI and the annual RW. Accordingly, the proposed framework is capable to estimate the renewable water of a given watershed for different climate change scenarios.

Use of a multiscalar GRACE-based standardized terrestrial water storage index for assessing global hydrological droughts

A multiscalar standardized terrestrial water storage index (STI) is proposed to assess global hydrological droughts in this study. STI is defined as the standardized statistical deviation of terrestrial water storage (TWS) measured by GRACE to the climatology at multiple time scales. Because of the statistical nature of its definition, the proposed index is comparable to other standardized drought indices in different time scales, e.g., standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI) across different regions at multiple time scales. Our comparisons of STI with multiscalar drought indices (i.e. SPI and SPEI) and uni-scalar indices (i.e. self-calibrating palmer drought severity index [scPDSI], standardized soil moisture index [SM-Z], standardized runoff index [R-Z], and GRACE-based drought severity index [DSI]) show that STI can capture drought events with less noise than other indicators in persistent drought events. As a hydrological drought index based on TWS (which includes groundwater, soil moisture, etc.), STI at short-term time scales (e.g. 3 and 6 months) have higher correlations (mostly > 0.7) with SPI/SPEI at long-term scales (e.g. 18 and 24 months). Since the TWS measured by GRACE incorporates the impacts of the accumulated changes in meteorological variables, hydrological processes, and human activities as well as the advantage of multiscale, STI better represents vegetation growth reflected by Normalized Difference Vegetation Index (NDVI) than uni-scalar drought indices including the GRACE-based DSI, suggesting STI is a robust indicator for hydrological droughts and vegetation changes.

A maximum entropy copula-based frequency analysis method for assessing bivariate drought risk: a case study of the Kaidu River Basin

Abstract In this study, a maximum entropy copula-based frequency analysis (MECFA) method is developed through integrating maximum entropy, copulas and frequency analysis into a general framework. The advantages of MECFA are that the marginal modeling requires no assumption and joint distribution preserves the dependence structure of drought variables. MECFA is applied to assessing bivariate drought frequency in the Kaidu River Basin, China. Results indicate that the Kaidu River Basin experienced 28 drought events during 1958–2011, and drought inter-arrival time is 10.8 months. The average duration is 6.2 months (severity 4.6), and the most severe drought event lasts for 35 months (severity 41.2) that occurred from June 1977 to March 1980. Results also disclose that hydrological drought index (HDI) 1 is suitable for drought frequency analysis in the target year of return periods of 5 and 10, HDI 3, HDI 6 and HDI 12 are fit for the target year of return periods of 20, 50 and 100. The joint return period can be used as the upper bound of the target return period, and the joint return period that either duration or severity reaches the drought threshold can be used as the lower bound of the target return period.

Analysis of Characteristics of Hydrological and Meteorological Drought Evolution in Southwest China

Based on the data of 82 meteorological stations and six representative hydrological stations in four provinces in Southwest China (Guizhou, Sichuan, Yunnan, Chongqing), this paper uses standardized precipitation evapotranspiration index (SPEI) and standardized runoff index (SRI) to analyze the spatial and temporal evolution characteristics of drought in the study area from 1968 to 2018. Combined with the Southwest monsoon index and historical drought data, the correlation of drought and the applicability of different drought indices were verified. The results show that: (1) SPEI-12 in Southwest China shows a downward trend from 1968 to 2018, with a linear trend rate of −0.074/10a, and SPEI-3 has a downward trend in four seasons, the maximum linear trend rate being −0.106/10a in autumn;(2) The change in SRI-12 and SRI-24 value directly reflected the decrease in SRI value, indicating that drought events are increasing in recent times, especially in the 21st century (3). Severe drought occurred in the south of Southwest China, as indicated by the increase of drought frequency in this area. The main reason for the variations in the frequency distribution of drought in Southwest China is the combined effect of the change of precipitation and evapotranspiration. (4) The correlation between hydrological drought index and disaster areas is stronger than the correlation between meteorological drought and disaster areas.

Adapting reservoir operation rules to hydrological drought state and environmental flow requirements

Negligence of environmental flow (e-flow) has severe adverse impacts on wetlands. Additionally, hydrological droughts, compounded by mismanagement of available water resources and anthropogenic activities, lead to water shortage and degrade wetland ecosystem. However, in dealing with reservoir operation/re-operation, a few studies have addressed inflow uncertainty and hydrological drought state to improve the monthly operation performance of an upstream reservoir in coordination with downstream wetland needs. Hence, the study objective is to adopt a linear/nonlinear reservoir operation rule to hydrological drought states and e-flow requirements. So, first, the soil and water assessment (SWAT) model is applied to simulate the natural daily flow regime by eliminating human impacts. Then, using the Tennant e-flow scheme, the intra/inter-annual variability of e-flow and the degree of alteration of the flow are measured. Afterward, using the Standardized Hydrological Drought Index (SHDI), the drought/wet severity and duration are determined. A linear/nonlinear simulation model adopted for the presentation of reservoir policies and the non-dominated sorting genetic algorithm-II (NSGA-II) was employed to determine the optimal monthly operation of the upstream reservoir. The simulated operation policies correspond to each hydrological drought state were evaluated with reliability, resilience, vulnerability, and water-deficit indices. Results indicated that incorporation of hydrological drought state could improve the overall system reliability by 23% over simply enforcing the monthly variable-parameter nonlinear decision rules. Also, the proposed methodology simultaneously maximized the downstream allocated water and e-flow of the terminal wetland. It also implicitly minimized the change in extreme flow metrics that are known to benefit ecosystem diversity. The proposed framework supports reservoir re-operation in regions subject to challenges in supplying water to different sectors while maintaining e-flow during drought periods.

Evaluation of time scale of meteorological, hydrological and agricultural drought indices

Evaluation of time scale of meteorological, hydrological and agricultural drought indices

Evaluation of drought indices correlation for drought frequency analysis of the Mosul dam watershed

In this paper, drought was evaluated for the Mosul Dam Watershed, Iraq, which is considered one of the most important dams in Iraq. The evaluation of drought was studied using two types of meteorological drought indices, namely Reconnaissance Drought Index (RDI) and Standardized Precipitation Index (SPI), and Hydrological drought index which is Streamflow Drought Index (SDI). Monthly precipitation and air temperature for the period 1979-2013 downloaded from Climate Forecast System Reanalysis (CFSR) were used as weather input data for the RDI and SPI. Streamflow obtained from the gauge station in the study area were used as input data for the SDI. The indices computed by the DrinC software for a 12-month time scale. The results were analysed and the Drought Frequency Patterns (DFPs) were computed for each index. The results showed that potential evapotranspiration plays a major role in causing drought, in addition to precipitation. The RDI gave more dry periods than the other two indices, as it depends on precipitation and potential evapotranspiration. Likewise, all indices are identical in that the period is divided into two parts: the first part, which extends from the beginning of the period 1979 to the end of the nineties 1990s, is considered wet and the second part, which extends to the end of the period 2013, is drier. The Hydrological Drought Index SDI gives a drought of greater severity (Moderate drought for the years 2002, 2003 and 2004) compared to the other two indices.