Reconnaissance Drought Index Research Articles

Drought Assessment and Monitoring for Rajshahi District Using Meteorological Drought Indices

An analysis for drought condition has been made in the Rajshahi district of Bangladesh. The assessment of drought for the country has become an urgent need. The agriculture of the country is dependent on the amount of precipitation. As drought and precipitation are inversely related so it is desired to know the condition of Rajshahi. The main characteristics that define drought are the absence of precipitation for a lengthy period and a low distribution of rainfall in contrast to the normal climatic condition. The monthly maximum average temperature data, monthly minimum average temperature data and total monthly precipitation data for the last 48 years (1971-2019) from Bangladesh Meteorological Department (BMD) are utilized for the analysis process. An online software named DrinC is preferred due to its open-source platform and its user-friendly interface. The meteorological data serve as inputs for the software. The Deciles Index (DI), the Standardized Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI) have been used to identify the drought condition of Rajshahi. The values of the DI and SPI are generated from the precipitation data. However, for the generation of the RDI, the precipitation data as well as the potential evapo-transpiration (PET) data, which is an intermediate data that is generated from the two different types of temperature data via DrinC software are used. The values of SPI, RDI and DI values are presented in a tabular as well as in graphical form. From the comparison of these data and from the study of the recurrence interval the state and the repetition of drought in Rajshahi district can be determined.

Spatio-temporal modeling of rangeland degradation in response to changing environment in the Upper Ewaso Ngiro River Basin, Kenya.

Rangelands primarily provide forage for grazing and browsing animals, yet their ecosystems are degraded due to natural causes and anthropogenic activities such as pastoralism, tourism, and ranching. Increased rangeland detrimental effects led the present research to model the severity of rangeland degradation in the Upper Ewaso Ngiro River Basin (UENRB) in Kenya between 1986 and 2021 and predict the future scenario for 2031. The severity of rangeland degradation was analysed using the multi-criteria analytic hierarchical process and principal component analysis, while the cellular automata Markov chain-analysis model was used for prediction. The models utilized datasets including land-use land cover, surface albedo, bareness index, vegetation health index, soil moisture index, topographic wetness index, reconnaissance drought index, k-factor, slope, and population density. The findings indicated that rangeland degradation varied sporadically, with the reconnaissance drought index being the significant influencing parameter, contributing to about 19.2% of the total degradation. In average, between the years under study, non-rangeland zones covered 10.4%, while low, moderate, high, and very high degradability severity covered 15.3%, 49.1%, 25.2%, and 0%, respectively. Prediction results for the year 2031 revealed that non-rangeland zones will cover 5.3%, whereas low, moderate, high and very high will cover 18.1%, 39.2%, 37.4%, and 0%, respectively. The hybrid model proved to be effective in modeling rangeland degradation. The study recommends the county and national governments to propose and adopt by-laws on legislation to regulate the exploitation of natural resources in the study area in order to restore the rangelands.

Evaluation energy flow and analysis of energy economy for agricultural yield production in different geographical regions of China

Abstract Dry and humid climates have different potentials for providing soil moisture. Agricultural drought is a confirmed criterion for evaluating production potential in agriculture, which is discussed in this research. Therefore, this research aims to investigate drought using meteorological and agricultural drought indicator data in four climatic regions of China (humid, semi-humid, semi-arid and dry). For this purpose, climatic information was collected in the last 20 years, and the values of the standard precipitation index (SPI) and reconnaissance drought index (RDI) were determined. Examining the indicators indicates that the indicators are high in all the years under review in dry areas. In the semi-arid region, there was a significant decrease in the average value of the indices in July and August in the years 2017–2022. Drought indicators did not show a critical situation in humid and semi-humid areas, and there was sufficient moisture for plants throughout the year. The results showed that there was a high correlation between the SPI and the RDI in all the identified areas. In addition to rainfall, the RDI also includes transpiration and is more sensitive, especially in dry areas where transpiration is higher than rainfall.

Comparison of CMIP5 models for drought predictions and trend analysis over Mojo catchment, Awash Basin, Ethiopia

Climate change's effects on water, such as floods, droughts, ocean acidification, and increasing sea levels, are expected to worsen in the future years. In the context of the development the subject of mitigating the anticipated effects of drought has become highly important. Droughts in the African mainland are immediately in danger of hunger, particularly in Ethiopia. The creation of Global Climate Models (GCMs) and analysis of impending catastrophic events are two of the finest ways to handle this issue. This study analyzes the future Meteorological and Hydrological Drought with a comparison of GCM models on the Mojo Catchment using the Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), and Streamflow Drought Index (SDI) methodologies, under three slice periods at near (2006-2030), mid (2031-2055) and far (2056-2080) under the Representative Concentration Pathways (RCPs) including RCP4.5 and RCP8.5 scenario data. Overall Meteorological drought results show that at yearly time scale drought incidence is lower than biannual time scale in both indices. MIROC-MIROC5 and MPI-M-MPI-ESM-LR models are conducted for drought analysis. Under SPI6 (biannual period), 22% of the highest drought observed at far future periods at a scenario of RCP4.5 and for time scale SPI12 the frequency of drought decreases. On the catchment, there is no significant trend increase but both the meteorological and hydrological drought indices show the catchment to be highly susceptible to moderate range of drought and then severely drought takes place. Model MPI-M-MPI-ESM-LR is the most significant contributor to the analysis of the size and frequency of droughts and station Ejere is more venerable to drought as compared with other stations under the MPI-M-MPI-ESM-LR model. The occurrence of SDI12 drought on the MPI-M-MPI-ESM-LR model has a direct relation with the anomaly's climate variables (temperature and Precipitation). the maximum driest drought variability occurred at the same GCM model (anomalies up to -3.7mm) in the year 2041. Additionally, the frequency occurrence of drought at the annual timescale (SDI12) under RCP8.5 at the far future period of the catchment is suspected to 22% probability of drought occurrence. Overall, the Mojo catchment is moderately vulnerable to drought, and SPI-6 is more suspected to high frequency on drought under RCP4.5 especially near and mid periods. It is crucial for an organization, individuals, academics, water resource professionals, and disaster risk management in the catchment on alerting and planning water projects.

Meteorological and hydrological drought analysis of Sinop, Kastamonu, Bartın provinces in the Western Black Sea

In recent years, a significant change has been observed in the climate characteristics of Turkey. These changes, which are compatible with the general trend of global climate change, are also felt in the Western Black Sea Basin. This paper reported on the calculation of drought indices, with emphasis on two recently developed indices, the Reconnaissance Drought Index (RDI) and the Stream flow Drought Index (SDI) using specialized software package, named Drin C (Drought Indices Calculator). Additionally, Drin C includes a module for the estimation of potential evapo transpiration (PET) through temperature-based methods (Hargreaves, Blaney - Criddle, and Thornthwaite) that can be used for the calculation of RDI. Therefore, in this study carried out in the Western Black Sea region, meteorological drought analyzes for1-, 3-, 6- ve 12 months were made for 3 precipitation observation stations in the provinces of Sinop, Kastamonu, Bartın, and monthly total precipitation and monthly average temperature data were used to determine the meteorological drought.

Probabilistic modelling of interarrival time of drought for different operational drought indices used in Pakistan

AbstractAdverse climate changes in Pakistan undermine the water resources and give rise to transient and permanent droughts in different parts of the country. The occurrence of drought cannot be prevented, but its detrimental impacts can be brought down by proper pre‐drought planning. Effective planning owes to using scientific and probabilistic approaches. In this paper, the interarrival time of drought for seven operational drought indices in Pakistan is modelled by using the modified distribution of convolution derived from the Bivariate Affine Linear Exponential (BALE) distribution. A new stochastic variable, interarrival time of drought, is generated from the data of drought indices from 1951 to 2016 used in Pakistan by applying the theory of runs. The proposed model identifies Palmer drought severity index (PDSI) and Reconnaissance drought index (RDI) as the most appropriate meteorological indices for the under‐study country. The quantiles of the model are computed that provide information about the interarrival time of drought and help to anticipate this phenomenon. In addition, return periods for all the seven indices are calculated to report the frequency and duration of drought over a certain period. Finally, some recommendations for the National Drought Monitoring Centre (NDMC) and Pakistan Council of Research in Water Resources (PCRWR) are stated for better future planning to avoid the adverse impacts of drought.

Analysis of future meteorological, hydrological, and agricultural drought characterization under climate change in Kessie watershed, Ethiopia

The objective of this study is to analyze future drought characteristics in meteorological, hydrology, and agricultural droughts under the impact of changing climate in the Kessie watershed, upper Blue Nile Basin, using three drought indices; Reconnaissance Drought Index (RDI), Streamflow Drought Index (SDI) and Agricultural Standardized Precipitation Index (aSPI), respectively. The study used baseline data (1985–2014) and future (2041–2100) downscaled from Coupled Model Intercomparison Project 6 (CMIP-6) based on the three Global Climate Models (GCMs) projections under two scenarios of the Shared Socioeconomic Pathways (SSP) (SSP2-4.5, SSP5-8.5) with well-calibrated Soil and Water Assessment Tool (SWAT) model to simulate future streamflow for two future time horizons 2050s (2041–2070) and 2080s (2071–2100). Based on the yearly time scale, our results indicate that droughts of a high magnitude and rising frequency would affect most of the research area. These droughts will be either meteorological (RDI), agricultural (aSPI), or hydrological (SDI). Short-term agricultural and hydrological droughts are also anticipated to occur more frequently. The projected increases in frequency and trend of agricultural and hydrological droughts in this area are greater due to the anticipated drop in annual rainfall and the larger increase in mean annual temperature in the middle of Kessie. Furthermore, compared to hydrological and agricultural droughts, meteorological drought is less vulnerable to climate change; but, as the accumulation period lengthens, a stronger association develops between hydrological and agricultural droughts. These findings may be useful for water resources management and future planning for mitigation and adaptation to the climate change impact in the study area.

Drought characterization using different indices, theory of run and trend analysis in bilate river watershed, rift valley of Ethiopia

Droughts have become more powerful and frequent, affecting more people for longer periods than any other natural disaster, particularly in eastern Africa. The unprecedented climate change has increased the severity, duration, and frequency of droughts. The objectives of this study were to evaluate performances of different drought indices for spatiotemporal drought characterization in the Bilate river watershed that represents part of the rift valley drylands in Ethiopia. Historical data for 39 years (1981–2019) from seven stations were used for drought analyses using the following indices: Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Reconnaissance Drought Index (RDI), enhanced Reconnaissance Drought Index (eRDI) with different time scale and Self-Calibrated Palmer Drought Severity Index (scPDSI). Among them, SPI, SPEI, RDI and eRDI with 6-month and 9-month time scales were found to be the best correlated drought indices to characterize the historical drought events. Then, using the selected drought indices, temporal drought analysis showed occurrence of major drought events in the years: 1984/85, 1999/2000, 2002/3, and 2009. Some of these years are well known as famine years in some parts of Ethiopia including the study area. The results revealed spatial variation the severity of drought with extreme droughts occurred in the southern part of the Bilate watershed. Application of the theory of run confirmed that the maximum severity and duration of drought were observed at the Bilate Tena station that is located in the southern part of the watershed; the most severe being observed on a 9-month scale during 1984/85. Hossana and Wulbareg stations showed the highest frequency of drought over the study period. The Mann-Kendal trend test statistics showed an increasing trend of drought conditions in the study watershed.

APPRAISAL OF CLIMATE CHANGE DISASTER AND FOOD SECURITY IN KHYBER PAKHTUNKHAW PAKISTAN USING GEOSPATIAL TECHNOLOGIES

Abstract. Drought is one of the most severe natural disasters. It negatively influences the crop output considerably as water resources are inadequate across large geographic areas. Food insecurity, hunger, and poverty are widespread in three Pakistani provinces: Baluchistan, Khyber Pakhtunkhwa, and Sindh. Until March/April 2022, roughly 4.66 million people, or 25% of the population examined, were severely food insecure. Historically, the severity of a drought is evaluated using several different indicators, with the selection of indices depending on the data that is easily accessible. By multiple aspects, including potential evapotranspiration and rainfall data on a standard timescale and computing monthly, seasonal, or annual data, the Reconnaissance Drought Index (RDI) has a significant advantage over the other indices in determining drought severity. When rainfall in a region is significantly lower than the amount of potential evapotranspiration, drought situations can occur on the earth. Rainfall will often occur in locations with an increase in the relative humidity of the air. The reconnaissance drought index (RDI) for Khyber Pakhtaunkhawa province in Pakistan was calculated from 1981 to 2020 for 3-, 6-, and 12-month timeframes. This research employed rainfall, minimum and maximum temperature data from KPK province to describe drought using the DrinC program, the Man Kendall test, and Sen's slope calculator to determine drought patterns and variation. All regions had moderate, severe, and extreme droughts, with RDIs between -1.0 and -3.5. RDI 3-, 6-, and 12-month evaluations highlighted 1991, 1994, 2000, 2010, 2015, and 2017. These results indicate that productivity declines in dry and subsequent years due to seasonal rainfall and soil moisture retention.Wheat production averaged 1.7 tones ha-1 year-1, with significant variations. Between October and December, it is an essential to have favorable growing conditions to increase productivity. If droughts occur in this era, the yield will be substantially more negatively impacted than if they occur later in the growing cycle. The 3-month RDI values offer a more accurate description of this variation than the six-month and twelve-months RDI Values. Geospatial drought analysis indicates a considerable variance in drought patterns across the Kpk province over the last 4 decades. Because long-term droughts sometimes affect water quality, especially groundwater, a rise in the intensity of long-term droughts could pose a risk to the water management in the regions that have been examined. Analysis of the Landsat TM 30 m data took place. At the same time, Landsat imageries were utilized to generate the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for drought evaluation. The findings of the NDWI indicate an increase in value from 0.58 to 0.7 for the KPK region.

Meteorological and hydrological drought monitoring and trend analysis for selected gauged watersheds in the Lake Tana basin, Ethiopia: Under future climate change impact scenario

Since most of the countries in Africa rely on Agriculture supported by rainfall, they are especially vulnerable to dangers brought on by climate change, such as floods and droughts. Droughts in the Horn of Africa are immediately in danger of starvation, particularly in Ethiopia. This study uses ensemble GCM data at the Coupled Model Intercomparison Project 5 (CMIP5) to examine the future metrological and hydrological drafts of the chosen gauged watershed, specifically Gilgel Abay, Gumara, Megech, and Ribb. For the analysis, the Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), and Streamflow Drought Index (SDI) were used, along with data from the RCP4.5 and RCP8.5 scenarios. The SPI and RDI were used to analyze metrological drought, and the SDI was used to identify hydrological drought in the mid (2040–2079) to long-term (2050–2079) period. Overall meteorological drought results show that annual time scale drought occurrence is higher than biannual time scale in both indices. Under the biannual time scale (SPI6) and annual time scale (RDI12) for RCP8.5 scenarios of all time horizons, 20.69% of the maximum drought record was observed. While the (SPI12) indices of RCP4.5 and RCP8.5 for all time domains (mid and far) display a drought frequency of 17.24%. And according to the RCP8.5 scenario, RDI6 also predicts that droughts would occur 18.10% more frequently in the far future. Additionally, the RCP8.5 scenario has the greatest frequency record for hydrological droughts across all time horizons at 18.10%. Gumara and Ribb gauged watersheds are typically the most susceptible to drought in the mid periods. The study revealed that drought analysis is important to take planning activities for those who are working as researchers, water resource managers and other professions in the basin. As a result, the study will assist the government, policymakers, and decision-makers in putting drought adoptions and early warning actions in place.

Spatial and Temporal Assessment of Drought Hazard, Vulnerability and Risk in Three Different Climatic Zones in Algeria Using Two Commonly Used Meteorological Indices

Drought is considered a natural hazard and has become a recurrent phenomenon in Algeria since the 1970s. Algeria is characterized by three different climates, namely, sub-humid, semi-arid and arid climates. In this study, we aimed to spatiotemporally assess drought hazard, vulnerability and risk in the three climates of three sub-basins, namely, the Seybouse Maritime, Wadi Djelfa-Hadjia and Wadi M’Zi sub-basins. To this end, the standardized precipitation index (SPI) and the reconnaissance drought index (RDI) were used to evaluate drought physical characteristics on a 12-month timescale, based on precipitation and temperature monthly data covering the period of 1979–2019. High values of the coefficient of determination (R2) (0.76–0.99) confirmed by low values of the root-mean-square error (RMSE) (0.08–0.49) proved that the drought indices displayed a high correlation. Drought hazard and vulnerability were evaluated based on physical characteristics and socioeconomic aspects, respectively. The results led to the determination of a high correlation between the two indices used; the determination of the main drought events; and the mapping of the drought hazard, vulnerability and risk using a geographic information system (GIS). These findings suggest that the SPI provided the highest intensities, while the longest periods and the strongest magnitudes were given by the RDI. The spatiotemporal drought distributions varied with time from station to station and from sub-basin to sub-basin. Risk maps revealed that vulnerability based on socioeconomic factors controls drought risk.

A Stochastic Weather Model for Drought Derivatives in Arid Regions: A Case Study in Qatar

In this paper, we propose a stochastic weather model consisting of temperature, humidity, and precipitation, which is used to calculate a reconnaissance drought index (RDI) in Qatar. The temperature and humidity models include stochastic differential equations and utilize an adjusted Ornstein–Uhlenbeck (O–U) process. For the precipitation model, a first-order Markov chain is used to differentiate between wet and dry days and the precipitation amount on wet days is determined by a probability distribution. Five different probability distributions were statistically tested to obtain an appropriate precipitation amount. The evapotranspiration used in the RDI calculation incorporates crop coefficient values, depends on the growth stages of the crops, and provides a crop-specific and more realistic representation of the drought conditions. Five different evapotranspiration formulations were investigated in order to obtain the most accurate RDI values. The calculated RDI was used to assess the intensity of drought in Doha, Qatar, and could be used for the pricing of financial drought derivatives, a form of weather derivative. These derivatives could be used by agricultural producers to hedge against the economic effects of droughts.

Assessment of Meteorological Drought under the Climate Change in the Kabul River Basin, Afghanistan

Kabul River Basin is one of the most significant river basins in Afghanistan from a socio-economic perspective. Since the country is located in an arid climate zone with drastically varying climatic behavior, an effective assessment of meteorological drought is very essential to managing the limited availability of water resources. For this endeavor, the outputs of three general circulation models under two representative concentration pathways (RCP 4.5 and RCP 8.5) were used against the baseline period of 1961–1980. Different bias correction methods were applied, and the results show that the delta change method, quantile mapping method, and empirical quantile mapping all performed better for the precipitation, maximum temperature, and minimum temperature datasets, respectively. The ERA5-Land datasets and WorldClim Version 2 are used to validate the bias-corrected precipitation and temperature datasets, respectively, to determine their dependability, and the results were found to be promising. Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), Deciles Index (DI), and New Drought Index (NDI) were used to assess the drought condition in the past and forecast for the future periods of the 2020s, 2050s, and 2080s. The spatial distribution of assessed drought indices was mapped using the inverse distance weighting (IDW) method. Our results revealed that moderate to extreme droughts are consistent across the entire basin. This might be because the projected annual precipitation in the river basin shows a decline of 53–65% up to the end of this century (2100), and the average annual temperature is projected to increase by 1.8 °C, 3.5 °C, and 4.8 °C, respectively, for the three future periods of the 2020s, 2050s, and 2080s. Furthermore, the results show that the drought estimated by SPI and RDI for future climate scenarios is almost the same, whereas NDI estimates frequent drought events after the 2050s. However, for moderate drought, RDI, which includes the effects of evapotranspiration, was found to be far greater than SPI under both scenarios, and NDI considering temperature and precipitation also estimates a larger number of drought years, strengthening the possibility of its occurrence in the basin. A regional comparison of drought also indicates a decrease in precipitation in future periods, predominantly in high altitudes.

A comparative study of drought characteristics using meteorological drought indices over the central main Ethiopian Rift

Abstract Droughts are defined by a prolonged absence of moisture. For making drought assessments, a drought index is a crucial tool. This study aims to compare drought characteristics across the Central Main Ethiopian Rift using three drought indices – the Standardized Precipitation Index (SPI), the Reconnaissance Drought Index (RDI), and the Standardized Precipitation Evapotranspiration Index (SPEI) – from 1980 to 2017 at six climate sites in spring, summer, and a 6-month period (March - August). With 1% and 5% significance levels, Modified Mann-Kendall and Sen's Slope Estimators were used to determine trend and magnitude, respectively. The temporal fluctuations of the three drought indices revealed that droughts are frequent, unpredictable, and random. Furthermore, they behaved similarly and had significant links. At most places, the drought indices found no significant trends. However, in the spring season, Butajira (by the three indices) and Wulbareg (by the SPI) showed significantly decreasing trends (increasing drought severity), with change rates ranging from −0.03 to −0.04/year. A comparison of drought characteristics revealed that droughts have become more severe and frequent in recent decades, with spring being more common than summer. This study, which employed a variety of drought indices, could assist water resource planners better understand drought events.

Modified version of the cross-correlation function to measure drought occurrence time-delay correlation

Abstract According to the importance of assessing the presence of time delay between the occurrence of various hydrological and meteorological phenomena, the study aim is to introduce a new method (with high ability and non-sensitivity to the abnormality of datasets and the existence of outliers) for determining the time delay in mentioned data series. In this research, a new measure to detect the time delay between two stationary time series (Non-Parametric Cross-Correlation Function or NCCF, called Spearman's CCF or SCCF) is introduced, which has very low sensitivity to abnormality of data series and also the existence of outliers in the data series. The numerical studies verify the ability of the proposed measure. In standard uniform and exponential (with mean 1) time series, at 100% of numerical analyses and in standard Gaussian time series at more than 60% of numerical studies, the ability of SCCF was more than the CCF. The applicability of the proposed measure in practice was also studied using the Reconnaissance Drought Index (RDI) data series of 20 stations over Iran during 1967–2019 in 1, 3, and 12-month time scales. The results of the practical study also proved the appropriate performance of the proposed model in all time scales.

Performance evaluation of Standardized Copula‐based Drought Index with Reconnaissance Drought Index and Standardized Precipitation Temperature Index using severity–duration frequency curves over Balochistan, Pakistan

AbstractDrought and aridity are good measures to study climate change that are measured using several indices. In this study, the performance of Standardized Copula‐based Drought Index (SCDI) is compared with Standardized Precipitation Temperature Index (SPTI) and Reconnaissance Drought Index (RDI) for measuring drought in Balochistan province of Pakistan. These standardized drought indices combine the outcomes of De‐Martone Aridity Index (DAI) and UNEP Aridity Index (UAI). SCDI is calculated using probability distributions and copula models that combine the outcomes of UAI and DAI utilizing data of precipitation, potential evapotranspiration (PE), and temperature. Climate data from nine including arid and humid metrological stations are used to study drought conditions in Balochistan province. SCDI outcomes are graphically and numerically compared with SPTI and RDI. Selected drought indices are compared based on frequency of drought and wet events occurred in moderate, severe, and extreme drought categories, which indicate that SCDI has maximum number of events in these categories. It gives better results due to the utilization of more climate data as compared to RDI and SPTI. Severity–duration frequency curves are used to find future projections which shows that drought severity and drought duration have increasing trend with the passage of time. The indices have more differences in results of arid stations. The results indicate that there are no significant chances of drought at lower time scales, but the chances increase with the increase in time scale. The SCDI may also be more useful for agricultural and hydrological droughts due to temperature and PE variables. This study's results will be helpful for climate planning in Balochistan province.

Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data

ABSTRACT Using a multivariate drought index that incorporates important environmental variables and is suitable for a specific geographical region is essential to fully understanding the pattern and impacts of drought severity. This study applied feature scaling algorithms to MODIS time-series imagery to develop an integrated Multivariate Drought Index (iMDI). The iMDI incorporates the vegetation condition index (VCI), the temperature condition index (TCI), and the evaporative stress index (ESI). The 54,474 km2 Vietnamese Central Highlands region, which has been significantly affected by drought severity for several decades, was selected as a test site to assess the feasibility of the iMDI. Spearman correlation between the iMDI and other commonly used spectral drought indices (i.e. the Drought Severity Index (DSI–12) and the annual Vegetation Health Index (VHI–12)) and ground-based drought indices (i.e. the Standardized Precipitation Index (SPI–12) and the Reconnaissance Drought Index (RDI–12)) was employed to evaluate performance of the proposed drought index. Pixel-based linear regression together with clustering models of the iMDI time-series was applied to characterize the spatiotemporal pattern of drought from 2001 to 2020. In addition, a persistent area of LULC types (i.e. forests, croplands, and shrubland) during the 2001–2020 period was used to understand drought variation in relation to LULC. Results suggested that the iMDI outperformed the other spectral drought indices (r > 0.6; p < 0.005). The analysis revealed an increase in drought risk in some provinces of the Central Highlands including Gia Lai, Kon Tum, and Dak Lak. It was also found that changes in LULC patterns could minimize (reforestation) or exacerbate (deforestation) the impacts of drought. Our study suggests that applying a multivariate drought index enables a better understanding of drought patterns at the local scale. This provides valuable information for the development of appropriate land and environmental management practices that can affect and mitigate climate change effects.

DROUGHT ANALYSIS OF THE CEYHAN BASIN IN TURKIYE USING DIFFERENT INDICES BASED ON PRECIPITATION

Drought is one of the most important natural disasters threatening life, and its impact is becoming more evident daily. Therefore, it is important to analyse the drought regionally, which is expected to substantially impact the Mediterranean region, and develop adaptation strategies against it. In this study, drought analyses were carried out in the Ceyhan Basin for 12-month time periods with various indices using precipitation and temperature data between 1989-2019. Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), and Deciles Index (DI) methods were used to analyse the dry periods in the region. According to the results of these methods, the periods, including extremely dry and wet years, were analysed and compared. As a result, the driest year was found to be 2014, and the wettest year was found to be 2010. Keywords: Drought, Standardized Precipitation Index, Deciles Index, Reconnaissance Drought Index, Ceyhan Basin

Effects of climate change and drought attributes in Nigeria based on RCP 8.5 climate scenario

Historical observatory and future simulated datasets based on representative concentration pathway (RCP) 8.5 scenario covering from 1951 to 2100 were analysed. Four well accepted drought indices: the standardized precipitation index (SPI), standardized runoff index (SRI), reconnaissance drought index (RDI) and standardized precipitation and evapotranspiration index (SPEI) were used in drought characterization. The ensemble of the seven GCMs using RCA-4 captured some pragmatic features of the climatology of Nigeria in the historical period. The correlations between SRI and SPEI/SPI are significant. The major peaks can be divided into three drought cycles with 1–2, 2–4 and 8–16 periods (years). On a general note, there is a projection of an increase in both the frequency and intensity of drought in the five classified agroecological zones in Nigeria. Considering the output of drought characteristics as measured by SPI, future climates were predicted to be more wet when compared to the historical period as indicated in the study. In contrast, SPEI and RDI forecasted dryer climates. The prediction of SPEI and RDI must have been predicated on the influence of increasing temperature due to global warming as captured by RCP 8.5, which would in turn affect the rate of evapotranspiration. In view of drought analysis using the RCP 8.5 scenario, the frequency of severe/extreme drought will likely become more intense especially towards the end of the 21st century.

Declining water resources in response to global warming and changes in atmospheric circulation patterns over southern Mediterranean France

Abstract. Warming trends are responsible for an observed decrease of water discharge in southern France (northwestern Mediterranean). Ongoing climate change and the likely increase of water demand threaten the availability of water resources over the coming decades. Drought indices like the Reconnaissance Drought Index (RDI) are increasingly used in climate characterization studies, but little is known about the relationships between these indices, water resources, and the overall atmospheric circulation patterns. In this study, we investigate the relationships between the RDI, water discharge, and four atmospheric teleconnection patterns (TPs) for six coastal river basins in southern France, both for the historical period of the last 60 years and for a worst-case climatic scenario (RCP8.5) reaching the year 2100. We combine global and regional climate model (CGM and RCM, respectively) outputs with a set of observed climatic and hydrological data in order to investigate the past relationships between the RDI, water discharge, and TPs and to project their potential evolution in space and time. Results indicate that annual water discharge can be reduced by −49 % to −88 % by the end of the century under the extreme climate scenario conditions. Due to unequal links with TPs, the hydroclimatic evolution is unevenly distributed within the study area. Indeed a clustering analysis performed with the RDI time series detects two major climate clusters, separating the eastern and western part of the study region. The former indicates stronger relationships with the Atlantic TPs (e.g. the North Atlantic Oscillation (NAO) and the Scandinavian Oscillation (Scand) patterns), whereas the latter is more closely related to the Mediterranean TPs (Mediterranean Oscillation (MO) and Western Mediterranean Oscillation (WeMO)). The future climate simulations predict an antagonistic evolution in both clusters which are likely driven by decreasing trends of Scand and WeMO. The former provokes a general tendency of lower P in both clusters during spring, summer, and autumn, whereas the latter might partly compensate for this evolution by enhanced precipitation in the eastern cluster during autumn and winter. However, compared to observations, representation of the Mediterranean TPs WeMO and MO in the considered climate models is less satisfactory compared to the Atlantic TPs NAO and Scand, and further improvement of the model simulations therefore requires better representations of the Mediterranean TPs.