Assessment Of Meteorological Drought Research Articles

Meteorological Drought Assessment for Jhelum River Basin, Jammu and Kashmir\'s Sangam and Ram Munshi Bagh Stations

Abstract: Drought is a severe natural calamity that harms the global ecology. To examine drought conditions including intensity and duration, a variety of drought indicators (DIs) are commonly employed. This study assesses the occurrences of drought in Kashmir's Jhelum River Basin (JRB) at the Sangam and Ram Munshi Bagh stations. Several DI approaches, such as Deciles, Percent of Normal (PN), and Standardized Precipitation Index (Normal-, Log-, and Gamma-SPI), are used to analyse 20 years of monthly recorded precipitation data from 2002 to 2023. Every technique is used on the annual long-term precipitation data set. The findings indicate that nearly identical outcomes are obtained for the stations by the DI approaches. While the normalSPI indicates rainy and less droughty conditions, the log-SPI and gamma-SPI forecast moderate dry conditions. The results emphasize that the PN method predicts more moderate drought years in comparison with SPI method, however, Deciles method shows longer period of extreme and severe drought than other methods. As a result, the five methods indicate various drought intensities in 2017, 2018,2001, and 2002 with a peak moderate drought in 2017 in the Sangam station. The Ram Munshi Bagh station area experienced the less drought conditions from 2002 to 2023 with a moderate drought in 2017 and 2018 as well. Therefore, moderate drought conditions happened in 2017 -2018 in both stations confirm to the recorded drought reports for the same region.

Assessment of meteorological drought using Standard Precipitation Index model (SPI) in Herat, Afghanistan

Assessment of meteorological drought using Standard Precipitation Index model (SPI) in Herat, Afghanistan

Meteorological drought assessment in northern Bangladesh: A machine learning-based approach considering remote sensing indices

Meteorological drought, driven by inadequate precipitation, has significant repercussions for water resources, agriculture, and human well-being. This study conducted an extensive assessment of meteorological drought in northern Bangladesh, employing remote sensing indices and machine learning techniques. The main aim was to evaluate meteorological drought occurrences in northern Bangladesh from 2010 to 2019, utilizing seven drought parameters and a machine learning model. Utilizing a Random Forest (RF) model, this study employed the Standardized Precipitation Index (SPI) as the dependent variable and seven remote sensing indices as independent variables. Through this methodology, the study assessed the significance of these indices generated by the model and integrated them, culminating in the creation of a meteorological drought distribution map spanning 2010 to 2019. This approach offers novel insights by probing the interplay and collective impacts of these indices, shedding light on previously unexplored aspects of regional drought patterns of northern Bangladesh. The major findings showed that precipitation strongly influenced both short-term and long-term meteorological drought episodes. Moreover, land surface-related indices, such as Evapotranspiration (ET) and Normalized Difference Water Index (NDWI), exhibited a more pronounced impact on short-term drought occurrences, while vegetation-related indices like Normalized Multi-band Drought Index (NMDI) and Normalized Difference Vegetation Index (NDVI) demonstrated greater influence over long-term drought events. During this timeframe, the Rajshahi division experienced frequent extreme and severe drought events. Moderate droughts and abnormally dry conditions were widespread. The Barind tract area consistently faced moderate to extreme droughts, with exceptions in 2011, 2014, and 2019. On average, over 5% of the region had extreme droughts, while more than 12% experienced severe droughts during this decade. Long-term drought indicators (SPI 6 and SPI 9) consistently showed higher frequencies of extreme and severe droughts compared to short-term indicators (SPI 1 and SPI 3), emphasizing the influence of prolonged rainfall deficits on extreme droughts and the relevance of longer time frames for severe drought dynamics. The RF model demonstrated strong performance with accuracy ranging from 81% to 95%. Low prediction errors (RMSE 6% to 31%) and high out-of-bag (OOB) accuracy ranging from 76% to 98% highlighted its accuracy. The F1 score consistently exceeded 76%, indicating high precision and recall. Cross-validation values ranged from 78% to 94%, affirming reliable generalization to new data. Incorporating the main findings, this study contributes valuable insights for the formulation of targeted drought mitigation strategies in northern Bangladesh. It is imperative to note that the scope of this study is confined to the northern region of Bangladesh, and generalizing these findings to other regions should be exercised with caution. Nevertheless, the research methodology and approach can serve as a model for future studies in related fields, advancing knowledge of how to assess droughts using remote sensing and machine learning methods.

Evaluation of global precipitation products for meteorological drought assessment with respect to IMD station datasets over India

In-situ precipitation data is the most reliable data for the drought assessment. However, gridded datasets are commonly used due to limited availability of station data. However, the gridded data may exhibit bias compared to station data. The present study has been undertaken to examine the performance of gridded datasets from global precipitation products (gauge-interpolated, merged from various sources, re-analysis and satellite products) for the assessment of meteorological drought with reference to station datasets of the India Meteorological Department (IMD) from 2000 to 2019 at six drought zones over India. Various statistical metrics were used to check the performance of the precipitation products at four seasons of India. The Standardised Precipitation Index (SPI) was used at a 1-month timescale to evaluate the meteorological drought characteristics. The gridded precipitation products, in regions of high rainfall, tended to underestimate precipitation during the monsoon season and overestimate it during the non-monsoon season. When precipitation was analysed spatially using statistical parameters, the product Integrated Multi-Satellite Retrievals (IMERG Final-Run) outperformed all the other precipitation products in the four seasons and six drought zones over India. For drought characteristics evaluation, the products Climate Prediction Center (CPC) Morphing Technique (CMORPH) and Global Precipitation Climatology Centre (GPCC) outperformed all the other products in the six drought zones over India. Further, in order to rank the precipitation products, the Spearman rank correlation coefficient (SCC) and critical success index (CSI) were used. The analysis results showed that IMERG Final-Run, GPCC and Multi-Source Weighted-Ensemble Precipitation (MSWEP-PastNoGauge) ranked best out of all the precipitation products when analysed for drought monitoring and assessment at six drought zones over India. Overall, IMERG Final-Run outperformed all the other precipitation products for drought monitoring, while TerraClimate performed poorly in most of the drought zones and in India as a whole. The study's findings will help choose suitable gridded precipitation dataset for meteorological drought assessment across six homogeneous drought zones and in India as a whole, aiding operational drought risk management and early warnings.

Spatio-temporal drought assessment and comparison of drought indices under climatic variations in drought-prone areas of Pakistan

Abstract Climatic variations cause droughts which badly affect the environment. The study focused on monitoring droughts to aid decision-making and mitigate their negative impacts on water availability for agriculture and livelihoods in the face of increasing water demand and climate change. To assess the agricultural droughts, a new agricultural Standardized Precipitation Index (aSPI) was calculated which is not used earlier in Balochistan. Widely recommended Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used for meteorological drought assessment. Drought indices comparison was also conducted to check the applicability. Rainfall, maximum temperature, and minimum temperature data (1992 to 2021) were utilized to calculate SPI, aSPI, and SPEI at different timescales (3, 6, 9, and 12 months) using DrinC software and SPEI calculator. Indices results revealed the following severe to extreme drought years: 1998, 1999, 2000, 2001, 2002, 2004, 2008, 2011, 2014, 2016, and 2017. It was determined that Dalbandin, Quetta, Sibi, Kalat, Khuzdar, and Zhob experienced higher extreme drought frequencies. Both long- and short-term drought durations were observed. Indices comparison showed that SPI is the most efficient drought index compared to aSPI and SPEI. This study offers valuable insights for managing water resources in the face of climate-induced droughts.

Meteorological drought assessment and prediction in association with combination of atmospheric circulations and meteorological parameters via rule based models

The development of data-driven models along with the data that are recorded from satellites has made the study of water engineering more attractive. In this study, SPEI values were calculated with the meteorological variables and then a combination of the meteorological variables as well as atmospheric circulation values was considered as a data mining model input for estimating the droughts. The series of the SPEI values for 3-, 6-, 9-, 12-, 24- and 48-month time scales were obtained during 1988-2016. In this study, both M5 Tree model and Associate Rules were used to predict and analyze the meteorological drought at 6 synoptic stations in the basin considering both the atmospheric circulations (NAO, SO, MOgi, MOac, WeMO, Mediterranean, Red, Black, Caspian, and Persian Gulf SSTs) and the meteorological variables (lagged relative humidity, evapotranspiration, average temperature, minimum-maximum temperature and pressure). The results showed that making use of a combination of the atmospheric circulation indices and meteorological variables in the models increases the accuracy of the model and improves the results in a long term period. The best result in the study of drought in Urmia Lake basin is related to SPEI48 (R = 0.85, RMSE = 0.08, MAE = 0.11) and in the association rules the value of the lift index of best rule is 1.32. Although both approaches provided acceptable results, M5 Tree model had a comparative advantage due to simple and practical linear relationships.

Meteorological Drought Assessment and Trend Analysis in Puntland Region of Somalia

Drought assessment and trend analysis of precipitation and temperature time series are essential in the planning and management of water resources. Long-term precipitation and temperature historical records (monthly for 41 years, from 1980 to 2020) are used to investigate annual drought characteristics and trend analysis in Somalia’s northern region. Six drought indices of the normal Standardized Precipitation Index (normal-SPI), the log normal Standardized Precipitation Index (log-SPI), the Standardized Precipitation Index using the gamma distribution (Gamma-SPI), the Percent of Normal Index (PNI), the Discrepancy Precipitation Index (DPI), and the Deciles Index (DI) are used in this study for the annual drought assessment. The log-SPI, the gamma-SPI, the PNI, and the DPI could capture historical extreme and severe droughts that occurred in the early 1980s and over the last two decades. The results indicate that Somalia has gone through extended drought periods over the past quarter century, exacerbating the existing humanitarian situation. The normal-SPI, gamma-SPI, and PNI indicate less and moderate drought conditions, whereas log-SPI, DPI, and DI accurately capture historical extreme and severe drought periods; thus, these methods are recommended as annual drought assessment tools in the studied region. Not only are the PNI and DPI less correlated to each other, but their correlation coefficient (CC) with SPI-based drought indices are not as high as SPI-based indices which are close to unity. For the purpose of the trend analysis, the Mann Kendall (MK) test, the Spearman’s rho (SR) test, and the Şen test are used. Furthermore, the Pettitt test is implemented to detect the change points and the Thiel-Sen approach is used to estimate the magnitude of trend in the precipitation and temperature time series. The results indicate that there is overall warming in the region which has experienced a significant shift in trend direction since 2000. The trend analysis of annual precipitation data time series shows that Bossaso and Garowe stations have significant positive trends, while the Qardho station has no trend. In 1997 and 1998, respectively, abrupt changes in annual precipitation are detected at Qardho and Garowe stations. Due to the civil war of more than three decades in Somalia and the non-institutionalized governance to inform historical drought conditions in the country, determining the most appropriate meteorological drought index would help to develop a drought monitoring system for states and the entire country.

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.

Effects of Climate Change on Streamflow in the Ayazma River Basin in the Marmara Region of Turkey

This study investigates the effects of climate change on streamflow in the Ayazma river basin located in the Marmara region of Turkey using a hydrological model. Regional Climate Model (RCM) outputs from CNRM-CM5/RCA4, EC-EARTH/RACMO22E and NorESM1-M/HIRHAM5 with the RCP4.5 and RCP8.5 emission scenarios were utilized to drive the HBV-Light (Hydrologiska Byråns Vattenbalansavdelning) hydrological model. A trend analysis was performed with the Mann–Kendall trend test for precipitation and temperature projections. A meteorological drought assessment was presented using the Standardized Precipitation–Evapotranspiration Index (SPEI) method for the worst-case scenario (i.e., RCP8.5). The calibrated and validated hydrological model was used for streamflow simulations in the basin for the period 2022–2100. The selected climate models were found to produce high precipitation projections with positive anomalies ranging from 22 to 227 mm. The increase in annual mean temperatures reached up to 1.8 °C and 2.6 °C for the RCP4.5 and RCP8.5 scenarios, respectively. The trend results showed statistically insignificant upward and downward trends in precipitation and statistically significant upward trends in temperatures at 5% significance level for both RCP scenarios. It was shown that there is a significant increase in drought intensities and durations for SPEI greater than 6 months after mid- century. Streamflow simulations showed decreasing trends for both RCP scenarios due to upward trend in temperature and, hence, evapotranspiration. Streamflow peaks obtained with the RCP8.5 scenario were generally lower than those obtained with the RCP4.5 scenario. The mean values of the streamflow simulations from the CNRM-CM5/RCA4 and NorESM1-M/HIRHAM5 outputs were approximately 2 to 10% lower than the observation mean. On the other hand, the average value obtained from the EC-EARTH/RACMO 22E outputs was significantly higher than the observation average, up to 32%. The results of this study can be useful for evaluating the impact of climate change on streamflow and developing sustainable climate adaptation options in the Ayazma river basin.

Spatiotemporal Assessment of Meteorological Drought of Paschim Medinipur District, West Bengal, India

The drought phenomenon is linked to the water scarcity and these are the pressing issues that require careful and thoughtful consideration. Drought in India mostly affects regions that are part of numerous plateaus, including the Chottanagpur plateau and the Deccan plateau. The Paschim Medinipur District of West Bengal, which is located in the southern portion of the Chottanagpur plateau, has recently experienced extreme and severe drought on multiple occasions. The assessment of the drought scenario in this region is, nevertheless, still very far from being finalized. Using the Standardized Precipitation Evapotranspiration Index (SPEI) at various time intervals (e.g., 3 months, 6 months, 12 months and 48 months) between 1979 and 2014, we have evaluated drought both geographically and temporally in this study. Here, the drought evaluation metrics include peak intensity, average intensity, magnitude, occurrence rate (%) and trend. Peak intensity, magnitude, average drought intensity, and the frequency of Extreme to Severe (ES) droughts are all seen to decline noticeably as time steps move forward. The frequency of moderate droughts starts to rise as time moves forward. Peak intensity, magnitude, average drought intensity, drought duration, ES and moderate drought occurrence rate is high in southern and southwestern portions of Paschim Medinipur. Additionally, the Principal Component Analysis (PCA) composite scores used to identify the drought-prone zones are estimated using the aforementioned parameters at various time steps. As the time step increases the area under the high and high moderate drought prone zone decreases, but very low and low drought prone area increases. Overall 16% area is found under high to high moderate drought prone category, whereas, approximately, 65% area is found under the low to low moderate drought category. The outcome of this research may be helpful to combat with drought and to make a fruitful move to manage water resources in the Paschim Medinipur region, West Bengal. Additionally, the study makes use of a superb methodology to comprehend the spatiotemporal variation of meteorological drought, which is applicable to all parts of the globe.

Assessment of meteorological drought in Balod district, India through GIScience and Remote sensing

The present study was conducted in Balod district to assess meteorological drought events for prevention and future water resource management planning. The study area has a deficit in rainfall with a higher rainfall variation. From 1990 to 2020, the rainfall anomaly index (RAI) was used to calculate meteorological drought and monitor dry and wet periods in the area. Rainfall datasets from IMD daily gridded data of 0.25º × 0.25º spatial resolution were combined with the Geographic information system (GIS) environment. The results from the index revealed that the highest negative values were observed in the years 2000, 2002, 2008 and 2009 which have faced severe drought events. The findings from the study can assist in making a drought preparedness plan to mitigate the risk with effective water resources management.

Meteorological Drought Assessment in the Ziz Watershed (South East of Morocco)

Meteorological Drought Assessment in the Ziz Watershed (South East of Morocco)

Benchmark worst droughts during the summer monsoon in India

During the summer monsoon (June–September) season, drought poses challenges for agricultural activities and water availability in India. We develop a framework considering the timing, areal coverage and severity that can be used for the assessment of meteorological droughts as the monsoon season progresses. We estimate the benchmark worst droughts within the monsoon season (June, July, August and September) using the long-term (1901–2020) gridded rainfall. The benchmark worst droughts were identified considering the extent and severity of drought using the Drought Severity Coverage Index (DSCI). The worst meteorological drought in June, July, August and September occurred in 1923, 2002, 1937 and 1907 with a return period of 68, 200, 147, 188 years, respectively. The worst drought in the entire summer monsoon season occurred in 1918, which had a return period of 238 years. The benchmark droughts during June 1923, July 2002 and monsoon 1918 were associated with the warm sea surface temperature over the equatorial Pacific Ocean. The other two droughts (1937 and 1907) were linked with the off-equatorial warming over the Indo-Pacific region. The leading mode of variability in the Standardized Precipitation Index obtained from empirical orthogonal function analysis showed that the western and central parts of the country are most affected by droughts during the summer monsoon season. The second leading mode exhibited declining trends in rainfall with a bipolar spatial variability centred over the Gangetic Plain and southern India. The estimated DSCI for a 2–500 return period can be used for the drought assessment during the monsoon season in India.This article is part of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’.

Spatial and Temporal Pattern Assessment of Meteorological Drought in Tumakuru District of Karnataka during 1951–2019 using Standardized Precipitation Index

Spatial and Temporal Pattern Assessment of Meteorological Drought in Tumakuru District of Karnataka during 1951–2019 using Standardized Precipitation Index

Characterization of Meteorological Drought Using Monte Carlo Feature Selection and Steady-State Probabilities

Drought is a creeping phenomenon that slowly holds an area over time and can be continued for many years. The impacts of drought occurrences can affect communities and environments worldwide in several ways. Thus, assessment and monitoring of drought occurrences in a region are crucial for reducing its vulnerability to the negative impacts of drought. Therefore, comprehensive drought assessment techniques and methods are required to develop adaptive strategies that a region can undertake to reduce its vulnerability to drought substantially. For this purpose, this study proposes a new method known as a regional comprehensive assessment of meteorological drought (RCAMD). The Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Standardized Precipitation and Temperature Index (SPTI) are jointly used for the development of the RCAMD. Further, the RCAMD employs Monte Carlo feature selection (MCFS) and steady-state probabilities (SSPs) to comprehensively collect information from various stations and drought indices. Moreover, the RCAMD is validated on the six selected stations in the northern areas of Pakistan. The outcomes associated with the RCAMD provide a comprehensive regional assessment of meteorological drought and become the initial source for bringing more considerations to drought monitoring and early warning systems.

Computation of the spatio-temporal extent of rainfall and long-term meteorological drought assessment using standardized precipitation index over Kolar and Chikkaballapura districts, Karnataka during 1951-2019

Drought is an insidious meteorological and hydrological natural phenomenon causing many adverse impacts on the environment, agriculture, and socio-economic condition, especially over a semi-arid region like Kolar and Chikkaballapura district in the state of Karnataka. When the precipitation level is inadequate to meet the demands of human activities, it leads to drought extension over a season or longer period. The present article is investigating the application of the standard precipitation index (SPI) to detect drought incidents and is widely used to understand rainfall variability. Therefore, this study attempts to analyze drought at pre-monsoon, southwest monsoon, northeast monsoon, and annual time period using SPI for Kolar and Chikkaballapur districts of Karnataka state. The outcome of this analysis demonstrates that drought occurrences are frequent in particular years and seasons with negative values of SPI along with dry, wet, and normal events. To apply any management plan with the increasing nuisance, deep knowledge is essential about meteorological, agricultural, and hydrological drought. The rainfall data for the period 1951–2019 at the eleven grid points were acquired from the India Meteorological Department with (0.25⁰ × 0.25⁰) high spatial resolution. The result revealed that a moderate dry event was observed in the pre-monsoon period at Kolar in 1993 with −0.67. Extreme drought condition was recorded during the southwest monsoon season in 1980 at Kolar with −2.22 and for the northeast monsoon, −1.67 was observed at Malur station in 1988. The seasonal crops of the southwest monsoon (June–September) are heavily dependent on this rainfall and it contributes to 58% of the annual precipitation. If there is a drop in the rainfall received during the southwest monsoon, the effect on the growing stage of agricultural crops leads to reduced crop yield.

Rapid Diagnosis and Evaluation of Agricultural Drought Based on Machine Learning Language

Global climate change causes a decrease in precipitation in India, as in many other places around the globe. As a result, droughts have occurred over a large area and in a more drastic way than in the past. The present study focuses upon assessment of meteorological drought conditions with Machine learning algorithms, such as Support Vector Machine (SVM) and Decision Tree (DT) based upon the measurement of precipitation over 36 meteorological -subdivisions in India. Meteorological drought occurs when precipitation is less than average over a prolonged time scale. It usually precedes various kinds of drought and adversely affects the environmental, economic and social conditions. Among various methods for assessment of meteorological drought by using drought indices, Machine learning algorithms can be of vital use in evaluating systems. Operating on multi-sensory data sets like SVM and DT, we will have far-reaching impacts to overcome socio- economic vulnerability in India. However, this preliminary study is focused on precipitation impacting meteorological drought. Building on the Machine learning algorithms, it is now possible to timely conduct real- time evaluation by SVM and DT applications. This approach will help us to have a refined view of drought transformations, Timely comprehensive analysis, severity and trends of meteorological drought category. Other drought interactions that are still relatively unknown, particularly in India, are to be more focused.

Spatiotemporal assessment of meteorological drought in the Palamu district, Jharkhand using Reconnaissance Drought Index

Droughts are natural phenomena that occur when the distribution of rainfall is below the average. It is usually triggered by factors such as climate change and human actions and they are classified as meteorological, agricultural, hydrological and socioeconomic droughts. The study focused on the meteorological drought in the Palamu district of Jharkhand based on the Reconnaissance drought index (RDI) from 1985 to 2020. The precipitation datasets were obtained from CHIRPS while the temperature data was acquired from ERA5 re-analysis. The RDI calculates drought based on precipitation and potential evapotranspiration (PET) data. Further, the DrinC software was used to calculate PET in which RDI values were calculated at 3 and 6 month time scale and results revealed that the study area has faced moderate to extreme events. However, in years 1992, 1993, 2006, 2009 and 2015 extreme drought events were observed. Therefore, due to high sensitivity of RDI, the droughts events were evaluated and its spatial distribution was mapped at different time scale. The results from the study can be beneficial for monitoring and planning strategies related to mitigating drought risk.

Improved Monitoring and Assessment of Meteorological Drought Based on Multi-Source Fused Precipitation Data.

Meteorological drought, one of the most frequent climate-related disasters, causes great danger for human health and socioeconomic development. With an aim to improve the accuracy of meteorological drought monitoring, this study collected multi-source remotely-sensed precipitation products, i.e., the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement Mission (GPM), and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and compared their performance over Hubei Province, China. The geographic difference analysis was used to blend the best-fitted product with gauged precipitation data. Based on the fused dataset with verification, the spatio-temporal characteristics of drought were investigated. Results showed that GPM performed the best in precipitation numerical evaluation and event detection with a 5 mm/d threshold. The fused data accurately captured 80% of historical drought events and indicated that extreme annual droughts mainly occurred in the northern and northwestern regions, while slight, moderate, and severe droughts mainly occurred in the central and eastern parts. The short-term drought exhibited the highest frequency of 33% in summer and the lowest frequency of 27% in spring, while the medium-term drought showed a higher frequency in autumn and winter. This could be a preliminary assessment of drought based on multi-source fused precipitation data for precise drought outlook and risk management.

Assessment of Meteorological Drought in Climate Variability Context within the Comoe River Transboundary Watershed

In the context of climate variability resulting in a decrease in rainfall with a severe drought, a spatio-temporal study of this phenomenon remains imperative for the efficient management of water resources. This paper aims to assess the long-term rainfall drought trend and breakpoints within the Comoe River watershed. From monthly rainfall data series (1960-2000), Standardized Precipitation Index (SPI) values were calculated for a time scale of 3 months (SPI.3). Statistical tests for breaks (CUSUM, and t-Student) and trends (Man-Kendall and Linear Regression) as well as the Sen’ slope method for estimating the magnitude of trends was applied. The breaks dates observed are mostly located after the 1970s. Based on SPI.3 values below the threshold of 0.84 chosen as an indicator of drought, rarely has more than half of the catchment area been affected by drought. The average watershed affected is about 20% over the study period (1960-2000). The most representative years, in terms of spatial expansion of the drought, in decreasing order of importance are: 1983, 1992, 1972 and 1982. The years 1982 and 1983 stand out for their exceptional condition, as the drought-affected 50% to 90% of the total catchment area. SPI.3 series from 1960 to the various break dates recorded slopes between -0.01 and 0.00 with a slight drought trend for most of the catchment. After the break periods, almost the entire northern part of the basin is characterized by slight moisture with Sen’s slopes between 0.000 and 0.005. The southern part will remain slightly subject to normal rainfall conditions.