Meteorological Drought Monitoring Research Articles

Comparison of Meteorological Drought Indices in Tanzania Using the Meteorological Drought Monitoring (MDM) Software Package

Comparison of Meteorological Drought Indices in Tanzania Using the Meteorological Drought Monitoring (MDM) Software Package

Evaluating the accuracy of APHRODITE and CHIRPS satellite-based Precipitation products for meteorological drought monitoring

Evaluating the accuracy of APHRODITE and CHIRPS satellite-based Precipitation products for meteorological drought monitoring

Research on Drought Monitoring Based on Deep Learning: A Case Study of the Huang-Huai-Hai Region in China

As climate change intensifies, drought has become a major global engineering and environmental challenge. In critical areas such as agricultural production, accurate drought monitoring is vital for the sustainable development of regional agriculture. Currently, despite extensive use of traditional meteorological stations and remote sensing methods, these approaches have proven to be inadequate in capturing the full extent of drought information and adequately reflecting spatial characteristics. Therefore, to improve the accuracy of drought forecasts and achieve predictions across extensive areas, this paper employs deep learning models, specifically introducing an attention-weighted long short-term memory network model (AW-LSTM), constructs a composite drought monitoring index (CDMI) and validates the model. Results show that: (1) The AW-LSTM model significantly outperforms traditional long short-term memory (LSTM), support vector machine (SVM) and artificial neural network (ANN) models in drought monitoring, offering not only better applicability in meteorological and agricultural drought monitoring but also the ability to accurately predict drought events one month in advance compared to machine learning models, providing a new method for precise and comprehensive regional drought assessment. (2) The Huang-Huai-Hai Plain has shown significant regional variations in drought conditions across different years and months, with the drought situation gradually worsening in the northern part of Hebei Province, Beijing, Tianjin, the southern part of Huai North and the central part of Henan Province from 2001 to 2022, while drought conditions in the northern part of Huai North, southern Shandong Province, western Henan Province and southwestern Hebei Province have been alleviated. (3) During the sowing (June) and harvesting (September) periods for summer maize, the likelihood of drought occurrences is higher, necessitating flexible adjustments to agricultural production strategies to adapt to varying drought conditions.

Development of a nonstationary Standardized Precipitation Evapotranspiration Index (NSPEI) and its application across China

Drought is a natural disaster and its occurrence and development can be attributed to a range of driving factors, such as deficient precipitation, wind, temperature, land use/land cover changes, and so on. Therefore, attribution and monitoring of droughts are complicated and challenging. Standardized Precipitation Evapotranspiration Index (SPEI) has been widely used in meteorological drought monitoring. Nonstationarity has not been considered in SPEI-based drought monitoring. Here we propose a modified SPEI considering the nonstationarity of hydrometeorological processes, i.e. NSPEI hereafter. We observe nonstationarity SPEI in the Northeast, the Huang-Huai-Hai Plain, the Yangtze River Delta, and the Qinghai-Tibet Plateau. The goodness-of-fit can be determined for 76% of the total stations. Under the assumption of nonstationarity, the drought monitoring performance of the NSPEI is more robust than the traditional SPEI. Besides, we evaluate drought conditions across China under four shared socioeconomic pathways (SSPs), i.e. SSP126, SSP245, SSP370 and SSP585. We identify intensifying droughts in southwest China but a wetting tendency in northern China in winter under the SSP126 scenario; but a significant wetting tendency in southwest China and a drying tendency in northeast China under SSP245, SSP370 and SSP585 scenarios. This study provides a novel meteorological drought index and highlights a fresh picture of drought conditions across China under different SSPs.

Performance and uncertainties of five popular satellite-based precipitation products in drought monitoring for different climate regions

Satellite-based precipitation products (SPPs) can provide crucial precipitation estimations for large-scale, near-real-time meteorological drought monitoring. However, the performances and uncertainties of SPPs in capturing drought information may vary greatly by product and climate region and are still poorly understood. In this study, we comprehensively evaluated the accuracy of five popular SPPs (MSWEP, TMPA 3B42, PERSIANN-CDR, CHIRPS, and COMRPH) in characterizing meteorological drought in mainland China. (1) Under global warming conditions, the standardized precipitation evapotranspiration index (SPEI) identified a significant decrease in drought associated with higher potential evapotranspiration (PET), which indicated the essential role of PET in determining drought evolution over different climate regions. (2) Among all the SPPs investigated, MSWEP, TMPA 3B42, and PERSIANN perform best in capturing the spatiotemporal pattern of the SPEI trend in the arid Tibetan Plateau regions, transition regions, and humid regions, respectively. In terms of drought category and drought characteristics, MSWEP and TMPA 3B42 perform very well for humid regions, and CHIRPS is better for cold and arid regions, with the poorest performance being found in CMORPH. However, no product performs well for all climate regions for drought monitoring. (3) The optimized parameters (OPs) are the dominant factor for drought uncertainties, followed by the interaction effect and SPPs, with great regional dependence. The OPs contributed the most to drought uncertainties over the relatively arid area of northern China, the interaction of OPs and SPPs did so in the humid area of China, and SPPs were found to contribute the most in the central Tibetan Plateau and some parts of humid regions. Our findings provide guidelines on the choice and application of existing SPP datasets for drought monitoring in different climate zones, as well as some clues to further improve the products over areas where they perform poorly.

Machine learning algorithms for merging satellite-based precipitation products and their application on meteorological drought monitoring over Kenya

Machine learning algorithms for merging satellite-based precipitation products and their application on meteorological drought monitoring over Kenya

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.

Spatiotemporal analysis of meteorological drought across China based on the high-spatial-resolution multiscale SPI generated by machine learning

An accurate gridded Standardized Precipitation Index (SPI) at high spatial resolution is important for meteorological drought monitoring and assessment. However, the spatial estimation of the SPI solely derived from gridded precipitation products or interpolation of meteorological station-based data is subject to low accuracy and large uncertainty. Here, we developed a Gaussian process regression-based method by blending multiple precipitation products (GPR-BMP) with information from meteorological stations and topographical characteristics to improve the resolution and accuracy of spatial SPI. GPR-BMP was applied over China in this study, using five widely used precipitation products and meteorological station data, as well as elevation, slope, longitude and latitude. The gridded multiscale (i.e., 1-month, 3-month, 6-month, 9-month and 12-month) SPI datasets over China for 1984–2020 (GPR-BMP-SPI) were generated. Results showed that GPR-BMI-SPIs have higher accuracy than the SPIs derived from single precipitation products at all time scales. Additionally, GPR-BMP-SPIs can relatively accurately identify historical drought events. By analysing the drought spatiotemporal variation over China during 1984–2020, it was found that the annual drought frequency, severity and duration were relatively high in southeast China during 1984–2020. The annual trends of drought frequency, severity and duration showed a significant decrease on the Qinghai-Tibet Plateau. Winter drought in 1984–2020 exhibited the most distinctive features between the east and the west. Additionally, the drought area percentage showed an overall decreasing trend over the whole region of China on both annual and seasonal scales. It was also found that drought frequency contributed more to drought severity than drought duration. The GPR-BMP-SPI datasets generated in this study can serve as fundamental data support for future studies. The proposed model can be applied to other regions globally for drought monitoring and mitigation.

Evaluation of SPI and Rainfall Departure Based on Multi-Satellite Precipitation Products for Meteorological Drought Monitoring in Tamil Nadu

The prevalence of the frequent water stress conditions at present was found to be more frequent due to increased weather anomalies and climate change scenarios, among other reasons. Periodic drought assessment and subsequent management are essential in effectively utilizing and managing water resources. For effective drought monitoring/assessment, satellite-based precipitation products offer more reliable rainfall estimates with higher accuracy and spatial coverage than conventional rain gauge data. The present study on satellite-based drought monitoring and reliability evaluation was conducted using four high-resolution precipitation products, i.e., IMERGH, TRMM, CHIRPS, and PERSIANN, during the northeast monsoon season of 2015, 2016, and 2017 in the state of Tamil Nadu, India. These four precipitation products were evaluated for accuracy and confidence level by assessing the meteorological drought using standard precipitation index (SPI) and by comparing the results with automatic weather station (AWS) and rain gauge network data-derived SPI. Furthermore, considering the limited number of precipitation products available, the study also indirectly addressed the demanding need for high-resolution precipitation products with consistent temporal resolution. Among different products, IMERGH and TRMM rainfall estimates were found equipollent with the minimum range predictions, i.e., 149.8, 32.07, 80.05 mm and 144.31, 34.40, 75.01 mm, respectively, during NEM of 2015, 2016, and 2017. The rainfall data from CHIRPS were commensurable in the maximum range of 1564, 421, and 723 mm in these three consequent years (2015 to 2017) compared to AWS data. CHIRPS data recorded a higher per cent of agreement (>85%) compared to AWS data than other precipitation products in all the agro-climatic zones of Tamil Nadu. The SPI values were positive > 1.0 during 2015 and negative < −0.99 for 2016 and 2017, indicating normal/wet and dry conditions in the study area, respectively. This study highlighted discrepancies in the capability of the precipitation products IMERGH and TRMM estimates for low rainfall conditions and CHIRPS estimates in high rainfall regimes.

Drought Monitoring and Forecasting across Turkey: A Contemporary Review

One of the critical consequences of climate change at both local and regional scales is a change in the patterns of extreme climate events such as droughts. Focusing on the different types of droughts, their quantifying indices, associated indicators, and sources of data (remote sensing (RS)/in situ measurements), this article reviewed the recent studies (from 2010 to 2022) that have explored drought features in Turkey. To this end, a total of 71 articles were selected from the Web of Science (WoS) and Scopus databases. The selected papers were clustered into two categories: (i) drought monitoring studies and (ii) drought forecasting articles. Then, the representative papers were reviewed in detail regarding the implemented indices, models (techniques), case study area, and source of the indicators used to derive drought indices. The review results showed that most of the studies aimed at meteorological drought monitoring and forecasting. An increasing trend was also observed in the use of machine learning for short-term meteorological and hydrological drought prediction. On the other hand, the emerging RS technology and satellite-driven indicators were rarely used in the country. The review showed that there is room for more research on agricultural and hydrological drought monitoring, forecasting, and pattern detection in Turkey.

Assessment of GPM IMERG and GSMaP daily precipitation products and their utility in droughts and floods monitoring across Xijiang River Basin

The recent increasing concerns about floods and droughts have brought up a pressing need for high-precision and high-resolution precipitation records. As representative satellite precipitation products (SPPs) in Global Precipitation Measurement (GPM) era, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product and the Global Satellite Mapping of Precipitation (GSMaP) are widely acknowledged as reliable alternative sources. A quantitative evaluation of these new products is of great importance before their extended applications. This study comprehensively assesses the statistical performance and the application potential of IMERG (IMERG-Early, IMERG-Late and IMERG-Final) and GSMaP (GSMaP-NRT, GSMaP-Gauge-NRT, GSMaP-Gauge) products over Xijiang River Basin during 2009–2018 at a daily timescale. Both IMERG and GSMaP products have their own advantages. The results indicate that (1) at the grid scale, the IMERG-Final exhibits the highest correlation with gauge-based precipitation data while GSMaP-Gauge yields the lowest relative mean error. IMERG-Early and IMERG-Late have superiorities in almost all evaluation metrics relative to GSMaP-NRT and GSMaP-Gauge-NRT. (2) The temporal performance in specific months visually demonstrates that GSMaP-Gauge has a lower precipitation detection capacity in wet seasons due to a higher FAR, in opposite to other SPPs. (3) The precipitation detection ability of SPPs varies largely with different precipitation intensities. GSMaP-Gauge can identify nearly 90% rainfall events at the lowest threshold of 1 mm/day, but it tends to overestimate the non- and slight precipitation events up to small or moderate classes and highly underestimate precipitation amount of strong events. At extreme precipitation intensity (≥50 mm/day), >50% of extreme precipitation events were missed by SPPs resulting in unsatisfactory PODs below 0.5. (4) For meteorological drought monitoring, IMERG-Final exhibits highest CCs in all inspected timescales, succeed by GSMaP-Gauge. IMERG-Early and IMERG-Late still perform better than near-real-time GSMaPs in drought research. (5) In the hydrological simulation with the Soil and Water Assessment Tool (SWAT), GSMaP-Gauge-simulation shows the best accuracy regardless of wet or dry seasons with highest NSEs of 0.64 and 0.73, respectively. All near-real-time SPPs present an unacceptable hydrological utility and an underestimation of streamflow in dry seasons. The systematic evaluation of extreme streamflow indices highlights that of the four timelier products, IMERG-Early is most reliable in capturing extreme streamflow. GSMaP-NRT tends to exaggerate the flood magnitude and could be used as reference in estimating severe and continuous floods.

Non-parametric severity-duration-frequency analysis of drought based on satellite-based product and model fusion techniques.

Climate change has increased the severity and frequency of droughts over the last decades. To alleviate the adverse impacts of droughts, an effective planning and management framework requires high-resolution spatiotemporal data. TRMM multi-satellite precipitation analysis (TMPA) dataset provides sufficient accuracy with fine spatio-temporal resolution. However, it only covers a short temporal span, which limits its applicability for drought studies. This paper presents a methodology for efficient and accurate temporal extension of TMPA using four artificial intelligence (AI)-based models. To improve AI-based model precipitation estimations, fusion techniques including Orness, Orlike, and genetic algorithm (GA)-based weighting methods were employed. Results show that fusion approaches provide more accurate estimates of precipitation. Different timescales of n-SPI time series and drought spatial maps were prepared to visually evaluate the performance of long-term TMPA (LT-TMPA) alongside statistical error indices. The results confirm that this dataset is effective for meteorological drought monitoring over southern Iran. Finally, drought risk assessment was carried out to determine the spatiotemporal characteristics of droughts through severity-duration-frequency (SDF) contour maps. In contrast to the traditional SDF curves, SDF contour maps provide a superior understanding of drought for policymakers since they preserve spatial information.

Decadal spatio-temporal dynamics of drought in semi-arid farming regions of Zimbabwe between 1990 and 2020: a case of Mberengwa and Zvishavane districts

Drought severity and frequency are increasing in tropical regions and its occurrence is not uniform in space and time. The study assessed the spatio-temporal dynamics of agrometeorological drought in Mberengwa and Zvishavane districts between 1990 and 2020. An empirical research design was adopted in this study. GIS and remote sensing techniques were used to determine and analyze agricultural drought conditions and the standardized precipitation generator was used to compute SPI for meteorological drought monitoring and analysis. Microsoft Excel 2011 was adopted for the analysis of drought coverage and performing the Mann–Kendall trend test for precipitation trend analysis. Results indicated that the area covered by drought declined between the 1990 and 2000 and 2001–2010 decades before increasing during the 2011–2020 decade. Results indicated significant spatial dynamics of drought severity and frequency in Mberengwa and Zvishavane districts and the majority of wards experienced increased frequency of severe droughts during the 2011–2020 decade. It was highlighted that meteorological drought was not evenly distributed across all decades. Dry conditions in the first months (October and November) of the rainy season have been indicated during the 1990–2000 decade, followed by wetter conditions during the 2001–2010 decade and comparatively drier conditions in the same months during the 2011–2020 decade. This indicated the late onset of the rainfall season during the 1990–2000 and 2011–2020 decades compared to the 2001–2010 decade. It was also shown that rainfall cessation was earlier during the 2001–2010 and 2011–2020 decades and late during the 1990–2000 decade. The study concludes that drought has increased both in severity and frequency in Mberengwa and Zvishavane districts; hence, there is a need for more support to drought resilience-building initiatives in these areas. The government of Zimbabwe is advised to set up climate research centers in all provinces of the country to improve the availability of climate change–related data which is useful when addressing the impacts of climate change.

Evaluation of Three Long-Term Remotely Sensed Precipitation Estimates for Meteorological Drought Monitoring over China

Remotely sensed precipitation estimates (RSPEs) play an essential role in monitoring drought, especially in ungauged or sparsely gauged areas. In this study, we evaluated the ability of three popular long-term RSPEs (PERSIANN, CHIRPS, and MSWEP) in capturing the meteorological drought variations over the 10 first-level water resource basins of China, based on the standardized precipitation index (SPI). Drought events were identified by run theory, and the drought characteristics (i.e., duration, severity, and intensity) were also evaluated and compared with a gridded in situ observational precipitation dataset (CMA). The results showed that the three RSPEs could generally capture the spatial patterns and trends of the CMA and showed better performance in the wetter basins. MSWEP had the best performance for the categorical skill of POD, followed by CHIRPS and PERSIANN for the four timescales. SPI6 was the optimal timescale for identifying meteorological drought events. There were large skill divergences in the 10 first-level basins for capturing the drought characteristics. CHIRPS can efficiently reproduce the spatial distribution of drought characteristics, with similar metrics of MDS, MDI, and MDP, followed by MSWEP and PERSIANN. Overall, no single product always outperformed the other products in capturing drought characteristics, underscoring the necessity of multiproduct ensemble applications. Our study’s findings may provide useful information for drought monitoring in areas with complex terrain and sparse rain-gauge networks.

Hydrological and Meteorological Drought Monitoring and Trend Analysis in Abbay River Basin, Ethiopia

The definition of drought is very controversial due to its multi-dimensional impact and slow propagation in onset and end. Predicting the accurate occurrence of drought remains a challenging task for researchers. The study focused on hydrological and meteorological drought monitoring and trend analysis in the Abbay river basin, using the streamflow drought index (SDI), standardized precipitation index (SPI), and reconnaissance drought index (RDI), respectively, to fill this research gap. The study also looked into the interrelationships between the two drought indicators. The SDI, SPI, and RDI were calculated using long-term streamflow, precipitation, and temperature data collected from 1973 to 2014. The data were collected from eight streamflow stations and fifteen meteorological gauge stations. DrinC software (Drought Indices Calculator) was used to calculate the SDI, SPI, and RDI values. The result from meteorological drought using SPI12 and RDI12 shows that 1975, 1981, 1984, 1986, 1991, 1994, and 2010 were extreme drought years, whereas 1983, 1984, 2001, and 2010 were the most extreme hydrological drought years based on the SDI12 result. Except for Bahir Dar and Gondar, a severe drought occurs at least once a decade in all stations considered in this study. In general, the SPI, RDI, and SDI results indicated that the study area was exposed to the most prolonged severe and extreme drought from 1981 to 1991. The findings of this study also demonstrated that the occurrence of hydrometeorological droughts in the Abbay river basin has a positive correlation at long time scales of 6 and 12 months. The trend analysis using the Mann–Kendall test implied that there was a significant meteorological drought trend in two stations (Debre Berhan and Fiche) at SPI12 and RDI12 time scale, but for the remaining thirteen stations, there is no trend in all time scales. The hydrological drought trend analysis in the basin on a seasonal (SDI3) and yearly (SDI12) time scale also revealed that three streamflow stations have a positive trend (Kessie, Gummera, and Border). This implies that water resource management is still a vital tool for the sustainable development of the Abbay river basin in the future.

Meteorological drought monitoring in the Upper Olifants sub-basin, South Africa

Measured climate data are often scarce and expensive to collect through a network of measurement points. There are various reanalysis products that can be used in certain regions in studies concerned with drought monitoring. This study aimed at assessment of the adequacy of using the National Centers for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR) rainfall data and the ERA5 rainfall data from the European Centre for Medium-Range Weather Forecasts (ECMWF) in monitoring meteorological drought over the Upper Olifants sub-basin, South Africa. A comparison was made between the observed rainfall data and the two reanalysis products based on the Mean Error (ME), the Nash-Sutcliffe Efficiency (NSE) and percentage of bias (PBIAS). The data were also used in computing and comparing the results of the Standardised Precipitation Index (SPI). The CFSR were found to underestimate rainfall amounts, while the ERA5 overestimated the rainfall amounts except during winter. The ERA5 showed a better correlation which was R2 = 0.78 when compared to the one for the CFSR, R2 = 0.57. Also, the NSE, ME and PBIAS for the ERA5 products were generally better in almost all-time scales. The three products detected one of the major droughts but the CFSR were unable to detect the second major drought in the study area during the chosen study period. The study concludes that the ERA5 is a better product for meteorological drought monitoring in the study area and can be useful in predicting meteorological drought using artificial intelligence due to the spatiotemporal resolution of the product.

Meteorological Drought Monitoring Based on Satellite CHIRPS Product over Gamo Zone, Southern Ethiopia

Drought is a frequent occurrence in semidesert areas of southern Ethiopia that significantly affect regional, social, economic, and environmental conditions. Lack of rainfall monitoring network, instrument measurement, and failure are major bottlenecks for agro-and hydroclimate research in developing countries. The objectives of this study were to evaluate the performance of CHIRPS rainfall product and to assess meteorological drought using SPI for the period 2000 to 2020 over Gamo Zone, southern Ethiopia. The performance of CHIRPS v2 was assessed and compared to station observations (2000–2020) in the study domain to derive SPI on a three-month timescale. The Pearson correlation coefficient (R), bias, probability of bias (PBias), mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and Nash simulation efficiency (NSE) values across the zone for CHIRPS v2 were found to be 0.88, 1.02, 2.56, 0.25, 22.41, 33.14, and 0.77, respectively. The results indicate that CHIRPS performed good ability to analyze the drought characteristics in the Gamo Zone. The spatial and temporal distribution method of meteorological drought has been evaluated using the Climate Data Tool (CDT). The Standardized Precipitation Index (SPI) was computed using the gamma distribution method. The magnitude of (SPI-3) of monthly and seasonal (MAM) meteorological drought in the zone from 2000 to 2020. The result shows that the known historic drought years (2014, 2015, 2010, 2009, and 2008) were indicated very well. Furthermore, sever and extreme droughts were observed in 2008 and 2009 with drought duration of 6.7 and 6.3, respectively, in most areas of the zone. Hence, this study revealed that CHIRPS can be a useful supplement for measuring rainfall data to estimate rainfall and drought monitoring in this region.

An Evaluation of the Performance of Five Meteorological Drought Monitoring Indices Over an Arid and Semi-Arid Region of Gujarat (India)

Drought Monitoring plays an important role in drought risk assessment and management. Different meteorological drought indices are normally used to determine drought, which are essentially constant functions of rainfall and hydro-meteorological factors. A universal drought index cannot be used to evaluate the severity of drought in a specific region due to the essential difficulty of drought phenomena, hydro-climatic factors, and watershed characteristics. Classifying an appropriate drought index requires evaluating the performance of various drought indices. This study evaluated 5 meteorological drought indices for Gujarat, India utilizing a dataset from a total of 167 raingauge and climate stations having over 30 years (1986-2015 of the dataset). In various countries, several droughts indices have been introduced and utilized. This study evaluates the effectiveness of 5 meteorological drought monitoring indices in Gujarat. The following meteorological drought indexes were chosen based on data availability: Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Percent Departure from Normal (PDN), Effective Drought Index (EDI), and Reconnaissance Drought Index (RDI). The EDI is a time step independent drought indicator, with 1-, 3-, 6-, 9-, and 12-month time intervals defined for the four multi-time scale indices. This drought index was compared using several time scales of multi-time scale drought indices based on their association with the EDI. The evaluation of drought indices during the historic drought was assessed based on the occurrence and response of drought indices within the specific drought severity classes. In the present study area, the 9-month scale is suitable for comparisons of drought indices. The SPEI-9 had the highest relative occurrence in the 'severe dry' class, and it was subtle to 9-monthly rainfall in most districts. As a result of the study, SPEI-9 is considered the best drought index.

Meteorological and agricultural drought monitoring in Southwest of Iran using a remote sensing-based combined drought index

Meteorological and agricultural drought monitoring in Southwest of Iran using a remote sensing-based combined drought index

Meteorological drought monitoring across the main river basins of Ethiopia using satellite rainfall product

BackgroundDrought is a recurrent phenomenon emerging from an inter-annual and intra-seasonal deficit of water across the atmosphere-to-aquifer continuum and these events are reported to be very severe in regions of Ethiopia. Availability of accurate precipitation observations significantly impacts the drought monitoring systems. These observations are scarce and sparsely distributed in countries like Ethiopia. To overcome such a problem, the use of satellite rainfall estimates with continuous and timely data at different spatio-temporal scales is opportune, provided their accuracy is well known. Among the currently available satellite rainfall products studies specifically in Ethiopia shown that Climate Hazard Group Infrared Precipitation with Station version 2 daily rainfall product (hereafter CHIRPSv2) has better performance and recommended as a valuable substitute for gauge rainfall data. Therefore, the current study focused on assessing the applicability of CHIRPSv2 for meteorological drought monitoring in Ethiopia. Due to the wide spatio-temporal variability of Ethiopia's climate, the performance of CHIRPSv2 rainfall product for meteorological drought monitoring has been assessed in selected river basins (Awash, Blue Nile, Baro, Danakil, Omo and Tekeze) of Ethiopia. For drought estimations, two well-known meteorological drought indices such as Effective Drought Index (EDI) and the Standardized Precipitation Index (SPI) have been utilized.ResultThe obtained results show that the CHIRPSv2 based EDI and SPI are able to identify all the historical drought events reported between 1982 and 2016 (such as 1984, 1992, 2003, 2009, 2011, 2012, and 2015). The time series plots of EDI and SPI values show that most of the drought events in the selected river basins occurred during their corresponding main and smaller rainy months. Detailed spatio-temporal investigations of the two worst drought years (1984 and 2011) and one drought-free year (2007) show that both the EDI and SPI could enable to identify the drought and drought-free areas correctly when compared with the available recorded historical droughts (RHD) across each river basin. Similarly, the temporal trends of EDI and SPI identified drought shows that frequency and severity of drought were higher during 1980s and 2000s than 1990s.ConclusionsSuch good agreement between the identified drought and historical drought indicates that CHIRPSv2 is a promising rainfall dataset, which could be used to develop drought monitoring and early warning system across different river basins of Ethiopia. Besides, the study helps to provide useful information for decision makers to implement different adaptation and mitigation measures of drought in the study area. The finding also will support to improve the existing drought monitoring and early warning system and to build resilience to drought at the river basin level.