A Bibliometric Analysis of Drought Indices, Risk, and Forecast as Components of Drought Early Warning Systems

In this study, we apply a bibliometric analysis to characterize publication data on droughts, mainly focusing on drought indices (DIs), drought risk (DR), and drought forecast (DF). Data on publications on these selected topics were obtained through the Scopus database, covering the period from 1963 to June 2021. The DI-related publications, based on meteorological, soil moisture, hydrological, remote sensing, and composite/modeled Dis, accounted for 57%, 8%, 4%, 29%, and 2% of the scientific sources, respectively. DI-related studies showed a notable increase since the 1990s, due perhaps to a higher number of major droughts during the last three decades. It was found that USA and China were the two leading countries in terms of publication count and academic influence on the DI, DR, and DF studies. A network analysis of the country of residence of co-authors on DR and DF research highlighted the top three countries, which were the USA, China, and the United Kingdom. The most productive journal for the DI studies was found to be the International Journal of Climatology, whereas Natural Hazards was identified as the first-ranked journal for the DR and DF studies. In relation to individual researchers, Singh VP from the USA was found to be the most prolific author, having the greatest academic influence on DF study, whereas Zhang Q from China was identified as the most productive author on DR study. This bibliometric analysis reveals that further research is needed on droughts in the areas of risk management, water management, and drought management. This review maps trends of previous research in drought science, covering several important aspects, such as drought indices, geographic regions, authors and their collaboration paths, and sub-topics of interest. This article is expected to serve as an index of the current state of knowledge on drought warning systems and as guidance for future research needs.

Spatiotemporal Characteristics of Meteorological Drought During the Past Half Century in Different Climates Over Iran

The present study compares the main characteristics (intensity, duration, and frequency) of meteorological drought events in the four climates (Hyperarid, Arid, Semiarid, and Humid) of Iran. For this purpose, three drought indices, including Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), and Standardized Precipitation Evapotranspiration Index (SPEI), were employed at the timescales of 1-, 3-, 6-, 9- and 12-months. These indices were compared by utilizing long-term data of 41 synoptic meteorological stations for the recent half century, 1969–2019. The long-term analysis of drought indices indicates that the duration and intensity of drought events have temporally risen after the 1998–99 period. Iran has experienced the longest duration (40 months) of extreme drought during Dec 98–Mar 02 and Jan 18–Mar 18, respectively. Spatial patterns demonstrate that drought intensity uniformly increased in SPI1 to SPI12, and SPEI3 to SPEI12, from humid and semiarid to arid and hyperarid regions. The average drought duration in studied stations for SPI, SPEI and RDI indices equaled 9, 12, and 9 months, respectively. In addition, mean drought frequencies are calculated at 14, 17, and 13 percent for SPI, SPEI and RDI indices, respectively. Generally, SPEI compared to SPI and RDI shows greater duration and frequency of drought events, particularly in arid and hyperarid regions. The research shows the crucial role of climatic variables in detecting drought characteristics and the importance of selecting appropriate drought indices in various climates.

Forecasting the Future Drought Indices Due to the Effects of Climate Change in Al Najaf City, Iraq.

Drought is a natural disaster associated with a shortage of water availability for specified region within a specific time period. The impacts of drought are significant and extend to damage many important life aspects such as environmental, economic, and social activities. The forecasting of the drought events is an essential element for planning this disaster, reducing its effectiveness and response. The three characteristic frequency, intensity, and time period are the key parts for forecasting and assessment of droughts. Here, two drought indices (The Reconnaissance Drought Index (RDI), standardized precipitation index (SPI)) were used for forecasting of the future drought within Al Najaf city, Iraq. Thirty years meteorological data (average monthly precipitation and temperature) were used for the period (2021–2050) downloaded from the site of the Centre for Environmental Data Analysis (CEDA) for five grid points to cover overall study area. The computation of these indices conducted at a 12-month time scale and included the calculation of potential evapotranspiration by Thorthwaite method. The temporal drought intensity as well as drought frequency configurations were calculated and analyzed for each drought index. The results showed that the general average drought level expected will mildly dry while the maximum drought level expected will extremely dry. The more severe seasons of drought were forecasted in the years 2038, 2034 and 2021, respectively. Also, the prevailing event will be a one year drought and the maximum drought interval occurred within the study period will four consecutive years, with a 3.33% exceedance probability.

Meteorological Drought Assessment in Sharjah, UAE Using Drought Indices

Drought is a natural disaster that significantly affects environmental and socio-economic conditions. It occurs when there is a period of below average precipitation in a region, and it results in water supply shortages affecting various sectors and life adversely. Droughts impact the ecosystems, crop production, and erode livelihoods. Monitoring drought is essential especially in the United Arab Emirates (UAE) due to the scarcity of rainfall for an extended period of time. In this study, drought is assessed in Sharjah UAE using monthly precipitation and average temperature data recorded for 35 years (1981-2015) at the Sharjah International Airport. The standardized precipitation Index (SPI), and the Reconnaissance Drought Index (RDI) are selected to predict future droughts in the region. SPI and RDI are fitted to the statistical distribution functions (gamma and lognormal) in an annual time scale and then, a trend analysis of index values is carried out using Mann-Kendal test. The correlation between SPI and RDI indices was found to be high where both showed high drought frequencies and a tendency to get drier over time, thus indicating the need of appropriate drought management and monitoring.

Concurrent and lagged relationships among various drought indices in the United states

The purpose of this study is to examine the nature of the concurrent and lagged relationships among various drought type-specific measures of drought severity. Monthly values of average temperature (TEMPZ), total precipitation (PREZ), the Palmer moisture anomaly index (ZINX) the Palmer drought severity index (PDSl), and the Palmer hydrologic drought seventy Index (PHDI) were examined from a sample of climatic divisions, in the United States for the period,1931-1985. The relationships are examined at two levels through the use of simple correlations. Level one utilizes data from the entire study period. Data from selected drought events are employed in level two. The results show that the strongest relationships are between drought indices with similar rates of response to changes in moisture supply and demand. The correlations also show that lagged values of fast-response drought indices (ZINX, PREZ) arc more strongly correlated with the slow-response PHD1 than concurrent values. Intersite differences between correlated pairs of indices are generally small and follow consistent trends; cross the flow pattern sample for both level one and level two analyses. Intra-site differences are large for some pairs of correlated indices indicating that characteristics of individual droughts can deviate substantially from average or normal conditions

Study of drought indices in relation to rice crop Production over some States of India

Drought monitoring indices used by India Meteorological Department (IMD) and National Remote Sensing Agency (NRSA) have been discussed in relation to crop estimates during kharif seasons of 1989 and 1990 for some States over India. It was found that index used IMD showed moderate to severe drought over certain areas during certain periods, while NRSA reported non-drought conditions. On the other hand, the preliminary estimated rice crop by the respective States during 1989 suggested higher values in four States and lower in one State, with reference to the mean values based, on the previous eight years data. A high degree of correlation between NRSA and IMD drought indices was found, which agree partially with the yield estimates during 1989.

ANALISIS KETERKAITAN ANTARA KEKERINGAN METEOROLOGIS DENGAN INDEKS VEGETASI TERSTANDARISASI DI PULAU LOMBOK

Intisari Kekeringan merupakan bencana kompleks yang dapat menyebabkan kerugian masyarakat di berbagai sektor. Salah satu wilayah yang berisiko tinggi mengalami kekeringan adalah Pulau Lombok. Wilayah ini memiliki lahan yang berisiko terkena kekeringan seluas 405.985 ha. Tingkat keparahan kekeringan meteorologis dapat diukur dengan Standardized Precipitation Evapotranspiration Index (SPEI). Salah satu karakteristik kekeringan adalah kondisi vegetasi tanaman yang buruk, oleh karena itu Standardized Vegetation Index (SVI) digunakan sebagai acuan dalam monitoring kekeringan agrikultural. Penelitian ini bertujuan untuk mengetahui hubungan antara SPEI dengan SVI untuk setiap pos hujan di Pulau Lombok tahun 2001-2018. Penelitian ini menggunakan data bulanan tahun 2001-2018 yang meliputi data observasi curah hujan, suhu maksimum, suhu minimum, penginderaan jauh Normalized Differences Vegetation Index (NDVI) dengan resolusi 0,05°, model FLDAS kecepatan angin yang juga didapatkan dengan resolusi 0,5°, lama penyinaran matahari, lintang, dan elevasi. Metode yang digunakan yaitu menghitung indeks kekeringan SPEI dan SVI, kemudian menghitung korelasi dan signifikansi untuk kedua indeks kekeringan tersebut. Hasilnya menunjukkan bahwa SPEI1 lebih tinggi berkorelasi dengan SVI+1 dengan kategori cukup kuat. Untuk SPEI3, SPEI6, dan SPEI12 berkorelasi cukup kuat hingga kuat dengan SVI0. Hal ini menunjukkan bahwa kekeringan jangka panjang akan langsung mempengaruhi kekeringan agrikultural atau kekeringan vegetasi saat itu juga. Nilai korelasi yang lebih tinggi untuk setiap indeks tersebar di pos hujan yang terletak di tengah-tengah Pulau Lombok, karena pengaruh kondisi geografis dan demografis Abstract Drought is a complex disaster because it can cause loss to society in various sectors. One of the high-risk areas of drought is Lombok Island. This area has 405,985 ha of drought risk. The severity of meteorological drought can be measured by the Standardized Precipitation Evapotranspiration Index (SPEI). One of the characteristics of drought is the poor condition of plant vegetation, therefore the Standardized Vegetation Index (SVI) is used as a reference in monitoring agricultural drought. This study aims to determine the relationship of SPEI with SVI for each rainfall post in Lombok Island from 2001-2018. This study uses monthly data from 2001-2018, including observation data of rainfall, maximum temperature, minimum temperature, remote sensing Normalized Differences Vegetation Index (NDVI) 0.05 °, FLDAS model of wind speed 0.5 °, length of the day, latitude, and elevation. The use method is to calculate SPEI and SVI, then calculate the correlation and significance for the two drought indices. The result shows that SPEI1 is higher in correlation with SVI+1, which is in a strong enough category. For SPEI3, SPEI6, and SPEI12, the correlation is strong enough to strong with SVI0. This suggests that long-term drought will directly affect agricultural drought or immediate vegetation drought. The higher correlation values ??for each index are spread over the rain posts located in the middle of Lombok Island because geographic and demographic conditions influence them.

Introduction of the best criterion for evaluation of tolerance to drought stress in sorghum’s genotypes

<p class="042abstractstekst">Sorghum (<em>Sorghum bicolor</em> (L.) Moench) is the fifth important cereal considered a drought-tolerant crop. However, its reduction of grain yield considerably occurs in a shortage of water. In the current study, 10 sorghum genotypes were assessed for their grain yield under normal irrigation and water deficit irrigation. As well, the efficacy of several drought indices was evaluated for the selection of high-yield and drought-tolerant genotypes. The experiment was conducted as a split-plot considering three irrigation levels as main-plot and 10 genotypes as sub-plot. Correlation among the indices, clustering of the genotypes along with principal component analysis was employed. Yield production was significantly and positively correlated with indices MP (mean productivity), STI (stress tolerance index), GMP (geometric productivity), HM (harmonic mean), and YI (yield index) in all the irrigation levels. Therefore, these indices are more effective in the selection of high-yielding genotypes under different water conditions. Rank means of stress indices for each genotype revealed that genotype TN-04-79 in mild deficit irrigation and genotypes KGS23 and TN-04-79 in severe deficit irrigation were the most tolerant.</p>

DroughtCast: A Machine Learning Forecast of the United States Drought Monitor

Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods to forecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.