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.

Attentiveness to Early Warning Drought Information: Implications for Policy Support and Climate Risk Reduction in Ghana

Successful drought planning is dependent on the generation of timely and accurate early warning information. Yet there is little evidence to explain the extent to which crop farmers pay attention to and assimilate early warning drought information that aids in the policy formulation in support of drought risk reduction. A socioecological survey, using a structured questionnaire administered to 426 crop farming households, was carried out in the Talensi District of the Upper East Region, Ghana. The data analytic techniques used were frequency tables, relative importance index, and multinomial logistics embedded in SPSS v.20 software. The results show that crop farmers predominantly rely on agricultural extension officers for early warning drought information, with an estimated 78% of them paying little to very much attention to the information. The likelihood ratio Chi-square test showed that there is a significant improvement in fit as X2 (20) = 96.792, p < 0.000. Household status, average monthly income, and age were the significant predictors for crop farmers paying no attention at all to early warning drought information, while household status was the only significant factor among those paying a little attention. The drive to build a climate-resilient society with effective early warning centers across Ghana will receive 60% lower support from crop farmers paying no to a little attention as compared to farmers paying very much attention to early warning drought information. Broader stakeholder engagements should be carried out to harness inclusive support from crop farmers to build a climate-resilient society in Ghana.

Integrated drought risk assessment to support adaptive policy making in the Netherlands

Adaptive policy-making to prepare for current and future drought risks requires an integrated assessment of policy actions and combinations of those under changing conditions. This entails quantification of drought risks, integrating drought probability and socio-economic consequences for all relevant sectors that are potentially impacted by drought. The investment costs of proposed policy actions and strategies (various actions combined) can then be compared with the expected risk reduction to determine the cost-effectiveness. This paper presents a method to quantify drought risk in the Netherlands under changing future conditions and in response to policy actions. It illustrates how to use this information as part of a societal cost-benefit analysis and in building an adaptive long-term strategy. The method has been successfully applied to support decision making on the Netherlands’ national drought risk management strategy as part of the National Delta Program for climate change adaptation.

Drought assessment in paddy rice fields using remote sensing technology towards achieving food security and SDG2

PurposeThis research aims to monitor vegetation indices to assess drought in paddy rice fields in Mazandaran, Iran, and propose the best index to predict rice yield.Design/methodology/approachA three-step methodology is applied. First, the paddy rice fields are mapped by using three satellite-based datasets, namely SRTM DEM, Landsat8 TOA and MYD11A2. Second, the maps of indices are extracted using MODIS. And finally, the trend of indices over rice-growing seasons is extracted and compared with the rice yield data.FindingsRice paddies maps and vegetation indices maps are provided. Vegetation Health Index (VHI) combining average Temperature Condition Index (TCI) and minimum Vegetation Condition Index (VCI), and also VHI combining TCImin and VCImin are found to be the most proper indices to predict rice yield.Practical implicationsThe results serve as a guideline for policy-makers and practitioners in the agro-food industry to (1) support sustainable agriculture and food safety in terms of rice production; (2) help balance the supply and demand sides of the rice market and move towards SDG2; (3) use yield prediction in the rice supply chain management, pricing and trade flows management; and (4) assess drought risk in index-based insurances.Originality/valueThis study, as one of the first research assessing and mapping vegetation indices for rice paddies in northern Iran, particularly contributes to (1) extracting the map of paddy rice fields in Mazandaran Province by using satellite-based data on cloud-computing technology in the Google Earth Engine platform; (2) providing the map of VCI and TCI for the period 2010–2019 based on MODIS data and (3) specifying the best index to describe rice yield through proposing different calculation methods for VHI.

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.

Downscaling of Future Precipitation in China’s Beijing-Tianjin-Hebei Region Using a Weather Generator

To project local precipitation at the existing meteorological stations in China’s Beijing-Tianjin-Hebei region in the future, local daily precipitation was simulated for three periods (2006–2030, 2031–2050, and 2051–2070) under RCP 4.5 and RCP 8.5 emission scenarios. These projections were statistically downscaled using a weather generator (BCC/RCG-WG) and the output of five global climate models. Based on the downscaled daily precipitation at 174 stations, eight indices describing mean and extreme precipitation climates were calculated. Overall increasing trends in the frequency and intensity of the mean and extreme precipitation were identified for the majority of the stations studied, which is in line with the GCMs’ output. However, the downscaling approach enables more local features to be reflected, adding value to applications at the local scale. Compared with the baseline during 1961–2005, the regional average annual precipitation and its intensity are projected to increase in all three future periods under both RCP 4.5 and RCP 8.5. The projected changes in the number of days with precipitation are relatively small across the Beijing-Tianjin-Hebei region. The regional average annual number of days with precipitation would increase by 0.2~1.0% under both RCP 4.5 and RCP 8.5, except during 2031–2050 under RCP 8.5 when it would decrease by 0.7%. The regional averages of annual days with precipitation ≥25 mm and ≥40 mm, the greatest one-day and five-day precipitation in the Beijing-Tianjin-Hebei region, are projected to increase by 8~30% during all the three periods. The number of days with daily precipitation ≥40 mm was projected to increase most significantly out of the eight indices, indicating the need to consider increased flooding risk in the future. The average annual maximum number of consecutive days without precipitation in the Beijing-Tianjin-Hebei region is projected to decrease, and the drought risk in this area is expected to decrease.

Spatial Multi-Criterion Decision Making (SMDM) Drought Assessment and Sustainability over East Africa from 1982 to 2015

Droughts are ranked among the most devastating agricultural disasters that occur naturally in the world. East Africa is the most vulnerable and drought-prone region worldwide. In this study, four drought indices were used as input variables for drought assessment from 1982 to 2015. This work applied the SMDM algorithm to the integrated approach of OLR and Hurst exponent. The Detrended Fluctuation Analysis (DFA) and Ordinary Least Square (OLR) were merged to compute the trend and persistence (Hurst exponent) of the drought indices. Result indicates that the OLR at time scale 1, 6, and 12 shows a similar distribution with positive (negative) trends scattered in the Northwest (Northeast and Southern) parts of the study area which differs with the OLR aggregated at a 3-month time scale. The percentage pixel distribution for OLR-1, OLR-3, OLR-6, and OLR-12 is 18.2 (81.8), 72.5 (27.5), 32.9 (67.1), and 36.9 (63.1) for increasing (decreasing) trends respectively. Additionally, results indicate that DFA-1 is highly persistent with few random pixels scattered around Ethiopia, South Sudan and Tanzania, with percentage pixels as 88.7, 11.3 and 0.1 representing h > 0.5, h = 0.5, and h < 0.5, respectively. DFA-6 shows high (low) pixels representing h > 0.5 (h > 1), respectively. Meanwhile, for DFA-3 and DFA-12, the distribution shows persistence and a random walk, respectively. Drought conditions may eventually persist, reverse or vary drastically in an unpredictable manner depending on the driving forces. Overall, the drought risk map at 1-, 3-, and 6-month aggregates has shown severe degradation in Southern Kenya and Tanzania while noticeable improvements are seen in western Ethiopia and South Sudan.

Preface to Drought Risk Management to Reflect Changing Meteorological Conditions

Drought is one of the main extreme meteorological and hydrological phenomena which influence both the functioning of ecosystems and many important sectors of human economic activity [...]