Drought Risk Map Research Articles

Wavelet-Entropy Enhanced Clustering: A Comprehensive Analysis of Drought Patterns in the Southern Plains, USA

Abstract Droughts may exhibit spatiotemporal heterogeneity at regional scale. Effective drought assessment and management necessitates identifying homogeneous areas. However, previous studies often simplified clustering analysis by focusing only on a single variable. In this study, we present a novel drought risk map for the Southern Plains (SP) region of the United States by integrating the wavelet-entropy approach with k-means clustering algorithm to capture spatio-temporal patterns of drought-related variables across various resolutions while eliminating redundant information. We considered multiple drought indicators and indices including gridded precipitation (P), potential evapotranspiration (PET), normalized difference vegetation index (NDVI), and Standardized Precipitation Evapotranspiration Index (SPEI) as well as geographical coordinates and topography map. Through evaluating five different combinations of input datasets, we selected the one demonstrating optimal results based on Davies-Bouldin and Calinski-Harabasz criteria. In addition to P, PET, and NDVI, including the coordinates and elevation as secondary variables significantly enhanced the clustering performance. Using these variables, the region was subdivided into 21 clusters. The Pearson’s correlation coefficients for the SPEI between centroid members and corresponding cells within clusters averaged between 0.84 to 0.94. Comparison with an existing cluster map (DRA) for the region revealed that our proposed cluster map showed higher variability between clusters for P, PET, and NDVI, confirming the robustness of the clustering results for drought conditions in the SP. The new clustering framework is expected to provide valuable insights for understanding and addressing drought dynamics in the SP region.

Drought risk assessment based on hazard, vulnerability, and coping capacity concepts for hot and dry climate regions of Iran

Due to the complex and multi-dimensional nature of droughts, it is not possible to assess drought-induced damage and its consequences for various social, economic, and environmental aspects of societies by relying only on a univariate index such as precipitation-based drought indices. The present study aimed to develop a practical and scientific framework based on hazard, vulnerability (social, economic, and environmental), and coping capacity to generate a drought risk map for the hot and dry climate regions of Iran. Accordingly, the Drought Hazard Index (DHI), Drought Vulnerability Index (DVI), and Drought Coping Capacity Index (DCCI) were derived from “the Standardized Precipitation Evapotranspiration Index (SPEI)”, “16 social, economic and environmental variables” and “three social, economic variables”, respectively. The layers of all variables of the three indices in the GIS were provided, and they were combined in the form of an equation to produce a drought hazard map of central and southeastern Iran. The results indicate that the counties most and least vulnerable to drought were located in the southeast and west of the case study area, respectively. A number of large households, long distances from provincial centers, and soil erosion were the most important social, economic, and environmental factors making the southeast of the case study (including south of Sistan and Baluchestan and south of Kerman provinces) most vulnerable to drought. Due to their high drought coping capacity, counties located in the west of the case study (west of Kerman and south of Yazd provinces) were least vulnerable to drought. Extended support for low-income households by charitable organizations, tertiary education, and most importantly, a variety of jobs and career opportunities were the most important factors in reducing vulnerability in this part of Iran. Furthermore, our methodology by taking social, economic, and environmental dimensions into account as risk, vulnerability, and coping capacity indices can be far more efficient than the methods considering only risk and vulnerability factors.

Comprehensive drought risk assessment and mapping in Taiwan: An ANP-ANN ensemble approach

This study aims to comprehensively evaluate and map the risk of drought in Taiwan by employing a combination of two powerful models, the Analytic Network Process (ANP) and Artificial Neural Network (ANN). This innovative approach utilizes an ensemble learning method, where ANP constructs a logical network and assigns weights to various indicators. Subsequently, ANN leverages these weights to train the model effectively. A total of twenty indicators were incorporated into the analysis to create a holistic drought risk map for Taiwan. These indicators are thoughtfully categorized into three essential components: hazard, exposure, and vulnerability, providing a well-defined representation of drought risk. The trained ANN model showcases remarkable accuracy and performance, boasting values of 0.940 for accuracy, 0.946 for precision, 0.938 for recall, 0.942 for the F1 score, and 0.923 for the Kappa Index. These results unequivocally affirm the model's effectiveness in predicting drought risk. Furthermore, the final drought risk map underwent rigorous validation through fieldwork and statistical data. The validation process yielded high accuracies, ranging from 0.717 to 0.851, for assessing damage to crops, converted damaged areas, and estimated value product loss. This validation, conducted against multiple reference data sources, underscores the map's reliability and its alignment with various goodness-of-fit criteria. In summary, this study underscores the potency of the ANP-ANN ensemble approach, with the trained ANN model proving its robustness in swiftly predicting drought risk across diverse ecological and socioeconomic scenarios.

Drought risk in the Mahanadi River basin: a multi-dimensional approach for integrated urban-rural drought management strategies

ABSTRACT Droughts are one of the most spatially complex geohazards, having a significant impact on the economic status of any region. To mitigate drought risks, a comprehensive drought management plan is required, and the first step towards that goal is to assess the various aspects of drought risks in the preparation of a drought risk map. To produce an integrated drought risk map for Mahanadi River basin, India, the current study combines geospatial methodologies with the Analytical Hierarchy Process (AHP) technique to assess various dimensions, viz. hydrological, meteorological, agricultural and socio-economic drought risk in the region in view of integrated rural-urban management strategies. A total of 17 criteria from different aspects were taken into consideration in different groups. The results showed that districts like Raipur, Bilaspur, Durg and Ragnandgaon faced extreme drought conditions combining assessments of agriculture, water resources, socioeconomic factors and weather patterns, reveals varying levels of risk across the region. According to the findings, 22.01% of the region is vulnerable to extreme drought and 31.64% of the area is suffering from severe drought. The results indicate that the technique used for identifying the region's susceptibility to drought is effective, which will help planners develop strategies for mitigating drought.

Meteorological and Agricultural Drought Risk Assessment via Kaplan–Meier Survivability Estimator

Dry periods and drought are inherent natural occurrences. However, due to the increasing pressures of global warming and climate change, these events have become more frequent and severe on a global scale. These phenomena can be traced with various indicators and related indices proposed by various scholars. In general, drought risk assessment is done by modeling these indicators and determining the drought occurrence probabilities. The proposed adaptation introduces the “Kaplan–Meier estimator”, a non-parametric statistic traditionally used in medical contexts to estimate survival functions from lifetime data. The study aims to apply this methodology to assess drought risk by treating past droughts as “events” and using drought indicators such as the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Mapping these results for a better understanding of the drought risks on larger spatial scales such as a river basin is also within the expected outcomes. The adapted method provides the probability of non-occurrence, with inverted results indicating the likelihood of drought occurrence. As a case study, the method is applied to SPI and SPEI values at different time steps (3, 6, and 12 months) across 27 meteorological stations in the Gediz River Basin, located in Western Turkey—a region anticipated to be profoundly affected by global climate change. The results are represented as the generated drought risk maps and curves, which indicate that (i) drought risks increase as the considered period extends, (ii) drought risks decrease as the utilized indicator timescales increase, (iii) locally plotted drought curves indicate higher drought risks as their initial slope gets steeper. The method used enables the generation of historical evidence based spatially distributed drought risk maps, which expose more vulnerable areas within the river basin.

Drought risk assessment: The importance of vulnerability factors interdependencies in regional drought risk management

Recognizing the vulnerability factors is essential in assessing the drought risk of a region. Moreover, considering the interactions and interdependencies between the vulnerability factors will generate a more accurate and realistic analysis. The present study tries to depict the interdependencies between vulnerability factors and their covering effect on each other. Kashkan River basin in the west of Iran has hydrological, physical, social, and cultural diversity; hence, one of the main goals of the current research is to evaluate drought risk and detect its affecting factors in each rural district. Twelve vulnerability factors were selected and standardized based on regional risk management. To model interdependencies of vulnerability factors, the integration of the Fuzzy Analytic Network Process (FANP) and Decision-making Trial and Evaluation Laboratory (DEMATEL) method was compared with the Fuzzy Analytic Hierarchical Process (FAHP). The Drought Vulnerability Index (DVI), a weighted aggregation of factors, was multiplied by the Drought Hazard Index (DHI) to obtain a drought risk map. Based on the results, FANP-DEMATEL modifies the risk severity of 20 % of districts. Both methods introduce the western part of the basin with intense risk levels, indicating the irrefutable effect of the hazard index on drought risk maps. The scarcity of hydrological factors can cause profound risk changes in both methods. Furthermore, comparing weighting scenarios revealed that, in some cases, sophisticated weighting methods can be replaced by logical weighting strategies.

Spatio‐temporal risk analysis of agriculture and meteorological droughts in rainfed Potohar, Pakistan, using remote sensing and geospatial techniques

AbstractDroughts of various types are considered as a major threat to rainfed agriculture, because agricultural production is dependent on the spatio‐temporal distribution of rainfall. The rainfed regions of Pakistan, such as Potohar, have experienced several instances of drought since 2000. The most drought‐affected staple crop of the region is wheat, which ultimately affects regional food security. In this study, we examine the agricultural and the meteorological droughts in the Potohar region during 2000–2020, to create a drought risk map. This region comprises four districts, namely Attock, Chakwal, Jhelum and Rawalpindi. First, the agricultural and meteorological drought severity maps were created using rainfall data (meteorological data) and vegetation indices for three different periods (i.e., drought year, moderate drought year and normal year). The agricultural drought patterns and intensity were identified and evaluated using the MODIS products MOD09A1 and MOD11A2, while the meteorological droughts were identified using CHRIPS rainfall data. Afterwards, a combined drought risk map was generated by integrating the agricultural and meteorological adrought severity maps using a weighted overlay analysis. This drought risk map showed that Attock and Rawalpindi were expeiencing slight to no drought conditions, whereas the southwestern and central parts of Chakwal showed moderate drought conditions. Similarly, the western parts of Jhelum faced moderate drought conditions. Thus, the combined drought risk map may be a useful guide for decision makers in the local and provincial government. Using this map, they can identify adaptation practices in the drought‐prone areas of this region for enhancing agricultural productivity.

Application of GIS using NDVI and LST estimation to measure climate variability-induced drought risk assessment in Ethiopia

Abstract The contribution of this paper is to assess the drought patterns using the Land Surface Temperature (LST) estimation index and examine the correlation between the Normalized Difference Vegetation Index (NDVI). The main objective was to evaluate the spatiotemporal variation in agricultural drought patterns and severity in order to estimate and measure climate variability. According to the study's findings, the region experienced mild to severe meteorological drought periods 15–18 times over the study period. The majority of these periods (62.72%) were classified as mild drought (unusual dry circumstances), which usually only showed a slight departure from the distribution of rainfall that is close to normal. According to the findings, cropping seasons from 2013 to 2022 saw an increase in agricultural dryness and a decrease in grain yield, with varying degrees of severity in different geographic locations. Based on the result, the major difficulties and challenges identified were the shortage of drinking water, and impacts on air quality, and food and nutrition. Accordingly, agricultural drought risk mapping can be utilized to mitigate the risk associated with drought on agricultural productivity and guide decision-making processes in drought monitoring.

Agricultural Drought Risk Assessment Based on a Comprehensive Model Using Geospatial Techniques in Songnen Plain, China

Drought is a damaging and costly natural disaster that will become more serious in the context of global climate change in the future. Constructing a reliable drought risk assessment model and presenting its spatial pattern could be significant for agricultural production. However, agricultural drought risk mapping scientifically still needs more effort. Considering the whole process of drought occurrence, this study developed a comprehensive agricultural drought risk assessment model that involved all risk components (exposure, hazard, vulnerability and mitigation capacity) and their associated criteria using geospatial techniques and fuzzy logic. The comprehensive model was applied in Songnen Plain to justify its applicability. ROC and AUC techniques were applied to evaluate its efficiency, and the prediction rate was 88.6%. The similar spatial distribution of water resources further verified the model’s reliability. The southwestern Songnen Plain is a very-high-risk (14.44%) region, determined by a high vulnerability, very high hazardousness and very low mitigation capacity, and is the region that should be paid the most attention to; the central part is a cross-risk region of high risk (24.68%) and moderate risk (27.28%) with a serious disturbance of human agricultural activities; the northeastern part is a dry grain production base with a relatively optimal agricultural production condition of very low risk (22.12%) and low risk (11.48%). Different drought mitigation strategies should be adopted in different regions due to different drought causes. The findings suggest that the proposed model is highly effective in mapping comprehensive drought risk for formulating strong drought mitigation strategies and could be used in other drought-prone areas.

Hazard Assessment of Drought Using PNPI and GIS in Fars Province, Iran

Drought is among the natural disasters that seriously impact the environment and human life. This study aims to explore the spatial pattern of drought using the percent of normal precipitation index (PNPI) in Fars Province, located in the Southern part of Iran. To this end, a drought risk model based on data from 42 stations in Fars province from 1990 to 2019 was evaluated. The model includes three criteria of maximum drought intensity in the period, drought trend, and a maximum number of consecutive dry years. The final drought risk map was obtained with an arithmetic mean of three indicators of intensity, continuity, and trend. The final hazard map and the 3-criteria map were interpolated by the inverse distance weighting (IDW) method and were classified into five risk classes: none, mild, moderate, severe, and very severe. The final vulnerability map shows that moderate hazard areas (5% of the region), which are observed in the Sothern parts of the region, are less widespread than areas under severe hazard (83% of the region), which are observed in almost all parts of the region. According to the final vulnerability map, about 94% of the area of Fars province is under severe and very severe conditions. Overall, this study, regarding its simplicity and considering different dimensions of drought, may be utilised as a basic framework to evaluate drought hazards for other locations worldwide. In this respect, it is necessary to study the multiple sights of this phenomenon for land use planning, resource management, and prevention of water and food crises. Therefore, this model can help users and administrations with executive initiatives.

Agricultural drought risk assessment in the Caribbean region: The case of Haiti

Climate hazards are a particular concern for the Caribbean region, especially droughts, which cause significant damage in the agricultural sector. However, studies on agricultural drought risk mapping are lacking for the Caribbean region, including Haiti. Thus, a comprehensive agricultural drought risk assessment integrating all risk components with their respective influencing indicators is urgently needed to generate structured drought risk information for drought mitigation strategies and proactive response. This study performs an exhaustive assessment of agricultural drought risk, integrating all risk components (vulnerability, hazard, exposure, and the adaptive capacity) with their appropriate parameters essential to plant development. A total of eighteen corresponding spatial indicators are defined to develop agricultural drought risk maps. Fuzzy logic was applied to standardize the various drought aspects in a range of 0-1 followed by the aggregation of drought vulnerability, hazard, exposure, and adaptive capacity indicators to quantify and develop detailed agricultural drought risk maps. Drought risk was analyzed at two different stages: (i) using the risk equation without including the adaptive capacity, and (ii) including the adaptive capacity as a risk component in the risk equation. The first approach produces higher (45.8%) moderate to very-high drought risk than the second (30.7%). The resulting cartographic products can assist in visualizing critically drought-stressed areas and stakeholders, and policy makers can use them to make better decisions to cope with drought in the farming sector.

Drought risk assessment and mapping for the Murray–Darling Basin, Australia

Drought risk assessment and mapping for the Murray–Darling Basin, Australia

Drought Risk Evaluation in Iran by Using Geospatial Technologies

A drought risk map has been developed at the national scale by using remote-sensing satellite data over Iran by combining output layers resulting from three main components of a risk-evaluation procedure including Hazard Quantification (HQ), Vulnerability Assessment (VA) and Identification of Elements at Risk (IER) in a GIS environment. In this respect, Drought Severity (DS) was calculated by using the monthly Normalized Difference Vegetation Index (NDVI) (over 31 years from 1986–2016). Iran landcover classification and a slope map, population density maps, and irrigated farm percentages at the provincial scale were utilized within the drought risk evaluation (DRE) process. The final risk map reveals that the northwest of the country, with a climate similar to the central European weather conditions, is exposed to the maximum drought risk. In contrast, the areas with an arid climate, mainly located in the middle of Iran, exhibits minimum risk against drought. Based on the risk map, the southern part of the Caspian Sea shows very low drought risk due to the moderate and subtropical climate in this region. The outputs of this research will provide advice and warnings to help decision makers reduce drought risk consequences after prioritizing risk areas at the administrative scale.

Assessment of Maize Drought Risk in Midwestern Jilin Province: A Comparative Analysis of TOPSIS and VIKOR Models

Drought is a major natural disaster that causes a reduction in rain-fed maize yield. Agricultural drought risk assessment is conducive to improving regional disaster management ability, thereby reducing food security risks and economic losses. Considering the complexity of risk assessment research, an increasing number of researchers are focusing on the multiple-criteria decision-making (MCDM) method. However, the applicability of the MCDM method to agro-meteorological disaster risk assessments is not clear. Therefore, this study comprehensively evaluated hazard, exposure, vulnerability, and emergency response and recovery capability using the TOPSIS and VIKOR models to generate a maize drought risk map in mid-western Jilin Province and ranked the drought risk of each county. The results showed that: (1) maize drought risk in the middle and west of Jilin province showed an increasing trend. Spatially, the risk diminished from west to east. The drought risks faced by Tongyu, Changchun, and Dehui were more severe; (2) the evaluation results of the two models were verified using the yield reduction rate. The VIKOR model was found to be more suitable for agrometeorological drought risk assessments; (3) according to the damage degree of drought disaster to maize, the cluster analysis method was used to divide the study area into three sub-regions: safe, moderate drought, and severe drought. Combined with the characteristics of different regions, suggestions on disaster prevention and mitigation are proposed. The results of this study can provide a basis for formulating strategies to alleviate drought, reduce losses, and ensure the sustainable development of agriculture.

Drought Analysis for the Seyhan Basin with Vegetation Indices and Comparison with Meteorological Different Indices

Various drought indices have been developed to monitor drought, which is a result of climate change, and mitigate its adverse effects on water resources, especially in agriculture. Vegetation indices determined by remote sensing were examined by many recent studies and shed light on drought risk management. In the current study, one of the 25 drainage basins in Turkey—the Seyhan Basin, located in the south of the country—was investigated. The Normalized Difference Vegetation Index (NDVI) and the Vegetation Condition Index (VCI) are the most widely used vegetation indices and are very useful because they give results only based on satellite images. This study examined the Seyhan Basin using satellite data in which the vegetation transformation occurring due to the decline of agricultural and forest areas was seen. An increase in drought frequency was detected in the Seyhan Basin using the NDVI and VCI indices and compared with different indices. The results obtained revealed that climate change and drought is increasing with a linear uptrend. It is recommended that decision-makers take the necessary measures by considering the drought risk maps. Long-term drought management plans should also be prepared and implemented.

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.

Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand

The Lam Phaniang River Basin is one of the areas in Northeast Thailand that experiences persistent drought almost every year. Therefore, this study was focused on the assessment of drought severity and vulnerability in the Lam Phaniang River Basin. The evaluation of drought severity was based on the Drought Hazard Index (DHI), which was derived from the Standardized Precipitation-Evapotranspiration Index (SPEI) calculated for 3-month (short-term), 12-month (intermediate-term), and 24-month (long-term) periods. Drought vulnerability was assessed by the Drought Vulnerability Index (DVI), which relied on water shortage, water demand, and runoff calculated from the WEAP model, and the Gross Provincial Product (GPP) data. A drought risk map was generated by multiplying the DHI and DVI indices, and the drought risk level was then defined afterwards. The CNRM-CM5, EC-EARTH, and NorESM1-M global climate simulations, and the TerrSet software were used to evaluate the potential impacts of future climate under RCPs 4.5 and 8.5, and land use during 2021–2100, respectively. The main findings compared to baseline (2000–2017) revealed that the average results of future rainfall, and maximum and minimum temperatures were expected to increase by 1.41 mm, and 0.015 °C/year and 0.019 °C/year, respectively, under RCP 4.5 and by 2.72 mm, and 0.034 °C/year and 0.044 °C/year, respectively, under RCP 8.5. During 2061–2080 under RCP 8.5, the future annual water demand and water shortage were projected to decrease by a maximum of 31.81% and 51.61%, respectively. Obviously, in the Lam Phaniang River Basin, the upper and lower parts were mainly dominated by low and moderate drought risk levels at all time scales under RCPs 4.5 and 8.5. Focusing on the central part, from 2021–2040, a very high risk of intermediate- and long-term droughts under RCPs 4.5 and 8.5 dominated, and occurred under RCP 8.5 from 2041–2060. From 2061 to 2080, at all time scales, the highest risk was identified under RCP 4.5, while low and moderate levels were found under RCP 8.5. From 2081–2100, the central region was found to be at low and moderate risk at all time scales under RCPs 4.5 and 8.5. Eventually, the obtained findings will enable stakeholders to formulate better proactive drought monitoring, so that preparedness, adaptation, and resilience to droughts can be strengthened.

Mapping the spatial and temporal variation of agricultural and meteorological drought using geospatial techniques, Ethiopia

BackgroundGeographic Information System (GIS) and Remote Sensing play an important role for near real time monitoring of drought condition over large areas. The aim of this study was to assess spatial and temporal variation of agricultural and meteorological drought using temporal image of eMODIS NDVI based vegetation condition index (VCI) and standard precipitation index (SPI) from the year 2000 to 2016. To validate the strength of drought indices correlation analysis was made between VCI and crop yield anomaly as well as standardized precipitation index (SPI) and crop yield anomaly.ResultsThe results revealed that the year 2009 and 2015 was drought years while the 2001 and 2007 were wet years. There was also a good correlation between NDVI and rainfall (r = 0.71), VCI and crop yield anomaly (0.72), SPI and crop yield anomaly (0.74). Frequency of metrological and agricultural drought was compiled by using historical drought intensity map. The result shows that there was complex and local scale variation in frequency of drought events in the study period. There was also no year without drought in many parts of the study area. Combined drought risk map also showed that 8%, 56% and 35% of the study area were vulnerable to very severe, severe and moderate drought condition respectively.ConclusionsIn conclusion, the study area is highly vulnerable to agricultural and meteorological drought. There was also no year without drought in many parts of the study area. Thus besides mapping drought vulnerable areas, integrating socio-economic data for better understand other vulnerable factors were recommended.

Developing spatial agricultural drought risk index with controllable geo-spatial indicators: A case study for South Korea and Kazakhstan

Constant environmental degradation and increased frequency and severity of natural disasters have been evident over the past few decades worldwide. As such, scientific tools to predict and assess risks keep being developed. Assessing disaster risk is an important task in supporting the transition to a sustainable society. However, as disasters and systems become more complex, disaster models combining diverse aspects including climatic, social, economic, and environmental factors are necessary. For this study, we set a model using the concept of risk by identifying hazards, exposure, and vulnerability. Here, the vulnerability was classified into two domains, sensitivity and adaptive capacity, and two spheres, natural/built environment and human environment. Also, we stressed that controllable geo-spatial indicators should be included in risk assessments to effectively reduce risk and implement adequate spatio-temporal actions. The approach of this study was applied to Kazakhstan and South Korea as a pilot study to develop Agricultural Drought Risk Index (ADRI) and maps. As a result, the agricultural drought risk could be analyzed for South Korea and Kazakhstan. In addition, we performed additional spatial analyses at a reasonable scale for practical use. It was concluded that prioritizing risk areas at administrative and site level could contribute in decision and policy-making for risk reduction. Furthermore, spatial data availability and quality were found to be significant in assessing disaster risk.

GEOGRAPHIC INFORMATION SYSTEM FOR DROUGHT RISK MAPPING IN AUSTRALIA – DROUGHT RISK ANALYSER WEB APP

Abstract. Australia frequently experiences extended periods of severe droughts which have a significant negative impact on populations and economy. To improve preparedness for drought, decision-support tools which provide comprehensive information about current dry conditions are essential. In this paper, we present a conceptual design for a Drought Risk Analyser (DRA) – web-based information App for drought risk mapping developed using geographic information system (GIS). The developed DRA is based on combining Drought Hazard/Vulnerability/Exposure Indices (DHI, DVI and DEI respectively) into a final Drought Risk Index (DRI) for total of 542 Local Government Areas (LGA) in Australia. Drought indicators selected to compute drought hazard – the Standardised Precipitation Index (SPI), the Vegetation Health Index (VHI) and Soil Moisture – were obtained through the World Meteorological Organization (WMO) Space-based Weather and Climate Extremes Monitoring (SWCEM) international initiative. Australian Bureau of Statistics (ABS) census data were used to develop the drought-related population vulnerability index – DVI. Australian national Digital Elevation Model and catchment scale land use data were used to calculate the DEI. Implemented functionality of the designed DRA is illustrated using a case study for the 2019 drought in Australia. The DRA App will be beneficial for Australian farmers and rural communities to assist with decision making, as well as for LGA planners to gain insights on current state of drought risk at both local and national levels. The developed methodology of using space-based observations for assessing drought hazard could be applied for developing similar web-based information tools in drought-prone areas of other countries.