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Drought is a significant natural hazard in Australia, associated with financial hardship and damage to soils and vegetation. With droughts expected to become more frequent and more severe as climate change progresses, it is increasingly important to identify drought risk and assess community-level drought resilience at the appropriate spatial, temporal and administrative scale for decision-making. Here, we have used spatial multi-criteria analysis (MCA) to identify regional priority areas for the implementation of drought resilience interventions or investment in southwest Western Australia (WA). The MCA method involves the systematic integration of spatial data of different magnitudes and units through standardisation and weighting, and the visualisation of aggregated data to a single parameter output map. The study region is home to the WA grains industry, the largest agricultural contributor to the economy and one of the regions most impacted by climate change in Australia to date, experiencing consistent reduction in rainfall and recurrent drought over the last several decades. We modelled drought exposure, sensitivity and adaptive capacity for the region, incorporating spatially explicit climate, environmental, social and economic data, and produced a suite of priority areas maps that were sense-checked against a participatory mapping process. The maps highlight the northern and eastern Wheatbelt of WA as areas at high risk from drought and have the potential to serve as a powerful tool for local-level drought resilience decision-making. Most of the data we used are publicly available, and the vulnerability framework applied allows for wide replication within and beyond southwest WA. [ABSTRACT FROM AUTHOR]

Floods cause great damage to ecosystems and are among the main agents of soil erosion. Given the importance of soils for the functioning of ecosystems and development and improvement of bio-economic conditions, the risk and rate of soil erosion was assessed using the RUSLE model in Iran's Lorestan province before and after a period of major floods in late 2018 and early 2019. Furthermore, soil erosion was calculated for current and future conditions based on the Global Soil Erosion Modeling Database (GloSEM). Through the analysis of rainfall events, as the most important agent of soil erosion, the average R-factors for the period before and after flooding were 58.87 and 157.6 MJ mm ha− 1 h− 1 y− 1, respectively. The results showed that agricultural development and land use change are the main causes of land degradation in the southern and central parts of the study area. The impact of floods was also significant since our evaluations showed that soil erosion increased from 4.12 t ha− 1 yr− 1 before the floods to 10.93 t ha− 1 yr− 1 afterwards. Field surveys using 64 ground control points determined that erodibility varies from 0.17 to 0.49% in the study area. Orchards, farms, rangelands, and forests with moderate or low vegetation cover were the most vulnerable land uses to soil erosion. The results of GloSEM modeling revealed that climate change is the main cause of change in the rate of soil erosion. The results also established that when the combined effects of land use change and climate change are taken into account, soil erosion has increased under SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5 scenarios, so that about 80% of the region has experienced moderate to very high erosion. Therefore, both natural factors (e.g. climate change) and human factors (e.g. agricultural development, population growth, and overgrazing) are among the drivers of soil erosion in the study area. [ABSTRACT FROM AUTHOR]

Islands are highly vulnerable to natural disasters and extreme weather events due to their physical size, remoteness, and limited resources. This paper measured village-level drought risk in Marinduque, Philippines using Principal Component Analysis (PCA) and fuzzy logic. Standard Precipitation Index was used to measure drought hazards in the province utilizing publicly available rainfall. Principal component analysis was used to derive the drought hazard index from SPI-calculated drought magnitude and total drought event at different time scales. The fuzzy logic approach was used to delineate the physical vulnerability of the province to drought. The social vulnerability index was also derived from the socioeconomic and demographic data of Marinduque using PCA. Based on the results, villages with high drought risk were found in the northwest and eastern portion of the province. The results showed that topography and climate influence the hazard and physical vulnerability to drought in the area. Villages in high mountainous regions, and areas with low rainfall have higher drought hazard and physical vulnerability scores. Meanwhile, villages with high social vulnerability are also those with a large population of women, the elderly, and households engage in agriculture. [ABSTRACT FROM AUTHOR]

Drought is a prolonged dry period that has a serious impact on health, agriculture, economies, energy, and the environment. Thus, there have been numerous attempts to make this phenomenon more predictable for preventing the aforementioned effects. The present study aims to determine the best combination of input data sets and predict the Standardized Precipitation Evapotranspiration Index (SPEI) in 1,6, and 12-month time scales using Artificial Intelligence (AI) models (Multilayer Perceptron Neural Network (MLPNN), Support Vector Regression (SVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Ensemble Decision Tree (EDT)), which all models are hybridized with a wavelet transformation at three synoptic stations named Ardebil, Khalkhal, and Moghan. To this end, monthly lags of precipitation, temperature, and SPEI were used in northwestern Iran from 1987 to 2018. The methods were classified into single parameter and multiparameter, and each sub-method was designed based on a combination of the parameters. Moreover, Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), Effective Factor Elimination Technique (EFET), and Feature Scaling (FS) were used to determine the best lags of parameters. In this regard, Root Mean Square Error (RMSE), Correlation Coefficient (CC), and Nash–Sutcliffe Efficiency Index (NSE) were used as the statistical criteria to assess AI models, methods, and sub-methods. The results revealed that the sub-method (D) with W-MLPNN in the 1-month and 6-month time scales and the sub-method (D, P, T) with W-SVR in the 12-month time scale were the best models and sub-methods of this area, respectively. Moreover, based on the results, the efficiency of the AI was enhanced in longer time scales (more than the 6th month) and the longer the time scale, the more the number of lags (under the 6th month) in input data is decreased. [ABSTRACT FROM AUTHOR]

Drought forecasting is very important in reducing the drought damage and optimizing water resources. This paper focuses on confirming the advantage of wavelet long short-term memory network (WLSTMN) through comparison with wavelet artificial neural network (WANN) and wavelet support vector regression (WSVR) for drought forecasting in the west area of the Democratic People's Republic of Korea. The standardized precipitation index with 6 and 12-month timescales (SPI-6 and SPI-12) was used in this study. In order to increase the number of training samples for the development of data-driven models, SPIs were calculated at ten days' intervals and input data was lagged combinations of time series that decomposed using Haar wavelet mother function at 1–10 decomposition levels. The performances of the three models with several decomposition levels and lags at 1-month lead time were estimated with determination coefficient (R2), Lin's concordance correlation coefficient (LCCC), root-mean-square error (RMSE) and mean absolute error (MAE). Area-averaged performance measures of optimal models show that R2, LCCC, RMSE and MAE of WLSTMN for SPI-6 were 0.709, 0.806, 0.572 and 0.427, respectively, better than those of other models. And R2, LCCC, RMSE and MAE of WLSTMN for SPI-12 were 0.919, 0.950, 0.296 and 0.190, respectively. It has a better performance compared to the other models. Consequently, WLSTMN model for drought indices with two timescales outperformed traditional WANN and WSVR, which have smaller R2 and LCCC, larger RMSE and MAE. [ABSTRACT FROM AUTHOR]

Due to climate change, droughts will intensify in large parts of the world. Drought and its impacts on nature and agriculture have been studied thoroughly, but its effects on the urban environment is rather unexplored. But also the built environment is susceptible to droughts and estimation of its vulnerability is the first step to its protection. This article is focusing on assessing the vulnerability of a city to groundwater drought, using parts of the lowland city of Leiden, the Netherlands, as a case study. Using a new urban drought categorization framework, groundwater drought is separated from soil moisture drought, open water drought and water supply drought, as each has its own impacts. Vulnerability was estimated as the aggregation of drought exposure and damage sensitivity. Drought deficit and duration were used as exposure indicators. Both a Fixed and Variable threshold method was used to quantify these indicators. To quantify drought vulnerability weights were assessed for selected exposure and damage sensitivity indicators using an Analytical Hierarchy Process (AHP) with a small number of experts. Based on these weights the spatial variation in vulnerability for groundwater drought follows damage sensitivity patterns—rather than exposure ones. And, out of all damage sensitivity indicators used, 'land use', 'low income' and 'monuments' contributed the most to the spatial variation in vulnerability. Due to the fact that the number of drought experts' opinions in the AHP was limited these vulnerability results however remain uncertain. The proposed methodology however allows water managers to determine vulnerability of urbanized areas to groundwater drought, identify highly vulnerable areas and focus their mitigating actions. [ABSTRACT FROM AUTHOR]

The southern region of Iran is a dry land zone where the drought is a driving force of land-use changes controlling water resources and aquatic ecosystems, such as lakes and wetlands. This study aimed to predict the 2040 drought condition in the Bakhtegan Lake watershed (Iran) and its correlation with land use changes. Extracting from Landsat and MODIS imagery, we classify land-use classes in 2000–2020 and investigate its relationship with multiple drought indicators through principal components analysis (PCA) analysis. In addition, using the Markov and cellular automata (CA)-Markov chain, the 2040 prediction maps of land-use and drought indices were made. Then, the regression analysis was used to reveal the influence trend of desertification on land-use activities in the future. Finally, the ecosystem services value and changes in agricultural lands between 2000 and 2020 were studied in the study area. The observed results of drought indices and land-use maps in 2000–2020 indicated that the expansion of the drought susceptibility zone causes the increasing areas of salt and bare land in parts of the northwestern and southern region. Based on PCA analysis, Normalized Difference Vegetation Index, Enhanced Vegetation Index, and Vegetation Condition Index were chosen to be input data in the CA–Markov model for drought-indices prediction in 2040. The value of these indices would decrease in 2040, indicating more drought in the region. The land-use prediction results in 2040 found that it will increase the percentage of bare land to 0.91, salt land to 0.89, and decrease 0.93 and 0.9 percentages of agricultural land and water lakes. Regression results showed that land use changes were related to drought indices with high accuracy (R2 = 0.93). In addition, the unprincipled use of agricultural lands and their conversion into saline and barren lands has led to a decrease of more than one million and nine hundred thousand dollars annually in the ecosystem services value. [ABSTRACT FROM AUTHOR]

Drought risk assessment for the Murray–Darling Basin (MDB), a drought-prone agricultural region in Australia, was conducted. Region- and agricultural sector-specific indicators of drought hazard, exposure, and vulnerability were selected to calculate the drought risk index. To represent hazardous drought conditions, the Standardized Precipitation Index, Vegetation Health Index, and Root Zone Soil Moisture were used. Population density, land use, and elevation were chosen as drought exposure indicators. Agricultural occupation, soil sand percentage, and Socio-Economic Indexes for Areas (SEIFA) were selected as drought vulnerability indicators. Thematic layers of the drought risk index and its components, hazard, exposure, and vulnerability, were prepared using ArcGIS. A case study for the 2019 drought was investigated and monthly drought risk index maps at the Local Government Area (LGA) level were produced. Overall, the maps demonstrated the presence of extreme drought risk conditions in many LGAs during the austral winter, spring and summer, with the autumn months in lower drought risk categories. The LGAs in the northern and eastern parts of the MDB were in severe-to-extreme drought risk categories during most of the study period. In December, at the peak of drought, almost 90% of the MDB covering the majority of the northern, eastern, and central regions was categorised at severe-to-extreme drought risk levels. Furthermore, the analysis identified a flash drought event in the southern MDB in March–April. The region- and sector-specific drought risk index developed in this study can enhance the user relevance of early warning system information and assist decision-makers in implementing proactive drought adaptation strategies. [ABSTRACT FROM AUTHOR]

Urmia Lake, the largest saline lake in Iran and the Middle East, in the northwest of Iran, has shrunk over the past decades. The reduced water level has increased the area of dry land around the lake allowing new environmental hazard such as sand dunes encroachment, particularly on the western side of the lake. New land has emerged as a consequence of lake shrinkage, and this new land is a major sediment source for the creation of sand dunes around the lake. This shrinking of the lake has created emerging lands. These lands play a major role in creating sand dunes around the lake. There are five terrain types that could contribute sediment to the dunes, and it is the main aim of this research to identify the contributions to the dunes of each terrain type. Fifteen surface samples were collected from the five most erodible terrain types, and eight samples were collected from the dunes both downwind and upwind from the lake, and major element components were measured using X-ray fluorescence. According to the Besler classification, all samples are in the saline class. Also, the chemical index of alteration values in all samples were less than 50, indicating weak weathering. Based on multivariate statistical analysis, suitable tracers were selected and were imported to the sourcing equations. Quantification of uncertainty and the creation of two new fingerprinting models for aeolian sediments based on both Bayesian and GLUE procedures were used. The highest proportion comes from the salty and puffy lands (44.2%) followed by salty polygon land (23.5%), clay-salty areas, puffy-flaky lands (7.01%), the terminus of the fine sandy alluvial fan (13.2%) and clay-salty abandoned lands (12.1%). It is concluded that if land managers use these results, they can more efficiently decrease the hazards posed by dune formation, reactivation and migration through implementation of soil conservation on the affected lands around the dried lake. [ABSTRACT FROM AUTHOR]

Drought vulnerability is the extent to which an area is susceptible to damage as well as causing a threat to human society. Drought frequently occurs in the Indian state of Rajasthan, and so far, very little attention has been paid towards its vulnerability assessment. Therefore, the present study focuses on a spatial multi-criteria integrated technique for an all-out drought vulnerability assessment and mapping consisting of geographic information systems (GIS) and analytic hierarchy process (AHP) techniques. The data have been acquired from various secondary sources pertaining to a total of 16 indicators under meteorological (rainfall, temperature and evapotranspiration), hydrological (hydrogeology, elevation, groundwater level, groundwater development and surface water bodies), agricultural (available water holding capacity of soils, land use and slope) and socio-economic (density of population, female-to-male ratio, irrigated land, agriculture-dependent population and deep tube wells) drought categories. Further, spatial layers for each category have been developed by various GIS operations followed by the calculation of weights for each drought category and type employing pair-wise comparison matrices by means of AHP criterion. Afterwards, individual drought category and comprehensive drought vulnerability maps have been prepared by employing the weighted overlay technique. The generated maps have effectively displayed the areal spreads and levels of drought vulnerability with respect to normal, mild, moderate, severe and extreme category of droughts. The findings from this study have demonstrated a proneness of severe to extreme drought vulnerability in 25% area of the state. Distinctively, the eastern, western, central and small pockets of south-western parts of the state have witnessed severe to extreme drought vulnerability, while the remaining areas have demonstrated normal to moderate drought vulnerability. The results of the overall drought vulnerability have been validated by employing normalized difference vegetation index and past occurrence of drought disasters, which revealed an accuracy of 81%. The obtained results prove the effectiveness of geoinformatics and AHP techniques in comprehensive drought vulnerability assessment and mapping. Finally, the findings of the present study may be easily applied for designing suitable drought mitigation strategies of the vulnerable areas. [ABSTRACT FROM AUTHOR]

Soil is a vital resource for the terrestrial ecosystem. Soil is a non-renewable natural resource on human time scales with its vulnerability to desertification and degradation relying on sophisticated interactions among factors, processes, and causes committing at a range of spatial and temporal scales. One of the major factors and widely considered in the desertification and degradation is the soil quality index. The basic purpose of this study is to assess soil quality index (SQI) related to desertification and degradation in the Akarçay Basin in Turkey falling within semi-arid terrestrial ecosystem using multi-criteria decision analysis associated with various geostatistical models and GIS techniques by taking into account nine soil criteria from the Desertification Model of Turkey (DTM), which was considered as the base for the current study. To determine weighting score for each criterion, Neutrosophic Fuzzy-AHP model was used to create sufficiently sensitive levels of significance of the criteria. Obtained results classified more than 22% of the total area of about 1766 km2 as low and very low quality, while only approx. 62% of the basin' soil were classified as high and very high quality by the SQI regard to desertification risk. [ABSTRACT FROM AUTHOR]

Desertification is a serious problem in most arid and semi-arid areas of the world, and especially in Iran. Combating desertification planning and any preparatory measures require detailed mapping of the current status of desertification as well as its evolvement during the time. This study, basically relying on remote sensing datasets, strives to map desertification in the Sabzevar area of Iran and identify its changes between the years 2001 and 2020. The indicators used in this study included Enhanced Vegetation Index, Vegetation Condition Index, Salinity Index, Synthetized Drought Index, Temperature Condition Index, Precipitation, Normalized Vegetation Index, and Land Surface Temperature. Analytical Hierarchical Process was used to determine the weight of the factors and the fuzzy overlay approach was used for synthesizing the thematic layer maps. According to the expert judgments, precipitation and salinity are the most important desertification factors in the area (~ 50% of the total weight). In terms of land-use changes over the past 20 years, urban, desert, croplands and bare lands grew in size at the expense of rangelands and open canopy forests which shrank greater than 3.5% (435 km2). Based on our findings, the total area of desertification classes has not changed considerably during the period but there has been a growth in very high and high desertification intensity classes by 12%. According to our findings, very high and high classes have increased between the years 2001 and 2020 mostly in the central and southern parts of the study area. The results obtained in this research are very important to the land managers of the Khorasan Razavi Province, for formulating sustainable development programs and avoiding the unwanted consequences of desertification. [ABSTRACT FROM AUTHOR]

Analysis of long-term land use and land cover (LULC) changes requires up-to-date remotely sensed data to assess their effects on erosion. This is a particularly important assessment for regions with semi-arid landscapes where soils tend to be scarce and proper management necessitates matching LULC to local conditions to achieve sustainable land use. This study evaluates the impact of LULC changes on erosion using Landsat satellite imagery and the Revised Universal Soil Loss Equation model on the plains around the Jarahi River and the Shadegan International Wetlands. Supervised-classification and maximum-likelihood methods were applied to pre-processed TM, ETM, and OLI images for 1989, 2003, and 2017 to prepare LULC maps. The areal extents of agricultural land, wetlands, waterbodies, and built-up regions increased by 9.48%, 2.52%, 3.44%, and 0.13% from 1989 to 2003, respectively. During this timeframe bare (or barren) lands and wetland vegetation decreased by 11.44% and 4.13%. Between 2003 and 2017, however, the areas of bare lands, waterbodies, and built-up areas increased 12.77%, 1.52%, and 0.30%, while agricultural lands, wetlands, and wetland vegetation decreased by 9.99%, 1.32%, and 3.27%. According to the results, the areal extent of erosion at a rate > 1.1–5 Mg ha−1 year−1 has been increased by about 45.56%, 50.06% and 52.24 between 1989, 2003 and 2017. LULC changes led to increased soil erosion on agricultural and bare lands. This highlights the need to plan and manage changes to LULC to reduce erosion to and below sustainable levels. Nature-based solutions can be effectively used to reduce erosion. [ABSTRACT FROM AUTHOR]

The Northern Murray–Darling Basin (MDB) is a key Australian agricultural region requiring efficient Agricultural Drought Management (ADM), focused on resilience. Although a need for resilience in local farming communities has long been recognised, previous studies assessing ADM in the Northern MDB did not consider two key elements of resilient management: proactivity (preparing for drought prior to a drought event) and suitability (localised drought management targeted at decision-makers). This study assessed the current ADM Strategy (ADMS) implemented within five selected Northern MDB Local Government Areas (LGAs) (Paroo Shire, Balonne Shire, Murweh Shire, Maranoa Region, and Goondiwindi Region), specifically investigating the extent of ADMS proactivity, effectiveness, and suitability. To investigate suitability, drought risk extent of each LGA was determined. A region-specific drought risk index consisting of hazard, vulnerability and exposure indices was developed; risk mapping was conducted. All LGAs displayed very high levels of drought risk due to hazardous climatic conditions, vulnerable socio-economic attributes, and drought-exposed geographical features. A Criteria-Based Ranking (CBR) survey produced a quantitative effectiveness and proactivity rank for each major ADMS used in the Northern MDB. Government Assistance was the most proactive and effective ADMS. Strategy effectiveness ranks of the major ADMS used and drought risk extent found in each LGA were correlated to determine ADMS suitability. Overall, Balonne Shire and the Goondiwindi Region were identified as high priority areas requiring improved ADM. A user-centred Integrated Early Warning System (I-EWS) for drought could potentially increase ADM proactivity and suitability in such areas, strengthening drought resilience of farming communities. [ABSTRACT FROM AUTHOR]

Due to the complex characteristics of drought, drought risk needs to be quantified by combining drought vulnerability and drought hazard. Recently, the major focus in drought vulnerability has been on how to calculate the weights of indicators to comprehensively quantify drought risk. In this study, principal component analysis (PCA), a Gaussian mixture model (GMM), and the equal-weighting method (EWM) were applied to objectively determine the weights for drought vulnerability assessment in Chungcheong Province, located in the west-central part of South Korea. The PCA provided larger weights for agricultural and industrial factors, whereas the GMM computed larger weights for agricultural factors than did the EWM. The drought risk was assessed by combining the drought vulnerability index (DVI) and the drought hazard index (DHI). Based on the DVI, the most vulnerable region was CCN9 in the northwestern part of the province, whereas the most drought-prone region based on the DHI was CCN12 in the southwest. Considering both DVI and DHI, the regions with the highest risk were CCN12 and CCN10 in the southern part of the province. Using the proposed PCA and GMM, we validated drought vulnerability using objective weighting methods and assessed comprehensive drought risk considering both meteorological hazard and socioeconomic vulnerability. [ABSTRACT FROM AUTHOR]

The analytic hierarchy process (AHP) in combination with geographic information system (GIS) provides an effective means for disaster monitoring. In the present study, GIS was used for the spatial analysis of drought for the Puruliya district, West Bengal, India. Fourteen parameters, such as annual rainfall, monthly rainfall, maximum temperature, monthly temperature, maximum evapotranspiration, monthly evapotranspiration, relative humidity, soil texture, landuse/landcover, slope, groundwater, cultivators, agricultural labourers and population were chosen and thematic maps for each parameter were developed. AHP was adopted to develop pairwise comparison matrices and to calculate the weightage factors of each parameter. Fourteen thematic maps were analysed, integrated and finally drought severity map was generated using GIS. The results showed that 70 % of the total area of Puruliya district is under severe drought affecting about 14 blocks. The output map thus obtained will provide information on severity of drought vulnerability which has practical relevance to agricultural importance and help in combating drought. [ABSTRACT FROM AUTHOR]

No method exists to increase the efficiency of controlling projects and reclamation of disturbed lands and avoid investment wasting, considering different criteria and alternatives and presenting optimal alternatives based on systematic and group perspectives in combating desertification. Recent proposed alternatives are usually non-systematic and non-comprehensive according to a scientific view. There is no record of application of systematic models such as multiple attribute decision-making in combating desertification. Therefore, in the present research, a fuzzy analytical hierarchy process (FAHP) method has been used to offer optimal alternatives challenging desertification. In the present study, the opinions of experts about alternatives and criteria have been assessed using the Delphi method and pairwise comparison. Then, the final priorities of the alternatives were obtained by a fuzzy decision matrix and the FAHP model. The model's ability in offering alternatives to combat desertification was assessed in the Khezr Abad region, Yazd province. Based on the obtained results, the alternative of groundwater harvesting modification with weighted average of 93 % was determined as the optimum alternative in the study area; other alternatives had no effective effect in control of desertification. [ABSTRACT FROM AUTHOR]