Drought Risk Assessment Research Articles

Vulnerability and drought risk assessment in Iran based on fuzzy logic and hierarchical analysis

Vulnerability and drought risk assessment in Iran based on fuzzy logic and hierarchical analysis

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

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

Spatiotemporal Variability of Drought and its Relationships to ENSO and IOD Indices in Somaliland

Drought is one of Somaliland’s most prevalent natural hazards, causing serious socioeconomic and environmental harm. This study investigated the spatial and temporal variability of drought and its relationships with El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) indices using the Standardized Evapotranspiration Index (SPEI) from 1981 to 2020. The Mann-Kendall trend test and Sen's slope estimator were used to assess the trends of annual and seasonal SPEI time series. The Empirical Orthogonal Function (EOF) was employed to examine the dominant modes of the SPEI series, and Pearson and partial correlation analyses were performed to investigate the associations between significant modes of drought variability and ENSO and IOD indices. The results demonstrated a statistically significant downward trend of SPEI (increasing drought) at 99% confidence level. The EOF analysis indicated two spatially distinct zones of drought variability in the west and east of the country. Drought variability had a statistically significant negative correlation with ENSO in the summer and winter seasons, and with IOD in the winter season and annual time series. The findings of this study will provide important information for drought risk assessment, mitigation, and predictability in Somaliland.
 Keywords: Drought, Standardized Precipitation Index, ENSO, IOD, Somaliland

Dynamic Evaluation of Agricultural Drought Hazard in Northeast China Based on Coupled Multi-Source Data

As the climate warms, the impact of drought on plants has increased. We aimed to construct a comprehensive drought index (CDI), coupling soil-vegetation-atmosphere drought and heat conditions based on multi-source information, and to combine it with static and dynamic drought hazard evaluation models to analyze the spatial and temporal distribution characteristics of agricultural drought disasters and hazards during the growing season (May to September) in Northeast China (NEC). The results demonstrated that the CDI could combine the benefits of meteorology (standardized precipitation evapotranspiration index, SPEI), vegetation (vegetation health index, VHI), and soil (standardized soil moisture condition index, SMCI) indices. This was performed using a relative weighting method based on the remote sensing data of solar-induced chlorophyll fluorescence (SIF) to determine the weights of SPEI, VHI, and SMCI. The CDI for drought monitoring has the advantages of broad spatial range, long time range, and high accuracy, and can effectively reflect agricultural drought; the growing season in NEC showed a trend of becoming drier during 1982–2020. However, the trends of the drought index, the impact range of drought events, and the hazard of agricultural drought all turned around 2000. The drought hazard was highly significant (p < 0.001) and decreased from 2000 to 2020. The frequency of drought disasters was the highest, and the hazard was the greatest in May. The best level of climatic yield anomalies in maize were explained by drought hazard in August (R2 = 0.28). In the center and western portions of the study area, farmland and grassland areas were where higher levels of hazard were most commonly seen. The dynamic hazard index is significantly correlated with climatic yield anomalies and can reflect the actual impact of drought on crop yield. The study results serve as a scientific foundation for drought risk assessment and management, agricultural planning, and the formulation of drought adaptation policies, as well as for ensuring food security in China.

Drought and flood risk assessment for rainfed agriculture based on Copula-Bayesian conditional probabilities

Rainfed agriculture ensures global food security. However, periodic droughts and floods endanger rainfed crop productivity. This work aims to identify drought and flood risks in rainfed agriculture using conditional probabilities and to reduce food losses caused by drought and floods. The standardized precipitation evapotranspiration index is produced from 1996 to 2020 using meteorological data from the three northeastern provinces and is used to establish four time-scale drought indicators. The Pearson correlation coefficient is then used to identify drought indicators related to rainfed crop production. The Copula-Bayesian conditional probabilities were used to calculate the probability of yield reduction of rainfed crops when drought indicators indicate varied drought and flood conditions. The results show that: (1) Droughts from May to July and floods from January to March had the most significant impact on rainfed crops in the three northeastern provinces. (2) The cities of Huludao, Changchun, Baishan, Baicheng, Yanbian, Hegang, and Jiamusi face a more significant danger of drought in rainfed crops. In contrast, Yichun and Suihua face a higher risk of flooding in rainfed crops. (3) When drought monitoring indicators decreased by one unit, rainfed crop output declined the most in Fuxin city; when the drought indicator increased by one unit, rainfed crop yield decreased the most in Liaoyuan city. The study’s findings can provide quantitative direction for food security work in China’s three northeastern regions.

Study on Agricultural Drought Risk Assessment Based on Information Entropy and a Cluster Projection Pursuit Model

Study on Agricultural Drought Risk Assessment Based on Information Entropy and a Cluster Projection Pursuit Model

A new method for assessment of regional drought risk: information diffusion and interval mapping adjustment based on k-means cluster points

Abstract Aiming at the defect that there is no ability for the conventional weighted comprehensive assessment method (WCA) to grade drought risk directly, a method based on k-means cluster points to realize the classification of drought risk is proposed in this paper. On the basis of calculating the drought risk value of cluster points, the inverse distance weight interpolation method (IDWI) and multidimensional normal diffusion method (MND) were used to quantify the drought risk value, and the discrimination between the risk value and grade was improved by interval mapping adjustment (IMA). In this paper, the drought risk of Anhui Province from 2000 to 2020 was calculated to verify the above method. The results show that: (1) The drought risk quantification method based on information redistribution of k-means cluster point can not only realize automatic risk classification, but also re-quantify the risk value of the assessment object in the same risk grade, which makes up for the defects that the conventional WCA cannot carry out grade division and the conventional clustering method cannot assign the risk value of the assessment object. (2) The result of information redistribution based on MND is closer to the actual drought situation and more reasonable than IDWI. (3) The dispersion effect of risk value obtained by information redistribution based on k-means cluster point can be improved by the IMA of drought risk. It improves the discrimination degree of risk value, so that the grades can be displayed more intuitively. The defect of the WCA is overcome by the new method proposed in this paper, the follow-up utilization space is widened, and the thinking of risk quantification in drought risk assessment is broadened.

Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk Assessment

Xilin Gol is a typical kind of grassland in arid and semi-arid regions. Under climate warming, the droughts faced by various grassland types tend to expand in scope and intensity, and increase in frequency. Therefore, the quantitative analysis of drought risk in different grassland types becomes particularly important. Based on multi-source data, a random forest regression algorithm was used to construct a grassland biomass estimation model, which was then used to analyze the spatiotemporal variation characteristics of grassland biomass. A quantitative assessment of drought risk (DR) in different grassland types was applied based on the theory of risk formation, and a structural equation model (SEM) was used to analyze the drivers of drought risk in different grassland types. The results show that among the eight selected variables that affect grassland biomass, the model had the highest accuracy (R = 0.90) when the normalized difference vegetation index (NDVI), precipitation (Prcp), soil moisture (SM) and longitude (Lon) were combined as input variables. The grassland biomass showed a spatial distribution that was high in the east and low in the west, gradually decreasing from northeast to southwest. Among the grasslands, desert grassland (DRS) had the highest drought risk (DR = 0.30), while meadow grassland (MEG) had the lowest risk (DR = 0.02). The analysis of the drivers of drought risk in grassland biomass shows that meteorological elements mainly drive typical grasslands (TYG) and other grasslands (OTH). SM greatly impacted MEG, and ET had a relatively high contribution to DRS. This study provides a basis for managing different grassland types in large areas and developing corresponding drought adaptation programs.

Agro-meteorological drought risk assessment in the groundnut basin of Senegal: the case of the municipalities of Djilor, Diossong, Keur Samba Gueye and Toubacouta

Agriculture drought is a recurrent phenomenon that affects most parts of Sahel regions. With the advent of various satellite data, drought risk assessment has become more accessible, but the challenge lies in choosing a well-defined time scale. This study aims to contribute to agricultural drought monitoring, based on the calculation of composite indices using the MODIS sensor in the Senegalese Groundnut basin. The approach is based upon vegetation condition and health (VCI, VHI) and temperature (TCI) indices. Subsequently, those indices were evaluated based upon crop yield data, and a Holt-Winter forecasting was performed to determine the driest month in 2020-2021. Here we show that all investigated communes are sensitive to drought, especially Keur-Samba Gueye (in July, September, and October). Based on its significant correlation with VCI and TCI (R2>0.57), VHI was proven to be a reliable water stress indicator. Our approach demonstrates the Groundnut basin's vulnerability to climate change.

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.

Quantification of Water Resource Sustainability in Response to Drought Risk Assessment for Afghanistan River Basins

Drought in Afghanistan has many impacts from the interaction between environmental and socio-economic factors in the agriculture and water supply sectors. The complex topography and climate change impacts cause high spatial and temporal variation in the precipitation pattern and pose several challenges in managing water resources. Therefore, this paper aimed to quantify the sustainability of water resources against the progression of drought. Observed monthly precipitation data monitored by 23 stations located across five river basins covering the entire country for the period 1970–2016 were used to demonstrate the potential impacts of drought on water resources sustainability. Based on severe drought estimation, the year 2000 and 2001 were identified as change points. Accordingly, datasets pertaining to before and after the change points were partitioned to analyze the long-term temporal shift of drought. The spatiotemporal variability of sustainability indicators was estimated using reliability resiliency and vulnerability concept. The results indicated a positive trend of precipitation in most of the river basins after the change points leading to an increase in sustainability. However, the major portion in the upstream of the Kabul River basin showed a decrease in sustainability of around 15% due to a reduction in precipitation. As the Kabul city has high population density, it needs immediate attention in effectively planning and managing available water resources. Furthermore, the comprehensive analyses reported in this paper discuss the possible implications of drought risk assessment and its impact on largely varying sustainability both spatially and temporally in Afghanistan.

Identification and risk assessment of flash drought in the Pearl River basin based on the Standardized Evaporative Stress Ratio

Identification and risk assessment of flash drought in the Pearl River basin based on the Standardized Evaporative Stress Ratio

Drought risk assessment of millet and its dynamic evolution characteristics: A case study of Liaoning Province, China

• Fitting drought intensity and yield loss rate to construct sensitivity curves. • Considering millet planting climatic suitability as a potential exposure index. • A perfect index system suitable for millet drought risk assessment was built. • Distribution and dynamic migration characteristics of millet drought risk were analyzed. Due to the improvement of living standards and the adjustment of dietary structure, the cereal industry shows excellent growth prospects. Clarifying millet drought risk distribution and determining its dynamic evolution characteristics are therefore of great importance for guiding millet production, preventing drought risks, and ensuring food security. In this study, SPRI was used to identify millet drought, and drought intensity-yield loss rate curves were constructed to characterize millet drought sensitivity. The potential exposure of millet was explored by considering millet climatic suitability and farmland proportion. In light of this, a comprehensive assessment of millet drought risk in different growth periods was made using natural disaster risk theory. The results showed that: (1) The sensitivity model for each growth period had passed the F-test of α = 0.05 (R 2 were 0.544, 0.408 and 0.347 respectively), reaching a significant level, which proved that the model can be used to assess millet drought sensitivity. (2) More than 70 % of Liaoning Province had a suitable climatic environment for millet production. Combined with the farmland proportion, the area with high potential exposure was distributed in the central region of Liaoning. (3) The millet drought risk in different growth periods showed a characteristic distribution of high in the northwest and low in the southeast. Among them, Chaoyang had the largest high and extremely high drought risk area in the three growth periods, accounting for 92.11 %, 99.34 % and 98.86 % respectively. At the same time, the center-of-gravity model shows that the barycenter of millet drought risk has a trend of spreading from the west of Liaoning to the northeast and southwest. To sum up, the millet drought risk area in Liaoning may increase in the future, measures should be taken to prevent and mitigate millet drought from three aspects: before, during and after the disaster.

Dynamic agricultural drought risk assessment for maize using weather generator and APSIM crop models

Dynamic agricultural drought risk assessment for maize using weather generator and APSIM crop models

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

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

Drought risk assessment in the coupled spatial–temporal dimension of the Sichuan Basin, China

Drought risk assessment in the coupled spatial–temporal dimension of the Sichuan Basin, China

Drought assessment of terrestrial ecosystems in the Yangtze River Basin, China

Droughts resulting from global warming severely hamper the healthy development of terrestrial vegetation ecosystems. This paper defines a terrestrial vegetation ecological drought index, i.e., the Standardized Water Supply–Demand Index (SSDI), based on the water balance method using the same calculation model as the SPEI. The theory of run is used to analyze the spatial and temporal distribution of the drought characteristics in the Yangtze River Basin (YRB). The results show that (1) the drought events monitored by the SSDI were in good agreement with historical drought events, and the correlation coefficient between SSDI and NDVI (0.94) was significantly higher than that between SPEI and NDVI (0.88). (2) About 55.4% of the regions in the YRB experienced more than 13 droughts, and about 41.4% of the regions had an average drought duration of more than 3.1 months. In the northern part of the YRB, droughts tend to develop in a short-term high frequency, while in the southern part of the YRB, there is a long-term low-frequency development trend. Nearly 24.5% of the regions have an average drought intensity of more than 1.4, and most of them are located in the lower reaches of the Yangtze River. (3) Over the last 18 years, about 79.5% of the regions in the YRB showed an increasing trend in SSDI, of which 36.8% showed an increasing trend of more than 0.04/yr. In addition, 40.4% of the regions showed a significant upward trend. Precisely analyzing the spatiotemporal variation of drought characteristics in the YRB is critical for drought risk assessment and sustainable development of water resources and could provide a theoretical basis for effective water resource allocation and drought relief.

Identification of groundwater potential zone using analytical hierarchical process (AHP) and multi-criteria decision analysis (MCDA) for Bhavani river basin, Tamil Nadu, southern India

The present study aimed to identify groundwater potential zones was involving the collection of both spatial and non-spatial datasets for water management practice. The Bhavani river basin groundwater is the major source for agriculture, domestic, industry, etc. The basin area is divided into lower and upper sub-basins of an area of 4207 km2. To delineate the Groundwater Potential Zone (GWPZ) ten thematic layers (geology, geomorphology, land use land cover, drainage and lineament density, slope, elevation, soil texture, rainfall, groundwater) were prepared using Arc GIS 10.3 software. The Analytical Hierarchical Process (AHP) based Multi-Criteria Decision Analysis (MCDA) was used to calculate the weightage value for each parameter. For parameter validation consistency index and consistency ratio were used. The identified groundwater potential zone was categorized into five classes based on the groundwater potential availability, as very high (1599 km2), high (1430 km2), moderate (547 km2), low (421 km2), and very low (210 km2). In the middle portion of the basin, 38% of the area falls under very good GWPZ and is followed by high (34%), moderate (13%), low (10%), and very low (5%) identified the different location of the basin. Further, GWPZ were classified based on firka and compared to the Public Work Department (PWD) 2011 and 2020 reports. From the report, 22 firkas are over-exploited from 2011 to 2020. In the present study GIS coupled with the AHP model gives accurate groundwater recharge zones. It's more helpful for drought risk assessment and planning recharge structures for agriculture engineers and government agencies.

Development of a GIS based hazard, exposure, and vulnerability analyzing method for monitoring drought risk at Karachi, Pakistan

Droughts have an adverse influence on agriculture, the environment, water supplies, and the global economy. The drought risk was computed using an integrated prospective approach: drought hazard, exposure, and vulnerability based on biophysical and socio-economic conditions over Karachi, Pakistan during 2000–2019. Drought hazard map (DHM) was created using annual Palmer drought severity Index (PDSI). Drought exposure map (DEM) was derived using population density and gross domestic product (GDP), as well as land surface temperature (LST), Normal difference vegetation index (NDVI), Night light images (NTL), land use land cover (LULC), and Distance to water were used for drought vulnerability map (DVM). An estimation of drought Risk (EDR) was derived by integrating layers of DHM, DEM, and DVM. Results showed that Central, South, and East regions of Karachi were at high risk, whereas the North East and North were less affected by the drought. The estimated average drought hazard (EDH) was 0.84, with minimum (maximum) value of 0.68 (1). Similarly, the average estimated drought exposure (estimated drought vulnerability) for EDE (EDV) was 0.27 (0.42), with the maximum value of 0.55 (0.84) and the minimum value of 0 (0). The drought risk assessment map (DRAM) shows that the average risk values is 0.18 while highest value is 0.36.

Proportional odds model for identifying spatial inter-seasonal propagation of meteorological drought

Drought is probably the most multifaceted environmental disaster that results from a precipitation deficiency. It perhaps has directly and indirectly potential effects on people's lives, the economy, and other environmental resources worldwide. However, for reducing the potential negative impacts of drought, the understanding and information about seasonal drought frequency and persistence are crucial for drought early warning and mitigations policies. Therefore, the current research examines the selected stations' seasonal meteorological drought frequency and persistence. For this purpose, ordinal outcomes of the current research are modelled under the set of cumulative Logit Models (CLM). The estimation of the CLM is made from the logit link function. Further, the Brant Test (BT) is used to check the parallel line assumptions. The BT substantiates that the odds ratios are the same across the several drought classes. Thereby the POM is a ubiquitous choice for the current analysis. Therefore, the Proportional Odds Model (POM) is utilized to compute the odds and Probability of Drought Persistence (PDP) in varying seasons (March, April, May (Spring); June, July, August (Summer); September, October, November (Autumn); December, January, February (Winter). Further, Standardized Precipitation Index (SPI) for a certain time scale (i.e. three-month time scale SPI-3) is mainly utilized in POM. Amid SPI and various seasons, the relationship is found significant at a 5% significance level in various stations, including Murree, Rawalpindi, Sialkot, Sargodha, Faisalabad, Bahawalnagar, Bahawalpur, Mianwali, Jhelum Multan, Khanpur, and Lahore. The potential of the current research is substantiated by twelve meteorological stations in a certain province of Punjab, Pakistan. The current research outcomes provide the direction to dynamically identify the spatial interseasonal propagation of meteorological drought. Moreover, the obtained results can be helpful in making useful policies for the early warning system, drought risk assessment, and management, and formulating the drought-reducing plans.