Impact Of Agricultural Drought Research Articles

Integrating Maize Yield and Agricultural Drought Analysis for Sustainable Food Security: A Provincial Study in South Africa (1993–2022)

ABSTRACTExtreme climatic events, such as droughts, hinder progress toward achieving the sustainable development goal of food security. South Africa is vulnerable to drought‐related agricultural losses, which have led to food insecurity. However, few studies have focused on the long‐term impacts of drought on crop production at a regional scale. Therefore, we aimed to examine the intensity, magnitude, and trend of rainfall‐based short‐term agricultural drought at the provincial scale in South Africa based on the Standardized Precipitation Index (SPI). Additionally, we analyzed the impact of agricultural drought on maize yield by calculating the Standardized Yield Residual Series (SYRS) and Crop Drought‐Resilience Factor (CDRF). To this end, we collected rainfall data from 29 stations across nine provinces along with maize yield data for the period of 1993–2022. Agricultural drought analyses based on the three‐month (SPI‐3) and six‐month (SPI‐6) SPIs demonstrated dynamic variations in occurrence, with Sen's slope indicating that 10 stations exhibited a significant increase in drought events across South Africa. Notably, SPI‐6 analysis showed that Gauteng, Free State, and North West provinces experienced the highest percentages of severe to extreme drought events during the study period, at 4.17%, 3.89%, and 3.61%, respectively. Furthermore, the majority of provinces in South Africa experienced an extreme SPI‐6 magnitude ranging from −46.03 in Western Cape Province to −61.6 in Free State Province. The dynamic effects of agricultural drought on maize yield revealed that the maximum yield loss of 13% occurred in 1993 in Eastern Cape Province, while some provinces experienced no yield loss during certain years. However, CDRF analyses identified Western Cape (CDRF [SPI‐3] = 0.52, CDRF [SPI‐6] = 0.62) and Mpumalanga (CDRF [SPI‐6] = 0.7) provinces as the most vulnerable to food insecurity due to the severe non‐resilience of maize to drought in these regions. This study reveals the complex interplay between climatic extremes and maize yield variability, providing valuable insights for managing regional food production systems and ensuring future food security in South Africa.

Impact assessment of agricultural droughts on water use efficiency in different climatic regions of Punjab Province Pakistan using MODIS time series imagery

AbstractDrought is the most destructive phenomenon that distresses the terrestrial carbon cycle balance and crop production. The variation in evapotranspiration (ET) and gross primary productivity (GPP) is a significant cause of agricultural drought effects on water use efficiency. This study aims to evaluate the impact of agricultural drought on WUE and it's anomalies in different climate regions. The standard vegetation index was used to measure the extent of agricultural drought. WUE was calculated using the ratio of ET, GPP, and climate classification using the De Martonne method. This study was conducted over the last 22 years, from 2001 to 2022. Meanwhile, 2001, 2002, 2014, and 2018 were considered high drought years based on a 22‐year analysis. According to remote sensing analysis of ET and GPP, the WUE increased throughout drought in all climate regions and more strongly in the arid zone than in humid regions. Humid areas were more vital due to drought than arid ones. WUE anomalies badge with increased drought severity across all climates except the very humid zone. Humid climates saw faster recovery times than arid ones, and areas experienced severe droughts. The findings of this research are essential for understanding the carbon and water cycles for agriculture management. The analysis of drought severity and WUE across arid climate regions helped analyse the varying impact of drought on WUE due to climate change. The significance of this study includes informing agricultural, water resource, and drought management planning in the Punjab Province, an agricultural region vulnerable to climate change. This study holds important learnings for agricultural regions worldwide. It has practical and scientific importance regarding agricultural systems' specific stresses and responses to droughts.

Assessing the Impact of Agricultural Drought on Yield over Maize Growing Areas, Free State Province, South Africa, Using the SPI and SPEI

Assessing the Impact of Agricultural Drought on Yield over Maize Growing Areas, Free State Province, South Africa, Using the SPI and SPEI

Development and Applicability Evaluation of Damage Scale Analysis Techniques for Agricultural Drought

In recent years, the intensity and frequency of droughts have been increasing with the advent of the climate crisis. Agricultural droughts have a significant economic and social impact. Agricultural drought is not only a natural disaster but also leads to food security threats and reduced economic activities, such as decreased productivity. Therefore, it is very important to specify the scale of agricultural drought and quantitatively estimate the economic damage. In this study, we developed an analytical methodology to quantitatively assess the economic damage of agricultural drought and estimated the damage of agricultural drought in 2018 and 2019 for the Republic of Korea. The 2018 agricultural drought was estimated to have caused USD 4.438 million in damage cost and USD 5.180 million in recovery cost. The 2019 drought was less damaging than the previous year, with an estimated damage cost of USD 286,000 and recovery costs of USD 218,000. The results suggest that the economic impact of agricultural drought varies by region depending on the frequency and intensity of the drought and confirm the importance of regional strategies for effective drought management and response. The impacts of agricultural drought go beyond short-term agricultural losses and lead to long-term economic burdens. Therefore, the results of this study are expected to be used as a basis for understanding the impacts of agricultural drought on national economies and for developing policies and strategies to minimize impacts.

Impact of Agricultural Drought on Barley and Wheat Yield: A Comparative Case Study of Spain and Germany

Given the growing interest in drought impacts on crops, this work studied the impact of agricultural drought on wheat and barley during the period 2001–2020. The study was carried out in the Spanish regions of Castilla y León and Castilla–La Mancha, with approximate areas of 94,000 km2 and 79,000 km2, respectively, and in the German regions of Nordrhein-Westfalen, Niedersachsen and Bayern, with approximate areas of 34,000 km2, 48,000 km2 and 71,000 km2, respectively. These are the main cereal-growing regions of Spain and Germany. Soil moisture (SM) in the root zone was extracted from the LISFLOOD model database, and SM anomalies were used as the agricultural drought index. Gross primary productivity (GPP) and leaf area index (LAI) variables were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the month in which SM is most influential on these crop state variables was identified. Crop yields in Spain and Germany were obtained from the Spanish Ministry of Agriculture, Fisheries and Food and the German Federal Statistical Office, respectively. Agricultural drought years and their impact on cereal yields were determined on a regional scale using three approaches based on the critical month with different time periods. These approaches were the use of the critical month and the two (before or after) and the three months (before and after) around the critical month. Two different analyses were used to identify the critical month, depending on the different environmental conditions in each country. These two approaches consisted of a monthly correlation analysis between SM anomalies and cereal yield in Spain and a monthly trend analysis of SM anomalies in Germany. The results showed a dependence of crop variables on SM in spring months in both countries and in summer months in Germany. Differences were found depending on the environmental conditions. A considerable reduction in cereal yields was obtained in Spain which exceeded 30%. Similarly, a worrying sign was observed in Germany, with a positive agricultural drought trend and a yield reduction of almost 5% in cereal crops. In view of future forecasts of the negative impact of climate change on global food production, this study provides valuable information for water and agricultural management under climate change scenarios. Both in regions that are already threatened and in those that until recently were not affected, it is necessary to study adaptation measures to avoid aggravating the impact of agricultural drought on crops, which could improve water productivity and future food security.

Grassland Model Based Evaluation of Drought Indices: A Case Study from the Slovenian Alpine Region

Considering that the relationships between agrometeorological drought indices and the impact of agricultural drought in Slovenia are not yet well understood, the aim of this study was to make an indicative evaluation of the success of selected drought indices, addressing topsoil layer and vegetation condition, in detecting drought in agriculture. In this study, the performance of two established drought indices—the SPEI (standardised precipitation evapotranspiration index) and the VHI (vegetation health index)—was evaluated with respect to yield values calculated with the LINGRA-N model, specifically, the ratio between actual and potential transpiration, also known as drought factor (TRANRF), actual root zone water content (SMACT), leaf area index (LAI), reserve dry weight (WRE), and root dry weight (WRT). The two grassland species selected for analysis were Dactylis glomerata L. (dg) and Lolium perenne L. (lp). For the period 2002–2020 or 2015–2020, three farm sites in Slovenia were considered for evaluation, with two farms at a higher altitude site and one farm at a lower altitude site in the Alpine region. Evaluation of the yield data with the drought indices showed that the r2 values of the linear regression for the selected years with the highest drought impacts (2003, 2013, and 2017) were highest between the two SPEI indices (SPEI-2, SPEI-3) and the model variables TRANRF, SMACT, and WRT, with r2 higher than 0.5 and statistically significant for the lower situated farm in 2013. For 2003 and 2017, the r2 values were less significant as well as for the model variable WRE for all three years selected for analysis (2003, 2013, and 2017).

The Impact of Agricultural Drought on Smallholder Livestock Farmers: Empirical Evidence Insights from Northern Cape, South Africa

The Northern Cape Province of South Africa faces drought that limits human endeavours, and which explains the unpredictable progress in livestock production over time. This study evaluated the impact of agricultural drought on smallholder farming households’ resilience in the Northern Cape Province. Data from 217 smallholder livestock farmers were collected and analysed descriptively and with the Agricultural Drought Resilience Index (ADRI), and Household Food insecurity Access Scale (HFIAS). This study found that most smallholder livestock farmers (79%) were not resilient to agricultural drought. Further, the findings revealed that agricultural drought significantly impacted resources, food security, and government policy. This implies that a lack of access to resources, food insecurity, and ineffective government policy affected already vulnerable smallholder livestock farmers coping with severe agricultural drought. This study is significant in providing policymakers and other stakeholders with evidence-based recommendations for developing strategies and implementing policies for improving the resilience of smallholder livestock farmers by improving access to resources, including access to land. They will be better able to deal with challenges that come their way if they become more resilient, resulting in reduced household loss. This study recommends that government and stakeholders enhance the resilience of smallholder farmers by supporting less resilient farmers. The government needs to work with stakeholders to improve access to land and funds to enhance farmers’ resilience. As a result, these policies can help smallholder farmers be more resilient in times of climatic shock.

Impact of Agricultural Drought on Sunflower Production across Hungary

In the last few decades, agricultural drought (Ag.D) has seriously affected crop production and food security worldwide. In Hungary, little research has been carried out to assess the impacts of climate change, particularly regarding droughts and crop production, and especially on regional scales. Thus, the main aim of this study was to evaluate the impact of agricultural drought on sunflower production across Hungary. Drought data for the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) were collected from the CARBATCLIM database (1961–2010), whereas sunflower production was collected from the Hungarian national statistical center (KSH) on regional and national scales. To address the impact of Ag.D on sunflower production, the sequence of standardized yield residuals (SSYR) and yield losses YlossAD was applied. Additionally, sunflower resilience to Ag.D (SRAg.D) was assessed on a regional scale. The results showed that Ag.D is more severe in the western regions of Hungary, with a significantly positive trend. Interestingly, drought events were more frequent between 1990 and 2010. Moreover, the lowest SSYR values were reported as −3.20 in the Hajdu-Bihar region (2010). In this sense, during the sunflower growing cycle, the relationship between SSYR and Ag.D revealed that the highest correlations were recorded in the central and western regions of Hungary. However, 75% of the regions showed that the plantation of sunflower is not resilient to drought where SRAg.Dx < 1. To cope with climate change in Hungary, an urgent mitigation plan should be implemented.

A Study on Establishment of Drought Index for Agricultural Disaster Prediction

This study aims to establish a drought index for disaster prediction in Gyeongsangnam-do, where the most agricultural drought damage occurred from 1965 to 2018. The drought index was analyzed for each duration (3, 6, 9, 12 months) targeting the SPI. Damage characteristics of the duration of agricultural drought were calculated. SPI for each duration of agricultural drought damage period in Gyeongsangnam-do was at least -2.0 or less, and the maximum was -1.0 or more, and weak and moderate drought were analyzed. However, due to the heavy rain effect during the rainy season, the average SPI12 was -1.06, and the impact of agricultural drought was negligible. It was analyzed that the correlation between the damage period of agricultural drought and the SPI by duration was high. However, there is not much difference in SPI for each duration to determine the occurrence of damage. In this study, the criterion for disaster prediction of agricultural drought was calculated as representative drought index by year as the minimum drought index of SPI for each duration of damage occurrence period of past agricultural drought. The Standard of drought index for disaster prediction was set to -1.64, the average of the SPI for each duration of year in which damage occurred in the past.

Impact of green water anomalies on global rainfed crop yields

The importance of green water (moisture from rain stored in soils) for global food and water security is widely recognized, with process-based simulation models and field-level studies demonstrating its role in supporting rainfed agriculture. Despite this evidence, the relationship between green water anomalies and rainfed agriculture has not yet been investigated using statistical models that identify a causal relationship between the variables. Here, we address this gap and use disaggregated statistical regression (panel data analysis) at the 30 arc-min grid level to study the response of observed yields (1982–2010) of four main crops (maize, rice, soybean and wheat) to green water anomalies globally over rainfed areas. Dry green water anomalies (1 or 2 standard deviations below long-term average) decrease rainfed crop yields worldwide. This effect is more pronounced for wheat and maize, whose yields decline by 12%–18% and 7%–12% respectively. Globally, agricultural production benefits from wet green water anomalies. This effect is intensified in arid climates and weakened in humid climates where, for wheat, soybean and rice, periods of green water availability 2 standard deviations above long-term averages lead to declines in crop yield. This confirms existing evidence that excess soil moisture is detrimental to crop yield. These findings (1) advance our understanding of the impact of green water on rainfed food production and (2) provide empirical evidence supporting arguments for better management of local green water resources to reduce the impact of agricultural drought and waterlogging on rainfed crop production and capture the yield increasing effects of positive green water anomalies.

CHARACTERIZATION, FORECASTING AND ASSESSMENT OF AGRICULTURAL DROUGHT IMPACTS IN THE SUDANO-SAHELIAN CLIMATE OF GOURMA PROVINCE IN BURKINA FASO

Natural hazards such as agricultural droughts impact negatively on crop yields and economic activities. Characterization of agricultural droughts provides precise and accurate information for decision making processes during agricultural drought events. Planning and responding to the hazards by government, and non-governmental organizations in the Sudano-Sahelian belt has been limited in the past due to knowledge gap on the nature and impact of the hazard. This study seeks to characterize historical agricultural droughts, assess their impact on crop yields and people’s susceptibility to undernourishment and through forecasting, unravel what the future holds. Annual effective reconnaissance drought index values are computed using mean monthly potential evapotranspiration and effective precipitation data. To assess the impact of agricultural drought, the index’s values are compared to crop yields and prevalence to undernourishment data. Results show that agricultural drought events of 1983 and 2008 are mild and ephemeral while the 1999 – 2006 event is severe and protracted. While there is 26% chance of materialization of an agricultural drought in Gourma, the chance of being ephemeral and of moderate category is the highest (8%). It has been determined that an ephemeral and moderate agricultural drought would trigger below average yields for maize, sorghum and millet. Mild, moderate and severe events increase prevalence to undernourishment by 2.9 %, 4.3 % and 5.8 % respectively. From 2020 to 2030, a continued materialization of agricultural droughts is expected

Local-scale agricultural drought monitoring with satellite-based multi-sensor time-series

ABSTRACT Globally, drought constitutes a serious threat to food and water security. The complexity and multivariate nature of drought challenges its assessment, especially at local scales. The study aimed to assess spatiotemporal patterns of crop condition and drought impact at the spatial scale of field management units with a combined use of time-series from optical (Landsat, MODIS, Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel 1) data. Several indicators were derived such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Land Surface Temperature (LST), Tasseled cap indices and Sentinel-1 based backscattering intensity and relative surface moisture. We used logistic regression to evaluate the drought-induced variability of remotely sensed parameters estimated for different phases of crop growth. The parameters with the highest prediction rate were further used to estimate thresholds for drought/non-drought classification. The models were evaluated using the area under the receiver operating characteristic curve and validated with in-situ data. The results revealed that not all remotely sensed variables respond in the same manner to drought conditions. Growing season maximum NDVI and NDMI (70–75%) and SAR derived metrics (60%) reflect specifically the impact of agricultural drought. These metrics also depict stress affected areas with a larger spatial extent. LST was a useful indicator of crop condition especially for maize and sunflower with prediction rates of 86% and 71%, respectively. The developed approach can be further used to assess crop condition and to support decision-making in areas which are more susceptible and vulnerable to drought.

Smart method of agricultural drought regionalization: A winter wheat case study

Abstract The occurrence of drought during flowering (usually from the end of May to the beginning of June) is the most hazardous timing in terms of the possible negative impact of agricultural drought on winter wheat, which is the most cultivated crop in the Czech Republic (about 800000 ha). Lack of water, often accompanied by high temperatures, negatively affects the number of grains in the wheat ear and the tissue development of the developing grain, with consequent impacts on yield and quality of product. With the use of a) long-term time series of agrometeorological data (1961–2010), b) long-term phenological time series of winter wheat (1981–2010), and c) soil conditions data (available water capacity of soils of the Czech Republic) for the arable soil, the ratio of actual evapotranspiration and potential evapotranspiration for the period of 1961–2010, used as an indicator of agricultural drought (lack of water) for wheat, was calculated. The innovative aspect of this categorization of the territory of the Czech Republic according to the risk of occurrence of agricultural drought for winter wheat is considering drought from the aspect of the plant, i.e., evaluation based on the actual consumption of water by the vegetation. This is a very sophisticated procedure. Frequently, water content in soils data, presented as an output of some models, do not fully indicate the possible negative impacts on yield generation because the plants themselves are typically not considered. The method used in this study is universally applicable and allows comparisons of regions at the local, regional, and supra-regional levels. For estimation of the development of agronomic drought in the future, the basic water balances in the growing seasons of 1961–2010 and 2071–2100 were compared using a climate scenario. The forecast indicates a significant deterioration of agricultural drought in the region with probable direct impacts on agricultural production.

ENHANCING CAPACITY OF LOCAL COMMUNITY TOWARDS AGRICULTURAL DROUGHT (CASE IN PLOSO VILLAGE, YOGYAKARTA)

This paper reveals the capacity enhancement efforts for local community and assessing their vulnerability level regarding the drought event in Ploso Village of Gunungkidul Regency – Yogyakarta Special Province. The vulnerability assessment was conducted to measure the impact of agricultural drought and also focusing on enhancing the community’s absorbing capacity, buffering capacity and local response capacity. The village drought vulnerability assessment was conducted by using indicator-based calculation towards 50 family-heads of Ploso Village as the assessment samples. Absorbing capacity enhancement was conducted by facilitating and educating local farmers to conduct organic farming as well as increasing their local response capacity by focusing on rainwater harvesting, while buffering capacity enhancement is conducted by empowering the housewives for creating alternate household income The disaster preparedness community is created as the centre of intervention to educate local people about drought disaster and preparing the local teenagers to become future focal points in their area.

Analysis of Agricultural Drought in East Java Using Vegetation Health Index

Drought is a natural hazard indicated by the decreasing of rainfall and water storage and impacting agricultural sector. Agricultural drought assessment has been used to monitor agricultural sustainability, particularly in East Java as national agricultural production center. Identification of drought characteristics –correlated with El Nino-Southern Oscillation, and agricultural impact on paddy fields and rice production using VHI (Vegetation Health Index) were conducted. VHI is produced by TCI (Temperature Condition Index) and VCI (Vegetation Condition Index) derived from MODIS satellite data, LST (Land Surface Temperature) and EVI (Enhanced Vegetation Index), respectively. The results showed agricultural drought usually started in June, maximum in October and ended in November. Onset and end time drought tends to follow monsoonal rainfall pattern. El Nino 2015 showed long duration of agricultural drought (i.e. ± 5 months), high severity (i.e. mild-extreme drought; VHI 0-40) and areal extent of drought approx. 197,343 km2, while during La Nina 2010 the areal extent was approx. 28,685 km2 with mild-severe drought (VHI 10-40). Impact of agricultural drought on paddy fields showed wider (smaller) areal extent in sub-round 3 (sub-round 1) from September-December (January-April). Areal extent of drought was negatively correlated with rice production (r=-0.79) which significant in 99 % confidence level.

Assessing multi-satellite remote sensing, reanalysis, and land surface models' products in characterizing agricultural drought in East Africa

Heavy reliance of East Africa (EA) on rain-fed agriculture makes it vulnerable to drought-induced famine. Yet, most research on EA drought focuses on meteorological aspects with little attention paid on agricultural drought impacts. The inadequacy of in-situ rainfall data across EA has also hampered detailed agricultural drought impact analysis. Recently, however, there has been increased data availability from remote sensing (rainfall, vegetation condition index – VCI, terrestrial water storage – TWS), reanalysis (soil moisture and TWS), and land surface models (soil moisture). Here, these products were employed to characterise EA droughts between 1983 and 2013 in terms of severity, duration, and spatial extent. Furthermore, the capability of these products to capture agricultural drought impacts was assessed using maize and wheat production data. Our results show that while all products were similar in drought characterisation in dry areas, the similarity of CHIRPS and GPCC extended over the whole EA. CHIRPS and GPCC also identified the highest proportion of areas under drought followed closely by soil moisture products whereas VCI had the least coverage. Drought onset was marked first by a decline/lack of rainfall, followed by VCI/soil moisture, and then TWS. VCI indicated drought lag at 0–4 months following rainfall while soil moisture and TWS products had variable lags vis-à-vis rainfall. GLDAS mischaracterized the 2005–2006 drought vis-à-vis other soil moisture products. Based on the annual crop production variabilities explained, we identified CHIRPS, GPCC, FLDAS, and VCI as suitable for agricultural drought monitoring/characterization in the region for the study period. Finally, GLDAS explained the lowest percentages of the Kenyan and Ugandan annual crop production variances. These findings are important for the gauge data deficient EA region as they provide alternatives for monitoring agricultural drought.

Impact of agricultural drought on main crop yields in the Republic of Moldova

Crop loss due to drought is a complex issue, because it changes according to the drought intensity and duration, and the developmental stage of the plants when drought occurs. In order to assess the drought-induced decline in crop harvest, drought variability and the yield sensitivity of winter wheat, maize, sugar beet, and sunflower to drought during their growing seasons is investigated in the Republic of Moldova. This is then used as an example of the response of non-irrigated crops to increasing drought tendency in south-eastern Europe. The quantification of drought was done by using the standardized precipitation evapotranspiration index (SPEI) at 1- to 12-month lags during the period from 1951 to 2012. The relationship between drought at various time scales and the standardized yield residuals series (SYRS) for individual crops over the country and the Balti chernozem steppe of Moldova (represented by Balti experimental site) for the 1962–2012 farming years were investigated. In order to detect the trends and the shifts in the SPEI time series over 62 years, the non-parametric, Mann–Kendall and Pettitt tests were used for each month of the year to cover the main life cycle of the crops. The trend analysis of agricultural drought emphasizes an increasing trend from June to October, and becomes significant in the southern region at the 95% level during July to September. The SPEI highlights the main periods of dry/wet persistence and the regional characteristics of drought which are present in the Southern region, and make this region more prone to severe drought persistence, mostly during the last decade. Drought during the plant reproductive stages may significantly reduce grain yield potential, the relation between the SYRS and the SPEI explaining up to 62% of the low-yield variability.

Developing seasonal rainfall scenarios for food security early warning

Rainfed agriculture in Sub-Saharan Africa accounts for 95 % of the local cereal production, impacting hundreds of millions of people. Early identification of poor rainfall conditions is a critical indicator of food security. As such, monitoring accumulated seasonal rainfall gives an important mid-season estimate of final accumulated totals. However, characterizing the remaining uncertainty in a season has largely been ignored by the food security community. This paper presents a new technique describing rainfall conditions over the duration of a crop-growing cycle by combining estimated rainfall-to-date with potential scenarios for the remaining season based on available satellite rainfall estimates, the common tool for rainfall analysis in Africa. The limited historical record provided by satellite rainfall estimates using previous seasons provides only a coarse view of likely seasonal totals. To combat this, scenarios developed by bootstrapping dekadal data to create synthetic seasons allow for a finer understanding of potential seasonal accumulations. Updating this throughout the season shows a narrowing envelope of seasonal totals, converging on the final seasonal result. The resulting scenarios inform the expectations for the final seasonal rainfall accumulation, allowing analysts to quantify and visualize the uncertainty in seasonal totals. Giving decision makers a tool for understanding the likelihood of specific rainfall amounts provides additional time to enact and mobilize efforts to reduce the impact of agricultural drought.

Severity–duration–frequency curves in the mitigation of drought impact: an agricultural case study

Severity–duration–frequency (SDF) curves are very useful in the analysis of drought phenomena. Station-level information obtained from SDF curves can be interpolated to obtain severity maps for fixed return period, in order to jointly analyse the spatial variability of drought characteristics (e.g. severity, duration and frequency). This approach is limited because the severity is usually quantified through indices that use hydrological and meteorological data, depending on the type of requirements. Therefore, drought indices can only reflect hydrological conditions, but are unable to quantify economic losses associated with droughts. In other words, SDF curves do not allow effective quantification of the impact expected with a certain return period. This paper proposes the methodology drought economic risk assessment (DERA) as an approach that emphasizes the importance of the relationship between a generic drought index (which quantifies water deficit) and the economic impact of the failure to meet water demand. Using integrated SDF curves, this relationship enables drought severity and corresponding impacts to be mapped. This procedure was applied to agricultural droughts (sunflower crop) in Umbria Region (central Italy). The agricultural drought impact variable was identified by sunflower yield (Y); the economic impact variable by net benefit depletion (EL); and the drought index by Relative Severity Index (RSI), which is quantifiable by a soil–water balance model. The relationships Y = g(RSI) and EL = f(Y) were specifically determined. Using DERA, it was possible to derive curves for SDF, impact–duration–frequency, and economic losses–duration–frequency (ELDF), which were then used to map severity, impact and economic losses for the assigned return period and duration. From the ELDF curves, further information was obtained by mapping critical drought durations for the assigned return period and economic loss threshold. The case study supports the potential of the proposed approach, both in the planning and real-time management of drought effects.

Monitoring and forecasting drought impact on dryland farming areas

AbstractFrequent drought amplifies the need for a warning system and forecasting models for damage to crop yields. This study developed an operational model to assess agricultural drought impact. The dryland areas of Kermanshah Province (Iran) were selected to test the proposed modelling system. The model predicted the consequences of drought damage on wheat crop during critical stages of growth (emergence, vegetative growth, initiation of flowering, grain filling, and maturity) as a drought loss indicator. Two types of input were evaluated to correlate climate conditions versus drought losses. The first group comprises the Palmer Drought Severity Index, Z‐index, Crop Moisture Index, Crop‐Specific Drought Index (CSDI), Standardized Precipitation Index, and Effective Drought Index with one‐ to three‐month timescales used as meteorological indices. The second group, which is consistent of the vegetation condition index and temperature condition index, is based on satellite data. Also a new satellite‐based version of CSDI, so‐called standardized CSDI (S‐CDSI), where evapotranspiration was estimated using surface energy balance algorithm for land, is used. Adaptive Neuro‐Fuzzy Inference Systems (ANFIS) technique was used for forecasting with genetic algorithms applied to select appropriate inputs from among the large number of indices. It was concluded that the combination of meteorological and satellite indices performed best in forecasting crop yield. As expected, accuracy improved over the growth stages as the crop developed. Enhancement of the model with a GIS platform made it possible to present the results more suitably, hence helping users to make more realistic decisions. Copyright © 2012 Royal Meteorological Society