Milk production as an indicator of drought vulnerability of cities located in the brazilian semiarid region

Several spectral indices have been used to estimate droughts, however, these indicators only give evidence of a dry spell leaving out its impacts on significant economic activities performed in a given region. In this context, livestock breeding in one of the most important activities to analyze in the Brazilian semiarid region. The aim of this study was to evaluate the relationship between the drought indices, obtained through remote sensory devices, and annual milk production (2004 – 2014), identifying the most affected cities by the drought and were considered the most vulnerable and in need of special attention during dry periods. In order to analyze the data, the hierarchical grouping technique and correlation analyses between milk production and VCI - Vegetation Condition Index, TCI - Temperature Condition Index, VHI - Vegetation Health Index, PCI - Precipitation Condition Index and SDCI - Scaled Drought Condition indices were used. The intense correlation between milk production and the drought indices may be related to the dependency of the cities’ economies on natural resources. On the other hand, the diversification of the cities’ economic activities may enable access to various resources and drought vulnerability reductioninfo:eu-repo/semantics/publishedVersio

Spatial-Temporal Patterns of Agricultural Drought in Upper Progo Watershed Based on Remote Sensing and Land Physical Characteristics

Agricultural drought is alarmed by meteorological drought characterized by lower year-to-year rainfall. Under long period and continuous water deficits, plants may demonstrate stress symptoms and wilt or die. Furthermore, agricultural drought leads to crop failures and threaten the food security of an area. Progo Hulu sub-watershed is a major agricultural area in Temanggung Regency. Spatial-temporal pattern-based information about agricultural drought can be a basis for decision making in drought mitigation. This study aims to analyze spatial and temporal distribution patterns of drought, analyze the physical characteristics of land and their influence on drought pattern, and establish a prediction model of drought distribution patterns based on four physical characteristics of the land. Landsat 8 imagery is used to determine the spatial and temporal patterns of agricultural drought in Upper Progo watershed using an improved Temperature vegetation Dryness Index (iTVDI). Slope, land use, landform, and soil texture are the physical characteristics of land as the variables to determine the most influential factor of drought pattern. They are analyzed using multiple regression analysis techniques. Pixel samples are obtained through purposive sampling method based on land units. The results reveal that the spatial-temporal distribution of agricultural drought occurs rapidly on the slopes and foothills of Sumbing and Sindoro. These areas have the highest average value of the iTVDI index. Agricultural drought extends gradually in line with the number of days without rainfall. Landform is a physical characteristic that most influences the distribution of agricultural drought. The established model by utilizing four variables of physical characteristics generates an average value which almost similar to the iTVDI value produced by remote sensing data. The model can be useful to estimate drought distribution based on the number of days without rainfall

Drought Monitoring and Prediction using K-Nearest Neighbor Algorithm

Drought is a climate phenomenon which might occur in any climate condition and all regions on the earth. Effective drought management depends on the application of appropriate drought indices. Drought indices are variables which are used to detect and characterize drought conditions. In this study, it was tried to predict drought occurrence, based on the standard precipitation index (SPI), using k-nearest neighbor modeling. The model was tested by using precipitation data of Kerman, Iran. Results showed that the model gives reasonable predictions of drought situation in the region. Finally, the efficiency and precision of the model was quantified by some statistical coefficients. Appropriate values of the correlation coefficient (r=0.874), mean absolute error (MAE=0.106), root mean square error (RMSE=0.119) and coefficient of residual mass (CRM=0.0011) indicated that the present model is suitable and efficien

A new station-enabled multi-sensor integrated index for drought monitoring

Remote sensing data are frequently incorporated into drought indices used widely by research and management communities to assess and diagnose current and historic drought events. The integrated drought indices combine multiple indicators and reflect drought conditions from a range of perspectives (i.e., hydrological, agricultural, meteorological). However, the success of most remote sensing based drought indices is constrained by geographic regions since their performance strongly depends on environmental factors such as land cover type, temperature, and soil moisture. To address this limitation, we propose a framework for a new integrated drought index that performs well across diverse climate regions. Our framework uses a geographically weighted regression model and principal component analysis to composite a range of vegetation and meteorological indices derived from multiple remote sensing platforms and in-situ drought indices developed from meteorological station data. Our new index, which we call the station-enabled Geographically Independent Integrated Drought Index (GIIDI_station), compared favorably with other common drought indices such as Microwave Integrated Drought Index (MIDI), Optimized Meteorological Drought Index (OMDI), Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Vegetation Condition Index (VCI). Using Pearson correlation analyses between remote sensing and in-situ drought indices during the growing season (April to October) from 2002 to 2011, we show that GIIDI_station had the best correlations with in-situ drought indices. Across the entire study region of the continental United States, the performance of GIIDI_station was not affected by common environmental factors such as precipitation, temperature, land cover and soil conditions. Taken together, our results suggest that GIIDI_station has considerable potential to improve our ability of monitoring drought at regional scales, provided local meteorological station data are available

The sensitivity of satellite solar‐induced chlorophyll fluorescence (SIF) to meteorological drought

Solar‐induced chlorophyll fluorescence (SIF) could provide information on plant physiological response to water stress (e.g., drought). There are growing interests to study the effect of drought on SIF. However, to what extent SIF responds to drought and how the responses vary under different precipitation, temperature and potential evapotranspiration conditions are not clear. In this regard, we evaluated the relationship between satellite‐based SIF product and four commonly used meteorological drought indices (Standardized Precipitation‐Evapotranspiration Index, SPEI; Standardized Precipitation Index, SPI; Temperature Condition Index, TCI; and Palmer Drought Severity Index, PDSI) under diverse climate regions in the continental United States. The four drought indices were used because they estimate meteorological drought conditions from either single or combined meteorological factors such as precipitation, temperature, and potential evapotranspiration, representing different perspectives of drought. The relationship between SIF and meteorological drought varied spatially and differed for different ecosystem types. The high sensitivity occurred in dry areas characterized by a high mean annual growing season temperature and low vegetation productivity. Through random forest regression analyses, we found that temperature, gross primary production, precipitation, and land cover are the major factors affecting the relationships between SIF and meteorological drought indices. Taken together, satellite SIF is highly sensitive to meteorological drought but the high sensitivity is constrained to dry regions

Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI); 21-year drought monitoring in Iran using satellite imagery within Google Earth Engine

Remote Sensing (RS) offers efficient tools for drought monitoring, especially in countries with a lack of reliable and consistent in-situ multi-temporal datasets. In this study, a novel RS- based Drought Index (RSDI) named Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI) was proposed. To the best of our knowledge, TVMPDI is the first RSDI using four different drought indicators in its formulation. TVMPDI was then validated and compared with six conventional RSDIs including VCI, TCI, VHI, TVDI, MPDI and TVMDI. To this end, precipitation and soil temperature in-situ data have been used. Different time scales of meteorological Standardized Precipitation Index (SPI) index have also been used for the validation 2 of the RSDIs. TVMPDI was highly correlated with the monthly precipitation and soil temperature in-situ data at 0.76 and 0.81 values respectively. The correlation coefficients between the RSDIs and 3-month SPI ranged from 0.07 to 0.28, identifying the TVMPDI as the most suitable index for subsequent analyses. Since the proposed TVMPDI could considerably outperform the other selected RSDIs, all spatiotemporal drought monitoring analyses in Iran were conducted by TVMPDI over the past 21 years. In this study, different products of the Moderate Resolution Imaging Spectrometer (MODIS), Tropical Rainfall Measuring Mission (TRMM), and Global Precipitation Measurement (GPM) datasets containing 15206 images were used on the Google Earth Engine (GEE) cloud computing platform. According to the results, Iran experienced the most severe drought in 2000 with a 0.715 TVMPDI value lasting for almost two years. Conversely, the TVMPDI showed a minimum value equal to 0.6781 in 2019 as the lowest annual drought level. The drought severity and trend in the 31 provinces of Iran have also been mapped. Consequently, various levels of decrease over the 21 years were found for different provinces, while Isfahan and Gilan were the only provinces showing an ascending drought trend (with a 0.004% and 0.002% trendline slope respectively). Khuzestan also faced a worrying drought prevalence that occurred in several years. In summary, this study provides updated information about drought trends in Iran using an advanced and efficient RSDI implemented in the cloud computing GEE platform. These results are beneficial for decision-makers and officials responsible for environmental sustainability, agriculture and the effects of climate change.Peer ReviewedPostprint (author's final draft

SUNRISE: Drought monitoring in China - a brief review

Drought is one of the most complex and costly natural hazards. It develops slowly and can affect a large area meaning it can be difficult to pinpoint the start and/or the end of an event. Drought is primarily driven by a deficit in precipitation but an additional level of complexity is introduced when these deficits in precipitation propagate to other parts of the hydrological cycle such as soil moisture, river flows and groundwater levels over different time scales

Drought characterization: A systematic literature review

This study examined the worsening severity of global droughts caused by climate change. However, the multiple definitions and varied range of drought indices pose challenges in effectively monitoring and assessing the prevalence and severity of droughts. This study aims to give a comprehensive overview of the various drought definitions found in the literature and how they have evolved based on their applications. Specifically, the focus was to shed light on the dynamic nature of drought characterization and offer insights into the factors that shaped its conceptualization over time. Within this context, this study explored three primary categories of drought indices: climatic, remote sensing, and composite. Each category was discussed in relation to its utility in specific fields, such as meteorological, agricultural, and hydrological drought assessments, along with an analysis of their strengths and limitations. Furthermore, this study presents modified meteorological drought indices that have been adapted to better monitor agricultural droughts. Additionally, the authors used geographic information systems to create a map showing the distribution of drought-related publications globally over the past decade. The findings showed that countries with arid and semi-arid climates are more actively involved in drought research, highlighting their particular interest and concern regarding the subject matter. The implications of this study emphasize the urgent need for immediate and coordinated efforts to address the escalating issue of droughts caused by climate change. By improving monitoring and assessment methods and focusing on tailored strategies in vulnerable regions, it is possible to mitigate the far-reaching consequences of drought and to build more resilient communities and ecosystems

Meteorological Drought Analysis and Return Periods over North and West Africa and Linkage with El Niño−Southern Oscillation (ENSO)

Droughts are one of the world’s most destructive natural disasters. In large regions of Africa, droughts can have strong environmental and socioeconomic impacts. Understanding the mechanism that drives drought and predicting its variability is important for enhancing early warning and disaster risk management. Taking North and West Africa as the study area, this study adopted multi-source data and various statistical analysis methods, such as the joint probability density function (JPDF), to study the meteorological drought and return years across a long term (1982−2018). The standardized precipitation index (SPI) was used to evaluate the large-scale spatiotemporal drought characteristics at 1−12-month timescales. The intensity, severity, and duration of drought in the study area were evaluated using SPI−12. At the same time, the JPDF was used to determine the return year and identify the intensity, duration, and severity of drought. The Mann-Kendall method was used to test the trend of SPI and annual precipitation at 1−12-month timescales. The pattern of drought occurrence and its correlation with climate factors were analyzed. The results showed that the drought magnitude (DM) of the study area was the highest in 2008−2010, 2000−2003, and 1984−1987, with the values of 5.361, 2.792, and 2.187, respectively, and the drought lasting for three years in each of the three periods. At the same time, the lowest DM was found in 1997−1998, 1993−1994, and 1991−1992, with DM values of 0.113, 0.658, and 0.727, respectively, with a duration of one year each time. It was confirmed that the probability of return to drought was higher when the duration of drought was shorter, with short droughts occurring more regularly, but not all severe droughts hit after longer time intervals. Beyond this, we discovered a direct connection between drought and the North Atlantic Oscillation Index (NAOI) over Morocco, Algeria, and the sub-Saharan countries, and some slight indications that drought is linked with the Southern Oscillation Index (SOI) over Guinea, Ghana, Sierra Leone, Mali, Cote d’Ivoire, Burkina Faso, Niger, and Nigeria