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Estimating the dependence structure of concurrent extremes is a fundamental issue for accurate assessment of their occurrence probabilities. Identifying the extremal dependence behavior is also crucial for scientific understanding of interactions between the variables of a multidimensional environmental process. This study investigates the suitability of parametric multivariate extreme value models to correctly represent and estimate the dependence structure of concurrent extremes. Probabilistic aspects of multivariate extreme value theory with point process representation are discussed and illustrated with application to the concurrence of rainfall deficits, soil moisture deficits, and high temperatures. Application is concerned with the investigation of extremal behavior and risk assessment in Marathwada, a drought‐prone region of Maharashtra state, India. To characterize the multivariate extremes, marginal distributions are specified first and transformed into unit Fréchet margins. Standardized distributions are represented by a Poisson point process and coordinates of data points are transformed to pseudo‐polar coordinates to make the dependence form more explicit. The extremal dependence structure is described through angular densities on the unit simplex. Strong dependence is observed between soil moisture deficits and high temperatures, whereas rainfall deficits are mildly dependent on these two variables. Overall, a weak dependence is observed between the variables considered. Estimated extremal dependence is further used to compute probabilities of a few critical extreme combinations. Results demonstrate the ability of parametric multivariate models to characterize the complex dependence structure of concurrent extremes. These models can provide a powerful new perspective for appropriate statistical analysis of dependent hydroclimatic extremes in higher dimensions. Key Points: Utility of parametric multivariate extreme value models is explored for modeling concurrent hydroclimatic extremes in Marathwada, IndiaExtremal dependence structure of rainfall, soil moisture, and temperatures is explicitly described through angular densities on two‐dimensional simplexThe results demonstrate the suitability of parametric models to disentangle the complex interactions of multidimensional concurrent extremes [ABSTRACT FROM AUTHOR]

The main objective of this study is to evaluate suitability of shrink-swell soils for surface irrigation system based upon a parametric evaluation system in low irrigation potential (7%) of cotton growing Yavatmal district, Maharashtra, India. The thirty three shrink-swell soil series on basaltic landforms were identified from reconnaissance soil survey on 1:50,000 scale and evaluated for surface irrigation methods using Geographic Information System (GIS). The standard weekwise rainfall data showed that the rainfall is less than 20% of total precipitation during September and December, the top A horizon reaches to wilting point and needs supplementary protective irrigation to cotton based cropping systems . It was estimated that ten soil mapping units (1.8 Mha and 13.89%) of shrink-swell soils on moderate slopes (5 to 8%) were evaluated as suitable for surface methods and calculated the irrigation intervals that vary from 8.61±1.35 days for cotton to 8.9±1.4 days for wheat and 10±1.64 days for sugar cane. The study emphasized the utility of soil resource maps helps to delineate the soils with large PAWC(>200mm) with slight yield advantage and will serve as benchmark sites to monitor the interrelationships of soil water dynamics with respect to climate and cotton yields. [ABSTRACT FROM AUTHOR]

Abstract: Tree growth-climate relationship analysis revealed the important role of soil moisture availability in development of annual growth rings. Winter season (October–March) Palmer Drought Severity Index (PDSI) is an important factor in developing annual tree-rings during subsequent growing seasons. The chronology was strongly correlated with winter PDSI in recent few decades, more so than in the earlier period, indicating that tree growth is more dependent on climate. Many low growth years detected during El Nino events are probably related to moisture deficiency. The result indicates that the tree growth is mostly affected by the soil moisture availability during the recent warm periods over the region. [Copyright &y& Elsevier]

Cry1Ac toxin concentration was assessed in leaves of Bt transgenic cotton hybrid grown on shallow (<60 cm) and deep (>90 cm) black soils of Nagpur, Maharashtra, India. Cry toxin concentration increased up to 80 days after sowing followed by a steep decline. In general, toxin concentration was greater on the deep black soils than the shallow soil. This was because of greater water-holding capacity of the deep soils. Cry toxin concentration was closely related to the soil water content. Beyond (excess moisture) and below (moisture deficit) field capacity, toxin concentration declined. A cubic polynomial best described the relationship between Cry toxin concentration and soil moisture content (R2 = 0.95). [ABSTRACT FROM AUTHOR]

Drought is recognized as one of the most damaging natural hazard caused by a temporary water supply deficit relative to some long term average condition. Although there is not a universal definition of drought in the scientific community, previous studies have defined four types of droughts, intimately interrelated to each other, which occur at different time scales: meteorological, agricultural, hydrological and socio-economic. Soil moisture represents one of the most suitable variable to assess the effects of agricultural droughts and define different drought parameters, which include intensity, duration, severity and spatial extent. In fact, agricultural drought occurs when there is not enough soil moisture to support crop production. As ground-based soil moisture measurements are extremely hard to compare to large scale data sets because of their point-based nature, their limited coverage, and the well known high variability of soils, many studies have promoted the use of synoptic, timely and spatially continuous remote sensing soil moisture data from active and passive microwave sensors to assess agricultural drought conditions over large areas where ground monitoring instruments are sparse or non-existent.In this work satellite soil moisture observations derived from the long-term ESA CCI soil moisture product from 1981 to 2016 (Dorigo et al., 2017) were used to obtain a drought index, the Standardized Soil Moisture Index (SSI). Finally, to evaluate the suitability and potential of SSI index for assessing agricultural drought impacts, we tested it against agricultural productivity.We will show results of our analysis over the Districts of Maharashtra and Karnataka States in India, which have been affected by severe historical droughts in recent years. In order to integrate drought analysis with crop phenology, we used 18 years annual crop yield data (1998-2015) for two different crops: maize for the monsoon season (July to October); wheat for the winter season (October to March). Crop yield anomalies datasets for every district of the two states were compared with SSI. For a robust statistical analysis SSI was also compared with the Standardized Precipitation Index (SPI; McKee et al, 1993), computed by ground-based rainfall observations in India. The performance of SSI and SPI against agricultural productivity for different crops in the Districts of Maharashtra and Karnataka were tested and SSI showed higher correlation both in the monsoon period as in the winter season. On the contrary, SPI displayed good performance in the monsoon season and low performance for wheat in the winter season. In general, these results have led to the assumption that ESA-CCI soil moisture product can be consistently used for agricultural drought characterization. [ABSTRACT FROM AUTHOR]