No abstract

No abstract

No abstract

Drought analysis is an important part of risk management plan. Drought is usually characterized by variables such as severity and duration. Using standardized precipitation index (SPI) at an aggregated scale of 12 months, we construct different copula models for different river basins of India. Based on goodness of fit tests, suitable distributions are selected for duration and severity. These marginal distributions are then used to construct copula models from amongst-Frank, Gumbel, Clayton and Student's t copula. It is found that for some river basins Frank copula can capture the dependence structure between duration and severity whereas for others Gumbel copula is effective. Exceedance probability, conditional probability and joint return period of different drought events are calculated which allude to differing drought resilience and persistence conditions in river basins. The river basins in the western India have a longer joint return period and smaller exceedance probability compared to the river basins in south India. We explore the conjunctive use for joint return period and exceedance probability to qualitatively assess the resilience of river basins to meteorological drought. The results suggest that the drought events in the south Indian river basins are less severe and more frequent whereas the ones in Central and Western India are severe and longer. The results of this study can provide useful information for drought mitigation strategies at a national scale.

No abstract

<p><strong>Abstract.</strong> Aquatic macrophytes are important elements of freshwater ecosystems, fulfilling a pivotal role in the ecological functions of these environments and biogeochemical cycles. Although aquatic macrophytes are beneficial, some species can hinder human activity. They can clog reservoirs and reduce water availability for human needs. Surveys of macrophytes are hindered by logistic problems, and remote sensing represents a powerful alternative, allowing comprehensive assessment and monitoring. The objectives of this study was to map temporal changes in the macrophytes using time series multispectral dataset over Tapi River, Surat. The field trip was conducted over the Tapi River on 22nd June 2018, where <i>in-situ</i> spectral response dataset were acquired using ASD Spectroradiometer. Water samples were also collected over three locations, one before entering the city (Kamrej), second at the Sarthana water treatment plant and third at the outer end (causeway). The nutrient concentration was less before entering the city (Ammonical Nitrogen 0.056<span class="thinspace"></span>mg/L and phosphate 0.0145<span class="thinspace"></span>mg/l), while higher concentration (Ammonical Nitrogen 0.448<span class="thinspace"></span>mg/l and phosphate 0.05<span class="thinspace"></span>mg/l) was observed within the city. Maps of aquatic macrophytes fractional cover were produced using Resourcesat-2/2A (LISS-III) dataset covering a period of 2012&amp;ndash;2018. Maximum extent was observed in February-March of every year. Although during monsoon, lot of agriculture run-off and nutrients will come into the river, but main flow of water will dilute its concentration. During summer, the same nutrient concentration will boost these macrophytes due to less availability of stream water. Within the area of 16<span class="thinspace"></span>km² between Kamrej and causeway, 3.35<span class="thinspace"></span>% was covered by macrophytes during March 2013. This area coverage increase to 36.41<span class="thinspace"></span>% in March 2018. Based on these maps, we discuss how remote sensing could support monitoring strategies and provide insight into spatial variability, and by identifying hotspot areas where invasive species could become a threat to ecosystem functioning.</p>

<p><strong>Abstract.</strong> Dynamics, distribution and quality of water has a direct impact on environment and its dependent human activities. Regular monitoring of these hydrological processes help in understanding water cycle and better management policy making. Recent increase in remote sensing satellites offer multiple observations with high spatial and temporal resolution, thus calling for extensive use of high end computational resources. Google Earth Engine(GEE) is an open Application Programing Interface (API), which offers free computational resources and satellite data on cloud computational platform minimising the users need for computational resources and data availability. Five year Landsat-8 imagery (2013&amp;ndash;18) from GEE database has been used to study the surface water extent of large inland water bodies (surface area greater than 6000<span class="thinspace"></span>ha) of India. We have used a pixel based classification system to delineate water and non-water pixels. A knowledge based Decision Tree (DT) model has been employed to cluster the classes according to Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (MNDWI) distribution. We report an anomalous departure from the 5-year trend line suggesting that the maximum decrease of water extent was found in year 2015&amp;ndash;2016. Analysis of the decay pattern of reservoirs can provide timely inputs for better policy making and management of water resources. To understand the decay pattern, a Modified Gaussian model fit on time series of surface extent helps to determine maximum water extent, peak extent day and storage cycle of the water body.</p>

No abstract