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Climate change has a huge impact on the change pattern of rainfall, which ultimately lead us to either flood or drought, in any part of the world. A long term rainfall data of a particular area and its variability analysis provides vital information about past and future climatic conditions. This research was to analysis the rainfall variability and intensity in a long term monthly basis from (1980 to 2021) using Precipitation Concentration Index (PCI). The rain fall data was collected from Power Data Access Viewer (https://power.larc.nasa.gov/data-access-viewer/). The PCI was calculated as PCIAnnual, PCIWinter, PCIPre-monsoon, PCIMonsoon, PCIPost-monsoon as seasonal and decadal basis. The PCIAnnual shows the lowest rainfall average in the year of 1987 and highest rainfall in 2003 in the last 41 years in our study area. The study recommends that the Talcher Development Authority (TDA) must follow or adopt a constructive well planned water management plan. [ABSTRACT FROM AUTHOR]

In the world floods and droughts occur in everywhere. Main cause of these natural disasters is sudden change in climatic condition and precipitation patterns. Heavy rainfall in the area results flood and less or no rainfall result drought. So for managing the water resource rainfall variability analysis plays a vital role which used Precipitation Concentration Index (PCI). In this research 41 year long-term monthly precipitation data collected for the Kanchipuram district of Tamilnadu. PCI classified into 5 classes as annual, pre-monsoon, monsoon, post-monsoon and winter rainfall. According to this study highest PCIannual is 32.41 which is recorded in the year 2015 and lowest PCIannual are 13.92 which are recorded in year 2013. The average 41 year PCIannual of the study area recorded as 19.16. According to the range and categories of PCIannual 39.02% of years are in SPID (Strong Irregularity of Precipitation Distribution) while 14.63% years are in MPD (Moderate Precipitation Distribution). Most years 46.34% are in IPD (Irregular Precipitation Distribution). Results of this research indicate that PCI is very useful to manage and developed the natural resources of the study area. [ABSTRACT FROM AUTHOR]

Flash floods are a recurring phenomenon in the Kanchipuram watershed due to its high annual rainfall. These floods have significant impacts on various aspects such as agriculture, land use/land cover (LU/LC), and even human lives. To address this issue, our research focuses on the construction of a flash flood hazard map utilizing remote sensing techniques. Morphometry parameters and Geographic Information System (GIS) techniques are employed to identify the areas susceptible to flash floods. The first step involved generating a slope map using the ASTER-DEM image, which provided valuable information about the terrain's steepness. Additionally, we analyzed hydrological factors such as flow direction, flow accumulation, and basin borders, which helped in understanding the flow patterns and drainage characteristics of the watershed. Our study monitored a comprehensive set of 53 morphometric parameters for the entire watershed. To unravel the relationships among these parameters and their relevance to flash flood hazards, we employed Principal Component Analysis (PCA). This statistical technique allowed us to identify the key variables driving flash flood risks and determine their interdependencies. Furthermore, our research focused on analyzing flash flood hazards across six sub-watersheds within the study area. To communicate the varying degrees of risk, we classified the flash flood hazard into five distinct classes: very low, low, moderate, high, and very high. Each class represents a different level of vulnerability, with "very low" indicating areas with the lowest risk and "very high" representing regions with the highest risk of flash floods. By integrating the obtained results into a comprehensive flash flood hazard map, we provide valuable insights for effective land and water management in the Kanchipuram watershed. This map enables decision-makers, urban planners, and local communities to identify high-risk areas, implement preventive measures, and plan appropriate land use strategies to mitigate the potential impacts of flash floods. our research combines remote sensing techniques, morphometric parameters, and GIS analysis to construct a flash flood hazard map for the Kanchipuram watershed. By monitoring and analyzing a wide range of variables, we provide a comprehensive understanding of the flash flood risks in the area. This information can serve as a valuable resource for informed decision-making and proactive measures to minimize the impacts of flash floods, ultimately safeguarding lives, agriculture, and the overall environment. [ABSTRACT FROM AUTHOR]

The aim was to assess the rainfall and temperature extreme in last 37 years (1982–2018) in Western Agro-climatic zone of Punjab using Climate Change Detection and Indices (CCDI). The existence of statistical significance was studied using Mann-Kendall test and linear regression analysis. Statistical analysis revealed that the annual mean maximum temperature (Tmax) is decreasing significantly by 0.02 °C yearly, while, annual mean minimum (Tmin) indicated an increment tendency and found statistically non-significant. The variability in respect of temperature would have an effect on agricultural production due to decrease in rate of photosynthesis and respiration process during night-time beyond of normal limit. Moreover, the magnitude and frequency of coolness of day-time when the maximum temperature < 15 °C in addition to night-time having minimum temperature < 5 °C were found rising at Bathinda, Punjab. Additionally, rainfall based analysis of indices, annual total wet day precipitation (PRCPTOT), consecutive wet days (CWD), number of rainy days, when Rain ≥ 20 mm (R20) indicated decreasing order (but non-significant). However, rising tendency in simple daily intensity index (SDII), declining trend of rainy days (R2.5) and consecutive dry days (CDD) have denoted good allocation of rainfall. Due to insignificant decreasing pattern in RX1day, RX5day, R95p along with R99p indices, probability of non-heavy rainfall situations was possessed in the study region. [ABSTRACT FROM AUTHOR]