Kerala is the gateway of the Indian southwest monsoon. The Tropical Rainfall Measurement Mission (TRMM) rainfall data is an efficient approach to rainfall measurement. This study explores the temporal variability in rainfall and trends over Kerala from 1998-2019 using TRMM data and observatory data procured from India Meteorological Department (IMD). Direct comparison with observatory data at various time scales proved the reliability of the TRMM data (monthly, seasonal and annual). The temporal rainfall converted by averaging the data on an annual, monthly and seasonal time scale, and the results have confirmed that the rainfall estimated based on satellite data is dependable. The station wise comparison of rainfall in monsoon season provides satisfactory results. However, estimation of rainfall in mountainous areas is challenging task using the TRMM. In the basins of humid tropical regions, TRMM data can be a valuable source of rainfall data for water resource management and monitoring with some vigilance. In Kerala, the study found an insignificant increase in the southwest monsoon and winter season rainfall during last two decades. The rainfall over Kerala showed uncertainty in the distribution of monthly, seasonal and yearly time scales. This study provides a preview of recent weather patterns that would enable us to make better decisions and improve public policy against climate change.
Maternal Health Care (MHC) is very essential for improvement in the health status of the mother and children. The present study attempts to show the role of mass media on the utilization of MHC services in India. The entire study depends on secondary data collected from the National Family Health Survey (NFHS-4, 2015-16). Initially, the data has been analyzed by some descriptive statistics and for the proper depiction of the result, binary logistic regression has been conducted. The unadjusted odds ratio (UOR) has shown media exposure positively and significantly associated with the utilization of the majority of MHC services. The adjusted odds ratio has a less effective association with the MHC services compare to the unadjusted odds ratio. Other controlling variables including maternal age, age at marriage, birth order, education, caste, religion, wealth index, place of residence, and the region has also affect the health care services.
This study was designed to demarcate the Ecotourism Potential Zones (ETPZs) of West Bengal using the Analytic Hierarchy Process (AHP) and weighted linear algorithm by considering three sustainable tourism parameters and sixteen indicators. Those three parameters are 1) physical (P), 2) social (S), and 3) availability of scenic beauty and infrastructures (ASI). Overall, 5 parameters are merged under physical (P), 2 parameters are integrated under social (S), and 9 parameters are incorporated under availability of scenic beauty and infrastructures (ASI). A 4-step procedure has been adopted for this study: 1) a simple hierarchical structure has been outlined, 2) pair-wise comparison matrices are formed, 3) weighted linear algorithm technique is utilized to get the ecotourism potentiality zone, and 4) ecotourism potentiality map is classified into high, moderate and low categories based on the principle of Dominant and Distinctive Function (DDF). As a result, about 61.65% area is identified with high ecotourism potential zone, 17.86% area is observed under the moderate ecotourism potential zone, and 20.48% area is recognized as the low ecotourism potential zone. Thus, the study considers an exceptional methodological framework that is applicable in any region of the world.
The present study is focused on analysis of rainfall in the Oued El-Abid watershed, which is characterized by an important potential in water supply of the Bin El Ouidane dam and the recharging groundwater of the plains downstream. The aim of the present research is to characterize the meteorological drought in the Oued El-Abid watershed, located in the Beni Mellal-Khenifra region (Central High Atlas, Morocco). The study focused on the analysis of the meteorological drought detection indices such as the deviation from the mean (DM), the rainfall index (RI) and the standardized precipitation index (SPI) based on annual precipitation for the three stations (Tilouguit, Ait Ouchen and Tizi N'Isli) generally experienced alternating periods of surplus and deficit. The results of these indices allowed us to determine the most remarkable and common drought years are: 1981, 1983, 1990, 1998, 2001, 2005, 2017 and 2019. This study is helpful for water resource managers to make decisions and develop tools for adaptation and mitigation of climate change impacts.
Freshwater scarcity is a major issue in Rayalaseema region in Andhra Pradesh (India). Groundwater is the primary source of drinking and irrigation water in Anantapur district, Andhra Pradesh, India. Therefore, it is important to identify areas having groundwater potential; however, the current methods of groundwater exploration consume a lot of time and money. Analytic Hierarchy Process (AHP)-based spatial model is used to identify groundwater potential zones in Anantapur using remote sensing and GIS-based decision support system. Thematic layers considered in this study were geology, geomorphology, soils, land use land cover (LULC), lineament density (LD), drainage density (DD), slope, and rainfall. According to Saaty’s AHP, all these themes and individual features were weighted according to their relative importance in groundwater occurrence. Thematic layers were finally combined using ArcGIS to prepare a groundwater potential zone map. The high weighted value area was considered a groundwater prospecting region. Accordingly, the GWPZ map was classified into four categories: very good, good, moderate, and poor. The very good GWPZ area is 77.37 km2 (24.93%) of the total study area. The northeastern and southeastern sections of the study area, as well as some medium patches in the center and western regions, are covered by moderate GWPZs, which cover an area of 53.07 km2 (17.10%). However, the GWP in the study area’s central, southwestern, and northern portions is poor, encompassing an area of approximately 79.31 km2 (25.56%). Finally, RS and GIS techniques are highly effective and useful for identifying GWPZs.