The Himachal Pradesh district’s biggest natural disaster is the forest fire. Forest fire threat evaluation, model construction, and forest management using geographic information system techniques will be important in this proposed report. A simulation was conducted to evaluate the driving forces of fires and their movement, and a hybrid strategy for wildfire control and geostatistics was developed to evaluate the impact on forests. The various methods we included herein are those based on information, such as knowledge-based AHP-crisp for figuring out forest-fire risk, using such variables as forest type, topography, land-use and land cover, geology, geomorphology, settlement, drainage, and road. The models for forest-fire ignition, progression, and action are built on various spatial scales, which are three-dimensional layers. To create a forest fire risk model using three different methods, a study was made to find out how much could be lost in a certain amount of time using three samples. Precedent fire mapping validation was used to produce the risk maps, and ground truths were used to verify them. The accuracy was highest in the form of using “knowledge base” methods, and the predictive value was lowest in the use of an analytic hierarchy process or AHP (crisp). Half of the area, about 53.92%, was in the low-risk to no-risk zones. Very-high- to high-risk zones cover about 24.66% of the area of the Sirmaur district. The middle to northwest regions are in very-high- to high-risk zones for forest fires. These effects have been studied for forest fire suppression and management. Management, planning, and abatement steps for the future were offered as suitable solutions.
Natural capital is the wealth of nations that determine their economic status. Worldwide, vulnerable people depend on natural capital for employment, salaries, wealth, and livelihoods and, in turn, this determines the developmental index of the nation to which they belong. In this short review, we have tried to sum up the ideas and discussions over natural capital’s role in ascribing economic status to countries as well as the need for natural resource management and sustainability. This paper aimed to discuss how humanity’s prosperity is intertwined with the services that ecosystems provide, and how poor natural resource management (NRM) has adversely affected human well-being. Our preselected criteria for the review paper led us to evaluate 96 peer-reviewed publications from the SCOPUS database, which is likely the most comprehensive archive of peer-reviewed scientific literature as well as WoS, PUBMED, and Google Scholar databases. Our review revealed that the availability of ecological services is crucial for clean water and air, food and fodder, and agricultural development. We further discussed important concepts regarding sustainability, natural capital and economics, and determinants of human well-being vis-à-vis the intergenerational security of natural wealth. To ensure current and future human well-being, we conclude that an in-depth understanding of the services that ecosystems provide is necessary for the holistic management of the Earth system.
Urban floods are very destructive and have significant socioeconomic repercussions in regions with a common flooding prevalence. Various researchers have laid down numerous approaches for analyzing the evolution of floods and their consequences. One primary goal of such approaches is to identify the areas vulnerable to floods for risk reduction and management purposes. The present paper proposes an integrated remote sensing, geographic information system (GIS), and field survey-based approach for identifying and predicting urban flood-prone areas. The work is unique in theory since the methodology proposed finds application in urban areas wherein the cause of flooding, in addition to heavy rainfall, is also the inefficient urban drainage system. The work has been carried out in Delhi’s Yamuna River National Capital Territory (NCT) area, considered one of India’s most frequently flooded urban centers, to analyze the causes of its flooding and supplement the existing forecasting models. Research is based on an integrated strategy to evaluate and map the highest flood boundary and identify the area affected along the Yamuna River NCT of Delhi. In addition to understanding the causal factors behind frequent flooding in the area, using field-based information, we developed a GIS model to help authorities to manage the floods using catchment precipitation and gauge level relationship. The identification of areas susceptible to floods shall act as an early warning tool to safeguard life and property and help authorities plan in advance for the eventuality of such an event in the study area.
Understanding the likely impacts of climate change (CC) and Land Use Land Cover (LULC) on water resources (WR) is critical for a water basin’s mitigation. The present study intends to quantify the impact of (CC) and (LULC) on the streamflow (SF) of the Parvara Mula Basin (PMB) using SWAT. The SWAT model was calibrated and validated using the SWAT Calibration Uncertainty Program (SWAT-CUP) for the two time periods (2003–2007 and 2013–2016) and (2008–2010 and 2017–2018), respectively. To evaluate the model’s performance, statistical matrices such as R2, NSE, PBIAS, and RSR were computed for both the calibrated and validated periods. For both these periods, the calibrated and validated results of the model were found to be very good. In this study, three bias-corrected CMIP6 GCMs (ACCESS-CM2, BCC-CSM2-MR, and CanESM5) under three scenarios (ssp245, ssp370, and ssp585) have been adopted by assuming no change in the existing LULC (2018). The results obtained from the SWAT simulation at the end of the century show that there will be an increase in streamflow (SF) by 44.75% to 53.72%, 45.80% to 77.31%, and 48.51% to 83.12% according to ACCESS-CM2, BCC-CSM2-MR, and CanESM5, respectively. A mean ensemble model was created to determine the net change in streamflow under different scenarios for different future time projections. The results obtained from the mean ensembled model also reveal an increase in the SF for the near future (2020–2040), mid future (2041–2070), and far future (2071–2100) to be 64.19%, 47.33%, and 70.59%, respectively. Finally, based on the obtained results, it was concluded that the CanESM5 model produces better results than the ACCESS-CM2 and BCC-CSM2-MR models. As a result, the streamflow evaluated with this model can be used for the PMB’s future water management strategies. Thus, this study’s findings may be helpful in developing water management strategies and preventing the pessimistic effect of CC in the PMB.
Surfactant liquid-membrane type sensors are usually made of a PVC, ionophore and a plasticizer. Plasticizers soften the PVC. Due to their lipophilicity, they influence the ion exchange across the membrane, ionophore solubility, membrane resistance and, consequently, the analytical signal. We used the DMI-TPB as an ionophore, six different plasticizers [2-nitrophenyl-octyl-ether (P1), bis(2-ethylhexyl) phthalate (P2), bis(2-ethylhexyl) sebacate (P3), 2-nitrophenyl phenyl ether (P4), dibutyl phthalate (P5) and dibutyl sebacate (P6)] and a PVC to produce ionic surfactant sensors. Sensor formulation with P1 showed the best potentiometric response to four usually used cationic surfactant, with the lowest LOD, 7 × 10−7 M; and potentiometric titration curves with well-defined and sharp inflexion points. The sensor with P6 showed the lowest analytical performances. Surfactant sensor with P1 was selected for quantification of cationic surfactant in model solutions and commercial samples of disinfectants and antiseptics. It showed high accuracy and precision in all determinations, with recovery from 98.2 to 99.6, and good agreement with the results obtained with surfactant sensor used as a referent one, and a standard two-phase titration method. RDS values were lower than 0.5% for all determinations.
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