Abstract. Accident costs are an important component of external costs of traffic, a substantial part is related to fatal accidents. The evaluation of fatal accident costs crucially depends on the availability of an estimate for the economic value of a statistical life. This paper aims to develop a model for road accident through systems dynamics approach. To build an accident model, various factors causing the road accident and cost were identified. This model is capable of calculating the accident rate and its costs for the future. In this study the accident caused by bus alone is considered. The cost model is dealt more in this study as it requires more complex assessment. The accident model is built on the year 2000 data and predicted the accidents up to 2020 for every 5-year interval. The accident model is valuated by comparing the predicted and actual accident data for the year 2005. Three scenarios were studied by changing the income growth rate and discount rate. Finally, best scenario is suggested for implementation. The outcome of the study is highly useful for the planners, administrators and police to make their decisions effectively for road safety investment projects.
Flooding is one of the most disastrous global hazards, which has been occurring more frequently in recent times. It is observed that climate change is likely to increase the intensity and the frequency of floods and river basins have become more vulnerable to fluvial flooding. In this study, the impact of climate change on fluvial flooding was analyzed over the Adyar sub-basin. This study applied statistically downscaled Global Climate Model (GCM) data in a CMIP5 dataset of IPCC Assessment Report 5 (AR5). Based on the performance to simulate the observed climate, four GCMs, namely, cesm1-cam5, mpi-esm-mr, ncar-ccsm4, and bnu-esm, for RCP 4.5 were selected for projections of the future scenario. The Intensity-Duration-Frequency (IDF) curves for the past and future scenarios were derived from the IMD-observed and GCM-projected rainfall data. Integrated flood modeling was performed with hydrologic (HEC-HMS) and hydraulic (HEC-RAS) models. Finally, in order to visualize the inundation areas according to the future climate projection, flood inundation maps were prepared geospatially using the ArcGIS software. For the 100-year return period, the results predict that the peak discharge for the future climate scenario would increase by 34.3%–91.9% as compared to the present climate scenario. Similarly, the future projections show an increase in the flooded area ranging from 12.6% to 26.4% based on GCMs. This simulation helps in understanding the flood risk over the Adyar sub-basin under the changing climate and the requirement for the regulation of developmental activities over the flood-prone areas.
This study examines sedimentation rate and its consequences on the bathymetry, capacity and internal phosphorus loading of Krishnagiri Reservoir in Tamil Nadu, South India, utilizing an acoustic Doppler profiler and remote sensing data in an ArcGIS environment. There was a significant change in the reservoir bathymetry for the year 2012, compared with the 2007. The sedimentation rate was 0.818 MCM from 1960 to 1990 and 0.83 MCM over past 5 years. The present reservoir volume is 35.57 MCM, having been reduced to nearly half of its original capacity over a 55 year span, pointing to a seriously threatened lifespan. The sediment total phosphorus (TP) load spatially varied from 6.84 to 23 394 kg, depending on the sediment deposition zones. Sequential extraction indicated the dominance of phosphate fractions to be Al‐P> Fe‐P> Ca‐P> SRP, with an average TP value of 27.27 mg g−1 dry weight. Aluminium‐ (35%) and iron(25%)‐bound forms are the major sediment phosphorus fractions, suggesting temperature, pH and redox or related chemical reactions may be important means of sediment P release in Krishnagiri Reservoir. The sediment phosphorus load in Krishnagiri Reservoir is estimated to be 44.50 tons, with an average TP release of 40.97 mg m−2 (range of 10.22–70 mg m−2). The measured pore water TP concentration and calculated sediment phosphate release exhibited a linear relation. Even with a reduced external P load, the eutrophication of Krishnagiri Reservoir cannot be reduced immediately because of its high internal load and nutrient remobilization.
Krishnagiri Reservoir exhibits a hypereutrophic status and continuously receives external sediment and nutrient loads, in addition to its internal phosphorus loading, both affecting the reservoir water quality. Increased nutrient loading attributable to changing anthropogenic activities in the catchment area will further exacerbate the deteriorating trophic status. Temporal Satellite imageries can play a crucial role in the rapid assessment of the trophic status of the reservoir over a large spatial extent. The eutrophication status of freshwater systems is directly related to the chlorophyll‐a (Chl‐a) concentration, which represents a major trophic state indicator by reflecting green and absorbing violet‐blue and orange‐red light of the solar energy spectrum. The present study was undertaken to map seasonal Chl‐a concentration variations using Landsat 8 Operational Land Imager (OLI) images. Multiple regression equations developed using reflectance in the Green, Near Infrared, Shortwave Infrared 1 and 2 and Coastal bands (R2 = .635) were found to be the best fit of the model in mapping the Chl‐a concentration variations in Krishnagiri Reservoir. The derived regression model also can be used to determine the trophic state of the reservoir and can facilitate a more rapid assessment for developing management strategies for sustainable reservoir water quality management.
The Krishnagiri reservoir is the main source of irrigation in Tamil Nadu, India. It has been reported to be hypereutrophic for over a decade with sediment and nutrient load sources responsible for the degradation of water quality. Remotely sensed satellite imagery analysis plays a significant role in assessing the water quality for developing a management strategy for reservoirs. The present study is an attempt to demonstrate the improvement in the chlorophyll-a (chl-a) estimation in the Krishnagiri reservoir by integrating remote sensing and in-situ measurements. Multiple regression equations were developed with the reflectance of Green, Red, NIR and SWIR1 bands of the Operational Land Imager (OLI) sensor of Landsat 8 satellite yielded the coefficient of determination for chlorophyll-a (chl-a) as 0.812, Total Dissolved Solids (TDS) as 0.945 and Electrical Conductivity (EC) as 0.960 respectively. The developed regression model was further utilised to forecast chl-a and EC of the reservoir through the Seasonal Auto Regressive Integrated Moving Average (SARIMA) model. It is found that chl-a prediction showed that the reservoir continued to be hypereutrophic and EC significantly changed from a class C3 (high salinity) to class C4 (very high salinity). These results are alarming and an immediate reduction of the external load from the catchment through effective watershed management programs should be implemented.
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