Abstract

Events during an emergency unfold in an unpredictable fashion which makes management of traffic duringemergencies pretty challenging. Furthermore, some vehicles would need to be evacuated faster than otherse.g., emergency vehicles or large vehicles carrying a lot more people. The Prioritized Routing Assistant forFlow of Traffic (PRAFT) enables prioritized routing during emergencies. However, the PRAFT solution doesnot compute multiple plans that can help handle better dynamic nature of emergencies. PRAFT maps theprioritized routing problem to the Minimum-Cost Maximum-Flow (MCMF) problem, hence its solution canaccommodate maximum flow while routing vehicles based on priority (maps higher priority vehicles to betterquality routes (i.e., ones with minimum cost)). We build upon the PRAFT solution to make the followingcontributions: (a) Develop a Pareto Minimum-Cost Maximum-Flow (Pareto-MCMF) algorithm which cancompute all the possible MCMF solutions. (b) Through a series of experiments performed using the wellknown traffic simulator SUMO, we could show that all the solutions generated by Pareto-MCMF indeed haveproperties similar to a MCMF solution thus providing multiple high quality options for traffic police to pickfrom depending on the situation.

Abstract

Residential energy (electricity) use in India is expected to grow four-fold by 2030, yet there is paucity of data that is essential for developing energy policy or programme. This paper undertakes a meta-study of residential energy studies in India to characterise their scope, data collection methods and findings. An in-depth review of literature was undertaken using Google Scholar, Web of Science, Scopus and ScienceDirect to identify relevant journal publications, conference papers and reports. A series of expert workshops were held to identify any data gaps and stakeholders’ needs. The identified studies were characterised using a meta-data framework that included criteria such as climate, dwelling form, income group, methods and study duration. An online searchable platform (REACT) was created to make the identified studies more accessible using the meta-data criteria. Of the 29 studies identified, only 16 were published in peer-reviewed publications (10 in journals and 6 in conferences proceedings). About 11 studies focussed on dwelling-level energy use, while the remaining used econometric data and statistical modelling at a macro-scale. To develop evidence based energy policies, it is vital to gather empirical data on residential energy in different climatic zones. Robust data sharing policies should be implemented to avoid duplication of effort.

Abstract

The urban agglomeration is the unplanned growth of a city into its surrounding peri/rural areas causing unsustainable exploitation of natural resources. This leads to an increase in the land surface temperature that in turn results in climatic issues ranging from local to global scales. In the current study, an attempt has been made to map the urban growth and its associated land surface temperature variations in and around Vijayawada city of Andhra Pradesh state, India. Temporal Landsat satellite images from 4 years, viz. 1990, 2000, 2010, and 2018, were used to generate land use/land cover maps with four major classes such as built-up, vegetation, water body, and others. Change detection and transition of the natural land cover to man-made land use were statistically computed for the study area. Sprawl analysis of the city was carried out by generating multiple buffer rings over the study region to evaluate …

Abstract

Personalized Comfort Systems (PCS) have been seen as an effective alternative to provide thermal comfort along with reducing energy consumption. This paper contains the methodology and results of a study conducted in a laboratory environment that aims to evaluate Thermally Activated Furniture (TAF) panels. These panels mounted in front and on the side of a workstation as space partition panels, can provide thermal comfort and have the potential to save energy. The experiments were carried out in a controlled thermal comfort chamber using a thermal mannequin having 22 body segments and TAF. A total of 15 cases were examined against a reference case with an aim to understand the impact of TAF on the change in PMV, Skin Temperature (Tskin) and Heat Flux (U) of 22 individual body segments and change in the energy consumption. The combinations of three Room Air Temperatures (Tair) i.e. 26
C, 28
C and 30
C were tested with a combination of four surface temperature (Tsur) ranging from the difference between Tair and Tsur 2 to 8
C. For the entire study, Relative Humidity (RH) was maintained at 50%. The results indicate a change in PMV by about 0.15 votes towards the cooler side when the difference between Tair and Tsur was D2
C for the three Tair cases. The PMV changed by about 0.5 votes in the case of the D8
C difference between Tair and Tsur. The increase in DT values was found to increase the cooling thermal energy consumption relative to the baseline case without having PCS in operation, whereas the increase in Tair caused a decrease in the cooling thermal energy consumption

Abstract

Rapid urbanization and associated land-use changes in cities cause an increase in the demand for electricity by altering the local climate. The present study aims to examine the variations in total energy and cooling energy demand in a calibrated building energy model, caused by urban heat island formation over Delhi. The study used Sentinel-2A multispectral imagery for land use and land cover (LULC) of mapping of Delhi, and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery for land surface temperature (LST) mapping during March 2018. It was observed that regions with dense built-up areas (i.e., with built-up area greater than 90%) had a higher annual land surface temperature (LST), i.e., 293.5 K and urban heat island intensity (UHII) ranging from 0.9 K–5.9 K. In contrast, lower annual values of LST (290K) and UHII (0.0–0.4 K) were observed in regions with high vegetation cover (53%). Statistical analysis reveals that a negative correlation exists between vegetation and nighttime LST, which is further confirmed by linear regression analysis. Energy simulations were performed on a calibrated building model placed at three different sites, identified on the basis of land use and land cover percentage and annual LST. Simulation results showed that the site located in the central part of Delhi displayed higher annual energy consumption (255.21 MWh/y) compared to the site located in the rural periphery (235.69 MWh/y). For all the three sites, the maximum electricity consumption was observed in the summer season, while the minimum was seen in the winter season. The study indicates that UHI formation leads to increased energy consumption in buildings, and thus UHI mitigation measures hold great potential for energy saving in a large city like Delhi.

Abstract

This study aimed to estimate water quality–based minimum environmental flow (Eflow) of a river under the impact of different plausible scenarios. The water quality model QUAL2K was deployed to simulate levels of dissolved oxygen (DO) and biochemical oxygen demand (BOD) in a river during the dry season. Hypothetical scenarios of pollution were generated by varying BOD in contributing drains, and climate change scenarios were generated by altering air temperature over the study region. DO and BOD levels were simulated under the impact of various scenarios. Corresponding to each scenario, minimum Eflow throughout the river stretch—that is, the headwaters flow (quantity and quality) meeting desirable river water quality standards (as proposed by the Indian Central Pollution Control Board)— was explored. Water quality charts were developed for direct and user-friendly estimation of Eflows. The proposed approach was applied to the Yamuna and Bhadra rivers in India. The results reveal the inefficiency of existing river flow conditions in maintaining permissible water quality standards. Adverse effects of pollution load, upstream diversions, and climate change are highlighted. DOI: 10.1061/(ASCE) HE.1943-5584.0001780. © 2019 American Society of Civil Engineer

Abstract

Parametric models of actual evapotranspiration (AET) based on precipitation (P) and potential evapotranspiration (PET) are region-specific and purely climate-induced and limited to represent the hydrological water balances. Basin-averaged model parameters considering P, AET, and runoff (R) using a machine learning algorithm, ensemble regression model, is proposed. Hydrologically calibrated model parameters allowed the study of AET under alterations of water use for current and for future scenarios under climate change. The effect of climate, water, and land use changes on AET was studied for the post-change period of 2004–2014 compared to pre-change period of 1965–2003 over Krishna river basin (KRB), India. The AET has increased under climate and water use changes while there is both increase and decreases of AET under land use changes for post-change period compared to pre-change period over the basin. Severe water shortages were estimated under pronounced increase of temperature (1.29
C) compared to precipitation increase (2.19%) based on Coordinated Regional Downscaling Experiment (CORDEX) projections for the period 2021–2060. Hydrologically induced AET changes were more pronounced than climate for current climate; whereas climate-induced AET changes were found to be more prominent for projected climate signals over the basin

Abstract

This study aimed to estimate water quality–based minimum environmental flow (Eflow) of a river under the impact of different plausible scenarios. The water quality model QUAL2K was deployed to simulate levels of dissolved oxygen (DO) and biochemical oxygen demand (BOD) in a river during the dry season. Hypothetical scenarios of pollution were generated by varying BOD in contributing drains, and climate change scenarios were generated by altering air temperature over the study region. DO and BOD levels were simulated under the …

Abstract

The Standardized Precipitation and Evapotranspiration Index (SPEI) became one of the popular drought indices due to the consideration of difference between precipitation (P) and potential evapotranspiration (PET), which represents the energy-based climatic water balance. Implementation of actual evapotranspiration (AET), which accounts for both water and energy-based climatic evaporative demand in drought characterization studies, is limited. This study proposes a meteorological drought index with the structure of the SPEI and actual evapotranspiration modeled with empirical formulations and remote sensing data integrated with surface energy models at annual scale. The proposed drought index imposes the effect of precipitation, PET, and AET using operational meteorological data sets of precipitation and temperatures. The present study aimed to test how a drought index based on PET and P can outperform with the inclusion of AET at a river basin scale at 12-month scale. The proposed hypothesis was tested considering Krishna River basin, India, as a case study for which most of the basin is in arid climate. The performance of drought indices was compared using historical droughts in terms of severity, areal extent, frequency, and duration based on empirical AET models along with satellite-based land surface ET data-based drought indices. The proposed AET-based drought indices have effectively captured the historical drought years over the Krishna River basin. The empirical AET formulation-based drought index was identified as a more reliable measure in the estimation of drought characteristics by comparing with satellite-based land surface AET-based drought index. The AET-based drought indices were able to drive the areas into moderate, which or otherwise categorized under severe drought regions with PET-based drought indices. Inclusion of AET in the drought characterization along with precipitation and PET can drive the highly intensified drought events determined by SPEI into moderate and less frequent droughts with short durations over a large river basin with arid climate.

Abstract

Regional water resource modelling is important for evaluating system performance by analyzing the reliability, resilience and vulnerability criteria of the system. In water resource systems modelling, several uncertainties abound, including data inadequacy and errors, modeling inaccuracy, lack of knowledge, imprecision, inexactness, randomness of natural phenomena, and operational variability, in addition to challenges such as growing population, increasing water demands, diminishing water sources and climate change. Recent advances in modelling techniques along with high computational capabilities have facilitated rapid progress in this area. In India, several studies have been carried out to understand and quantify uncertainties in various basins, enumerate large temporal and regional mismatches between water availability and demands, and project likely changes due to warming. A comprehensive review of uncertainties in water resource modelling from an Indian perspective is yet to be done. In this work, we aim to appraise the quantification of uncertainties in systems modelling in India and discuss various water resource management and operation models. Basic formulation of models for probabilistic, fuzzy and grey/inexact simulation, optimization, and multi-objective analyses to water resource design, planning and operations are presented. We further discuss challenges in modelling uncertainties, missing links in integrated systems approach, along with directions for future.