Yield, water, and carbon footprint of rainfed rice production under the lens of mid-century climate change: a case study in the eastern coastal agro-climatic zone, Odisha, India.

Water and carbon footprint assessment can be a good indicator of sustainable agricultural production. The present research quantifies the potential impact of near-future (2026-2050) climate change on water footprint (WF) and carbon footprint (CF) of farm-level kharif rice production of three locally grown varieties (Khandagiri, Lalat, and Swarna) in Odisha, India, under the two RCP scenarios of 4.5 and 8.5. The crop yield, water resources utilization, and greenhouse gas (GHG) emissions were estimated using the calibrated and validated DSSAT crop simulation model. The precipitation and temperature estimates from three regional climate models (RCM), namely HadGEM3-RA, RegCM4, and YSU-RSM were downscaled using the quantile mapping method. The results revealed a considerably high increase in the total WF of the Khandagiri, Lalat, and Swarna rice varieties elevating up to 101.9%, 80.7%, and 71.8% respectively during the mid-century for RCP 4.5 scenario, and 67.3%, 66.6%, and 67.2% respectively for RCP 8.5 scenario relative to the baseline WF. Moreover, compared to the green WF, the blue WF was projected to increase significantly (~ 250-450%) in the future time scales. This could be attributed to increasing minimum temperature (~ 1.7 °C) and maximum temperature (~ 1.5 °C) and reduced precipitation during the rice-growing periods. Rice yield was projected to continually decline in the future period (2050) with respect to the baseline (1980-2015) by 18.8% and 20% under RCP 4.5 and 8.5 scenarios respectively. The maximum CF of Swarna, Lalat, and Khandagiri rice were estimated to be 3.2, 2.8, and 1.3 t CO2eq/t respectively under RCP 4.5 and 2.7, 2.4, and 1.3 t CO2eq/t respectively under RCP 8.5 scenario. Fertilizer application (40%) followed by irrigation-energy use (30%) and farmyard manure incorporation (26%) were the three major contributors to the CF of rice production. Subsequently, management of N-fertilizer dose was identified as the major mitigation hotspot, simultaneously reducing carbon footprint and grey water footprint in the crop production process.

Development and performance evaluation of SCS-CN based hybrid model.

In this study, a hybrid approach has been used to increase the predictive efficiency of the SCS-CN model. A recently proposed Ajmal model (developed after randomized configuration) that ignored initial abstraction and maximum potential retention has been given the conceptual framework of the SCS-CN model and a new outcome-based hybrid model (Miv) was formulated. A total of 78 watersheds (7817 events) were used for calibration and the remaining 36 watersheds (3967 events) for validation to develop this hybrid model. The numerical value of hybrid model parameters Lc, λ and S were calibrated using calibration dataset and a simple non-linear one-parameter model has been developed. The performance of the Ajmal (Miii) and hybrid model (Miv) was compared with the original SCS-CN method (λ = 0.2 as Mi and λ = 0.05 as Mii). The performance of models was compared by using four statistical error indices i.e. RMSE, NSE, PBIAS, and n(t) and applying ranking and grading system (RGS). The mean RMSE, NSE, PBIAS, and n(t) values were found superior for Miv (5.60 mm, 0.71, 6.97%, 1.15) model followed by Miii (5.98 mm, 0.65, 16.52%, 1.01), Mii (6.27 mm, 0.61, 20%, 0.90) and Mi (6.98 mm, 0.46, 24.2%, 0.72) model for tested watersheds. The hybrid model (Miv) exhibited consistently well performance for all size watersheds. On the basis of the agreement between watershed runoff coefficient (C) and calibrated model parameter (Lc or CN), R2 value was found relatively higher for hybrid model (Miv) than other models.

2-D simulation of atmospheric boundary layer in and around Delhi to determine air pollution scenarios during winter morning.

In this paper, a 2-D turbulent closure model, based on the pollutant mass conservation equation, is adopted to estimate the local and background pollutants in the predominant wind direction for the stable atmosphere during winter mornings. The background concentration of pollutants can severely affect the regional pollution level, and its monitoring is a challenging task. Here, the turbulent closure model is employed across three cities in India, viz., Patiala, Delhi, and Agra, to estimate SO2 and NOx concentration along the predominant wind direction to demonstrate the potential of numerical models. The direction of the prevailing wind in this area during January 2003 was NNW (330°). Patiala is followed by Delhi and then Agra in the predominant wind direction. The sensitivity analysis of surface temperature on pollutant concentration reveals that concentration would increase by its square as temperature dips. So, during low or no horizontal wind, pollution episodes will be inevitable. Thus, the pollution hotspots are also identified in these three cities. Delhi had a high pollution load. So, the impact of local pollution in Delhi, through dispersion, was found significant in Agra. NOx hot spots (exceed the 30 µg/m3 limit) are found all across Delhi, except IGI Airport and two other locations. However, no SO2 hotspot (exceed the 60 µg/m3 limit) is found in Delhi. The proposed model output is verified with the WRF-CFD model results. Compared to the WRF-CFD model, the proposed model has overestimated NOx and SO2 concentration maximum by 14.4% and 23.5%, respectively. The overestimation occurred primarily due to ignoring atmospheric chemical reactions (e.g., acid condensation, etc.) for which the atmospheric factors were not so conducive.

Evaluating hotspots for stormwater harvesting through participatory sensing.

Geographic Information Systems have been widely accepted to manage and manipulate spatial data associated with the hydrologic response of a watershed. Due to climate change and drought impacts, there is a need to conserve freshwater resources, which can be accomplished by introducing the concept of stormwater harvesting. Apart from hotspot identification and site screening, several economic, social, cultural, environmental aspects need to be considered before finalizing the suitable site for stormwater harvesting. The shortlisted sites are commonly ranked by considering various parameters, i.e. water demand, availability of stormwater and distance to end-use locations, which relate to economic aspects. In the present study, socio-environmental considerations are also constituted by adopting a web-GIS based approach. The geospatial datasets and metadata associated with the study area are organized as a repository in the open source database server (PostgreSQL/PostGIS), which is further assessed and analyzed by using GeoServer. This technique publishes the geospatial datasets to the public domain websites that can be accessed and visualized around the clock and across the world. This will help stakeholders gather and store responses from water planners and inhabitants, while minimizing the time and cost associated with field visits for collecting individual responses. In this respect, a questionnaire is prepared that includes queries associated with site selection and the responses are gathered from various institutions, water professionals, stakeholders and residents. Once the responses are collected, the Analytic Hierarchy Process has been implemented to compute the relative weights of each criterion with respect to the responses collected. The weights thus obtained assisted the planners in deciding the suitable stormwater harvesting site for Dehradun city in India. In context to responses gathered the sites 'B' and 'D' are given the maximum weightage to be the suitable sites in the study region. Also, the socio-environmental criteria such as 'community acceptance', 'recreational activities' and 'need for water reuse' have gathered the maximum weightage from the responses for the specific sites. Hence, the proposed methodology demonstrated how water professionals, civilians, planners, stakeholders and public can be included as participants in water-related decision making processes.

Sediment in-situ bioremediation by immobilized microbial activated beads: Pilot-scale study.

A field study was carried out to investigate the sediment in-situ bioremediation by adding microbial activated beads. In this work, Calcium carbonate, silicon dioxide, activated carbon powder, attapulgite powder, sodium alginate, microbial liquid and polyvinyl alcohol were utilized to make the immobilized microbial activated beads. Field experiment results showed that the removal rate of NH4+-N, TN and COD in overlying water reached about 61.8%, 87.5% and 87.1%, respectively. The initial concentration of NH4+-N, TN and COD was 159 mg/L, 6.24 mg/L and 7.28 mg/L, whereas and the final concentration was 58 mg/L, 0.78 mg/L and 0.94 mg/L when water temperature, DO, pH and C/N ratio were 25-30 °C, 2-3 mg/L, 7.0-8.0 and 10-15, respectively. Moreover, under optimal temperature condition (25-30 °C), the removal rate of TOC, TN, heterotrophic bacteria and sulfur bacteria in the river sediment reached to 46.5%, 50.7%, 39.2% and 73.2%, respectively.

Evaluation of re-aeration equations for river Ghataprabha, Karnataka, India and development of refined equation.

Stream re-aeration is an extremely important component to enhance the self-purification capacity of streams. To estimate the dissolved oxygen (DO) present in the river, estimation of re-aeration coefficient is mandatory. Normally, the re-aeration coefficient is expressed as a function of several stream variables, such as mean stream velocity, shear stress velocity, bed slope, flow depth and Froude number. Many empirical equations have been developed in the last years. In this work, 13 most popular empirical re-aeration equations, used for re-aeration prediction, have been tested for their applicability in Ghataprabha River system, Karnataka, India, at various locations. Extensive field data were collected during the period March 2008 to February 2009 from seven different sites located in the river to observe re-aeration coefficient using mass balance approach. The performance of re-aeration equations have been evaluated using various error estimations, namely, the standard error (SE), mean multiplicative error (MME), normalized mean error (NME) and correlation statistics. The results show that the predictive equation developed by Jha et al. (Refinement of predictive re-aeration equations for a typical Indian river. Hydrological Process. 2001;15(6):1047-1060), for a typical Indian river, yielded the best agreement with the values of SE, MME, NME and correlation coefficient r. Furthermore, a refined predictive equation has been developed for river Ghataprabha using least-squares algorithm that minimizes the error estimates.

Application of artificial neural network, fuzzy logic and decision tree algorithms for modelling of streamflow at Kasol in India.

The prediction of streamflow is required in many activities associated with the planning and operation of the components of a water resources system. Soft computing techniques have proven to be an efficient alternative to traditional methods for modelling qualitative and quantitative water resource variables such as streamflow, etc. The focus of this paper is to present the development of models using multiple linear regression (MLR), artificial neural network (ANN), fuzzy logic and decision tree algorithms such as M5 and REPTree for predicting the streamflow at Kasol located at the upstream of Bhakra reservoir in Sutlej basin in northern India. The input vector to the various models using different algorithms was derived considering statistical properties such as auto-correlation function, partial auto-correlation and cross-correlation function of the time series. It was found that REPtree model performed well compared to other soft computing techniques such as MLR, ANN, fuzzy logic, and M5P investigated in this study and the results of the REPTree model indicate that the entire range of streamflow values were simulated fairly well. The performance of the naïve persistence model was compared with other models and the requirement of the development of the naïve persistence model was also analysed by persistence index.

Applying Fuzzy logic and the point count system to select landfill sites.

The treatment of solid waste is currently one of the major environmental problems facing municipalities. Thousands of tonnes of waste are generated each day, requiring a large area for disposal purposes. It is difficult to find suitable areas for the construction of such sanitary landfills as numerous criteria must be met, and landfill sites vary considerably in terms of their sophistication. The selection criteria for landfill sites should be as simple as possible, and with this in mind, we have evaluated a large number of random cases for the suitability of the site for landfill purposes using the recently advocated fuzzy approach. Using the fuzzy classification, we have attempted to develop a simple classification which uses only certain point values for available attributes. A normalized average of such attributes based on the proposed classifier is further evaluated using additionally generated random data sets. The results appear to be encouraging and indicate that the present classifier can be used as a substitute for the fuzzy-based ranking of landfill sites.