Ashok Mishra

Moisture stress and the water use efficiency of mustard

Abstract Studies conducted under a wide variety of limited irrigation conditions of mustard crop, showed the crop was most sensitive to the moisture stress from the vegetative to early-flowering stage. The first preference for irrigation should be given to this stage. The studies also showed that moisture stress in all growth stages reduced the grain yield significantly. The water use efficiency in vegetative to early pod-filling period reduced considerably, but increased significantly during pod-filling.

Detecting rainfall trends in twentieth century (1871–2006) over Orissa State, India

Climate change has affected the temperature and rainfall characteristics worldwide. However, the changes are not equal for all regions and have localized intensity and must be quantified locally to manage the natural resources. Orissa is an eastern state in India where agricultural activities mainly depends on the rainfall and thus face problems due to changing patterns of rainfall due to changing climate. In the present study, attempts were made to study temporal variation in monthly, seasonal and annual rainfall over the state during the period from 1871 to 2006. Long term changes in rainfall characteristics were determined by both parametric and non-parametric tests. The analysis revealed a long term insignificant decline trend of annual as well as monsoon rainfall, where as increasing trend in post-monsoon season over the state of Orissa. Rainfall during winter and summer seasons showed an increasing trend. Statistically monsoon rainfall can be considered as very dependable as the coefficient of variation is 14.2%. However, there is decreasing monthly rainfall trend in June, July and September, where as increasing trend in August. This trend is more predominant in last 10xa0year. Based on departure from mean, rainfall analysis also showed an increased number of dry years compared to wet years after 1950. This changing rainfall trend during monsoon months is major concern for the rain-fed agriculture. More over, this will affect hydro power generation and reservoir operation in the region.

Evaluation of the SWAT Model for Assessing Sediment Control Structures in a Small Watershed in India

Stormwater runoff is a major pathway for transporting sediment and other nonpoint-source pollutants from watersheds to stream systems and other surface water bodies. In this study, the Soil and Water Assessment Tool (SWAT) model was used to assess sediment transport from the 17 km2 Banha watershed located in northeast India, which is characterized by mixed land use and on-stream sediment control structures called checkdams. Calibration (1996) and validation (1997-2001) of surface runoff and sediment yield were performed with SWAT on both a daily and monthly basis by comparing model estimates versus measured data. The calibration R2 and Nash-Sutcliffe modeling efficiency (NSE) statistics were found to range between 0.70 to 0.99 for surface runoff and 0.82 to 0.98 for sediment loss. The corresponding validation period statistics ranged from 0.60 to 0.92 for surface runoff and 0.58 to 0.89 for sediment loss. Following calibration and validation, the SWAT model was executed with and without checkdams to test its capability in visualizing the impacts of sediment control structures in the watershed. The model estimates showed that sediment loss from the watershed could be reduced more than 64% by adopting checkdams as a barrier for sediment. The results also revealed the potential for using SWAT to assess sediment transport from specific subwatersheds within a watershed, and to prioritize the siting of sediment control structures within a watershed to obtain the most effective reduction of sediment losses to surface water. Overall, the study showed that SWAT can be a useful tool for studying how checkdams can be used to manage and control sediment loss from small watersheds located in sub-humid climate conditions.

Combining synchronous fluorescence spectroscopy with multivariate methods for the analysis of petrol–kerosene mixtures

Synchronous fluorescence spectroscopy (SFS) is a rapid, sensitive and nondestructive method suitable for the analysis of multifluorophoric mixtures. The present study demonstrates the use of SFS and multivariate methods for the analysis of petroleum products which is a complex mixture of multiple fluorophores. Two multivariate techniques principal component regression (PCR) and partial least square regression (PLSR) have been successfully applied for the classification of petrol-kerosene mixtures. Calibration models were constructed using 35 samples and their validation was carried out with varying composition of petrol and kerosene in the calibration range. The results showed that the method could be used for the estimation of kerosene in kerosene-mixed petrol. The model was found to be sensitive, detecting even 1% contamination of kerosene in petrol.

Spatial variability of climate change impacts on yield of rice and wheat in the Indian Ganga Basin

Indian Ganga Basin (IGB), one of the most densely populated areas in the world, is facing a significant threat to food grain production, besides increased yield gap between actual and potential production, due to climate change. We have analyzed the spatial variability of climate change impacts on rice and wheat yields at three different locations representing the upper, middle and lower IGB. The DSSAT model is used to simulate the effects of climate variability and climate change on rice and wheat yields by analyzing: (i) spatial crop yield response to current climate, and (ii) impact of a changing climate as projected by two regional climate models, REMO and HadRM3, based on SRES A1B emission scenarios for the period 2011-2040. Results for current climate demonstrate a significant gap between actual and potential yield for upper, middle and lower IGB stations. The analysis based on RCM projections shows that during 2011-2040, the largest reduction in rice and wheat yields will occur in the upper IGB (reduction of potential rice and wheat yield respectively by 43.2% and 20.9% by REMO, and 24.8% and 17.2% by HadRM3). In the lower IGB, however, contrasting results are obtained, with HadRM3 based projections showing an increase in the potential rice and wheat yields, whereas, REMO based projections show decreased potential yields. We discuss the influence of agro-climatic factors; variation in temperature, length of maturity period and leaf area index which are responsible for modeled spatial variability in crop yield response within the IGB.

Reservoir Inflow Forecasting Using Ensemble Models Based on Neural Networks, Wavelet Analysis and Bootstrap Method

Accurate and reliable forecasting of reservoir inflow is necessary for efficient and effective water resources planning and management. The aim of this study is to develop an ensemble modeling approach based on wavelet analysis, bootstrap resampling and neural networks (BWANN) for reservoir inflow forecasting. In this study, performance of BWANN model is also compared with wavelet based ANN (WANN), wavelet based MLR (WMLR), bootstrap and wavelet analysis based multiple linear regression models (BWMLR), standard ANN, and standard multiple linear regression (MLR) models for inflow forecasting. Robust ANN and WANN models are ensured considering state of the art methodologies in the field. For development of WANN models, initially original time series data is decomposed using wavelet transformation, and wavelet sub-time series are considered to develop WANN models instead of standard data used for development of ANN model. To ensure a robust WANN model different types of wavelet functions are utilized. Further, a comparative analysis is carried out among different approaches of WANN model development using wavelet sub time series. Seven years of reservoir inflow data along with outflow data from two upstream reservoirs in the Damodar catchment along with rainfall data of 5 upstream rain gauge stations are considered in this study. Out of 7xa0years daily data, 5xa0years data are used for training the model, 1xa0year data are used for cross-validation and remaining 1xa0year data are used to evaluate the performance of the developed models. Different performance indices indicated better performance of WANN model in comparison with WMLR, ANN and MLR models for inflow forecasting. This study demonstrated the effectiveness of proper selection of wavelet functions and appropriate methodology for wavelet based model development. Moreover, performance of BWANN models is found better than BWMLR model for uncertainty assessment, and is found that instead of point predictions, range of forecast will be more reliable, accurate and can be very helpful for operational inflow forecasting.

Impact of Human Intervention and Climate Change on Natural Flow Regime

According to the ‘natural flow paradigm’, any departure from the natural flow condition will alter the river ecosystem. River flow regimes have been modified by anthropogenic interventions and climate change is further expected to affect the biotic interactions and the distribution of stream biota by altering streamflow. This study aims to evaluate the hydrologic alteration caused by dam construction and climatic changes in a mesoscale river basin, which is prone to both droughts and monsoonal floods. To analyse the natural flow regime, 15xa0years of observed streamflow (1950–1965) prior to dam construction is used. Future flow regime is simulated by a calibrated hydrological model Soil and Water Assessment Tool (SWAT), using ensemble of four high resolution (~25xa0km) Regional Climate Model (RCM) simulations for the near future (2021–2050) based on the SRES A1B scenario. Finally, to quantify the hydrological alterations of different flow characteristics, the Indicators of Hydrological Alteration (IHA) program based on the Range of Variability Approach (RVA) is used. This approach enables the assessment of ecologically sensitive streamflow parameters for the pre- and post-impact periods in the regions where availability of long-term ecological data is a limiting factor. Results indicate that flow variability has been significantly reduced due to dam construction with high flows being absorbed and pre-monsoon low flows being enhanced by the reservoir. Climate change alone may reduce high peak flows while a combination of dam and climate change may significantly reduce variability by affecting both high and low flows, thereby further disrupting the functioning of riverine ecosystems. We find that, in the Kangsabati River basin, influence of dam is greater than that of the climate change, thereby emphasizing the significance of direct human intervention.

Modeling Hydrologic Processes and NPS Pollution in a Small Watershed in Subhumid Subtropics Using SWAT

The soil and water assessment tool (SWAT) has been calibrated and validated to predict stream flow, and to transport sediment and non-point source (NPS) pollutants to the downstream water resources from a small (1,695 ha) watershed in sub-humid subtropics that receives variable monsoon rains. Observed rainfall, temperature, stream flow, and sediment yield data for three years have been utilized to test the models prediction capability for daily stream flow and sediment yield during the monsoon months from June to October. Because of the variability of monsoon rains, the model has been calibrated for a normal rainfall year (M-SD M-SD and RF < 0:8M). The results reveal that a calibrated model for a normal rainfall year can be used successfully for predicting hydrologic processes and NPS pollution for a relatively dry rainfall year. However, for the medium rainfall year the model prediction shows more deviations from the measured values. The Nash-Sutcliffe efficiencies in dry and medium rainfall years are 0.70 and 0.62 for daily stream flow and 0.73 and 0.69 for daily sediment yield. NPS pollutants simulation results indicate that a calibrated SWAT model is used in estimating hydrologic responses related to water quality prob- lems of watersheds situated in monsoon regions in which the nature of rainfall shows varying characteristics every year. The results of the study have implications for watershed management to reduce the sediment and NPS pollutants load into downstream water bodies. DOI: 10.1061/(ASCE)HE.1943-5584.0000458.

Evapotranspiration Modeling Using Second-Order Neural Networks

AbstractThis study introduces the utility of the second-order neural network (SONN) method to model the reference evapotranspiration (ET0) in different climatic zones of India. The daily climate data of minimum and maximum air temperatures, minimum and maximum relative humidity, wind speed, and solar radiation from 17 different locations in India were used as the inputs to the SONN models to estimate ET0 corresponding to the FAO-56 Penman-Monteith (FAO-56 PM) method. With the same inputs, for all 17 locations the first-order neural networks such as feed forward back propagation (FFBP-NN) models were also developed and compared with the SONN models. The developed SONN and FFBP-NN models were also compared with the estimates provided by the FAO-56 PM method. The performance criteria adopted for comparing the models were root-mean-squared error (RMSE), mean-absolute error (MAE), coefficient of determination (R2), and the ratio of average output to average target ET0 values (Rratio). Based on the comparisons,...

Association between Uncertainties in Meteorological Variables and Water-Resources Planning for the State of Texas

Because of the complexity and rapidly occurring changes in the dynamics of human demography and water demands, it is difficult to assess the future adequacy of limited freshwater resources. The planning of water resources largely depends on the meteorological variables (precipitation and evaporation) in terms of their distribution in space and time. Considering precipitation and evaporation as natural input and output without any human intervention for water-resources systems that can be perceived to represent the potential water-resources availability of an area, an uncertainty study was carried out for different water-resources regions in Texas. The entropy method was used for measuring the uncertainty in meteorological variables. It was observed that critical water-deficit regions based on meteorological variables are mostly located in the western part of Texas. The Mann-Kendall test was employed to understand the trend in precipitation, evaporation, and the meteorological excess index (MEI) in deficit...