Abstract: The rainfall Intensity-Duration-Frequency (IDF) relationship is the primary input for storm water management and other engineering design applications across the world and it is developed by fitting an appropriate theoretical probability distribution to annual maximum (AM) series or partial duration series (PDS) of rainfall. The existing IDF relationship devel- oping methods consider the extreme rainfall series as a stationary series. There exist few studies that compared AM and PDS datasets for developing rainfall IDF relationship in a stationary condition. However, during the last few decades, the intensity and frequency of extreme rainfall events are increasing due to global climate change and creating a non- stationary component in the extreme rainfall series. Therefore, the rainfall IDF relationship developed with the stationary assumption is no longer tenable in a changing climate. Hence, it is inevitable to develop non-stationary rainfall IDF relationship and to understand the differ- ences in non-stationary rainfall IDF relationships derived using AM and PDS datasets. Consequently, the objectives of this study are: (1) to develop non-stationary rainfall IDF relationships using both AM and PDS datasets; (2) to compare them in terms of return level estimation. In particular, the non-linear trend in different durations’ PDS and AM datasets of Hyderabad city (India) rainfall is modeled using Multi-objective Genetic Algorithm (MGA) generated Time based covariate. In this study, the PDS datasets are modeled by the Generalized Pareto Distribution (GPD) while the AM datasets are modeled by the Generalized Extreme Value Distribution (GEVD). The time-varying component is introduced in the scale parameter of the GPD and the location parameter of the GEVD by linking the MGA generated covariate. In addition, the complexity of each non-stationary model is identified using the corrected Akaike Information Criteria (AICc) and the statistical signifi- cance of trend parameter in the non-stationary models is estimated using the Likelihood Ratio (LR) test. Upon detecting significant superiority of non-stationary models, the return levels of extreme rainfall event for 2-, 5-, 10- and 25-year return periods are calculated using non- stationary models. From the results, it is observed that the non-stationary return levels estimated with PDS datasets are higher than those estimated with AM datasets for short durations and smaller return periods while the non-stationary return levels estimated with AM datasets are higher than those estimated with PDS datasets for long durations and higher return periods.

Abstract: Present infrastructure design is primarily based on rainfall Intensity-Duration-Frequency (IDF) curves with so-called stationary assumption. However, in recent years, the extreme precipitation events are increas- ing due to global climate change and creating non-stationarity in the series. Based on recent theoretical developments in the Extreme Value Theory (EVT), recent studies proposed a methodology for developing non-stationary rainfall IDF curve by incorporating trend in the parameters of the Generalized Extreme Value (GEV) distribution using Time covariate. But, the covariate Time may not be the best covariate and it is important to analyze all possible covariates and find the best covariate to model non-stationarity. In this study, five physical processes, namely, urbanization, local temperature changes, global warming, El Niño-Southern Oscillation (ENSO) cycle and Indian Ocean Dipole (IOD) are used as covariates. Based on these five covariates and their possible combinations, sixty-two non-stationary GEV models are con- structed. In addition, two non-stationary GEV models based on Time covariate and one stationary GEV model are also constructed. The best model for each duration rainfall series is chosen based on the cor- rected Akaike Information Criterion (AICc). From the findings of this study, it is observed that the local processes (i.e., Urbanization, local temperature changes) are the best covariate for short duration rainfall and global processes (i.e., Global warming, ENSO cycle and IOD) are the best covariate for the long dura- tion rainfall of the Hyderabad city, India. Furthermore, the covariate Time is never qualified as the best covariate. In addition, the identified best covariates are further used to develop non-stationary rainfall IDF curves of the Hyderabad city. The proposed methodology can be applied to other situations to develop the non-stationary IDF curves based on the best covariate.

Abstract: To estimate alteration in physico-chemical and hydrological properties of soil of eucalyptus plantation (Eu) in comparison to soil of natural grassland (NG). To estimate spatio-temporal variations in soil moisture under eucalyptus plantation. To develop relationship of temperature, relative humidity, leaf area index with available soil moisture (ASM).

Abstract: This manuscript presents importance of fertigation, types of fertilizers, their solubility and compatibility problems, fertig and types of fertigation equipment. The research work carried out on fertigation with drip in fruits, vegetable, field crops and rose in India and abroad is also presented in this manuscript. Extensive work carried out by the author and his research group in the Preci Development Centre project at IIT Kharagpur is also presented in this paper. The study shows that the use of micro irrigation improves the water and fertilizer use efficiency

Abstract: A field experiment was carried out in two crop seasons in the lateritic sandy loam soils of Kharagpur, West Bengal, India, to investigate the response of banana (Musa acuminata L.) cv. Grand Naine at different levels of nitrogen, phosphorous and potassium nutrients applications through drip fertigation and plastic mulch. A randomized complete block design was used with four fertigation levels in conjunction with mulch and without mulch. Fertigation levels caused a significant increase in fruit yield and determined the response to N, P and K fertilizers. The results of recommended dose of fertilizers application through drip either alone or in conjunction with black plastic mulch conditions were compared with other fertigation treatments in terms of growth and crop of yield. Both the main and ratoon crops performed best for 80 per cent of the recommended fertigation dose (160 N: 48 P: 240 kg plant-1 year-1) covered with plastic mulch in respect of (a) growth parameters; maximum plant height, stem girth, functional leaves, yield parameters and shortened total crop duration for 34 days and for (b) quality parameters; higher levels of TSS, reducing sugar and non-reducing sugar, pulp:peel ratio and lower content of acidity. Hence, fertigation with 80 per cent of the recommended dose coupled with plastic mulch was found to be optimum and economical for banana cultivation.

Abstract: India leads the world in banana production, producing around 18 % of the worldwide crop of 139 million metric tonnes. In spite of this, its exports are minimal for various reasons. External appearance, internal and market quality of bananas are influenced by several factors, including production practices. Banana bunch cover is a physical protection method which will improves the visual quality of fruit by promoting skin colouration and reducing blemishes, but can also change the micro-environment for fruit development, which can have several beneficial effects on internal fruit quality. Bunch cover can also reduce the incidence of disease, insect pest and/or mechanical damage, sunburn of the skin, fruit cracking, agrochemical residues on the fruit, and bird damage. Bunch covering is laborious and its benefit cost ratio must be investigated in order to promote adoption of the method in much of the World. Few researches have been conducted studies on the effects of banana bunch covers in different parts of the World, but the results have not been compiled. We have therefore attempted to compile all the scattered information on banana bunch cover to assist researchers and extension personnel working in this area.

Abstract: The Olidih watershed hydrology was affected by opencast mines for the past five decades. This study explores the potential hydrological effect of these mines using Soil and Water Assessment Tool (SWAT2012). The calibration and validation of the model was performed using daily streamflow and sediment yield data (2005–2008) at the outlet of the water shed. The model performed satisfactorily during simulation when tested with statistical indicators. The alternative scenario of no-mines was also modelled to assess the potential impact of abandoned opencast mines for the period 2005–2010. Results show that the abandoned opencast mines play a crucial role in altering hydrological processes of the watershed with 16% increase in the annual sediment yield and reduction of 51% and 6% in annual surface flow and water yield, respectively. This may be due to surface soil disturbance and accumulation of surface runoff in large depressions that resulted in less surface runoff and 13% more groundwater flow. The contribution of this analysis is the application of SWAT in modelling potential hydrological effect of abandoned opencast mines by defining large opencast mines as pothole during simulation.

Abstract: Land cover/land use (LCLU) maps are essential inputs for environmental analysis. Remote sensing provides an opportunity to construct LCLU maps of large geographic area in a timely fashion. Remote sensing is one of the tool which is very important for the production of land use and land cover maps through a process called image classification. For the image classification process to be successfully, several factors should be considered including availability of quality of imagery and secondary data, a precise classification process and user’s experiences and expertise of the procedures. The objective of this research is to classify the landuse/land cover map and its accuracy assessment. In this study the LISS III imagery is used and classified into five categories like forest, agriculture land, waste land, built up and water bodies. In this study it is found that the overall accuracy derived from the stratified random sampling method is 76.00 % with an overall kappa coefficient of 0.67.The kappa coefficient is rated as substantial and hence the classified image found to be fit for further research. This study is essential source of information whereby planners and decision makers can use to sustainably plan the environment

Abstract: The purpose of the present study is to recognize the best infiltration model in soil among the infiltration methods available in HEC-HMS model for an agricultural dominated watershed upstream of the Hadaf dam. The area is predominant with agricultural land and falls under semiarid zone, where water resources planning and management is necessary for irrigation scheduling, water harvesting, flood control, drought mitigation and design of various engineering structures. In this respect, the hydrologic HECHMS model is used to compare various infiltration models and the best model that gives some results more likely to be real

Abstract: Variation of precipitation extremes over the relatively small spatial scales of urban areas could be significantly different from those over larger regions. An understanding of such variation is critical for urban infrastructure design and operation. Urban climatology and sparse spatial data lead to uncertainties in the estimates of spatial precipitation. In this paper, Bayesian hierarchical model is used to obtain spatial distribution of return levels of precipitation extremes in urban areas and quantify the associated uncertainty. The 10 Generalised Extreme Value (GEV) distribution is used for modelling precipitation extremes. 11 A spatial component is introduced in the parameters of the GEV through a latent spatial 12 process by considering geographic and climatologic covariates. A Markov Chain Monte 13 Carlo algorithm is used for sampling the parameters of GEV distribution and the latent 14 process model. Applicability of the methodology is demonstrated with data from telemetric rain gauge stations in Bangalore city, India. For this case study, it is inferred that the elevation and mean monsoon precipitation are the predominant covariates for annual maximum precipitation. Variation of seasonal extremes is also examined in the paper. For the monsoon maximum precipitation, it is observed that the geographic covariates dominate while for the summer maximum precipitation, elevation and mean summer precipitation are the predominant covariates. A significant variation in spatial return levels of extreme precipitation is observed over the city.

Abstract: Since the substantial evidence of non-stationarity in the extreme rainfall series is already reported, the current realm of hydrologic research focusing on developing methodologies for a non-stationary rainfall condition. As the rainfall intensity duration frequency (IDF) curve is primarily used in storm water management and infrastructure design, developing rainfall IDF curves in a non-stationary context is a current interest of water resource researchers. In order to construct non-stationary rainfall IDF curve, the probability distribution’s parameters are allowed to change according to covariate value and the current practice is to use time as a covariate. However, the covariate can be any physical process and the recent studies show that the direct use of time as a covariate may increase the bias. Moreover, the significance of selecting covariate in developing non-stationary rainfall IDF curve is yet to be explored. Therefore, this study aims to find the uncertainties in rainfall return levels due to the choice of the covariate (covariate uncertainty). In addition, since the uncertainty in parameter estimates (parameter uncertainty) is the major source of uncertainty in the stationary IDF curve, the relative significance of covariate uncertainty, when compared to the parameter uncertainty, is also explored. The study results reveal that the covariate uncertainty is significant, especially when a number of covariates produce significantly superior non-stationary model and, remarkably, it is nearly equivalent to the parameter uncertainty in such cases.

Abstract: Water resources and agriculture are often affected by the weather anomalies in India resulting in disproportionate damage. While short to sub-seasonal prediction systems and forecast products are available, a skilful hydrologic forecast of runoff and root-zone soil moisture that can provide timely information has been lacking in India. Using precipitation and air temperature forecasts from the Climate Forecast System v2 (CFSv2), the Global Ensemble Forecast System (GEFSv2) and four products from the Indian Institute of Tropical Meteorology (IITM), here we show that the IITM ensemble mean (mean of all four products from the IITM) can be used operationally to provide a hydrologic forecast in India at a 7–45-day accumulation period. The IITM ensemble mean forecast was further improved using bias correction for precipitation and air temperature. Bias corrected precipitation forecast showed an improvement of 2.1 mm (on the all India median mean absolute error – MAE), while all-India median bias corrected temperature forecast was improved by 2.1 degree C for a 45-day accumulation period. Moreover, the Variable Infiltration Capacity (VIC) model simulated forecast of runoff and soil moisture successfully captured the observed anomalies during the severe drought years. The findings reported herein have strong implications for providing timely information that can help farmers and water managers in decision making in India

Abstract: The advent of the sensor-cloud framework empowers the traditional wireless sensor networks (WSNs) in terms of dynamic operation, management, storage, and security. In recent times, the sensor-cloud framework is applied to various real-world applications. In this paper, we highlight the benefits of using sensor-cloud framework for the efficient addressing of various agricultural problems. We address the specific challenges associated with designing a sensor-cloud system for agricultural applications. We also mathematically characterize the virtualization technique underlying the proposed sensor-cloud framework by considering the specific challenges. Furthermore, the energy optimization framework and duty scheduling to conserve energy in the sensor-cloud framework is presented. The existing works on sensor cloud computing for agriculture does not specifically define the specific components associated with it. We categorize the distinct features of the proposed model and evaluated its applicability using various metrics. Simulation-based results show the justification for choosing the framework for agricultural applications.

Abstract: The depletion of groundwater resources threatens food and water security in India. However, the relative influence of groundwater pumping and climate variability on groundwater availability and storage remains unclear. Here we show from analyses of satellite and local well data spanning the past decade that long-term changes in monsoon precipitation are driving groundwater storage variability in most parts of India either directly by changing recharge or indirectly by changing abstraction. We find that groundwater storage has declined in northern India at the rate of 2 cm yr-1 and increased by 1 to 2 cm yr-1 in southern India between 2002 and 2013. We find that a large fraction of the total variability in groundwater storage in north-central and southern India can be explained by changes in precipitation. Groundwater storage variability in northwestern India can be explained predominantly by variability in abstraction for irrigation, which is in turn influenced by changes in precipitation. Declining precipitation in northern India is linked to Indian Ocean warming, suggesting a previously unrecognized teleconnection between ocean temperatures and groundwater storage.

Abstract: With the limited availability of meteorological variables in many remote areas, estimation of evapotranspiration (ET) at different spatio-temporal scales for efficient irrigation water management and hydro-meteorological studies is becoming a challenging task. Hence, in this study, the indirect ET estimation methods, such as, the MODIS satellite-based remote sensing techniques and the water budget approach in built into the semi-distributed variable infiltration capacity (VIC-3L) land surface model are evaluated using the FAO-56 Penman-Monteith (PM) equation and crop coefficient approach. To answer the research question whether the regional or local controls of a river basin with tropical monsoon-type climatology affect the accuracy of the VIC and MODIS-based ET estimates, these methodologies are applied in the Kangsabati River basin in eastern India at 25 km×25 km resolutions attributed with dominant paddy land uses. The results reveal that the VIC-estimated ET values are reasonably matched with the FAO-56 PM based ET estimates with the Nash-Sutcliffe efficiency (NSE) of 54.14-71.94%; however, the corresponding MODIS-ET values are highly underestimated with a periodic shift which may be attributed to the cloud cover and leaf shadowing effects. To enhance the field applicability of the satellite-based MODIS-ET products, these estimates are standardized by using the genetic algorithm-based transformation that improves the NSE from -390.83% to 99.57%. Hence, this study reveals that there is the need of a regional-scale standardization of the MODIS-ET products using the FAO-56 PM or lysimeters data or possible modification in the MOD16A2 algorithm built-into the MODIS for generalization. Conversely, the satisfactory grid-scale ET estimates by the VIC model shows that this model could be reliably used for the world river basins; although at smaller temporal scales, the estimates could be slightly inconsistent