Regionalization of precipitation in data sparse areas using large scale atmospheric variables - A fuzzy clustering approach

Delineation of homogeneous precipitation regions (regionalization) is necessary for investigating frequency and spatial distribution of meteorological droughts. The conventional methods of regionalization use statistics of precipitation as attributes to establish homogeneous regions. Therefore they cannot be used to form regions in ungauged areas, and they may not be useful to form meaningful regions in areas having sparse rain gauge density. Further, validation of the regions for homogeneity in precipitation is not possible, since the use of the precipitation statistics to form regions and subsequently to test the regional homogeneity is not appropriate. To alleviate this problem, an approach based on fuzzy cluster analysis is presented. It allows delineation of homogeneous precipitation regions in data sparse areas using large scale atmospheric variables (LSAV), which influence precipitation in the study area, as attributes. The LSAV, location parameters (latitude, longitude and altitude) and seasonality of precipitation are suggested as features for regionalization. The approach allows independent validation of the identified regions for homogeneity using statistics computed from the observed precipitation. Further it has the ability to form regions even in ungauged areas, owing to the use of attributes that can be reliably estimated even when no at-site precipitation data are available. The approach was applied to delineate homogeneous annual rainfall regions in India, and its effectiveness is illustrated by comparing the results with those obtained using rainfall statistics, regionalization based on hard cluster analysis, and meteorological sub-divisions in India. (C) 2011 Elsevier B.V. All rights reserved.

Climate change and Indian forests

An assessment of the impact of projected climate change on forest ecosystems in India based on climate projections of the Regional Climate Model of the Hadley Centre (HadRM3) and the global dynamic vegetation model IBIS for A1B scenario is conducted for short-term (2021-2050) and long-term (2071-2100) periods. Based on the dynamic global vegetation modelling, vulnerable forested regions of India have been identified to assist in planning adaptation interventions. The assessment of climate impacts showed that at the national level, about 45% of the forested grids is projected to undergo change. Vulnerability assessment showed that such vulnerable forested grids are spread across India. However, their concentration is higher in the upper Himalayan stretches, parts of Central India, northern Western Ghats and the Eastern Ghats. In contrast, the northeastern forests, southern Western Ghats and the forested regions of eastern India are estimated to be the least vulnerable. Low tree density, low biodiversity status as well as higher levels of fragmentation, in addition to climate change, contribute to the vulnerability of these forests. The mountainous forests (sub-alpine and alpine forest, the Himalayan dry temperate forest and the Himalayan moist temperate forest) are susceptible to the adverse effects of climate change. This is because climate change is predicted to be larger for regions that have greater elevations.

Climate change vulnerability profiles for North East India

Climate change vulnerability profiles are developed at the district level for agriculture, water and forest sectors for the North East region of India for the current and projected future climates. An index-based approach was used where a set of indicators that represent key sectors of vulnerability (agriculture, forest, water) is selected using the statistical technique principal component analysis. The impacts of climate change on key sectors as represented by the changes in the indicators were derived from impact assessment models. These impacted indicators were utilized for the calculation of the future vulnerability to climate change. Results indicate that majority of the districts in North East India are subject to climate induced vulnerability currently and in the near future. This is a first of its kind study that exhibits ranking of districts of North East India on the basis of the vulnerability index values. The objective of such ranking is to assist in: (i) identifying and prioritizing the most vulnerable sectors and districts; (ii) identifying adaptation interventions, and (iii) mainstreaming adaptation in development programmes.

Exploring use of climate information for optimal design of water resources infrastructure

We conducted surveys of fire and fuels managers at local, regional, and national levels to gain insights into decision processes and information flows in wildfire management. Survey results in the form of fire managers’ decision calendars show how climate information needs vary seasonally, over space, and through the organizational network, and help determine optimal points for introducing climate information and forecasts into decision processes. We identified opportunities to use climate information in fire management, including seasonal to interannual climate forecasts at all organizational levels, to improve the targeting of fuels treatments and prescribed burns, the positioning and movement of initial attack resources, and staffing and budgeting decisions. Longer-term (5–10 years) outlooks also could be useful at the national level in setting budget and research priorities. We discuss these opportunities and examine the kinds of organizational changes that could facilitate effective use of existing climate information and climate forecast capabilities.

Groundwater Management in India:Physical, Institutional and Policy Alternatives

Groundwater constitutes a vital natural resource for sustaining India’s agricultural economy and meeting the country’s social, ecological and environmental goals. It is a unique resource, widely available, providing security against droughts and yet it is closely linked to surface-water resources and the hydrological cycle. Its availability depends on geo-hydrological conditions and characteristics of aquifers, from deep to alluvium, sediment crystalline rocks to basalt formations; and agro-climate from humid to subhumid and semi-arid to arid. Its reliable supply, uniform quality and temperature, relative turbidity, pollution-safe, minimal evaporation losses, and low cost of development are attributes making groundwater more attractive compared to other resources. It plays a key role in the provision of safe drinking water to rural populations. For example, already almost 80% of domestic water use in rural areas in India is groundwater-supplied, and much of it is being supplied to farms, villages and small towns. Inadequate control of the use of groundwater, indiscriminate application of agrochemicals and unrestrained pollution of the rural environment by other human activities make groundwater usage unsustainable, necessitating proper management in the face of the twin demand for water of good quality for domestic supply and adequate supply for irrigation, ensuring equity, efficiency and sustainability of the resource. Groundwater irrigation has overtaken surface irrigation in the early 1980s, supported by well energization. It is estimated that there are about 24 million energised wells and tube wells now and it is driven by demand rather than availability, evident through the greater occurrence of wells in districts with high population densities. Apart from aquifer characteristics, land fragmentation and landholding size are the factors that decide the density of wells. The ‘rise and fall’ of local economies dependent on groundwater can be summarized as: the green revolution of 1980s, groundwaterbased agrarian boom, early symptoms of groundwater overdraft, and decline of the groundwater socio-ecology. The social characteristics and policy interventions typical of each stage provide a fascinating insight into the human-resource dynamics. This book is a compilation of nine research papers discussing various aspects of groundwater management. It attempts to integrate knowledge about the physical system, the socio-economic system, the institutional set-up and the policy environment to come out with a more realistic analysis of the situation with regard to the nature, characteristics and intensity of resource use, the size of the economy the use generates, and the negative socioeconomic consequences. Complex variables addressed in this regard focusing on northern Gujarat are the stock of groundwater available in the region, its hydrodynamics, its net outflows against inflows, the economics of its intensive use (particularly irrigation in semi-arid and arid regions), its criticality in the regional hydroecological regime, ethical aspects and social aspects of its use. The first chapter by Dinesh Kumar and Singh, dwells on complex groundwater socio-ecology of India, while emphasizing the need for policy measures to address indiscriminate over-exploitation of dwindling resources. The chapter also explores the nature of groundwater economy and the role of electricity prices on it. The next chapter on groundwater issue in north Gujarat provides a description of groundwater resource characteristics followed by a detailed analysis of the groundwater depletion and quality deterioration problems in the region and their undesirable consequences on the economy, ecosystem health and the society. Considering water-buyers and wellowning farmers individually, a methodology for economic valuation of groundwater in regions where its primary usage is in agriculture, and as assessment of the groundwater economy based on case studies from north Gujarat is presented in the fourth chapter. The next chapter focuses on the extent of dependency of milk production on groundwater, which includes the water embedded in green and dry fodder and animal feed. The study made a realistic estimate of irrigation water productivity in terms of the physics and economics of milk production. The sixth chapter analyses the extent of reduction in water usage, increase in yield and overall increase in physical productivity of alfalfa with the use of the drip irrigation system. The chapter also provides a detailed synthesis of the costs and benefits associated with the use of drip irrigation systems. A linear programmingbased optimization model with the objective to minimize groundwater use taking into account the interaction between two distinct components – farming and dairying under the constraints of food security and income stability for different scenarios, including shift in cropping pattern, introduction of water-efficient crops, water- saving technologies in addition to the ‘business as usual’ scenario is presented in the seventh chapter. The results show that sustaining dairy production in the region with reduced groundwater draft requires crop shifts and adoption of water-saving technologies. The eighth chapter provides evidences to prove that the presence of adequate economic incentive would encourage farmers to adopt water-saving irrigation devices, based on the findings of market research with reference to the level of awareness among farmers of technologies and the factors that decide the adoption of water-saving technologies. However, now the marginal cost of using electricity for agricultural pumping is almost zero. The economic incentives are strong and visible only when the farmers are either water-buyers or have to manage irrigation with limited water from tube-well partnerships. The ninth chapter explores the socio-economic viability of increasing the power tariff and inducing groundwater rationing as a tool for managing energy and groundwater demand, considering the current estimate of the country’s annual economic loss of Rs 320 billion towards electricity subsidy in the farm sector. The tenth chapter suggests private tradable property rights and development of water markets as the institutional tool for achieving equity, efficiency and sustainability of groundwater use. It identifies the externalities for local groundwater management and emphasizes the need for managing groundwater by local user groups, supported by a thorough analysis of groundwater socio-ecology in India. An institutional framework for managing the resource based on participatory approach that is capable of internalizing the externalities, comprising implementation of institutional and technical alternatives for resource management is also presented. Major findings of the analyses and key arguments in each chapter are summarized in the concluding chapter. Case studies of the social and economic benefits of groundwater use, where that use could be described as unsustainable, are interesting. The benefits of groundwater use are outlined and described with examples of social and economic impacts of groundwater and the negative aspects of groundwater development with the compilation of environmental problems based on up-to-date research results. This publication with a well-edited compilation of case studies is informative and constitutes a useful publication for students and professionals.

Climate forcing and response to idealized changes in surface latent and sensible heat

Land use and land cover changes affect the partitioning of latent and sensible heat, which impacts the broader climate system. Increased latent heat flux to the atmosphere has a local cooling influence known as `evaporative cooling', but this energy will be released back to the atmosphere wherever the water condenses. However, the extent to which local evaporative cooling provides a global cooling influence has not been well characterized. Here, we perform a highly idealized set of climate model simulations aimed at understanding the effects that changes in the balance between surface sensible and latent heating have on the global climate system. We find that globally adding a uniform 1 W m(-2) source of latent heat flux along with a uniform 1 W m(-2) sink of sensible heat leads to a decrease in global mean surface air temperature of 0.54 +/- 0.04 K. This occurs largely as a consequence of planetary albedo increases associated with an increase in low elevation cloudiness caused by increased evaporation. Thus, our model results indicate that, on average, when latent heating replaces sensible heating, global, and not merely local, surface temperatures decrease.

Coupled Climate Model on Grids

Community Climate System Model (CCSM) is a Multiple Program Multiple Data (MPMD) parallel global climate model comprising atmosphere, ocean, land, ice and coupler components. The simulations have a time-step of the order of tens of minutes and are typically performed for periods of the order of centuries. These climate simulations are highly computationally intensive and can take several days to weeks to complete on most of today’s multi-processor systems. ExecutingCCSM on grids could potentially lead to a significant reduction in simulation times due to the increase in number of processors. However, in order to obtain performance gains on grids, several challenges have to be met. In this work,we describe our load balancing efforts in CCSM to make it suitable for grid enabling.We also identify the various challenges in executing CCSM on grids. Since CCSM is an MPI application, we also describe our current work on building a MPI implementation for grids to grid-enable CCSM.

A comparison of three methods for downscaling daily precipitation in the Punjab region

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K-nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue-type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright (C) 2011 John Wiley & Sons, Ltd.

Dynamics between patent latent variables and patent price

This paper focuses on studying the relationship between patent latent variables and patent price. From the existing literature, seven patent latent variables, namely age, generality, originality, foreign filings, technology field, forward citations, and backward citations were identified as having an influence on patent value. We used Ocean Tomo's patent auction price data in this study. We transformed the price and the predictor variables (excluding the dummy variables) to its logarithmic value. The OLS estimates revealed that forward citations and foreign filings were positively correlated to price. Both the variables jointly explained 14.79% of the variance in patent pricing. We did not find sufficient evidence to come up with any definite conclusions on the relationship between price and the variables such as age, technology field, generality, backward citations and originality. The Heckman two-stage sample selection model was used to test for selection bias. (C) 2011 Elsevier Ltd. All rights reserved.

Climate change and crop yields in the Indian Cardamom Hills, 1978-2007 CE

In this study we analyzed climate and crop yields data from Indian cardamom hills for the period 1978-2007 to investigate whether there were significant changes in weather elements, and if such changes have had significant impact on the production of spices and plantation crops. Spatial and temporal variations in air temperatures (maximum and minimum), rainfall and relative humidity are evident across stations. The mean air temperature increased significantly during the last 30 years; the greatest increase and the largest significant upward trend was observed in the daily temperature. The highest increase in minimum temperature was registered for June (0.37A degrees C/18 years) at the Myladumpara station. December and January showed greater warming across the stations. Rainfall during the main monsoon months (June-September) showed a downward trend. Relative humidity showed increasing and decreasing trends, respectively, at the cardamom and tea growing tracts. The warming trend coupled with frequent wet and dry spells during the summer is likely to have a favorable effect on insect pests and disease causing organisms thereby pesticide consumption can go up both during excess rainfall and drought years. The incidence of many minor pest insects and disease pathogens has increased in the recent years of our study along with warming. Significant and slight increases in the yield of small cardamom (Elettaria cardamomum M.) and coffee (Coffea arabica), respectively, were noticed in the recent years.; however the improvement of yield in tea (Thea sinensis) and black pepper (Piper nigrum L.) has not been seen in our analysis.