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Highlights • Proposed framework identifies the similarity among the weather condition over years. • A powerful domain specific weather-based decision support system (DSS) can be built using this framework. • Significant reuse of the developed content in a weather-based DSS can be achieved using this framework. Abstract Weather-based decision support systems (DSSs) are being built to improve the efficiency of the production systems in the domains of healthcare, agriculture, transport, governance and so on. Normally, a weather condition (WC) is represented by the statistical values of weather variables for a given duration (e.g. a day or a week). In a weather-based DSS, given a WC, the domain experts prepare the appropriate suggestions to improve the efficiency of the stakeholders. Normally, once the domain experts prepare a suggestion for a given WC that belongs to certain period (e.g. year or season), there is a scope to reuse the same suggestion for the similar WCs of other period(s). As a result, the performance of the DSS could be improved. In this paper, to improve reuse , we have proposed a notion of category-based WC (CWC) which is formed by using the categories of weather variables in the respective domain. By considering the context of agromet advisory service operated by India Meteorological Department (IMD) and the corresponding weather categories provided by IMD, we have analyzed the extent of reuse among CWCs by conducting the experiments on 30 years of weather data collected at Rajendranagar, Hyderabad, Telangana state. The experiments are conducted by considering two types of CWCs with the duration equal to one day and five days. By varying the number of weather variables in CWC from one to five, we have computed the extent of reuse among CWCs of different periods of the following period types: year, season, and phenophases (i.e., growth stages) of the Rice crop. The results show that there is a significant similarity among the CWCs of the given period and the CWCs of the preceding periods of each period type. For any domain including agriculture, the results provide an opportunity to improve the efficiency of weather-based agricultural DSSs by improving the reuse of the weather-based suggestions. [ABSTRACT FROM AUTHOR]

The development of methodologies for predicting crop yield, in real-time and in response to different agro-climatic conditions, could help to improve the farm management decision process by providing an analysis of expected yields in relation to the costs of investment in particular practices. Based on the use of crop models, this paper compares the ability of two methodologies to predict wheat yield ( Triticum aestivum L.), one based on stochastically generated climatic data and the other on mean climate data. It was shown that the numerical experimental yield distribution could be considered as a log-normal distribution. This function is representative of the overall model behaviour. The lack of statistical differences between the numerical realisations and the logistic curve showed in turn that the Generalised Central Limit Theorem (GCLT) was applicable to our case study. In addition, the predictions obtained using both climatic inputs were found to be similar at the inter and intra-annual time-steps, with the root mean square and normalised deviation values below an acceptable level of 10% in 90% of the climatic situations. The predictive observed lead-times were also similar for both approaches. Given (i) the mathematical formulation of crop models, (ii) the applicability of the CLT and GLTC to the climatic inputs and model outputs, respectively, and (iii) the equivalence of the predictive abilities, it could be concluded that the two methodologies were equally valid in terms of yield prediction. These observations indicated that the Convergence in Law Theorem was applicable in this case study. For purely predictive purposes, the findings favoured an algorithm based on a mean climate approach, which needed far less time (by 300-fold) to run and converge on same predictive lead time than the stochastic approach. [ABSTRACT FROM AUTHOR]

Abstract: Crop models often simulate drought impacts with full and no irrigation scenarios, while planners are more interested in whether the current available irrigation water can cope with the future more serious droughts. This paper addresses a key constraint common to modeling studies: the limited representation of actual irrigation water supply. We present a data-driven approach to identify a benchmark for agronomic drought risk levels as defined by water availability thresholds at the baseline climate (1980–2008) using reported crop yields, climate and irrigation statistics. Then, holding the current irrigation supplies, we adopted Bayesian formula to estimate magnitude of the future water availability and the associated probability of crops yields being decreased to rainfall-deficiency under climate conditions in 2030s (2020–2040) based on the RegCM3 climate model output driven by IPCC SRES A1B scenario. Results reveal that future drought stress would overwhelm the irrigation capacity of current supplies in northern and western China, while drought remains at baseline climate levels in the central, eastern and southern regions. The largest increases in the probability of projected drought risk were in northeast and southwest, ranging from 14% to 28% above baseline climate. Regional drought impacts for grain self sufficiency are discussed. [Copyright &y& Elsevier]

Abstract: Agro-meteorological metrics are indicators of weather determined environmental conditions on which agricultural management decisions are made. Metrics derived from an estimated future climate provide an opportunity to characterise the impacts of climate change on a wide range of agricultural systems, land use practices and ecosystem services. Such indications are vital for determining how changes in the biophysical environment can lead to land management and policy adaptations to achieve multiple objectives of financial viability, food security, biodiversity conservation and environmental sustainability. They provide valuable links between probable management adaptation responses and capacity for mitigating greenhouse gas emissions. However, there are large uncertainties associated with projected future climates, including the climate models’ spatial scale of representation and those at which agro-meteorological metrics are applied. This paper describes the estimation of agro-meteorological metrics derived from observed weather and downscaled Regional Climate Model projection data for 12 sites in Scotland. Results show that projected changes to seasonal rainfall distribution, growing season length, soil moisture deficits and accessibility will be substantially different from the present climate. Fundamentally, the metrics indicate a substantial shift in land management requirements and potential need for substantial changes in agricultural systems and land use that will have implications across a wide range of research disciplines. [Copyright &y& Elsevier]

Abstract: There are clear indications that in the future the Alpine region will increasingly suffer from droughts. In this paper, a simple method is devised for determining probability of drought occurrence and exceedance of severity thresholds. A reconstruction of drought occurrence during the past 100 yr indicates that the method is able to detect major events and also to correctly gauge their relative severity. The procedure is used in conjunction with climate simulations for the European region valid for 2071–2100 to study the impact of climate change on the likelihood and severity of droughts. The climate scenario used for the analysis refers to a SRES A2 emission pathway and specifies in particular a decrease in the frequency of wet days of about 20% with respect to the growing season of summer crops (April to September). Under these conditions the frequency of droughts is shown to increase from about 15% to more than 50%. In addition, the results indicate an overall shift in the distribution toward higher severity scores. The average severity increases by a factor of two, but also at the upper end of the spectrum severity increases by more than 20%. It is argued that this will affect the perception of extreme droughts, i.e. of those events rarer than the 10th percentile of the distribution. If this scenario comes true, by the end of the 21st century droughts comparable in severity to the 2003 event would represent the norm rather than the exception. [Copyright &y& Elsevier]