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Hydrological and climatological modeling is increasingly being used with the intent of supporting community-based climate change adaptation (CCA) and disaster risk reduction (DRR) initiatives in the Hindu Kush-Himalaya (HKH), as well as filling critical data gaps in a region that contributes significantly to the water resources and ecosystem diversity of Asia. As the case studies presented in the previous chapters illustrate, the utility of modeling in informing and supporting CCA and DRR initiatives depends on a number of criteria, including:•appropriate model selection;•ability to interpret models to local contexts; and•community engagement that incorporates and addresses underlying vulnerabilities within the community.

There are significant challenges to meeting all three of these criteria. However, when these criteria are met, we find:•There is a clear role for modeling to support CCA. The climate is changing now and will continue to do so for several centuries, even if carbon emissions were to stabilize tomorrow. Models, and other scenario development tools, provide our best insight into what the future climate might be and resulting impacts on dynamic social, environmental, political, and economic systems.•There is a clear role for local CCA. The impacts of climate change will be felt mostly at local levels, necessitating community adaptation responses. At the same time, most of the HKH communities and countries engaged in CCA initiatives have pressing, immediate development and livelihood needs. Making current development and livelihood initiatives incorporate climate adaptation considerations is the best way to ensure that the choices made today can set us on paths of increasing resilience, rather than almost inevitable disaster, for the future.•To achieve the best of both modeling and CCA requires thoughtful and patient application of modeling, tailored to local needs, conditions, and politics, with communities engaged around all stages of generating, interpreting, and applying the results. This requires a rare combination of technical skill, cultural sensitivity, political awareness, and above all, the time to continually engage with and build relationships within the community in order to foster resilient change.

A Bayesian approach to demand forecasting to optimize spare parts inventory that requires periodic replenishment is examined relative to a non-Bayesian approach when the demand rate is unknown. That is, optimal inventory levels are decided using these two approaches at consecutive time intervals. Simulations were conducted to compare the total inventory cost using a Bayesian approach and a non-Bayesian approach to a theoretical minimum cost over a variety of demand rate conditions including the challenging slow moving or intermittent type of spare parts. Although Bayesian approaches are often recommended, this study’s results reveal that under conditions of large variability across the demand rates of spare parts, the inventory cost using the Bayes model was not superior to that using the non-Bayesian approach. For spare parts with homogeneous demand rates, the inventory cost using the Bayes model for forecasting was generally lower than that of the non-Bayesian model. Practitioners may still opt to use the non-Bayesian model since a prior distribution for the demand does not need to be identified.

The purpose of this study is to present a hybrid approach to model and predict long-term energy peak load using Bayesian and Holt–Winters (HW) exponential smoothing techniques.

Bayesian inference is administered by Markov chain Monte Carlo (MCMC) sampling techniques. Machine learning tools are used to calibrate the values of the HW model parameters. Hybridization is conducted to reduce modeling uncertainty. The technique is applied to real load data. Monthly peak load forecasts are calculated as weighted averages of HW and MCMC estimates. Mean absolute percentage error and the coefficient of determination (R²) indices are used to evaluate forecasts.

The developed hybrid methodology offers advantages over both individual combined techniques and reveals more accurate and impressive results with R² above 0.97. The new technique can be used to assist energy networks in planning and implementing production projects that can ensure access to reliable and modern energy services to meet the sustainable development goal in this sector.

This is original research.

The goal of the paper is to analyse the various issues attached to the valuation of weather derivatives. We focus our study on temperature‐related contracts since they are the most widely traded at this point and try to address the following questions: (i) should the quantity underlying the swaps or options contracts be defined as the temperature, degree‐days or cumulative degree‐days? This discussion is conducted both in terms of the robustness of the statistical modelling of the state variable and the mathematical valuation of the option (European versus Asian). (ii) What pricing approaches can tackle the market incompleteness generated by a non‐tradable underlying when furthermore the market price of risk is hard to identify in other traded instruments and unlikely to be zero? We illustrate our study on a database of temperatures registered at Paris Le Bourget and compare the calls and puts prices obtained using the different methods most widely used in weather markets.

The purpose of this paper is to assess the spatial and temporal variations of extreme hot days (H*) and heat wave frequencies across Iran.

The authors used daily maximum temperature (T_max) data of 27 synoptic stations in Iran. These data were standardized using the mean and the standard deviation of each day of the year. An extreme hot day was defined when the Z score of daily maximum temperature of that day was equal or more than a given threshold fixed at 1.7, while a heat wave event was considered to occur when the Z score exceeds the threshold for at least three continuous days. According to these criteria, the annual frequency of extreme hot days and the number of heat waves were determined for all stations.

The trend analysis of H* shows a positive trend during the past two decades in Iran, with the maximum number of H* (110 cases) observed in 2010. A significant trend of the number of heat waves per year was also detected during 1991-2013 in all the stations. Overall, results indicate that Iran has experienced heat waves in recent years more often than its long-term average. There will be more frequent and intense hot days and heat waves across Iran until 2050, due to estimated increase of mean air temperature between 0.5-1.1 and 0.8-1.6 degree centigrade for Rcp2.6 and Rcp8.8 scenarios, respectively.

The trend analysis of hot days and heat wave frequencies is a particularly original aspect of this paper. It is very important for policy- and decision-makers especially in agriculture and health sectors of Iran to make some adaptation strategies for future frequent and intense hot days over Iran.

The purpose of this paper is to present the results obtained from numerical models of radiant energy exchange in instruments typically used to measure various characteristics of the Earth's ocean‐atmosphere system.

Numerical experiments were designed and performed in a statistical environment, based on the Monte Carlo ray‐trace (MCRT) method, developed to model thermal and optical systems. Results from the derived theoretical equations were then compared to the results from the numerical experiments.

A rigorous statistical protocol is defined and demonstrated for establishing the uncertainty and related confidence interval in results obtained from MCRT models of radiant exchange.

The methodology developed in this paper should be adapted to predict the uncertainty of more comprehensive parameters such as the total radiative heat transfer.

Results can be used to estimate the number of energy bundles necessary to be traced per surface element in a MCRT model to obtain a desired relative error.

This paper offers a new methodology to predict the uncertainty of parameters in high‐level modeling and analysis of instruments that accumulate the long‐term database required to correlate observed trends with human activity and natural phenomena. The value of this paper lies in the interest in understanding the climatological role of the Earth's radiative energy budget.

Climate change will affect tourism at several temporal and spatial levels. This chapter focuses on the quantification of effects and the development of strategies to reduce extremes and frequencies as well as thresholds in tourism areas. Knowledge about possibilities for mitigation and adaptation of current and expected climate conditions requires interdisciplinary approaches and solutions. Several examples are presented, including the effects of trees against climate change and extreme events (heat waves), behavior adaptations, urban and regional planning measures, bioclimatic conditions in the Mediterranean and human–biometeorological conditions under climate change conditions, and user-friendly computer tools for the quantification of urban bioclimate conditions.

Presents the development and implementation of a four‐dimensional variational (4D‐var) data assimilation technique for a comprehensive Eulerian chemistry‐transport model. The method aims at analysing the chemical state of the atmosphere on the basis of trace gas observations with arbitrary distribution in time and space, a chemistry‐transport model, and a priori knowledge as available from climatological records or preceding model runs. The model under consideration is the University of Cologne EURAD‐CTM2 with the full RADM2 gas phase mechanism. Describes the storage and recalculation strategy of a parallel implementation of the 4D‐var method and first experiences of its performance, when model generated data are provided as artificial observations. The problem of pre‐scaling the minimization problem is discussed in some detail. It is found that the algorithm is well suited to adapt the model trajectory to the observation data.

The purpose of this paper is to examine the short-run monetary policy response to five different types of natural disasters (geophysical, meteorological, hydrological, climatological and biological) with respect to developed and developing countries, respectively.

An augmented Taylor rule monetary policy model is estimated using systems generalized method of moments panel estimation over the period 2000–2018 for a panel of 40 developed and 77 developing countries, respectively.

In the case of developed countries, the greatest nominal interest rate response originates from geophysical, meteorological, hydrological and climatological disasters, whereas for developing countries the nominal interest rate response is the greatest for geophysical and meteorological disasters. For both developed and developing countries, the results suggest the monetary authorities will pursue expansionary monetary policies in the short-run to lower nominal interest rates; however, the magnitude of the monetary response varies across the type of natural disaster.

First, unlike previous studies, which focused on a specific type of natural disaster, this study examines whether the short-run monetary policy response differs across the type of natural disaster. Second, in relation to previous studies, the analysis encompasses a much larger panel data set to include 117 countries differentiated between developed and developing countries.

This paper aims to review the performance of the flood forecasting, warning, and response system (FFWRS) during the 1997 Red River of the North flood to identify the factors that contributed to FFWRS underperformance during this flood disaster.

The individual components of the FFWRS are reviewed – data collection, flood forecasting, forecast dissemination, decision‐making, and action implementation, as well as the communication linkages between each system category. The unique challenges and breakdowns that occurred at each system category and communication linkage are identified for this catastrophic flood event.

Forecast uncertainty was poorly communicated by flood forecasters, and misunderstood by decision makers. Both forecasters and decision makers were rigidly committed to probability‐thinking based on what they thought was most likely to happen; neither group adequately considered the possibility of a worst‐case scenario.

Forecast uncertainty must be clearly communicated to and understood by local decision makers. Significant efforts at improved knowledge transfer to decision makers should be made to improve their ability to make rapid and informed decisions during catastrophic hazard events.

Decision makers would benefit from adopting a possibility‐thinking approach that thoroughly considered the possibility of a worst‐case scenario before such an event actually occurred.