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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.

This book aims to examine how modeling can be applicable toward local adaptation to climate change, using the Hindu Kush-Himalayas (HKH) as a case study. This introductory chapter sets the stage by summarizing mountain systems and change in the context of the HKH, especially highlighting the importance of involving mountain peoples in any discussion and work. Then, each chapter is summarized. In the final section, limitations and extensions of the work here are reported, focused on developing, testing, and implementing solutions on the terms of the people most affected without losing sight of wider contexts. Modeling is one knowledge system among many that is needed for adaptation and other development work in the HKH and other mountain areas.

It is now established by the global scientific community that climate change is a hard reality but the changes are complex in nature and to a great extent uncertain. Global circulation models (GCMs) have made significant contributions to the theoretical understanding of potential climate impacts, but their shortcomings in terms of assessing climate impacts soon became apparent. GCMs demonstrate significant skill at the continental and hemispheric scales and incorporate a large proportion of the complexity of the global system. However, they are inherently unable to represent local subgrid-scale features and dynamics. The first generation approaches of climate change impact and vulnerability assessments are derived from GCMs downscaled to produce scenarios at regional and local scales, but since the downscaled models inherit the biases of their parent GCM, they produce a simplified version of local climate. Furthermore, their output is limited to changes in mean temperature, rainfall, and sea level. For this reason, hydrological modeling with GCM output is useful for assessing impacts. The hydrological response due to change in climate variables in the Amu Darya River Basin was investigated using the Soil and Water Assessment Tool (SWAT). The modeling results show that there is an increase in precipitation, maximum and minimum temperature, potential evapotranspiration, surface runoff, percolation, and water yields. The above methodology can be practiced in this region for conducting adaptation and mitigation assessments. This initial assessment will facilitate future simulation modeling applications using SWAT for the Amu Darya River Basin by including variables of local changes (e.g., population growth, deforestation) that directly affect the hydrology of the region.

Climate change data and predictions for the Himalayas are very sparse and uncertain, characterized by a “Himalayan data gap” and difficulties in predicting changes due to topographic complexity. A few reliable studies and climate change models for Nepal predict considerable changes: shorter monsoon seasons, more intensive rainfall patterns, higher temperatures, and drought. These predictions are confirmed by farmers who claim that temperatures have been increasing for the past decade and wonder why the rains have “gone mad.” The number of hazard events, notably droughts, floods, and landslides are increasing and now account for approximately 100 deaths in Nepal annually. Other effects are drinking water shortages and shifting agricultural patterns, with many communities struggling to meet basic food security before climatic conditions started changing.

The aim of this paper is to examine existing gaps between current climate models and the realities of local development planning through a case study on flood risk and drinking water management for the Municipality of Dharan in Eastern Nepal. This example highlights current challenges facing local-level governments, namely, flood and landslide mitigation, providing basic amenities – especially an urgent lack of drinking water during the dry season – poor local planning capacities, and limited resources. In this context, the challenge for Nepal will be to simultaneously address increasing risks caused by hazard events alongside the omnipresent food security and drinking water issues in both urban and rural areas. Local planning is needed that integrates rural development and disaster risk reduction (DRR) with knowledge about climate change considerations. The paper concludes with a critical analysis of climate change modeling and the gap between scientific data and low-tech and low capacities of local planners to access or implement adequate adaptation measures. Recommendations include the need to bridge gaps between scientific models, the local political reality and local information needs.

Ladakh is an isolated arid environment in the Western Himalayas whose population relies mainly on glacial melt water. If the predicted adverse impacts of climate change occur, rising temperatures would accelerate the retreat of glaciers and place immense stress on the traditional Ladakhi agriculture and way of life. Very few studies in hydrology and glaciology currently document physical processes happening in Ladakh and only one project has combined climate data based upon measurements of temperature and precipitation, collected by the Indian Air Force in Leh town, and perceptions of local communities in order to explore the potential impacts of climate change in the area. This information constitutes the basis for climate change-related interventions of nongovernmental organizations (NGOs), both local and international, and could help inform any future climate modeling. However, the quality of this data can be questioned on several points, it terms of accuracy, availability and, most importantly, usefulness. Moreover, this chapter discusses the relevance of this kind of data when the focus is placed upon the adaptation of local communities to global environmental changes where climate change may not be the primary cause. For instance, the region is also currently undergoing a rapid transition from subsistence farming to a market-based economy due to the integration of Ladakh into India and the growing influx of tourism. When addressing the broader context of environmental change, reliable, accurate, and available climate data and models could be useful only if used as part of a holistic approach. This approach requires research and interventions to combine scientific information with local knowledge and perceptions about the impacts of climate change to root the physical data in a “real world” context. It must also acknowledge other drivers of environmental changes such as unsustainable development.

This chapter reviews research on multi-level organizational justice. The first half of the chapter provides the historical context for this issue, discusses organizational-level antecedents to individual-level justice perceptions (i.e., culture and organizational structure), and then focuses on the study of justice climate. A summary model depicts the justice climate findings to date and gives recommendations for future research. The second half of the chapter discusses the process of justice climate emergence. Pulling from classical bottom-up and top-down climate emergence models as well as contemporary justice theory, it outlines a theoretical model whereby individual differences and environmental characteristics interact to influence justice judgments. Through a process of information sharing, shared and unique experiences, and interactions among group members, a justice climate emerges. The chapter concludes by presenting ideas about how such a process might be empirically modeled.

Climate control needs have reached momentum. While scientists call for stabilizing climate and regulators structure climate change mitigation and adaptation efforts around the globe, economists are concerned with finding proper and fair financing mechanisms. In an overlapping-generations framework, Sachs (2014) solves the climate change predicament that seems to pit today’s against future generations. Sachs (2014) proposes that the current generation mitigates climate change financed through bonds to remain financially as well-off as without mitigation while improving environmental well-being of future generations through ensured climate stability. This intergenerational tax-and-transfer policy turns climate change mitigation into a Pareto improving strategy. Sachs’ (2014) discrete model is integrated in contemporary growth and resource theories. The following article analyzes how climate bonds can be phased-in, in a model for a socially optimal solution and a laissez-faire economy. Optimal trajectories are derived partially analytically (e.g., by using the Pontryagin maximum principle to define the optimal equilibrium), partially data driven (e.g., by the use of modern big market data), and partially by using novel cutting-edge methods – for example, nonlinear model predictive control (NMPC), which solves complex dynamic optimization problems with different nonlinearities for infinite and finite decision horizons. NMPC will be programed with terminal condition in order to determine appropriate numeric solutions converging to some optimal equilibria. The analysis tests if the climate change debt adjusted growth model stays within the bounds of a sustainable fiscal policy by employing NMPC, which solves complex dynamic systems with different nonlinearities.

A variety of stressors have been identified that threaten the sustainability of water resources. The availability and predictability of water resources are at the core of considering the role of climate for humans and natural ecosystems. The hydrological cycle defines available water resources in a river basin, but to ensure sustainability, it is important to examine other factors within river basin borders influencing the quality and quantity of water. Preparing for pressures and building adaptive capacity require a holistic assessment of the current status and possible future impacts on the freshwater resources.

This chapter describes a case study focusing on the Irrawaddy and Salween Rivers that form a major part of Myanmar's water resources. Despite their importance, these basins have been little studied. The basins were divided according to ecological zones and terrain slope into subareas, and a vulnerability assessment based on 22 indicators was conducted. Indicators represent publicly available global spatial data on temperature, precipitation, hydrology, glaciers, state of wetlands, population distribution, land cover, nitrogen load, and water use. Indicators were based either on model outputs or on land cover and land-use information, representing variably current situations or future projections.

Besides describing the case study, this chapter discusses the challenges and opportunities of linking large-scale spatial modeling results to local-level management and adaptation planning. Challenges arise first from the process of modeling and input data characteristics that manifest as questions of scale and uncertainty. Secondly, the process of distributing the results for the relevant stakeholders (if identified and reached) can turn out to be tricky. Opportunities exist if attention is given to impact of scale and unit of analysis in (especially spatial) data ensuring best applicability in local-scale management. Also improving information management with a systematic approach in identifying knowledge gaps and synthesizing existing information is crucial for improving linkages between researchers, policy-makers, and local decision-makers. Finally, modeling should be developed toward acknowledging the value of the process of modeling rather than the actual results. This would provide possibilities for translating the increasing amounts of information into understanding among the relevant stakeholders.

In 2001, the Intergovernmental Panel on Climate Change's Third Assessment Report revealed an important increase in the level of consensus concerning the reality of human-caused climate warming. The scientific basis for global warming has thus been sufficiently established to enable meaningful planning of appropriate policy responses to address global warming. As a result, the world's policy makers, governments, industries, energy producers/planners, and individuals from many other walks of life have increased their attention toward finding acceptable solutions to the challenge of global warming. This laudable increase in worldwide attention to this global-scale challenge has not, however, led to a heightened optimism that the required substantial reductions in carbon dioxide (CO2) emissions deemed necessary to stabilize the global climate can be achieved anytime soon. This fact is due in large part to several fundamental aspects of the climate system that interact to ensure that climate change is a phenomenon that will emerge over extensive timescales.

Although most of the warming observed during the 20th century is attributed to increased greenhouse gas concentrations, because of the high heat capacity of the world's oceans, further warming will lag added greenhouse gas concentrations by decades to centuries. Thus, today's enhanced atmospheric CO2 concentrations have already “wired in” a certain amount of future warming in the climate system, independent of human actions. Furthermore, as atmospheric CO2 concentrations increase, the world's natural CO2 “sinks” will begin to saturate, diminishing their ability to remove CO2 from the atmosphere. Future warming will also eventually cause melting of the Greenland and Antarctic ice sheets, which will contribute substantially to sea level rise, but only over hundreds to thousands of years. As a result, current generations have, in effect, decided to make future generations pay most of the direct and indirect costs of this major global problem. The longer the delay in reducing CO2 and other greenhouse gas emissions, the greater the burden of climate change will be for future life on earth.

Collectively, these phenomena comprise a “global warming dilemma.” On the one hand, the current level of global warming to date appears to be comparatively benign, about 0.6°C. This seemingly small warming to date has thus hardly been sufficient to spur the world to pursue aggressive CO2 emissions reduction policies. On the other hand, the decision to delay global emissions reductions in the absence of a current crisis is essentially a commitment to accept large levels of climate warming and sea level rise for many centuries. This dilemma is a difficult obstacle for policy makers to overcome, although better education of policy makers regarding the long-term consequences of climate change may assist in policy development.

The policy challenge is further exacerbated by factors that lie outside the realm of science. There are a host of values conflicts that conspire to prevent meaningful preventative actions on the global scale. These values conflicts are deeply rooted in our very globally diverse lifestyles and our national, cultural, religious, political, economic, environmental, and personal belief systems. This vast diversity of values and priorities inevitably leads to equally diverse opinions on who or what should pay for preventing or experiencing climate change, how much they should pay, when, and in what form. Ultimately, the challenge to all is to determine the extent to which we will be able to contribute to limiting the magnitude of this problem so as to preserve the quality of life for many future generations of life on earth.