Climate Change Impact Models Research Articles

Improving the representation of adaptation in climate change impact models

Climate change adaptation is a complex human process, framed by uncertainties and constraints, which is difficult to capture in existing assessment models. Attempts to improve model representations are hampered by a shortage of systematic descriptions of adaptation processes and their relevance to models. This paper reviews the scientific literature to investigate conceptualisations and models of climate change adaptation, and the ways in which representation of adaptation in models can be improved. The review shows that real-world adaptive responses can be differentiated along a number of dimensions including intent or purpose, timescale, spatial scale, beneficiaries and providers, type of action, and sector. However, models of climate change consequences for land use and water management currently provide poor coverage of these dimensions, instead modelling adaptation in an artificial and subjective manner. While different modelling approaches do capture distinct aspects of the adaptive process, they have done so in relative isolation, without producing improved unified representations. Furthermore, adaptation is often assumed to be objective, effective and consistent through time, with only a minority of models taking account of the human decisions underpinning the choice of adaptation measures (14%), the triggers that motivate actions (38%) or the time-lags and constraints that may limit their uptake and effectiveness (14%). No models included adaptation to take advantage of beneficial opportunities of climate change. Based on these insights, transferable recommendations are made on directions for future model development that may enhance realism within models, while also advancing our understanding of the processes and effectiveness of adaptation to a changing climate.

Impact of climate change on hydropower generation in Rio Jubones Basin, Ecuador

This study attempted to use the soil and water assessment tool (SWAT), integrated with geographic information systems (GIS), for assessment of climate change impacts on hydropower generation. This methodology of climate change impact modeling was developed and demonstrated through application to a hydropower plant in the Rio Jubones Basin in Ecuador. ArcSWAT 2012 was used to develop a model for simulating the river flow. The model parameters were calibrated and validated on a monthly scale with respect to the hydro-meteorological inputs observed from 1985 to 1991 and from 1992 to 1998, respectively. Statistical analyses produced Nash-Sutcliffe efficiencies (NSEs) of 0.66 and 0.61 for model calibration and validation, respectively, which were considered acceptable. Numerical simulation with the model indicated that climate change could alter the seasonal flow regime of the basin, and the hydropower potential could change due to the changing climate in the future. Scenario analysis indicates that, though the hydropower generation will increase in the wet season, the plant will face a significant power shortage during the dry season, up to 13.14% from the reference scenario, as a consequence of a 17% reduction of streamflow under an assumption of a 2.9°C increase in temperature and a 15% decrease in rainfall. Overall, this study showed that hydrological processes are realistically modeled with SWAT and the model can be a useful tool for predicting the impact of climate change.

A System Dynamics Approach to Modeling Climate Change Impact: A Case Study of Wood Materials for Buildings

This paper presents a new system dynamics approach to modeling climate change impact. The approach we proposed is a physically based stochastic dynamic method. To illustrate the applicability of the model, a case study of wooden church building is conducted to simulate the impacts of climate change scenarios and the adaptive capacity of buildings’ wooden materials. Model predictions and forecasts are in good agreement to the measurements. Results show that wooden building materials offer an effective and resilient response to anticipated future climate changes. The developed model provides a sound basis for studying climate change impacts.

Environmental Modelling of Climate Change Impact on Grapevines: Case Study from the Czech Republic

Environmental Modelling of Climate Change Impact on Grapevines: Case Study from the Czech Republic

Cellular automata approach for modelling climate change impact on water resources

ABSTRACTIn this work, we consider the cellular automata (CA) approach for modelling the climate change impact on water resources. This consists in: constructing a CA model that describes the water cycle dynamics taking into account physical terrain attributes and climatic constraints; coupling the CA model with climate projection scenarios for a considered region as input data; determining and analysing in output the variations of the underground, surface and evaporated water. We present these variations per time interval and per zone of influence. As an application, we consider simulation for a basin in northern Morocco using a simulation software we have designed in Java Object Oriented Programming.We consider cellular automata (CA) approach for modelling climate change impact on water resources. This consists in, first constructing a CA model that describes the water cycle dynamics taking into account physical terrain attributes and climatic constraints, then coupling the CA model with climate projection scenarios for a considered region as input data, and we determine and analyze in output the variations of the water resources (groundwater and surface water). We present these variations per time interval and per zone of influence. The approach application is for a basin in northern Morocco for which we use simulation software that we have designed in Java Object Oriented Programming. Digital terrain model, geological map and satellites image are used for input data.

Regional modeling of climate change impacts on smallholder agriculture and ecosystems in Central America

Climate change will have serious repercussions for agriculture, ecosystems, and farmer livelihoods in Central America. Smallholder farmers are particularly vulnerable due to their reliance on agriculture and ecosystem services for their livelihoods. There is an urgent need to develop national and local adaptation responses to reduce these impacts, yet evidence from historical climate change is fragmentary. Modeling efforts help bridge this gap. Here, we review the past decade of research on agricultural and ecological climate change impact models for Central America. The results of this review provide insights into the expected impacts of climate change and suggest policy actions that can help minimize these impacts. Modeling indicates future climate-driven changes, often declines, in suitability for Central American crops. Declines in suitability for coffee, a central crop in the regional economy, are noteworthy. Ecosystem models suggest that climate-driven changes are likely at low- and high-elevation montane forest transitions. Modeling of vulnerability suggests that smallholders in many parts of the region have one or more vulnerability factors that put them at risk. Initial adaptation policies can be guided by these existing modeling results. At the same time, improved modeling is being developed that will allow policy action specifically targeted to vulnerable groups, crops, and locations. We suggest that more robust modeling of ecological responses to climate change, improved representation of the region in climate models, and simulation of climate influences on crop yields and diseases (especially coffee leaf rust) are key priorities for future research.

Ensemble Averaging Methods for Quantifying Uncertainty Sources in Modeling Climate Change Impact on Runoff Projection

AbstractThe study discusses future runoff projection and uncertainty arising from different choices of global circulation models (GCMs), different scenarios of future emission, and different statis...

Implications of between-isolate variation for climate change impact modelling of Haemonchus contortus populations

The impact of climate change on parasites and parasitic diseases is a growing concern and numerous empirical and mechanistic models have been developed to predict climate-driven spatial and temporal changes in the distribution of parasites and disease risk. Variation in parasite phenotype and life-history traits between isolates could undermine the application of such models at broad spatial scales. Seasonal variation in the transmission of the haematophagous gastrointestinal nematode Haemonchus contortus, one of the most pathogenic helminth species infecting sheep and goats worldwide, is primarily determined by the impact of environmental conditions on the free-living stages. To evaluate variability in the development success and mortality of the free-living stages of H. contortus and the impact of this variability on future climate impact modelling, three isolates of diverse origin were cultured at a range of temperatures between 15°C and 37°C to determine their development success compared with simulations using the GLOWORM-FL H. contortus model. No significant difference was observed in the developmental success of the three isolates of H. contortus tested, nor between isolates and model simulations. However, development success of all isolates at 37°C was lower than predicted by the model, suggesting the potential for overestimation of transmission risk at higher temperatures, such as those predicted under some scenarios of climate change. Recommendations are made for future climate impact modelling of gastrointestinal nematodes.

Quantifying Uncertainties in Modeling Climate Change Impacts on Hydropower Production

Climate change will have large impacts on water resources and its predictions are fraught with uncertainties in West Africa. With the current global drive for renewable energy due to climate change, there is a need for understanding the effects of hydro-climatic changes on water resources and hydropower generation. A hydrological model was used to model runoff inflow into the largest hydroelectric dam (Kainji) in the Niger Basin (West Africa) under present and future conditions. Inflow to the reservoir was simulated using hydro-climatic data from a set of dynamically downscaled 8 global climate models (GCM) with two emission scenarios from the CORDEX-Africa regional downscaling experiment, driven with CMIP5 data. Observed records of the Kainji Lake were used to develop a hydroelectricity production model to simulate future energy production for the reservoir. Results indicate an increase in inflow into the reservoir and concurrent increases in hydropower production for the majority of the GCM data under the two scenarios. This analysis helps planning hydropower schemes for sustainable hydropower production.

Climate change impact modelling needs to include cross-sectoral interactions

Climate change impact assessments often apply models of individual sectors such as agriculture, forestry and water use without considering interactions between these sectors. This is likely to lead to misrepresentation of impacts, and consequently to poor decisions about climate adaptation. However, no published research assesses the differences between impacts simulated by single-sector and integrated models. Here we compare 14 indicators derived from a set of impact models run within single-sector and integrated frameworks across a range of climate and socio-economic scenarios in Europe. We show that single-sector studies misrepresent the spatial pattern, direction and magnitude of most impacts because they omit the complex interdependencies within human and environmental systems. The discrepancies are particularly pronounced for indicators such as food production and water exploitation, which are highly influenced by other sectors through changes in demand, land suitability and resource competition. Furthermore, the discrepancies are greater under different socio-economic scenarios than different climate scenarios, and at the sub-regional rather than Europe-wide scale.

Modelling Climate Change Impacts and Adaptation Strategies for Sunflower in Pakistan

Climate change, food security, water scarcity and environmental sustainability have all become major global challenges. As a consequence, improving resource use efficiency is an important aspect of increasing crop productivity. Crop models are increasingly being used as tools for supporting strategic and tactical decision making under varying agro-climatic and socioeconomic conditions. These tools can also support climate change assessment and the evaluation of adaptation strategies to limit the adverse impacts of climate change. In this paper, the authors report on a case study conducted to assess the potential impact of climate change on grain yield in sunflower under arid, semi-arid and subhumid conditions in the Punjab region of Pakistan. Experimental data obtained between 2008 and 2009 were used for model evaluation. The study focused on the impacts of incremental temperature change on sunflower production. The modelling suggests that grain yield could reduce by up to 15% by the 2020s with an average increase in temperature of +1°C, and by up to 25% if temperatures increased by up to 2°C for the 2050s. Adaptation strategies showed that, if the crop were sown between 14 days (for 2020) and 21 days (for 2050) earlier than the current date (last week in February), yield losses could potentially be reduced.

Metrics to identify meaningful downscaling skill in WRF simulations of intense rainfall events

Dynamical downscaling attempts to provide regional detail to climate change projections that subsequently can be used as input to climate change impact models. However, unlike forecasts by numerical weather prediction models, downscaled projections cannot be tested for skill because the future of interest is decades away. Nevertheless, models can be tested in terms of how well they simulate current weather or climate, thus giving an indication of skill in representing the process of interest. Here, six configurations using different combinations of three microphysics and two planetary boundary layer schemes are assessed on their skill to simulate desired characteristics in daily rainfall fields from three two week simulations in southeast Australia; ‘desired’ meaning desirable in relation to the intended application. Of different metrics and analysis assessed, a metric based on variography analysis, summarising characteristics about spatial variability and dissimilarity, is shown to provide the most informative guidance relative to the desirable characteristics.

Curonian Lagoon drainage basin modelling and assessment of climate change impact

Summary The Curonian Lagoon, which is the largest European coastal lagoon with a surface area of 1578 km 2 and a drainage area of 100,458 km 2 , is facing a severe eutrophication problem. With its increasing water management difficulties, the need for a sophisticated hydrological model of the Curonian Lagoon's drainage area arose, in order to assess possible changes resulting from local and global processes. In this study, we developed and calibrated a sophisticated hydrological model with the required accuracy, as an initial step for the future development of a modelling framework that aims to correctly predict the movement of pesticides, sediments or nutrients, and to evaluate water-management practices. The Soil and Water Assessment Tool was used to implement a model of the study area and to assess the impact of climate-change scenarios on the run-off of the Nemunas River and the Minija River, which are located in the Curonian Lagoons drainage basin. The models calibration and validation were performed using monthly streamflow data, and evaluated using the coefficient of determination ( R 2 ) and the Nash-Sutcliffe model efficiency coefficient ( NSE ). The calculated values of the R 2 and NSE for the Nemunas and Minija Rivers stations were 0.81 and 0.79 for the calibration, and 0.679 and 0.602 for the validation period. Two potential climate-change scenarios were developed within the general patterns of near-term climate projections, as defined by the Intergovernmental Panel on Climate Change Fifth Assessment Report: both pessimistic (substantial changes in precipitation and temperature) and optimistic (insubstantial changes in precipitation and temperature). Both simulations produce similar general patterns in river-discharge change: a strong increase (up to 22%) in the winter months, especially in February, a decrease during the spring (up to 10%) and summer (up to 18%), and a slight increase during the autumn (up to 10%).

Climate for Culture: assessing the impact of climate change on the future indoor climate in historic buildings using simulations

The present study reports results from the large-scale integrated EU project “Climate for Culture”. The full name, or title, of the project is Climate for Culture: damage risk assessment, economic impact and mitigation strategies for sustainable preservation of cultural heritage in times of climate change. This paper focusses on implementing high resolution regional climate models together with new building simulation tools in order to predict future outdoor and indoor climate conditions. The potential impact of gradual climate change on historic buildings and on the vast collections they contain has been assessed. Two moderate IPCC emission scenarios A1B and RCP 4.5 were used to predict indoor climates in historic buildings from the recent past until the year 2100. Risks to the building and to the interiors with valuable artifacts were assessed using damage functions. A set of generic building types based on data from existing buildings were used to transfer outdoor climate conditions to indoor conditions using high resolution climate projections for Europe and the Mediterranean. The high resolution climate change simulations have been performed with the regional climate model REMO over the whole of Europe including the Mediterranean region. Whole building simulation tools and a simplified building model were developed for historic buildings; they were forced with high resolution climate simulations. This has allowed maps of future climate-induced risks for historic buildings and their interiors to be produced. With this procedure future energy demands for building control can also be calculated. With the newly developed method described here not only can outdoor risks for cultural heritage assets resulting from climate change be assessed, but also risks for indoor collections. This can be done for individual buildings as well as on a larger scale in the form of European risk maps. By using different standardized and exemplary artificial buildings in modelling climate change impact, a comparison between different regions in Europe has become possible for the first time. The methodology will serve heritage owners and managers as a decision tool, helping them to plan more effectively mitigation and adaption measures at various levels.

Modeling climate change impact on potato crop phenology, and risk of frost damage and heat stress in northern Europe

Potato (Solanum tuberosum) is one of the main food crops in northern Europe, considered to be the fourth most important crop on a global scale after rice, wheat and maize. Climate change leading to longer growing seasons may call for adjustments in timing of planting and harvesting, and in this modeling study we assess potential effects of a warmer climate on potato crop phenology and temperature stress. A phenological potato model was parameterized with three planting dates to assess management impact on the timing of emergence and maturation of both early and late potato. Estimates on phenological development and occurrence of temperature stress were analyzed by comparing two developmental thresholds (0°C and +2°C) and three temperature response functions. The potato model was driven by observed gridded climate data and two sets of bias corrected climate model data, representing RCP4.5 and RCP8.5 for the period 1991–2100.The future simulations indicated that a warmer climate and earlier planting may move the timing of harvest up to 1 month earlier, however, potato emergence early in the year will be associated with an increased risk of frost damage in most parts of northern Europe. The areas of west Europe most prone to frost damage today may experience a risk of frost damage in response to climate change. The simulation of early potato development was sensitive to the setting of the developmental threshold, while late potato development was sensitive to the optimum temperature setting. While a linear temperature response function is essentially sufficient for current climate conditions in northern Europe, optimum and upper thresholds should be considered in climate change impact assessments. The potato model runs with temperature data corrected according to quantile-mapping indicated in general a slightly higher risk of temperature stress than the corresponding runs with temperature data corrected by linear scaling.

Implication of calibration period on modelling climate change impact on future runoff

Abstract. This paper explores the consideration and implication of calibration period on the modelled climate change impact on future runoff. The results show that modelled runoff and hydrologic responses can be influenced by the choice of historical data period used to calibrate and develop the hydrological model. Modelling approaches that do not take this into account may therefore underestimate the range and uncertainty in future runoff projections. Nevertheless, the uncertainty associated with the choice of hydrological models and consideration of calibration dataset for modelling climate change impact on runoff is likely to be small compared to the uncertainty in the future rainfall projections.

Modelling climate change impact on the spatial distribution of fresh water snails hosting trematodes in Zimbabwe.

BackgroundFreshwater snails are intermediate hosts for a number of trematodes of which some are of medical and veterinary importance. The trematodes rely on specific species of snails to complete their life cycle; hence the ecology of the snails is a key element in transmission of the parasites. More than 200 million people are infected with schistosomes of which 95% live in sub-Saharan Africa and many more are living in areas where transmission is on-going. Human infection with the Fasciola parasite, usually considered more of veterinary concern, has recently been recognised as a human health problem. Many countries have implemented health programmes to reduce morbidity and prevalence of schistosomiasis, and control programmes to mitigate food-borne fascioliasis. As these programmes are resource demanding, baseline information on disease prevalence and distribution becomes of great importance. Such information can be made available and put into practice through maps depicting spatial distribution of the intermediate snail hosts.MethodsA biology driven model for the freshwater snails Bulinus globosus, Biomphalaria pfeifferi and Lymnaea natalensis was used to make predictions of snail habitat suitability by including potential underlying environmental and climatic drivers. The snail observation data originated from a nationwide survey in Zimbabwe and the prediction model was parameterised with a high resolution Regional Climate Model. Georeferenced prevalence data on urinary and intestinal schistosomiasis and fascioliasis was used to calibrate the snail habitat suitability predictions to produce binary maps of snail presence and absence.ResultsPredicted snail habitat suitability across Zimbabwe, as well as the spatial distribution of snails, is reported for three time slices representative for present (1980-1999) and future climate (2046-2065 and 2080-2099).ConclusionsIt is shown from the current study that snail habitat suitability is highly variable in Zimbabwe, with distinct high- and low- suitability areas and that temperature may be the main driving factor. It is concluded that future climate change in Zimbabwe may cause a reduced spatial distribution of suitable habitat of host snails with a probable exception of Bi. pfeifferi, the intermediate host for intestinal schistosomiasis that may increase around 2055 before declining towards 2100.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-014-0536-0) contains supplementary material, which is available to authorized users.

Soil depth affects simulated carbon and water in the MC2 dynamic global vegetation model

Climate change has significant effects on critical ecosystem functions such as carbon and water cycling. Vegetation and especially forest ecosystems play an important role in the carbon and hydrological cycles. Vegetation models that include detailed belowground processes require accurate soil data to decrease uncertainty and increase realism in their simulations. The MC2 DGVM uses three modules to simulate biogeography, biogeochemistry and fire effects, all three of which use soil data either directly or indirectly. This study includes a correlation analysis of the MC2 model to soil depth by comparing a subset of the model’s carbon and hydrological outputs using soil depth data of different scales and qualities. The results show that the model is very sensitive to soil depth in simulations of carbon and hydrological variables, but competing algorithms make the fire module less sensitive to changes in soil depth. Simulated historic evapotranspiration and net primary productivity show the strongest positive correlations (both have correlation coefficients of 0.82). The strongest negative correlation is streamflow (−0.82). Ecosystem carbon, vegetation carbon and forest carbon show the next strongest correlations (0.78, 0.74 and 0.74, respectively). Carbon consumed by forest fires and the part of each grid cell burned show only weak negative correlations (−0.24 and −0.0013 respectively). In the model, when the water demand is met (deep soil with good water availability), production increases and fuels build up as more litter gets generated, thus increasing the overall fire risk during upcoming dry periods. However, when soil moisture is low, fuels dry and fire risk increases. In conclusion, it is clear climate change impact models need accurate soil depth data to simulate the resilience or vulnerability of ecosystems to future conditions.

Water resources and climate change impact modelling on a daily time scale in the Peruvian Andes

Estimating water resources is important for adequate water management in the future, but suitable data are often scarce. We estimated water resources in the Vilcanota basin (Peru) for the 1998–2009 period with the semi-distributed hydrological model PREVAH using: (a) raingauge measurements; (b) satellite rainfall estimates from the TRMM Multi-satellite Precipitation Analysis (TMPA); and (c) ERA-Interim re-analysis data. Multiplicative shift and quantile mapping were applied to post-process the TMPA estimates and ERA-Interim data. This resulted in improved low-flow simulations. High-flow simulations could only be improved with quantile mapping. Furthermore, we adopted temperature and rainfall anomalies obtained from three GCMs for three future periods to make estimations of climate change impacts (Delta-change approach) on water resources. Our results show more total runoff during the rainy season from January to March, and temporary storages indicate that less water will be available in this Andean region, which has an effect on water supply, especially during dry season.Editor Z.W. Kundzewicz; Associate editor D. Gerten

Localized climate change scenarios of mean temperature and precipitation over Switzerland

There is a growing need of the climate change impact modeling and adaptation community to have more localized climate change scenario information available over complex topography such as in Switzerland. A gridded dataset of expected future climate change signals for seasonal averages of daily mean temperature and precipitation in Switzerland is presented. The basic scenarios are taken from the CH2011 initiative. In CH2011, a Bayesian framework was applied to obtain probabilistic scenarios for three regions within Switzerland. Here, the results for two additional Alpine sub-regions are presented. The regional estimates have then been downscaled onto a regular latitude-longitude grid with a resolution of 0.02° or roughly 2 km. The downscaling procedure is based on the spatial structure of the climate change signals as simulated by the underlying regional climate models and relies on a Kriging with external drift using height as auxiliary predictor. The considered emission scenarios are A1B, A2 and the mitigation scenario RCP3PD. The new dataset shows an expected warming of about 1 to 6 °C until the end of the 21st century, strongly depending on the scenario and the lead time. Owing to a large vertical gradient, the warming is about 1 °C stronger in the Alps than in the Swiss lowlands. In case of precipitation, the projection uncertainty is large and in most seasons precipitation can increase or decrease. In summer a distinct decrease of precipitation can be found, again strongly depending on the emission scenario.