Downscaled Climate Data Research Articles

Statistical testing of dynamically downscaled rainfall data for the Upper Hunter region, New South Wales, Australia

This study tests the statistical properties of downscaled climate data, concentrating on the rainfall which is required for hydrology predictions used in water supply reservoir simulations. The datasets used in this study have been produced by the New South Wales (NSW) / Australian Capital Territory (ACT) Regional Climate Modelling (NARCliM) project which provides a dynamically downscaled climate dataset for southeast Australia at 10 km resolution. In this paper, we present an evaluation of the downscaled NARCliM National Centers for Environmental Prediction (NCEP) / National Center for Atmospheric Research (NCAR) reanalysis simulations. The validation has been performed in the Goulburn River catchment in the Upper Hunter region of New South Wales, Australia. The analysis compared time series of the downscaled NARCliM rain-fall data with ground based measurements for selected Bureau of Meteorology rainfall stations and 5 km gridded data from the Australian Water Availability Project (AWAP). The initial testing of the rainfall was focused on autocorrelations as persistence is an important factor in hydrological and water availability analysis. Additionally, a cross-correlation analysis was performed at daily, fort-nightly, monthly and annually averaged time resolutions. The spatial variability of these statistics were calculated and plotted at the catchment scale. The auto-correlation analysis shows that the seasonal cycle in the NARCliM data is stronger than the seasonal cycle present in the ground based measurements and AWAP data. The cross-correlation analysis also shows a poor agreement between NARCliM data, and AWAP and ground based measurements. The spatial variability plots show a possible link between these discrepancies and orography at the catchment scale.

Drought Forecasting Using SPI and EDI under RCP-8.5 Climate Change Scenarios for Langat River Basin, Malaysia

In Malaysia, droughts often lead to water deficit and overcoming a lack of fresh water has become one of the important challenges in the country. Climate change have brought about a big environmental impact globally, such as the rise in sea levels, unavailability of fresh portal water and more extreme drought and flood events occurring and Malaysia is no different and not spared all this calamities. The Langat River Basin is located in a fast growing region in Peninsular Malaysia, the Greater Kuala Lumpur Valley and hence the implementation of the drought index in this basin is vital important and necessary. Normally drought characteristics can be determined or identified using the drought indices. The two drought indices were used in this study, namely the SPI (Standardized Precipitation Index) and the EDI (Effective Drought Index) to assess the severity, duration and extend of drought event. The CanESM2 outputs under Representative Concentration Pathway (RCP) 8.5 emission scenario of IPCC Fifth Assessment Report (AR5) were utilized to produce regionalized precipitation and temperature data. The GCM outputs were statistically downscaled using the Statistical Downscaling Model (SDSM) version 4.2.9. Next, the SPI for time scale period of 1-month, 6-months and 12-months (SPI-1, SPI-6 and SPI-12) and EDI were calculated for both the observed and statistically downscaled climate data to investigate and analyze the severity and extent of drought. Both indices were compared to get a more operational index between SPI-1, SPI-6, SPI-12 and EDI outlook for representing Malaysia drought events.

Implications of the Methodological Choices for Hydrologic Portrayals of Climate Change over the Contiguous United States: Statistically Downscaled Forcing Data and Hydrologic Models

Abstract Continental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation–Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5–3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded.

The role of meteorological processes in the description of uncertainty for climate change decision-making

Downscaled climate data are available at fine spatial scales making them desirable to local climate change practitioners. However, without a description of their uncertainty, practitioners cannot know if they provide quality information. We pose that part of the foundation for the description of uncertainty is an assessment of the ability of the underlying climate model to represent the meteorological or weather-scale processes. Here, we demonstrate an assessment of precipitation processes for the Great Lakes region using the Bias Corrected and Spatially Downscaled (BCSD) Coupled Model Intercomparison Project phase 3 (CMIP3) projections. A major weakness of the underlying models is their inability to simulate the effects of the Great Lakes, which is an important issue for most global climate models. There is also uncertainty among the models in the timing of transition between dominant precipitation processes going from the warm to cool season and vice versa. In addition, warm-season convective precipitation processes very greatly among the models. From the assessment, we discuss how process-based uncertainties in the models are inherited by the downscaled projections and how bias correction increases uncertainty in cases where precipitation processes are not well represented. Implications of these findings are presented for three regional examples: lake-effect snow, the spring seasonal transition, and summertime lake-effect precipitation.

Hydroregime Prediction Models for Ephemeral Groundwater-Driven Sinkhole Wetlands: a Planning Tool for Climate Change and Amphibian Conservation

Hydroregimes of ephemeral wetlands affect reproductive success of many amphibian species and are sensitive to altered weather patterns associated with climate change. We used 17 years of weekly temperature, precipitation, and water-depth measurements for eight small, ephemeral, groundwater-driven sinkhole wetlands in Florida sandhills to develop a hydroregime predictive model. To illustrate its utility for climate-change planning, we forecasted weekly wetland water-depths and hydroperiods (2012–2060) using our model and downscaled climate data from the CSIRO Mk3.5 Global Circulation Model under an A1B emissions scenario. We then examined how forecasted water depths and hydroperiods might alter reproductive success, and thereby populations, of five anuran species. Precipitation and water-depth from the prior week were significant predictors of water depth. Our model forecasted shallower depths and shortened hydroperiods for most wetlands when used with the CSIRO Mk3.5 A1B scenario. The forecasted hydroregimes would likely provide adequate reproductive opportunity for only one of the five species we examined. We demonstrate the utility of our model in examining how different climate-change scenarios might affect hydroregimes and, indirectly, biological diversity. Climate change uncertainty highlights the importance of retaining multiple, hydrologically diverse wetlands on landscapes to maximize the potential for successful reproduction by species having differing hydroregime requirements.

Modeling climate change impacts on the thermal dynamics of polymictic Oneida Lake, New York, United States

The thermal dynamics of lakes are affected by climate change, and in turn affect most aspects of lake ecology, including primary and secondary production, biogeochemistry, and biodiversity. These effects may be particularly large in polymictic lakes which have variable durations and degrees of thermal stratification. A one-dimensional model, the Dynamic Reservoir Simulation Model (DYRESM), was used to evaluate the impacts of predicted climate change on temperature and stability in Oneida Lake, a 207km2 shallow, polymictic lake in central New York State. Field data were collected on stream temperature and discharge, weather, and lake temperatures at varying depths to calibrate and validate the model for Oneida Lake. Modeled temperatures at 2m and 10m were generally within ±1°C of observed values, but only after wind speeds from a nearby airport were decreased by 25%. Downscaled climate data from three general circulation models and two emissions scenarios were used to assess the impacts of different anticipated climate scenarios on the lake for 2041–2050 and 2090–2099. By 2050, under the higher emissions scenario, water temperatures (April–November) at 2m and 10m depths indicated an increase of 1.36°C and 1.43°C, respectively; however, 2011 simulation temperatures already aligned with mid-century predictions. By the end of the century, the prediction of the higher emissions scenario indicated an increase in 2m and 10m water temperatures (April–November) of 3.70°C and 3.37°C, respectively, and an increase of 61 consecutive days of stratification in Oneida Lake. These impacts would lead to increased periods of lack of oxygen in the bottom waters of this lake and changes in the biotic species composition, including extirpation of a coldwater fish species, burbot (Lota lota). This study contributes to our understanding of the thermal regime of polymictic lakes in a warmer world.

Climate change impacts on phenology and yields of five broadacre crops at four climatologically distinct locations in Australia

Shifts in rainfall and rising temperatures due to climate change pose a formidable challenge to the sustainability of broadacre crop yields in Western and South-Eastern Australia. Output from18 Global Climate Models (GCMs) for the Special Report on Emission Scenarios (SRES) A2 scenario was statistically downscaled to four contrasting locations. For the first time in these regions, bias corrected statistically downscaled climate data were employed to drive the Agricultural Production Systems Simulator (APSIM) crop model that integrates the effects of soil, crop phenotype, and management options for a quantitative comparison of crop yields and phenology under an historical and a plausible projected climate. The dynamic APSIM simulation model explore the implications of climate change across multiple locations and multiple time periods (1961–2010, 2030, 2060 and 2090) for multiple key crops (wheat, barley, lupin, canola, field pea) grown in three different types of soil. On average, the ensemble of downscaled GCM projections show a decrease in rainfall in the future at the four locations considered, with increased variability at two locations. At all locations and for five crops, future changes in both crop biomass and grain yield are strongly associated with changes in rainfall (P = 0.05 to P = 0.001). The overall rainfall amount is critical in determining yields but, equally, higher future temperatures can contribute to reducing crop productivity primarily due to advanced crop phenology. For example, for wheat cropping at Hamilton (a higher rainfall site), there is a significant advancement in median flowering date for 2030, 2060, and 2090 of 10, 18, and 29 days respectively with a significant 0.50% grain yield changes for each percentage change in rainfall compared to significant 0.90% grain yield changes in Cunderdin (a lower rainfall site). At all sites except Hamilton, the change in crop grain yield is significantly correlated (P = 0.001) with the percentage change in the future rainfall and the impact increased progressively from higher rainfall to lower rainfall sites. However, the magnitude of the change in crop phenology and yield were not significantly different between soil types. These results help to define regions of concern and their relative importance in the coming years. In this future climate the negative consequences for crop yields and advancement of phenology relative to baseline are not uniform across crops and locations. Of the crops studied – wheat, barley, lupin, canola and field pea – field pea is the most sensitive to the projected future climate changes, and the ensemble median changes in field pea yield range from a decrease of 12% to a decrease of 45%, depending on location. These results highlight the importance of research and policy to support strategies for adapting to climate change, such as advances in agronomy, soil moisture conservation, seasonal climate forecasting and breeding new crop varieties.

Predicting the river's blue line for fish conservation.

River drying is increasing in frequency, duration, and extent in the United States, especially across the Sunbelt where drought has become the new norm (Fig. 1). Exceptional droughts and climate change are one cause of drying—surface water is scarcer and that trend is not changing (1⇓⇓–4). Increasing human appropriation of groundwater—especially during drought—is another cause (5, 6). River drying has clear implications for freshwater biodiversity, including mass mortality of commercially important fish if large reaches of a river dry completely (7). A much more subtle, but equally detrimental, impact of drying is habitat fragmentation, which can increase the risk of extinction from other causes (8, 9); however, data availability and a methodological void have left this critical issue unaddressed until now. In PNAS, Jaeger et al. (10) fill this void by integrating cutting edge and empirically grounded ecology with a tried-and-true surface water model that is in turn forced with forecasted and downscaled climate data. The authors use this intriguing model mashup to forecast network connectivity of flowing reaches in a desert river in Arizona at fine spatial scales. They then use this set of “blue lines” to forecast the persistence of the river's native fish populations. Their conclusions are dire: decreasing persistence of an endemic fauna. On the bright side, the mashup has real potential to be applied to address similar management problems in other basins. Fig. 1. ( Upper ) Mean (solid) and 95% CI (dashed) number of zero flow days for 23 stream gages across the US Sunbelt from the USGS National Water Information System (NWIS). Data cover a spatial extent that includes the states of Georgia, Florida, Kansas, Texas, New Mexico, and Arizona. ( Lower ) Mean (solid) and CI (dashed) number of days (per year) below the 40-y average (dotted black reference line) for 94 … [↵][1]1Email: john.l.sabo{at}asu.edu. [1]: #xref-corresp-1-1

Twenty-First-Century Projections of Snowfall and Winter Severity across Central-Eastern North America*,+

Abstract Statistically downscaled climate projections from nine global climate models (GCMs) are used to force a snow accumulation and ablation model (SNOW-17) across the central-eastern North American Landscape Conservation Cooperatives (LCCs) to develop high-resolution projections of snowfall, snow depth, and winter severity index (WSI) by the middle and late twenty-first century. Here, projections of a cumulative WSI (CWSI) known to influence autumn–winter waterfowl migration are used to demonstrate the utility of SNOW-17 results. The application of statistically downscaled climate data and a snow model leads to a better representation of lake processes in the Great Lakes basin, topographic effects in the Appalachian Mountains, and spatial patterns of climatological snowfall, compared to the original GCMs. Annual mean snowfall is simulated to decline across the region, particularly in early winter (December–January), leading to a delay in the mean onset of the snow season. Because of a warming-induced acceleration of snowmelt, the percentage loss in snow depth exceeds that of snowfall. Across the Plains and Prairie Potholes LCC and the Upper Midwest and Great Lakes LCC, daily snowfall events are projected to become less common but more intense. The greatest reductions in the number of days per year with a present snowpack are expected close to the historical position of the −5°C isotherm in December–March, around 44°N. The CWSI is projected to decline substantially during December–January, leading to increased likelihood of delays in timing and intensity of autumn–winter waterfowl migrations.

Impacts of Climate Change on Milk Production in the United States

Climate change is likely to affect milk production because of the sensitivity of dairy cows to excessive temperature and humidity. We use downscaled climate data and county-level dairy industry data to estimate milk production losses for Holstein dairy cows in the conterminous United States. On a national level, we estimate present-day production losses of 1.9 percent relative to baseline production and project that climate impacts could increase these losses to 6.3 percent by the end of the twenty-first century. Using present-day prices, this corresponds to annual losses of $670 million per year today, rising to $2.2 billion per year by the end of the century. We also find that there is significant geographic variation in production losses and that regions currently experiencing the greatest heat-related impacts are also projected to experience the greatest additional losses with climate change. Specifically, statewide average estimates of end-of-century losses range from 0.4 percent in Washington to a 25 percent loss in annual milk production in Florida. Given that the majority of these losses occur in the summer months, this has the potential to significantly impact operations in hotter climates.

Incorporating Future Climatic and Socioeconomic Variables in Water Demand Forecasting: A Case Study in Bangkok

With concerns relating to climate change, and its impacts on water supply, there is an increasing emphasis on water utilities to prepare for the anticipated changes so as to ensure sustainability in supply. Forecasting the water demand, which is done through a variety of techniques using diverse explanatory variables, is the primary requirement for any planning and management measure. However, hitherto, the use of future climatic variables in forecasting the water demand has largely been unexplored. To plug this knowledge gap, this study endeavored to forecast the water demand for the Metropolitan Waterworks Authority (MWA) in Thailand using future climatic and socioeconomic data. Accordingly, downscaled climate data from HadCM3 and extrapolated data of socioeconomic variables was used in the model development, using Artificial Neural Networks (ANN). The water demand was forecasted at two scales: annual and monthly, up to the year 2030, with good prediction accuracy (AAREs: 4.76 and 4.82 % respectively). Sensitivity analysis of the explanatory variables revealed that climatic variables have very little effect on the annual water demand. However, the monthly demand is significantly affected by climatic variables, and subsequently climate change, confirming the notion that climate change is a major constraint in ensuring water security for the future. Because the monthly water demand is used in designing storage components of the supply system, and planning inter-basin transfers if required, the results of this study provide the MWA with a useful reference for designing the water supply plan for the years ahead.

Estimation of the effects of climate change on flood-triggered economic losses in Japan

This study evaluates the effects of climate change on economic losses due to flood-related damage in Japan. Three selected GCM climate data were downscaled using an analytical method that uses observed precipitation data as the reference resolution. The downscaled climate data were used to estimate extreme rainfall for different return periods. The extreme rainfall estimates were then entered into a two-dimensional (2D) non-uniform flow model to estimate flood inundation information. A technique based on the land use type of the flood area was employed to estimate economic losses due to flood damages. The results of the rainfall analysis show that at present (in 2000), the Nankai region, and the area from Wakayama Prefecture to Kagoshima Prefecture and the mountains of the Japan Alps receive very high extreme rainfall. By 2050, in addition to these areas, the rainfall in the Tokai and Koshinetsu regions will be 1.2 to 1.3 times greater than at present. The overall variations show that the potential economic loss is greater for the SRES-B1, A2 and A1B scenarios for all return periods. These results clearly show that flood-related economic losses in Japan will increase significantly in the future as a result of climate change. It indicates that Japan needs to increase the capital investment to implement flood control and mitigation measures in the future. As this study presents comprehensive results in very fine resolution (1km×1km), the outcome of this study is more important for regional scale decision making processes will be useful to the public, economists, and policy and decision makers in planning and designing flood control measures.

Effects of climate change and wildfire on soil loss in the Southern Rockies Ecoregion

Forests in the Southern Rockies Ecoregion surround the headwaters of several major rivers in the western and central US. Future climatic changes will increase the incidence of wildfire in those forests, and will likely lead to changes in downstream water quality, including sediment loads. We estimated soil loss under the historic climate and two IPCC climate change emissions scenarios (A2 and B1); each scenario was modeled using statistically downscaled climate data from global circulation models (GCMs; ECHAM5 and HadCM3) for each of thirteen land cover types. We used the Revised Universal Soil Loss Equation (RUSLE) and developed a way to calculate rainfall erosivity, a key factor in RUSLE, to account for climate change. We also incorporated the effects of climate change on wildfire to create stochastic spatial distributions of wildfires and to inform changes in land cover. Based on 100 simulations of future wildfire applied to RUSLE for each GCM-scenario combination, we found that soil loss will likely increase above historic levels but that considerable uncertainty remains about the amount of increase. Across the GCM-scenario combinations, mean soil loss increased above historic levels by from 3% (HadCM3-A2) to 65% (ECHAM5-B1) for climate change only and the effects of wildfire increased soil loss an additional 3 to 5%.

Converting probabilistic tree species range shift projections into meaningful classes for management

The paper deals with the management problem how to decide on tree species suitability under changing environmental conditions. It presents an algorithm that classifies the output of a range shift model for major tree species in Europe into multiple classes that can be linked to qualities characterizing the ecological niche of the species. The classes: i) Core distribution area, ii) Extended distribution area, iii) Occasional occurrence area, and iv) No occurrence area are first theoretically developed and then statistically described. The classes are interpreted from an ecological point of view using criteria like population structure, competitive strength, site spectrum and vulnerability to biotic hazards. The functioning of the algorithm is demonstrated using the example of a generalized linear model that was fitted to a pan-European dataset of presence/absence of major tree species with downscaled climate data from a General Circulation Model (GCM). Applications of the algorithm to tree species suitability classification on a European and regional level are shown. The thresholds that are used by the algorithm are precision-based and include Cohen's Kappa. A validation of the algorithm using an independent dataset of the German National Forest Inventory shows good accordance of the statistically derived classes with ecological traits for Norway spruce, while the differentiation especially between core and extended distribution for European beech that is in the centre of its natural range in this area is less accurate. We hypothesize that for species in the core of their range regional factors like forest history superimpose climatic factors. Problems of uncertainty issued from potentially applying a multitude of modelling approaches and/or climate realizations within the range shift model are discussed and a way to deal with the uncertainty by revealing the underlying attitude towards risk of the decision maker is proposed.

Impact of Climate Change Effects on Contamination of Cereal Grains with Deoxynivalenol

Climate change is expected to aggravate feed and food safety problems of crops; however, quantitative estimates are scarce. This study aimed to estimate impacts of climate change effects on deoxynivalenol contamination of wheat and maize grown in the Netherlands by 2040. Quantitative modelling was applied, considering both direct effects of changing climate on toxin contamination and indirect effects via shifts in crop phenology. Climate change projections for the IPCC A1B emission scenario were used for the scenario period 2031-2050 relative to the baseline period of 1975-1994. Climatic data from two different global and regional climate model combinations were used. A weather generator was applied for downscaling climate data to local conditions. Crop phenology models and prediction models for DON contamination used, each for winter wheat and grain maize. Results showed that flowering and full maturity of both wheat and maize will advance with future climate. Flowering advanced on average 5 and 11 days for wheat, and 7 and 14 days for maize (two climate model combinations). Full maturity was on average 10 and 17 days earlier for wheat, and 19 and 36 days earlier for maize. On the country level, contamination of wheat with deoxynivalenol decreased slightly, but not significantly. Variability between regions was large, and individual regions showed a significant increase in deoxynivalenol concentrations. For maize, an overall decrease in deoxynivalenol contamination was projected, which was significant for one climate model combination, but not significant for the other one. In general, results disagree with previous reported expectations of increased feed and food safety hazards under climate change. This study illustrated the relevance of using quantitative models to estimate the impacts of climate change effects on food safety, and of considering both direct and indirect effects when assessing climate change impacts on crops and related food safety hazards.

Thermal controls of Yellowstone cutthroat trout and invasive fishes under climate change

We combine large observed data sets and dynamically downscaled climate data to explore historic and future (2050-2069) stream temperature changes over the topographically diverse Greater Yellowstone Ecosystem (elevation range = 824-4017 m). We link future stream temperatures with fish growth models to investigate how changing thermal regimes could influence the future distribution and persistence of native Yellowstone cutthroat trout (YCT) and competing invasive species. We find that stream temperatures during the recent decade (2000-2009) surpass the anomalously warm period of the 1930s. Climate simulations indicate air temperatures will warm by 1 °C to >3 °C over the Greater Yellowstone by mid-21st century, resulting in concomitant increases in 2050-2069 peak stream temperatures and protracted periods of warming from May to September (MJJAS). Projected changes in thermal regimes during the MJJAS growing season modify the trajectories of daily growth rates at all elevations with pronounced growth during early and late summer. For high-elevation populations, we find considerable increases in fish body mass attributable both to warming of cold-water temperatures and to extended growing seasons. During peak July to August warming, mid-21st century temperatures will cause periods of increased thermal stress, rendering some low-elevation streams less suitable for YCT. The majority (80%) of sites currently inhabited by YCT, however, display minimal loss (<10%) or positive changes in total body mass by midcentury; we attribute this response to the fact that many low-elevation populations of YCT have already been extirpated by historical changes in land use and invasions of non-native species. Our results further suggest that benefits to YCT populations due to warmer stream temperatures at currently cold sites could be offset by the interspecific effects of corresponding growth of sympatric, non-native species, underscoring the importance of developing climate adaptation strategies that reduce limiting factors such as non-native species and habitat degradation.

Assessment of Climate Change Impact on the Gharesou River Basin Using SWAT Hydrological Model

AbstractThe evaluation of climate change and its side effects on the hydrological processes of the basin can increasingly help in dealing with the challenges that water resource managers and planners face in future courses. These side effects are investigated using the simulation of hydrological processes with the help of physical rainfall‐runoff model. Hydrological models provide a framework for examining the relationship between climate and water resources. This research aims at the investigation of the effect of climate change on the runoff of Gharesou, which is one of the main branches of the “Karkheh” River in Iran during the periods 2040–2069. To achieve this, the distributed hydrological model Soil and Water Assessment Tool (SWAT) – a model that is sensitive to the changes in land, water, and climate – has been used with the aim of evaluating the impact of climate change on the hydrology of the Gharesou Basin. For this reason, first, the continuous distributed model of rainfall‐runoff SWAT for the period 1971–2000 has been calibrated and validated. Next, with the aim of evaluating the impact of climate change and global warming on the basin hydrology for the period 2040–2069, HadCM3‐AR4 global climate model data under the A2 scenario – from the SRES scenario set‐haves been downscaled. Eventually, the downscaled climate data haves been introduced in the SWAT model, and the future runoff changes have been studied. The results showed that the temperature increases in most of the months, and the precipitation rate exhibits a change in the range of ±30%. Moreover, the produced runoff in this period changes from −90 to 120% during different months.

Evaluating the fidelity of downscaled climate data on simulated wheat and maize production in the southeastern US

Crop models are one of the most commonly used tools to assess the impact of climate variability and change on crop production. However, before the impact of projected climate changes on crop production can be addressed, a necessary first step is the assessment of the inherent uncertainty and limitations of the forcing data used in these crop models. In this paper, we evaluate the simulated crop production using separate crop models for maize (summer crop) and wheat (winter crop) over six different locations in the Southeastern United States forced with multiple sources of actual and simulated weather data. The paper compares the crop production simulated by a crop model for maize and wheat during a historical period, using daily weather data from three sources: station observations, dynamically downscaled global reanalysis, and dynamically downscaled historical climate model simulations from two global circulation models (GCMs). The same regional climate model is used to downscale the global reanalysis and both global circulation models’ historical simulation. The average simulated yield derived from bias-corrected downscaled reanalysis or bias-corrected downscaled GCMs were, in most cases, not statistically different from observations. Statistical differences of the average yields, generated from observed or downscaled GCM weather, were found in some locations under rainfed and irrigated scenarios, and more frequently in winter (wheat) than in summer (maize). The inter-annual variance of simulated crop yield using GCM downscaled data was frequently overestimated, especially in summer. An analysis of the bias-corrected climate data showed that despite the agreement between the modeled and the observed means of temperatures, solar radiation, and precipitation, their intra-seasonal variances were often significantly different from observations. Therefore, due to this high intra-seasonal variability, a cautious approach is required when using climate model data for historical yield analysis and future climate change impact assessments.

Modeling of water balance response to an extreme future scenario in the Ötztal catchment, Austria

Abstract. The aim of the study was to investigate the impact of climate change on the water balance of the Ötztaler Ache catchment in Tyrol, Austria. For this purpose the conceptual hydrological model HBV-D REG was applied. First, the model was calibrated and validated using current observed climate and discharge data. Second, the calibrated model was applied with reanalysis data. Third, downscaled climate scenarios from 2010 to 2099 served as input data to the HBV-D REG. Thereby two extreme land cover scenarios were considered: for water balance modeling a constant glacier coverage from today and additionally for runoff simulations a complete loss of glaciered area. The downscaled climate data were generated with the expanded downscaling method. Scenario simulations indicated an increase in annual areal temperature by 3.4 °C and a slight decrease in annual areal precipitation by 89 mm in the next one hundred years. According to the hydrological modeling, these climate changes caused an increase in evapotranspiration and a decrease in snow coverage. Furthermore model simulations showed an increase in winter and spring runoff, whereas summer runoff was highly sensitive to glacier coverage and decreased with complete loss of glacier coverage.

Using an ensemble of downscaled climate model projections to assess impacts of climate change on the potential distribution of spruce and Douglas-fir forests in British Columbia

► We modelled future changes in climatic suitability for forests in British Columbia. ► We developed bioclimatic envelope models for spruce and Douglas-fir forests. ► Both models are more sensitive to changes in temperature than precipitation. ► Climatically suitable areas rapidly shift to higher elevations and latitudes. ► We quantified and mapped uncertainty due to differences among climate projections. Many of the world's forests are likely to face multiple stresses under a rapidly changing climate. Understanding the impact of climate change on tree species suitability is therefore crucial for forest management planning and policy development. We use the Douglas-fir and spruce (white spruce, Engelmann spruce, and interior spruce) forests of British Columbia as a case study. The impact of projected climate change on these forests was assessed using flexible bioclimatic envelope models appropriate for areas with sparse species locations records. Analysis of the model results focused on quantifying uncertainty due to differences between global climate models, emissions scenarios, and spatial resolution of climate data. To this end, future suitability was modelled using downscaled climate data from a collection of 10 climate projections that sampled across nine different global climate models and three different emissions scenarios (A2, A1B, and B1). All projections indicate a rapid shift in suitability for both spruce and Douglas-fir to higher elevations and latitudes relative to their current range. However, significant differences exist between the projections with regard to the pace, extent, and fine-scale details of these changes. This research was conducted as part of a collaborative interdisciplinary assessment involving both scientists and resource managers.