Hydrological change--climate change impact simulations for Sweden

Climate change resulting from the enhanced greenhouse effect is expected to give rise to changes in hydrological systems. This hydrological change, as with the change in climate variables, will vary regionally around the globe. Impact studies at local and regional scales are needed to assess how different regions will be affected. This study focuses on assessment of hydrological impacts of climate change over a wide range of Swedish basins. Different methods of transferring the signal of climate change from climate models to hydrological models were used. Several hydrological model simulations using regional climate model scenarios from Swedish Regional Climate Modelling Programme (SWECLIM) are presented. A principal conclusion is that subregional impacts to river flow vary considerably according to whether a basin is in northern or southern Sweden. Furthermore, projected hydrological change is just as dependent on the choice of the global climate model used for regional climate model boundary conditions as the choice of anthropogenic emissions scenario.     

Water Planning and Hydro-Climatic Change in the Murray-Darling Basin,Australia

More than a third of humanity lives in regions with less than 1 million liters of fresh water per person per year. Population growth will increase water demand while climate change in arid and semi-arid areas may reduce water availability. The Murray-Darling Basin in Australia is a region where water reform and planning have been used to reduce consumptive extraction to better sustain river ecosystems under climate variability. Using actual data and previously published models that account for climate variability and climate change, the trade-off between water extractions and water essential to the long-term ecological function of river systems is analysed. The findings indicate that better water planning and a more complete understanding of the effects of irrigation on regional climate evapotranspiration could: (1) increase the overall benefits of consumptive and non-consumptive water use; (2) improve riparian environments under climate variability; and (3) be achieved with only small effects on the profits and gross value of food and fiber production.     

Climate change effects on river flow to the Baltic Sea

River flow to the Baltic Sea originates under a range of different climate regimes in a drainage basin covering some 1,600,000 km2. Changes to the climate in the Baltic Basin will not only affect the total amount of freshwater flowing into the sea, but also the distribution of the origin of these flows. Using hydrological modeling, the effects of future climate change on river runoff to the Baltic Sea have been analyzed. Four different climate change scenarios from the Swedish Regional Climate Modelling Programme (SWECLIM) were used. The resulting change to total mean annual river flow to the Baltic Sea ranges from -2% to +15% of present-day flow according to the different climate scenarios. The magnitude of changes within different subregions of the basin varies considerably, with the most severe mean annual changes ranging from -30% to +40%. However, common to all of the scenarios evaluated is a general trend of reduced river flow from the south of the Baltic Basin together with increased river flow from the north.     

Variability in climate change simulations affects needed long-term riverine nutrient reductions for the Baltic Sea

Changes to runoff due to climate change may influence management of nutrient loading to the sea. Assuming unchanged river nutrient concentrations, we evaluate the effects of changing runoff on commitments to nutrient reductions under the Baltic Sea Action Plan. For several countries, climate projections point to large variability in load changes in relation to reduction targets. These changes either increase loads, making the target more difficult to reach, or decrease them, leading instead to a full achievement of the target. The impact of variability in climate projections varies with the size of the reduction target and is larger for countries with more limited commitments. In the end, a number of focused actions are needed to manage the effects of climate change on nutrient loads: reducing uncertainty in climate projections, deciding on frameworks to identify best performing models with respect to land surface hydrology, and increasing efforts at sustained monitoring of water flow changes.     

Climate change impact assessment on Northeast China’s grain production

The international community is paying more attention to climate change because a consensus has been reached that climate change has an adverse effect not only on the environment but also on agriculture. Therefore, in this study, present and future climate datasets (obtained from general circulation models) including atmospheric carbon concentration were used to assess the impact of climate change on grain production for an important base of China (Northeast). An empirical model has been developed using climate and other additional variables (effective irrigation area, fertilizer, and labor force) to assess the effect of climate change on grain production. The results revealed that maximum temperature is a key climate determinant in grain production of the study area. Atmospheric carbon concentration showed a significant impact on grain outputs in most of the cases. During the analysis, it was observed that precipitation displayed a declining trend while an effective irrigation area showed positive non-significant contribution to grain production. Analysis based on different representative concentration pathways exhibited that maximum temperature may contribute negatively to grain production in the future. Overall, the analysis showed that climate change has a significant contribution to grain production. In conclusion, the implications for future research and policymakers have been addressed. Particularly, the importance of considering regional differences in adaptation planning in agricultural regions was also considered.

     

Climate change impact on distribution and abundance of wildlife species: an analytical approach using GIS

An analytical approach to modelling the likely impact of climate change on the distribution and abundance of wildlife species is described using examples from Scotland. Data for present day distribution of wildlife and habitat are analysed using map data describing geographic variation in climatic factors. Climate data for the present day and under specified scenarios of change are themselves modelled within a GIS; climate modelling uses meteorological station data, climate change scenarios developed from GCMs and a variety of spatial interpolation techniques. The analytical procedure generates hypotheses defining ecological relationships between species distribution and climatic factors (monthly, seasonal and annual data). These relationships are then used to model the distribution of the species directly from climate and predict impacts of climate change. The analysis takes account of both direct impacts of climate on wildlife and indirect effects manifested through habitat response to climate change. The analytical procedure is implemented as a generic tool for inductive spatial analysis in GIS.     

Sensitivity of a forest ecosystem model to climate parametrization schemes

An analysis of the climate parametrization scheme adopted by conventional forest gap models revealed that most models assume a constant climate and are difficult to calibrate consistently. Tree growth showed unrealistically sensitive threshold effects along ecological gradients of temperature and precipitation. A new parametrization was compared with its predecessors in terms of the model's capability to predict realistic steady state species compositions at three test sites in the Alps. Applying the new model variant ForClim to some climate-change scenarios suggests that forest gap models are highly sensitive to climate pametrizations, regardless of the realism with which they simulate forests for the current climate. Moreover, the precision of climate scenarios based on General Circulation Models (GCM), for example, falls short of ForClim's sensitivity. Climate-dependent processes in forest gap models should be rehearsed before these models are used in impact studies of climatic change.     

The Swedish Regional Climate Modelling Programme, SWECLIM: a review

The Swedish Regional Climate Modelling Programme, SWECLIM, was a 6.5-year national research network for regional climate modeling, regional climate change projections and hydrological impact assessment and information to a wide range of stakeholders. Most of the program activities focussed on the regional climate system of Northern Europe. This led to the establishment of an advanced, coupled atmosphere-ocean-hydrology regional climate model system, a suite of regional climate change projections and progress on relevant data and process studies. These were, in turn, used for information and educational purposes, as a starting point for impact analyses on different societal sectors and provided contributions also to international climate research.     

Climate change: potential impact on plant diseases

Global climate has changed since pre-industrial times. Atmospheric CO(2), a major greenhouse gas, has increased by nearly 30% and temperature has risen by 0.3 to 0.6 degrees C. The intergovernmental panel on climate change predicts that with the current emission scenario, global mean temperature would rise between 0.9 and 3.5 degrees C by the year 2100. There are, however, many uncertainties that influence these predictions. Despite the significance of weather on plant diseases, comprehensive analysis of how climate change will influence plant diseases that impact primary production in agricultural systems is presently unavailable. Evaluation of the limited literature in this area suggests that the most likely impact of climate change will be felt in three areas: in losses from plant diseases, in the efficacy of disease management strategies and in the geographical distribution of plant diseases. Climate change could have positive, negative or no impact on individual plant diseases. More research is needed to obtain base-line information on different disease systems. Most plant disease models use different climatic variables and operate at a different spatial and temporal scale than do the global climate models. Improvements in methodology are necessary to realistically assess disease impacts at a global scale.     

Group method of data handling (GMDH) in modelling of growth dynamics of trees irrigated with wastewater

This paper describes the formulation and analysis of growth dynamics models for trees irrigated with wastewater. The models can be used to obtain the characteristics of a species, depending on treatment conditions and climate factors, at all stages of growth. The experiments were carried out at the University of Patras to identify the characteristics (height rate and mortality) of the forest tree Pinus brutia cultivated under different treatment conditions. The growth dynamics models are designed on the basis of the group method of data handling. This principle generates sets of estimation models with different complexity and accuracy. By analysing their structures, qualitative features of the models may be assessed, and general linear models for different treatment cases compiled.     

Support vector machine―an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river?

Water quality forecasting in agricultural drainage river basins is difficult because of the complicated nonpoint source (NPS) pollution transport processes and river self-purification processes involved in highly nonlinear problems. Artificial neural network (ANN) and support vector model (SVM) were developed to predict total nitrogen (TN) and total phosphorus (TP) concentrations for any location of the river polluted by agricultural NPS pollution in eastern China. River flow, water temperature, flow travel time, rainfall, dissolved oxygen, and upstream TN or TP concentrations were selected as initial inputs of the two models. Monthly, bimonthly, and trimonthly datasets were selected to train the two models, respectively, and the same monthly dataset which had not been used for training was chosen to test the models in order to compare their generalization performance. Trial and error analysis and genetic algorisms (GA) were employed to optimize the parameters of ANN and SVM models, respectively. The results indicated that the proposed SVM models performed better generalization ability due to avoiding the occurrence of overtraining and optimizing fewer parameters based on structural risk minimization (SRM) principle. Furthermore, both TN and TP SVM models trained by trimonthly datasets achieved greater forecasting accuracy than corresponding ANN models. Thus, SVM models will be a powerful alternative method because it is an efficient and economic tool to accurately predict water quality with low risk. The sensitivity analyses of two models indicated that decreasing upstream input concentrations during the dry season and NPS emission along the reach during average or flood season should be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data and even trimonthly data are available, the SVM methodology developed here can easily be applied to other NPS-polluted rivers.     

Simulating uncertainty in climate-pest models with fuzzy numbers

Inputs in climate-pest models are commonly expressed as point estimates ('crisp' numbers), which implies perfect knowledge of the system in study. In reality, however, all model inputs harbor some level of uncertainty. This is particularly true for climate change impact assessments where the inputs (i.e., climate projections) are highly uncertain. In this study, uncertainties in climate projections were expressed as 'fuzzy' numbers; these are uncertain numbers for which one knows that there is a range of possible values and that some values are 'more possible' than others. A generic pest risk model incorporating the combined effects of temperature, soil moisture, and cold stress was implemented in a fuzzy spreadsheet environment and run with three climate scenarios: (1) present climate (control run); (2) crisp climate change; and (3) fuzzy climate change. Under the crisp climate change scenario, winter and summer temperatures and precipitation were altered using best estimates (averaged predictions from the 1995 assessment report of the Intergovernmental Panel on Climate Change [IPCC]). Under the fuzzy scenario, climate changes were expressed as triangular fuzzy numbers, utilizing the extremes (lowest and highest predictions from the IPCC report) in addition to the best estimates. Under each scenario, environmental favorability was calculated for six locations in two geographical regions (Central North America and Southern Europe) with two hypothetical pest species having temperate or mediterranean climate requirements. Simulations with the crisp climate change scenario suggested only minor changes in overall environmental favorability compared with the control run. When simulations were conducted with the fuzzy climate change scenario, however, important changes in environmental favorability emerged, particularly in Southern Europe. In that region, the possibility of considerably increased winter precipitation led to increased values of environmental favorability. However, the simulations also showed that this result harbored a very broad range of possible outcomes. The results support the notion that uncertainty in climate change projections must be reduced before reliable impact assessments can be achieved.     

Impact of Climate Change on Fish Population Dynamics in the Baltic Sea: A Dynamical Downscaling Investigation

Understanding how climate change, exploitation and eutrophication will affect populations and ecosystems of the Baltic Sea can be facilitated with models which realistically combine these forcings into common frameworks. Here, we evaluate sensitivity of fish recruitment and population dynamics to past and future environmental forcings provided by three ocean-biogeochemical models of the Baltic Sea. Modeled temperature explained nearly as much variability in reproductive success of sprat (Sprattus sprattus; Clupeidae) as measured temperatures during 1973-2005, and both the spawner biomass and the temperature have influenced recruitment for at least 50 years. The three Baltic Sea models estimate relatively similar developments (increases) in biomass and fishery yield during twenty-first century climate change (ca. 28 % range among models). However, this uncertainty is exceeded by the one associated with the fish population model, and by the source of global climate data used by regional models. Knowledge of processes and biases could reduce these uncertainties.     

Climate change impact on water quality: model results from southern Sweden

Starting from six regional climate change scenarios, nitrogen leaching from arable-soil, water discharge, and nitrogen retention was modeled in the R?nne? catchment. Additionally, biological response was modeled in the eutrophic Lake Ringsj?n. The results are compared with similar studies on other catchments. All scenarios gave similar impact on water quality but varied in quantities. However, one scenario resulted in a different transport pattern due to less-pronounced seasonal variations in the hydrology. On average, the study shows that, in a future climate, we might expect: i) increased concentrations of nitrogen in the arable root zone (+50%) and in the river (+13%); ii) increased annual load of nitrogen from land to sea (+22%) due to more pronounced winter high flow; moreover, remote areas in the catchment may start to contribute to the outlet load; iii) radical changes in lake biochemistry with increased concentrations of total phosphorus (+50%), total nitrogen (+20%), and planktonic algae such as cyanobacteria (+80%).     

Enhanced science–stakeholder communication to improve ecosystem model performances for climate change impact assessments

In recent years, climate impact assessments of relevance to the agricultural and forestry sectors have received considerable attention. Current ecosystem models commonly capture the effect of a warmer climate on biomass production, but they rarely sufficiently capture potential losses caused by pests, pathogens and extreme weather events. In addition, alternative management regimes may not be integrated in the models. A way to improve the quality of climate impact assessments is to increase the science–stakeholder collaboration, and in a two-way dialog link empirical experience and impact modelling with policy and strategies for sustainable management. In this paper we give a brief overview of different ecosystem modelling methods, discuss how to include ecological and management aspects, and highlight the importance of science–stakeholder communication. By this, we hope to stimulate a discussion among the science–stakeholder communities on how to quantify the potential for climate change adaptation by improving the realism in the models.     

Atmospheric composition change: Climate–Chemistry interactions

Chemically active climate compounds are either primary compounds like methane (CH₄), removed by oxidation in the atmosphere, or secondary compounds like ozone (O₃), sulfate and organic aerosols, both formed and removed in the atmosphere. Man-induced climate–chemistry interaction is a two-way process: Emissions of pollutants change the atmospheric composition contributing to climate change through the aforementioned climate components, and climate change, through changes in temperature, dynamics, the hydrological cycle, atmospheric stability, and biosphere-atmosphere interactions, affects the atmospheric composition and oxidation processes in the troposphere. Here we present progress in our understanding of processes of importance for climate–chemistry interactions, and their contributions to changes in atmospheric composition and climate forcing. A key factor is the oxidation potential involving compounds like O₃ and the hydroxyl radical (OH). Reported studies represent both current and future changes. Reported results include new estimates of radiative forcing based on extensive model studies of chemically active climate compounds like O₃, and of particles inducing both direct and indirect effects. Through EU projects like ACCENT, QUANTIFY, and the AeroCom project, extensive studies on regional and sector-wise differences in the impact on atmospheric distribution are performed. Studies have shown that land-based emissions have a different effect on climate than ship and aircraft emissions, and different measures are needed to reduce the climate impact. Several areas where climate change can affect the tropospheric oxidation process and the chemical composition are identified. This can take place through enhanced stratospheric–tropospheric exchange of ozone, more frequent periods with stable conditions favoring pollution build up over industrial areas, enhanced temperature induced biogenic emissions, methane releases from permafrost thawing, and enhanced concentration through reduced biospheric uptake. During the last 5–10 years, new observational data have been made available and used for model validation and the study of atmospheric processes. Although there are significant uncertainties in the modeling of composition changes, access to new observational data has improved modeling capability. Emission scenarios for the coming decades have a large uncertainty range, in particular with respect to regional trends, leading to a significant uncertainty range in estimated regional composition changes and climate impact.     

Ensemble Modeling of the Baltic Sea Ecosystem to Provide Scenarios for Management

We present a multi-model ensemble study for the Baltic Sea, and investigate the combined impact of changing climate, external nutrient supply, and fisheries on the marine ecosystem. The applied regional climate system model contains state-of-the-art component models for the atmosphere, sea ice, ocean, land surface, terrestrial and marine biogeochemistry, and marine food-web. Time-dependent scenario simulations for the period 1960–2100 are performed and uncertainties of future projections are estimated. In addition, reconstructions since 1850 are carried out to evaluate the models sensitivity to external stressors on long time scales. Information from scenario simulations are used to support decision-makers and stakeholders and to raise awareness of climate change, environmental problems, and possible abatement strategies among the general public using geovisualization. It is concluded that the study results are relevant for the Baltic Sea Action Plan of the Helsinki Commission.     

Bias correction of regional climate model simulations for the impact assessment of the climate change in Egypt

This study projected the future temperature change for Egypt during the late of this century (2071–2100) for three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5), by correcting regional climate model (RCM) simulations of average, maximum, and minimum daily temperature with reference to observed data of 26 stations. Four commonly used methods of bias correction have been applied and evaluated: linear scaling, variance scaling, and theoretical and empirical quantile mapping. The compromise programing results of the applied evaluation criteria show that the best method is the variance scaling, and thus it was applied to transfer the correction factor to the projections. All temperature indices are expected to increase significantly under all scenarios and reach the highest record by the end of the century, i.e., the expected increase in average, maximum, and minimum temperature ranges between 4.08–7.41 °C, 4.55–7.89 °C, and 3.88–7.23 °C, respectively. The largest temperature rise will occur in the summer, with the highest increase in the maximum (minimum) temperature of 10.9 °C (10 °C) in July and August under RCP8.5. The maximum (minimum) winter temperature, on the other hand, will drop by a maximum of 2 °C (1.35 °C) under RCP2.6. The Western Desert and Upper Egypt are the regions most affected by climate change, while the northern region of Egypt is the least affected. These findings would help in impact assessment and adaptation strategies and encourage further investigation to evaluate various climate models in order to obtain a comprehensive assessment of the climate change impacts on different hydrometeorological processes in Egypt.

     

Power models and average ship parameter effects on marine emissions inventories

Maritime greenhouse gas emissions are projected to increase significantly by 2050, highlighting the need for reliable inventories as a first step in analyzing ship emission control policies. The impact of ship power models on marine emissions inventories has garnered little attention, with most inventories employing simple, load-factor-based models to estimate ship power consumption. The availability of more expansive ship activity data provides the opportunity to investigate the inventory impacts of adopting complex power models. Furthermore, ship parameter fields can be sparsely populated in ship registries, making gap-filling techniques and averaging processes necessary. Therefore, it is important to understand of the impact of averaged ship parameters on ship power and emission estimations. This paper examines power estimation differences between results from two complex, resistance-based and two simple, load-factor-based power models on a baseline inventory with unique ship parameters. These models are additionally analyzed according to their sensitivities toward average ship parameters. Automated Identification System (AIS) data from a fleet of commercial marine vessels operating over a 6-month period off the coast of the southwestern United States form the basis of the analysis. To assess the inventory impacts of using averaged ship parameters, fleet-level carbon dioxide (CO₂) emissions are calculated using ship parameter data averaged across ship types and their subtype size classes. Each of the four ship power models are used to generate four CO₂ emissions inventories, and results are compared with baseline estimates for the same sample fleet where no averaged values were used. The results suggest that a change in power model has a relatively high impact on emission estimates. They also indicate relatively little sensitivity, by all power models, to the use of ship characteristics averaged by ship and subtype.

Implications: Commercial marine vessel emissions inventories were calculated using four different models for ship engine power. The calculations used 6 months of Automated Identification System (AIS) data from a sample of 248 vessels as input data. The results show that more detailed, resistance-based models tend to estimate a lower propulsive power, and thus lower emissions, for ships than traditional load-factor-based models. Additionally, it was observed that emission calculations using averaged values for physical ship parameters had a minimal impact on the resulting emissions inventories.