Effect of Forecast Climate Changes on Water Needs of Giant Miscanthus Cultivated in the Kuyavia Region in Poland

Giant miscanthus is a vigorously growing energy plant, popularly used for biofuels production. It is a grass with low soil and water requirements, although its productivity largely depends on complementary irrigation, especially in the first year of cultivation. The aim of the study was to assess the impact of the forecast climate changes, mainly air temperature increase, on the water needs of giant miscanthus during the growing season in 2021–2050 in the Kuyavia region (central Poland). The years 1981–2010 as the reference period were applied. The meteorological data was based on the regional climate change model RM5.1 with boundary conditions from the global ARPEGE model for the SRES A1B emission scenario. Crop evapotranspiration, calculated using the Penman-Monteith method and crop coefficients, was assumed as a measure of water needs. The study results showed that in view of the expected temperature changes, in the forecast period 2021–2050, the giant miscanthus water needs will increase by 10%. The highest monthly increase may occur in August (16%) and in September (23%). In the near future, the increase in water needs of giant miscanthus will necessitate the use of supplementary irrigation. Hence the results of this study may contribute to increasing the efficiency of water use, and thus to the rational management of irrigation treatments and plant energy resources in the Kuyavia region.

Storylines of UK drought based on the 2010-2012 event

<p>Spatially extensive multi-year hydrological droughts threaten water resources availability and incur significant environmental and socio-economic consequences. Given the impacts of climate change, the UK is expected to remain vulnerable to future multi-year droughts. Existing approaches to quantify hydrological impacts of climate change are often scenario-driven and may miss out plausible outcomes with significant impacts. Event-based storyline approaches aim to quantify “storylines” of how a singular event with significant impacts could hypothetically have unfolded in alternative ways from plausible changes to its causal factors under present and future climate. This study uses the 2010-2012 UK drought, the most recent period of severe hydrological drought, as a basis, to create counterfactual storylines based on changes to 1) precondition severity, 2) temporal drought sequence and 3) climate change. Model simulations are performed using the GR4J hydrological model and drought characteristics for each counterfactual storyline is calculated using the Standardized Streamflow Index at multiple accumulation periods.</p><p>The storylines show that maximum intensity, mean deficit and duration of the 2010-2012 drought were highly conditioned by its meteorological preconditions. Recovery time from progressively drier preconditions reflect both spatial variation in drought characteristics and the influence of physical catchment characteristics, particularly hydrogeology, in the propagation of multi-year droughts. Plausible storylines of an additional dry year with dry winter conditions repeated before the observed drought or replacing the observed dramatic drought termination confirm the vulnerability of UK catchments to a “three dry winter” scenario. Application of the UKCP18 projections at four global warming levels explore the impacts of the drought in a warmer world. Drought conditions of the storylines could have matched and exceeded that experienced in past severe droughts, especially for southern catchments. The construction of storylines based on observed events can complement existing methods to stress test UK catchments against plausible unrealized droughts.</p>

THE IMPACTS OF CLIMATE CHANGE ON ENERGY-WATER SYSTEMS AND ECONOMIC ANALYSIS

In order to develop a valued decision-support system for climate alteration policy and planning, recognizing the regionally-specific features of the climate change, energy-water nexus, and the history of the current and possible future climate, water and energy supply systems is necessary. This paper presents an integrated climate change, water/energy modeling platform which allows tailored climate alteration and water-energy assessments. This modeling platform is established and described in details based on particular regional circumstances. The modeling platform involves linking three different models, including the climate change model from Coupled Model Intercomparison Project Phase 5 under the most severe scenario (Representative Concentration Pathways, Water Evaluation, and Planning system and the Long-range Energy Alternatives Planning system). This is to understand the impacts of climate variability (changes in temperature and precipitation) on water and electricity consumption in Zayandeh Rud River Basin (Central Iran) for the current (1971–2005) and future time period (2006–2040). Climate models have projected that the temperature will increase by 7 °C and precipitation will decrease by 44%, it is also proposed that electricity imports will rise during a severe dry scenario in the basin, while power generation will decrease around 8%.

Current and Future Potential Distribution Areas of Carphoborus minimus (Fabricius, 1798) in Turkey

Our study aims to model the current and future (2041-2060 and 2081-2100) distribution areas of Carphoborus minimus (Fabricius, 1798) according to SSP2 and SSP5 emission scenarios. Current and future potential distribution areas of the species were predicted using the maximum entropy (MaxEnt) method and the MIROC6 climate change model. Finally, change analysis was performed to reveal the distributional changes between the present and future distribution ranges of the species. Our study has made it clear that the most impactful bioclimatic factors on the distribution of the species are temperature seasonality, isothermality, and precipitation of the driest quarter. Model results showed that the suitable distribution range for C. minimus is western and southern Anatolia. Models presented that the species will expand its distribution area through northern Anatolia in the 2050s and 2090s due to the changing ecological environment. In addition to that, the results of the change analysis showed that suitable distribution areas for the species will increase between 7% and 13.5% with time. Therefore, the species can become a new threat to the forests of Northern Anatolia. As a result, state forestry authorities should take precautions against this bark beetle species in the pine stands of northern Turkey in the future. Moreover, land-use plans should be developed to prevent the degradation of forest areas and to plan suitable trees for afforestation.

An empirical model for the raw wood assortment price predicting – case study in Slovakia

Sales of timber, which represent the main source of forest management income, are essential for the economic welfare of forest businesses. Planning the timber sale management faces a certain amount of uncertainty and risk in such difficult conditions of climate change. Model scenarios make preparation for potential future development possible. The aim of the study was to create a prediction model of coniferous and non-coniferous sawlogs for the area of the Central Europe. The objective of the model was to estimate the variations in the price of coniferous or non-coniferous sawlogs following a linear regression equation in the analysed time series from 2001 to 2017. The price of coniferous sawlogs was significantly affected in a negative way by the amount of incidental fellings and in a positive way by the Gross Domestic Product. The price of the non-coniferous sawlogs was significantly affected in a positive way by the GDP and the volume of non-coniferous sawlog export. These factors caused a non-elastic response of the coniferous sawlog price. The impact of these factors depends to a great extent on the wood species composition of the forests in the Slovak Republic. The model also can be set for conditions of other countries when considering their economic indicators.

A Simplified Climate Change Model and Extreme Weather Model Based on a Machine Learning Method

The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 (“normal”), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada’s climate change. In 2025, the climate level of Canada will become “a little bad” based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change.

Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model

Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall–runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate change in terms of temperature and precipitation as an important predictor to minimize the gap between water supply and demand. In this study, temperature and precipitation were predicted for the period between 2046 and 2065, in the context of climate change, based on the A1B scenario and the HAD-CM3 model. Runoff volume was then predicted with the IHACRES model. A new, nature-inspired optimization algorithm, named the shark algorithm, was examined. Climate change model results were utilized by the shark algorithm to generate an optimal operation rule for dam and reservoir water systems to minimize the gap between water supply and demand for irrigation purposes. The proposed model was applied for the Aydoughmoush Dam in Iran. Results showed that, due to the decrease in water runoff to the reservoir and the increase in irrigation demand, serious irrigation deficits could occur downstream of the Aydoughmoush Dam.

Binned Relative Environmental Change Indicator (BRECI): A tool to communicate the nature of differences between environmental niche model outputs

Niche models are now widely used in many branches of the biological sciences and are often used to contrast the distribution of favouroble environments between regionsa or under changes in environmental conditions such as anthropogenic climate change. Model performance and quality assessment are accepted as best-practice when using these models. One aspect that has received far less attention is developing methods to communicate the degree and nature of changes between model outputs (typically as raster maps). The method described in this paper, Binned Environmental Change Index (BRECI), seeks to address this shortfall in communicating model results.