scholarly journals Formulating and testing a method for perturbing precipitation time series to reflect anticipated climatic changes

2016 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Stylianos Georgiadis ◽  
Ida Bülow Gregersen ◽  
Karsten Arnbjerg-Nielsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Models ◽  
Climate Change Impacts ◽  
Future Climate ◽  
High Temporal Resolution ◽  
Urban Water ◽  
Precipitation Time Series ◽  
Event Scale ◽  
The Impact

Abstract. Urban water infrastructure has very long planning horizons and planning is thus very dependent on reliable estimates on the impacts of climate change. Many urban water systems are designed using time series with a high temporal resolution. To assess the impact of climate change on these systems similarly high resolution precipitation time series for future climate are necessary. Climate models cannot at their current resolutions provide these time series at the relevant scales. Known methods for stochastic downscaling of climate change to urban hydrological scales have known shortcomings in constructing realistic climate changed precipitation time series at the sub-hourly scale. In the present study we present a deterministic methodology to perturb historical precipitation time series at minute scale to reflect non-linear expectations to climate change. The methodology shows good skill in meeting the expectations to climate change of extremes at event scale when evaluated at different timescales from the minute to the daily scale. The methodology also shows good skill with respect to representing expected changes to seasonal precipitation. The methodology is very robust to the actual magnitude of the expected changes as well as the direction of the changes (increase/decrease) even for situations where the extremes are increasing for seasons that in general should have a decreasing trend in precipitation. The methodology can provide planners with valuable time series representing future climate that can be used as input to urban hydrological models and give better estimates of climate change impacts on these systems.

scholarly journals Formulating and testing a method for perturbing precipitation time series to reflect anticipated climatic changes

2017 ◽  
Vol 21 (1) ◽  
pp. 345-355 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Stylianos Georgiadis ◽  
Ida Bülow Gregersen ◽  
Karsten Arnbjerg-Nielsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Models ◽  
Climate Change Impacts ◽  
Future Climate ◽  
High Temporal Resolution ◽  
Urban Water ◽  
Precipitation Time Series ◽  
Event Scale ◽  
The Impact

Abstract. Urban water infrastructure has very long planning horizons, and planning is thus very dependent on reliable estimates of the impacts of climate change. Many urban water systems are designed using time series with a high temporal resolution. To assess the impact of climate change on these systems, similarly high-resolution precipitation time series for future climate are necessary. Climate models cannot at their current resolutions provide these time series at the relevant scales. Known methods for stochastic downscaling of climate change to urban hydrological scales have known shortcomings in constructing realistic climate-changed precipitation time series at the sub-hourly scale. In the present study we present a deterministic methodology to perturb historical precipitation time series at the minute scale to reflect non-linear expectations to climate change. The methodology shows good skill in meeting the expectations to climate change in extremes at the event scale when evaluated at different timescales from the minute to the daily scale. The methodology also shows good skill with respect to representing expected changes of seasonal precipitation. The methodology is very robust against the actual magnitude of the expected changes as well as the direction of the changes (increase or decrease), even for situations where the extremes are increasing for seasons that in general should have a decreasing trend in precipitation. The methodology can provide planners with valuable time series representing future climate that can be used as input to urban hydrological models and give better estimates of climate change impacts on these systems.

scholarly journals Which climatic modeling to assess climate change impacts on vineyards?

OENO One ◽  
2017 ◽  
Vol 51 (2) ◽  
pp. 91 ◽  
Author(s):  
Hervé Quénol ◽  
Iñaki Garcia de Cortazar Atauri ◽  
Benjamin Bois ◽  
Andrew Sturman ◽  
Valérie Bonnardot ◽  
...  
Keyword(s):  
Climate Change ◽  
Climatic Change ◽  
Climate Models ◽  
Climate Change Impacts ◽  
Future Climate ◽  
Phenological Stages ◽  
Different Types ◽  
Assess Climate Change ◽  
The Impact

The impact of climatic change on viticulture is significant: main phenological stages appear earlier, wine characteristics are changing,... This clearly illustrates the point that the adaptation of viticulture to climate change is crucial and should be based on simulations of future climate. Several types of models exist and are used to represent viticultural climates at various scales. In this paper, we propose a review of different types of climate models (methodology and uncertainties) and then few examples of its application at the scale of wine growing regions worldwide.

scholarly journals Which climatic modeling to assess climate change impacts on vineyards?

OENO One ◽  
2017 ◽  
Vol 51 (2) ◽  
pp. 91-97 ◽  
Author(s):  
Hervé Quénol ◽  
Iñaki Garcia de Cortazar Atauri ◽  
Benjamin Bois ◽  
Andrew Sturman ◽  
Valérie Bonnardot ◽  
...  
Keyword(s):  
Climate Change ◽  
Climatic Change ◽  
Climate Models ◽  
Climate Change Impacts ◽  
Future Climate ◽  
Phenological Stages ◽  
Different Types ◽  
Assess Climate Change ◽  
The Impact

The impact of climatic change on viticulture is significant: main phenological stages appear earlier, wine characteristics are changing,... This clearly illustrates the point that the adaptation of viticulture to climate change is crucial and should be based on simulations of future climate. Several types of models exist and are used to represent viticultural climates at various scales. In this paper, we propose a review of different types of climate models (methodology and uncertainties) and then few examples of its application at the scale of wine growing regions worldwide.

scholarly journals Predicted Future Benefits for an Endemic Rodent in the Irano-Turanian Region

Climate ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 16
Author(s):  
Suzanna Meeussen ◽  
Anouschka Hof
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Species Distribution Modelling ◽  
Geographic Range ◽  
Future Climate ◽  
Future Climate Change ◽  
Distribution Ranges ◽  
Bioclimatic Variables ◽  
Suitable Area ◽  
The Impact

Climate change is expected to have an impact on the geographical distribution ranges of species. Endemic species and those with a restricted geographic range may be especially vulnerable. The Persian jird (Meriones persicus) is an endemic rodent inhabiting the mountainous areas of the Irano-Turanian region, where future desertification may form a threat to the species. In this study, the species distribution modelling algorithm MaxEnt was used to assess the impact of future climate change on the geographic distribution range of the Persian jird. Predictions were made under two Representative Concentration Pathways and five different climate models for the years 2050 and 2070. It was found that both bioclimatic variables and land use variables were important in determining potential suitability of the region for the species to occur. In most cases, the future predictions showed an expansion of the geographic range of the Persian jird which indicates that the species is not under immediate threat. There are however uncertainties with regards to its current range. Predictions may therefore be an over or underestimation of the total suitable area. Further research is thus needed to confirm the current geographic range of the Persian jird to be able to improve assessments of the impact of future climate change.

scholarly journals Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman–Scott weather generator

2016 ◽  
Vol 20 (4) ◽  
pp. 1387-1403 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Ole Bøssing Christensen ◽  
Karsten Arnbjerg-Nielsen ◽  
Peter Steen Mikkelsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Weather Generator ◽  
Spatial Extent ◽  
Urban Hydrology ◽  
Precipitation Time Series ◽  
Spatio Temporal

Abstract. Spatio-temporal precipitation is modelled for urban application at 1 h temporal resolution on a 2 km grid using a spatio-temporal Neyman–Scott rectangular pulses weather generator (WG). Precipitation time series used as input to the WG are obtained from a network of 60 tipping-bucket rain gauges irregularly placed in a 40 km  ×  60 km model domain. The WG simulates precipitation time series that are comparable to the observations with respect to extreme precipitation statistics. The WG is used for downscaling climate change signals from regional climate models (RCMs) with spatial resolutions of 25 and 8 km, respectively. Six different RCM simulation pairs are used to perturb the WG with climate change signals resulting in six very different perturbation schemes. All perturbed WGs result in more extreme precipitation at the sub-daily to multi-daily level and these extremes exhibit a much more realistic spatial pattern than what is observed in RCM precipitation output. The WG seems to correlate increased extreme intensities with an increased spatial extent of the extremes meaning that the climate-change-perturbed extremes have a larger spatial extent than those of the present climate. Overall, the WG produces robust results and is seen as a reliable procedure for downscaling RCM precipitation output for use in urban hydrology.

Timothy yield and nutritive value with a three-harvest system under the projected future climate in Canada

2014 ◽  
Vol 94 (2) ◽  
pp. 213-222 ◽  
Author(s):  
Qi Jing ◽  
Gilles Bélanger ◽  
Budong Qian ◽  
Vern Baron
Keyword(s):  
Climate Change ◽  
Nutritive Value ◽  
Climate Models ◽  
Phleum Pratense ◽  
Global Climate Models ◽  
Future Climate ◽  
Climate Scenarios ◽  
Intergovernmental Panel ◽  
Stochastic Weather Generator ◽  
The Impact

Jing, Q., Bélanger, G., Qian, B. and Baron, V. 2014. Timothy yield and nutritive value with a three-harvest system under the projected future climate in Canada. Can. J. Plant Sci. 94: 213–222. Timothy (Phleum pratense L.) is harvested twice annually in Canada but with projected climate change, an additional harvest may be possible. Our objective was to evaluate the impact on timothy dry matter (DM) yield and key nutritive value attributes of shifting from a two- to a three-harvest system under projected future climate conditions at 10 sites across Canada. Future climate scenarios were generated with a stochastic weather generator (AAFC-WG) using two global climate models under the forcing of two Intergovernmental Panel on Climate Change emission scenarios and, then, used by the CATIMO (Canadian Timothy Model) grass model to simulate DM yield and key nutritive value attributes. Under future climate scenarios (2040–2069), the additional harvest and the resulting three-harvest system are expected to increase annual DM yield (+0.46 to +2.47 Mg DM ha−1) compared with a two-harvest system across Canada but the yield increment will on average be greater in eastern Canada (1.88 Mg DM ha−1) and Agassiz (2.02 Mg DM ha−1) than in the prairie provinces of Canada (0.84 Mg DM ha−1). The DM yield of the first harvest in a three-harvest system is expected to be less than in the two-harvest system, while that of the second harvest would be greater. Decreases in average neutral detergent fibre (NDF) concentration (−19 g kg−1 DM) and digestibility (dNDF, −5 g kg−1 NDF) are also expected with the three-harvest system under future conditions. Our results indicate that timothy will take advantage of projected climate change, through taking a third harvest, thereby increasing annual DM production.

A comparative estimate of climate change impacts on cotton and maize in Greece

2018 ◽  
Vol 9 (4) ◽  
pp. 643-656 ◽  
Author(s):  
Dimitrios Voloudakis ◽  
Andreas Karamanos ◽  
Garyfalia Economou ◽  
John Kapsomenakis ◽  
Christos Zerefos
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Positive Impact ◽  
Climate Change Impacts ◽  
Emission Scenarios ◽  
Yield Increase ◽  
Yield Change ◽  
Maximum Value ◽  
The Impact ◽  
Western Greece

Abstract The impact of climate change on cotton and maize was estimated on the basis of three IPCC Emission Scenarios (A1B, A2, B2) for seven main agronomic areas during three periods, 1961–1990, 2021–2050 and 2071–2100. All climate models were assessed for their ability to identify the yield differences through the standardized discriminant function coefficients. Discriminant analysis was performed for each period. For cotton, using the A1B scenario, areas of Western Greece exhibited the most favourable results in terms of yield increase, compared to other regions, ranging up to the maximum value of +24%. This tendency became more pronounced towards the end of the century reaching an increase of +31%. In the A2 scenario, all the areas had a positive impact on their yield change rising up to 30% in areas of central Greece. A positive change for all regions was observed for scenario B2 ranging from +10% to +25%. In maize, the scenario A1B produced small changes in yields, not exceeding 5%. For A2 scenario, yield change varied from −5.7% to +3.6%. Scenario B2 gave more optimistic estimates of yield changes towards the end of the century, in some cases exceeding 5%.

scholarly journals Comparative study of conceptual versus distributed hydrologic modelling to evaluate the impact of climate change on future runoff in unregulated catchments

2019 ◽  
Vol 11 (2) ◽  
pp. 341-366 ◽  
Author(s):  
Hashim Isam Jameel Al-Safi ◽  
Hamideh Kazemi ◽  
P. Ranjan Sarukkalige
Keyword(s):  
Climate Change ◽  
Human Activities ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Future Climate ◽  
Distributed Model ◽  
Runoff Reduction ◽  
The Future ◽  
The Impact

Abstract The application of two distinctively different hydrologic models, (conceptual-HBV) and (distributed-BTOPMC), was compared to simulate the future runoff across three unregulated catchments of the Australian Hydrologic Reference Stations (HRSs), namely Harvey catchment in WA, and Beardy and Goulburn catchments in NSW. These catchments have experienced significant runoff reduction during the last decades due to climate change and human activities. The Budyko-elasticity method was employed to assign the influences of human activities and climate change on runoff variations. After estimating the contribution of climate change in runoff reduction from the past runoff regime, the downscaled future climate signals from a multi-model ensemble of eight global climate models (GCMs) of the Coupled Model Inter-comparison Project phase-5 (CMIP5) under the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios were used to simulate the future daily runoff at the three HRSs for the mid-(2046–2065) and late-(2080–2099) 21st-century. Results show that the conceptual model performs better than the distributed model in capturing the observed streamflow across the three contributing catchments. The performance of the models was relatively compatible in the overall direction of future streamflow change, regardless of the magnitude, and incompatible regarding the change in the direction of high and low flows for both future climate scenarios. Both models predicted a decline in wet and dry season's streamflow across the three catchments.

Some Pitfalls in Statistical Downscaling of Future Climate

2018 ◽  
Vol 99 (4) ◽  
pp. 791-803 ◽  
Author(s):  
John R. Lanzante ◽  
Keith W. Dixon ◽  
Mary Jo Nath ◽  
Carolyn E. Whitlock ◽  
Dennis Adams-Smith
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Climate Change Impacts ◽  
Future Climate ◽  
Added Value ◽  
Historical Period ◽  
Perfect Model

AbstractStatistical downscaling (SD) is commonly used to provide information for the assessment of climate change impacts. Using as input the output from large-scale dynamical climate models and observation-based data products, SD aims to provide a finer grain of detail and to mitigate systematic biases. It is generally recognized as providing added value. However, one of the key assumptions of SD is that the relationships used to train the method during a historical period are unchanged in the future, in the face of climate change. The validity of this assumption is typically quite difficult to assess in the normal course of analysis, as observations of future climate are lacking. We approach this problem using a “perfect model” experimental design in which high-resolution dynamical climate model output is used as a surrogate for both past and future observations.We find that while SD in general adds considerable value, in certain well-defined circumstances it can produce highly erroneous results. Furthermore, the breakdown of SD in these contexts could not be foreshadowed during the typical course of evaluation based on only available historical data. We diagnose and explain the reasons for these failures in terms of physical, statistical, and methodological causes. These findings highlight the need for caution in the use of statistically downscaled products and the need for further research to consider other hitherto unknown pitfalls, perhaps utilizing more advanced perfect model designs than the one we have employed.

scholarly journals Using 3PG to assess climate change impacts on management plan optimization of Eucalyptus plantations. A case study in Southern Brazil

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
João HN Palma ◽  
Rodrigo Hakamada ◽  
Gabriela Gonçalves Moreira ◽  
Silvana Nobre ◽  
Luiz Carlos Estraviz Rodriguez
Keyword(s):  
Climate Change ◽  
Climate Change Impacts ◽  
Management Plan ◽  
Empirical Models ◽  
Forest Growth ◽  
Future Climate ◽  
Empirical Equations ◽  
Complete Replacement ◽  
Physiological Models ◽  
The Impact

AbstractEucalyptus plantations around the world have been largely used by the paper industry. Optimizing the management of resources is a common practice in this highly competitive industry and new forest growth models may help to understand the impact of climate change on the decisions of the optimization processes. Current optimized management plans use empirical equations to predict future forest stands growth, and it is currently impractical to replace these empirical equations with physiological models due to data input requirements. In this paper, we present a different approach, by first carrying out a preliminary assessment with the process-based physiological model 3PG to evaluate the growth of Eucalyptus stands under climate change predictions. The information supplied by 3PG was then injected as a modifier in the projected yield that feeds the management plan optimizer allowing the interpretation of climate change impacts on the management plan. Modelling results show that although a general increase of rain with climate change is predicted, the distribution throughout the year will not favor the tree growth. On the contrary, rain will increase when it is less needed (summer) and decrease when it is most needed (winter), decreasing forest stand productivity between 3 and 5%, depending on the region and soil. Evaluation of the current optimized plan that kept constant the relation between wood price/cellulose ton shows a variation in different strategic management options and an overall increase of costs in owned areas between 2 and 4%, and a decrease of cumulated net present value, initially at 15% with later stabilization at 6–8%. This is a basic comparison to observe climate change effects; nevertheless, it provides insights into how the entire decision-making process may change due to a reduction in biomass production under future climate scenarios. This work demonstrates the use of physiological models to extract information that could be merged with existing and already implemented empirical models. The methodology may also be considered a preliminary alternative to the complete replacement of empirical models by physiological models. Our approach allows some insight into forest responses to different future climate conditions, something which empirical models are not designed for.

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