Downscaling of climate change scenarios for a high resolution, site–specific assessment of drought stress risk for two viticultural regions with heterogeneous landscapes

2021 ◽  
Author(s):  
Marco Hofmann ◽  
Claudia Volosciuk ◽  
Martin Dubrovský ◽  
Douglas Maraun ◽  
Hans R. Schultz

Abstract. Extended periods without precipitation observed for example in Central Europe including Germany during the seasons from 2018 to 2020, can lead to water deficit and yield and quality losses for grape and wine production. However, irrigation infrastructure is largely non–existent. Regional climate models project changes of precipitation amounts and patterns, indicating an increase in frequency of occurrence of comparable situations in the future. In order to assess possible impacts of climate change on the water budget of grapevines, a water balance model was developed, which accounts for the large heterogeneity of vineyards with respect to their soil water storage capacity, evapotranspiration as a function of slope and aspect, and viticultural management practices. The model was fed with data from soil maps (soil type and plant available water capacity), a digital elevation model, the European Union (EU) vineyard–register, observed weather data and future weather data provided by regional climate models and a stochastic weather generator. This allowed conducting a risk assessment of the drought stress occurrence for the wine–producing regions Rheingau and Hessische Bergstraße in Germany on the scale of individual vineyard plots. The simulations showed that the risk for drought stress varies substantially between vineyard sites but might increase for steep–slope regions in the future. Possible adaptation measures depend highly on local conditions and to make targeted use of the resource water, an intense interplay of different wine-industry stakeholders, research, knowledge transfer, and local authorities will be required.

2021 ◽  
Vol 11 (5) ◽  
pp. 2403
Author(s):  
Daniel Ziche ◽  
Winfried Riek ◽  
Alexander Russ ◽  
Rainer Hentschel ◽  
Jan Martin

To develop measures to reduce the vulnerability of forests to drought, it is necessary to estimate specific water balances in sites and to estimate their development with climate change scenarios. We quantified the water balance of seven forest monitoring sites in northeast Germany for the historical time period 1961–2019, and for climate change projections for the time period 2010–2100. We used the LWF-BROOK90 hydrological model forced with historical data, and bias-adjusted data from two models of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) downscaled with regional climate models under the representative concentration pathways (RCPs) 2.6 and 8.5. Site-specific monitoring data were used to give a realistic model input and to calibrate and validate the model. The results revealed significant trends (evapotranspiration, dry days (actual/potential transpiration < 0.7)) toward drier conditions within the historical time period and demonstrate the extreme conditions of 2018 and 2019. Under RCP8.5, both models simulate an increase in evapotranspiration and dry days. The response of precipitation to climate change is ambiguous, with increasing precipitation with one model. Under RCP2.6, both models do not reveal an increase in drought in 2071–2100 compared to 1990–2019. The current temperature increase fits RCP8.5 simulations, suggesting that this scenario is more realistic than RCP2.6.


2013 ◽  
Vol 13 (2) ◽  
pp. 263-277 ◽  
Author(s):  
C. Dobler ◽  
G. Bürger ◽  
J. Stötter

Abstract. The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.


2011 ◽  
Vol 11 (12) ◽  
pp. 3275-3291 ◽  
Author(s):  
M. Ruiz-Ramos ◽  
E. Sánchez ◽  
C. Gallardo ◽  
M. I. Mínguez

Abstract. Crops growing in the Iberian Peninsula may be subjected to damagingly high temperatures during the sensitive development periods of flowering and grain filling. Such episodes are considered important hazards and farmers may take insurance to offset their impact. Increases in value and frequency of maximum temperature have been observed in the Iberian Peninsula during the 20th century, and studies on climate change indicate the possibility of further increase by the end of the 21st century. Here, impacts of current and future high temperatures on cereal cropping systems of the Iberian Peninsula are evaluated, focusing on vulnerable development periods of winter and summer crops. Climate change scenarios obtained from an ensemble of ten Regional Climate Models (multimodel ensemble) combined with crop simulation models were used for this purpose and related uncertainty was estimated. Results reveal that higher extremes of maximum temperature represent a threat to summer-grown but not to winter-grown crops in the Iberian Peninsula. The study highlights the different vulnerability of crops in the two growing seasons and the need to account for changes in extreme temperatures in developing adaptations in cereal cropping systems. Finally, this work contributes to clarifying the causes of high-uncertainty impact projections from previous studies.


Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 23 ◽  
Author(s):  
Carlos Garijo ◽  
Luis Mediero

Climate model projections can be used to assess the expected behaviour of extreme precipitations in the future due to climate change. The European part of the Coordinated Regional Climate Downscalling Experiment (EURO-CORDEX) provides precipitation projections for the future under various representative concentration pathways (RCPs) through regionalised Global Climate Model (GCM) outputs by a set of Regional Climate Models (RCMs). In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX are analysed for the Iberian Peninsula. Precipitation quantiles for a set of probabilities of non-exceedance are estimated by using the Generalized Extreme Value (GEV) distribution and L-moments. Precipitation quantiles expected in the future are compared with the precipitation quantiles in the control period for each climate model. An approach based on Monte Carlo simulations is developed in order to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period. Thus, statistically significant changes are identified. The higher the significance threshold, the fewer cells with significant changes are identified. Consequently, a set of maps are obtained in order to assist the decision-making process in subsequent climate change studies.


2021 ◽  
Author(s):  
Paola Nanni ◽  
David J. Peres ◽  
Rosaria E. Musumeci ◽  
Antonino Cancelliere

&lt;p&gt;Climate change is a phenomenon that is claimed to be responsible for a significant alteration of the precipitation regime in different regions worldwide and for the induced potential changes on related hydrological hazards. In particular, some consensus has raised about the fact that climate changes can induce a shift to shorter but more intense rainfall events, causing an intensification of urban and flash flooding hazards. &amp;#160;Regional climate models (RCMs) are a useful tool for trying to predict the impacts of climate change on hydrological events, although their application may lead to significant differences when different models are adopted. For this reason, it is of key importance to ascertain the quality of regional climate models (RCMs), especially with reference to their ability to reproduce the main climatological regimes with respect to an historical period. To this end, several studies have focused on the analysis of annual or monthly data, while few studies do exist that analyze the sub-daily data that are made available by the regional climate projection initiatives. In this study, with reference to specific locations in eastern Sicily (Italy), we first evaluate historical simulations of precipitation data from selected RCMs belonging to the Euro-CORDEX (Coordinated Regional Climate Downscaling Experiment for the Euro-Mediterranean area) with high temporal resolution (three-hourly), in order to understand how they compare to fine-resolution observations. In particular, we investigate the ability to reproduce rainfall event characteristics, as well as annual maxima precipitation at different durations. With reference to rainfall event characteristics, we specifically focus on duration, intensity, and inter-arrival time between events. Annual maxima are analyzed at sub-daily durations. We then analyze the future simulations according to different Representative concentration scenarios. The proposed analysis highlights the differences between the different RCMs, supporting the selection of the most suitable climate model for assessing the impacts in the considered locations, and to understand what trends for intense precipitation are to be expected in the future.&lt;/p&gt;


Author(s):  
Toshichika Iizumi ◽  
Mikhail A. Semenov ◽  
Motoki Nishimori ◽  
Yasushi Ishigooka ◽  
Tsuneo Kuwagata

We developed a dataset of local-scale daily climate change scenarios for Japan (called ELPIS-JP) using the stochastic weather generators (WGs) LARS-WG and, in part, WXGEN. The ELPIS-JP dataset is based on the observed (or estimated) daily weather data for seven climatic variables (daily mean, maximum and minimum temperatures; precipitation; solar radiation; relative humidity; and wind speed) at 938 sites in Japan and climate projections from the multi-model ensemble of global climate models (GCMs) used in the coupled model intercomparison project (CMIP3) and multi-model ensemble of regional climate models form the Japanese downscaling project (called S-5-3). The capability of the WGs to reproduce the statistical features of the observed data for the period 1981–2000 is assessed using several statistical tests and quantile–quantile plots. Overall performance of the WGs was good. The ELPIS-JP dataset consists of two types of daily data: (i) the transient scenarios throughout the twenty-first century using projections from 10 CMIP3 GCMs under three emission scenarios (A1B, A2 and B1) and (ii) the time-slice scenarios for the period 2081–2100 using projections from three S-5-3 regional climate models. The ELPIS-JP dataset is designed to be used in conjunction with process-based impact models (e.g. crop models) for assessment, not only the impacts of mean climate change but also the impacts of changes in climate variability, wet/dry spells and extreme events, as well as the uncertainty of future impacts associated with climate models and emission scenarios. The ELPIS-JP offers an excellent platform for probabilistic assessment of climate change impacts and potential adaptation at a local scale in Japan.


2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Dongwoo Jang

Climate change scenarios are used for predicting future precipitation. More detailed regional climate change scenarios are being used through dynamic downscale based on global circulation model results. There is a global tendency to utilize simulated precipitation data from downscaled regional climate models (RCMs) suitable for each country. In Korea, there are studies for improving the accuracy of climate change scenario precipitation forecasts compared with observed precipitation. In this study, the precipitation of five regional climate models and actual observed precipitation provided in Korea are applied to ANN (artificial neural network), which suggests ways to improve prediction accuracy for precipitation. The ANN ensemble of RCMs simulates the actual observed precipitation more accurately than the individual RCM. In particular, it is more effective inland than in coastal areas, where precipitation patterns are complex. Pearson correlation coefficient of ANN is high as 0.04 compared with MRA. It is expected that more detailed analysis will be possible if it is applied not only to four cities but also to other regions in Korea. If observed precipitation data are collected in sufficient quantity, the applicability of the ANN model will widen.


Author(s):  
Andre Schardong ◽  
Slobodan P. Simonovic

Global Climate Models (GCMs) are currently the most powerful tools for accessing changes in the hydrological regime at the watershed scale due to climate change and variability. GCMs, however, have limitations due to their coarse spatial and temporal resolutions.  Regional Climate Models (RCMs) are often referred to as suitable alternatives due to their higher resolution of the long-term climate projections. It is expected that RCMs are better for simulating extreme conditions than the GCMs. This  present work, investigate the difference in updated IDF (Intensity-Duration-Frequency) relationships developed using GCMs and RCMs. The IDF updating method implemented with the IDF_CC tool for Canada has been used for comparison. The analyses are conducted using 369 selected Environment and Climate Change Canada hydro-meteorological stations from the IDF_CC tool database with record length longer than 20 years. Results for the future period (2020-2100), are based on multi-model ensembles of (i) the RCMs from the NA-CORDEX (North-American Coordinated Regional Climate Downscaling Experiment) project (ensemble 1) (ii) a sub-set of six GCMs from the GCMs available in the IDF_CC tool used as drivers for the RCMs (ensemble 2) and (iii) all 24 GCMs from the IDF_CC tool database (ensemble 3). One representative concentration pathway (RCP), RCP 8.5, is used in the analysis. The RCMs from the NA-CORDEX project selected for this study use six GCMs as drivers to produce the future predictions for the North American continent, including Canada. Two metrics are applied for the comparison of results: (i) the difference in projected precipitation using the multi-model ensemble median; and (ii) the difference in uncertainty range. The uncertainty range is defined in this study as the percentage projected change in future, 25 to 75 quantiles obtained using the RCMs a GCMs ensembles. The regional models from the NA-CORDEX project generated lower extreme precipitation projections than the GCMs for the stations located in the Canadian prairies (provinces of Alberta, Saskatchewan, Manitoba). Stations located at the East and West coasts of Canada show a smaller difference in the projected extremes obtained using GCMs and RCMs. The use of RCMs shows increase in uncertainty when compared to GCMs. This result indicates that even when using regional climate models, it’s advisable to extend the analyses and include as many as possible models from different climate centers.


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