English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Projections of Climate Change Impacts on Flowering-Veraison Water Deficits for Riesling and Müller-Thurgau in Germany

Yang, C., Menz, C., De Abreu Jaffe, M. S., Costafreda-Aumedes, S., Moriondo, M., Leolini, L., Torres-Matallana, A., Molitor, D., Junk, J., Fraga, H., van Leeuwen, C., Santos, J. A. (2022): Projections of Climate Change Impacts on Flowering-Veraison Water Deficits for Riesling and Müller-Thurgau in Germany. - Remote Sensing, 14, 6, 1519.
https://doi.org/10.3390/rs14061519

Item is

Files

show Files
hide Files
:
remotesensing-14-01519-v2.pdf (Publisher version), 42MB
Name:
remotesensing-14-01519-v2.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show

Creators

show
hide
 Creators:
Yang, Chenyao1, Author
Menz, Christoph2, Author              
De Abreu Jaffe, Maxim Simões1, Author
Costafreda-Aumedes, Sergi1, Author
Moriondo, Marco1, Author
Leolini, Luisa1, Author
Torres-Matallana, Arturo1, Author
Molitor, Daniel1, Author
Junk, Jürgen1, Author
Fraga, Helder1, Author
van Leeuwen, Cornelis1, Author
Santos, João A.1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

Content

show
hide
Free keywords: -
 Abstract: With global warming, grapevine is expected to be increasingly exposed to water deficits occurring at various development stages. In this study, we aimed to investigate the potential impacts of projected climate change on water deficits from the flowering to veraison period for two main white wine cultivars (Riesling and Müller-Thurgau) in Germany. A process-based soil-crop model adapted for grapevine was utilized to simulate the flowering-veraison crop water stress indicator (CWSI) of these two varieties between 1976–2005 (baseline) and 2041–2070 (future period) based on a suite of bias-adjusted regional climate model (RCM) simulations under RCP4.5 and RCP8.5. Our evaluation indicates that the model can capture the early-ripening (Müller-Thurgau) and late-ripening (Riesling) traits, with a mean bias of prediction of ≤2 days and a well-reproduced inter-annual variability for more than 60 years. Under climate projections, the flowering stage is advanced by 10–20 days (higher in RCP8.5) between the two varieties, whereas a slightly stronger advancement is found for Müller-Thurgau than for Riesling for the veraison stage. As a result, the flowering-veraison phenophase is mostly shortened for Müller-Thurgau, whereas it is extended by up to two weeks for Riesling in cool and high-elevation areas. The length of phenophase plays an important role in projected changes of flowering-veraison mean temperature and precipitation. The late-ripening trait of Riesling makes it more exposed to increased summer temperature (mainly in August), resulting in a higher mean temperature increase for Riesling (1.5–2.5 °C) than for Müller-Thurgau (1–2 °C). As a result, an overall increased CWSI by up to 15% (ensemble median) is obtained for both varieties, whereas the upper (95th) percentile of simulations shows a strong signal of increased water deficit by up to 30%, mostly in the current winegrowing regions. Intensified water deficit stress can represent a major threat for high-quality white wine production, as only mild water deficits are acceptable. Nevertheless, considerable variabilities of CWSI were discovered among RCMs, highlighting the importance of efforts towards reducing uncertainties in climate change impact assessment.

Details

show
hide
Language(s): eng - English
 Dates: 2022-02-032022-03-182022-03-212022-03-21
 Publication Status: Finally published
 Pages: 23
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3390/rs14061519
Organisational keyword: RD2 - Climate Resilience
PIKDOMAIN: RD2 - Climate Resilience
Working Group: Hydroclimatic Risks
Regional keyword: Germany
Research topic keyword: Food & Agriculture
Research topic keyword: Weather
Research topic keyword: Climate impacts
Model / method: Machine Learning
Model / method: Open Source Software
MDB-ID: yes - 3413
OATYPE: Gold Open Access
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Remote Sensing
Source Genre: Journal, SCI, Scopus, p3, OA
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 14 (6) Sequence Number: 1519 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals426
Publisher: MDPI