Climate Data for Agricultural Applications: Downscaling and Scenarios

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biometeorology".

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 14099

Special Issue Editors


E-Mail Website
Guest Editor
Agriculture and Agri-Food Canada, Ottawa, ON, Canada
Interests: climate change scenarios; climate change and agriculture; climate variability; climate change adaptation; statistical analysis

E-Mail Website
Guest Editor
Agriculture and Agri-Food Canada, Ottawa, ON, Canada
Interests: earth observation; agro-climate data; soil moisture; drought monitoring; crop yield estimation
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
College of Natural Resources and Environment, Northwest Agriculture and Forestry University, Yangling 712100, China
Interests: hydrological processes; water quality; tracer techniques; land use change effects
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Atmosphere dedicates this Special Issue to climate data for agricultural applications with a focus on climate downscaling and future climate scenarios. Climate data are essential for a wide range of agricultural applications, including climate risk assessements, crop yield forecasting, and climate change impacts and adaptation studies. In addition to climate data observed at climate stations, data derived from remote sensing data, numerical weather forecasts, and climate simulations are increasingly becoming essential for agricutlural applications. Spatial and temporal scales of these data are required to match those in the agricultural applications, where field sizes are small and often the base unit for crop simulation models. Downscaling the output of global climate models (GCMs), for example, is widely considered necessary for climate change impact studies to be relevant. Furthermore, future climate scenarios are the basis for climate change impacts and adaptation studies, and the scale of these data sets contributes to the uncertainty of the projected climate change impacts on the agricultural sector. The development and use of future climate scenarios will play an important role in climate change impacts and adaptation studies for the agricultural sector.

Original research and review papers, methodologies, and applications related to the development of climate data for use in the agricutural sector are welcome. Authors are especially encouraged to present studies on methodologies of downscaling weather forecast/climate model outputs for regional and local agricultural applications including, but not limited to, crop growth simulations, climate extremes and risk analysis, yield forecasting, and greenhouse gase emissions.Discussions on how to derive and interprete climate information for agricultural applications and the associated uncertainties are also encouraged.

Dr. Catherine Champagne
Dr. Budong Qian
Prof. Zhi Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Climate data
  • Agriculture
  • Downscaling
  • Climate scenarios
  • Climate extremes
  • Climate change impacts
  • Risk assessment
  • Agricultural water management
  • Remote sensing
  • Modelling

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

24 pages, 2445 KiB  
Article
Evaluation of Multiple Approaches to Estimate Daily Solar Radiation for Input to Crop Process Models
by Perdinan, Julie A. Winkler and Jeffrey A. Andresen
Atmosphere 2021, 12(1), 8; https://doi.org/10.3390/atmos12010008 - 23 Dec 2020
Cited by 2 | Viewed by 2452
Abstract
Daily solar radiation is a critical input for estimating plant growth and development, yet this variable is infrequently measured compared to other climate variables. This study evaluates the sensitivity of simulated maize and soybean production from the CERES-Maize and CROPGRO-Soybean modules of the [...] Read more.
Daily solar radiation is a critical input for estimating plant growth and development, yet this variable is infrequently measured compared to other climate variables. This study evaluates the sensitivity of simulated maize and soybean production from the CERES-Maize and CROPGRO-Soybean modules of the Decision Support System for Agrotechnology Transfer (DSSAT) to daily solar radiation estimates obtained from traditional (stochastic, empirical, and mechanistic models) and non-traditional (satellite estimation, reanalysis datasets, and regional climate model simulations) approaches, using as an example radiation estimates for Hancock, Wisconsin, USA. When compared to observations, radiation estimates obtained from empirical and mechanistic models and a satellite-based dataset generally had smaller biases than other approaches. Daily solar radiation estimates from a reanalysis dataset and regional climate model simulations overestimate incoming daily solar radiation. When the radiation estimates were used as an input to CERES-Maize, no significant differences were found for maize yield obtained from the different radiation estimates compared to yield from observed radiation, even though differences were found in the daily values of leaf area index, crop evapotranspiration, and crop dry weight (biomass). In contrast, significant differences were found in simulated soybean yield from CROPGRO-Soybean for the majority of the radiation estimates. Full article
(This article belongs to the Special Issue Climate Data for Agricultural Applications: Downscaling and Scenarios)
Show Figures

Graphical abstract

27 pages, 8504 KiB  
Article
A Long-Term, 1-km Resolution Daily Meteorological Dataset for Modeling and Mapping Permafrost in Canada
by Yu Zhang, Budong Qian and Gang Hong
Atmosphere 2020, 11(12), 1363; https://doi.org/10.3390/atmos11121363 - 16 Dec 2020
Cited by 3 | Viewed by 3947
Abstract
Climate warming is causing permafrost thaw and there is an urgent need to understand the spatial distribution of permafrost and its potential changes with climate. This study developed a long-term (1901–2100), 1-km resolution daily meteorological dataset (Met1km) for modeling and mapping permafrost at [...] Read more.
Climate warming is causing permafrost thaw and there is an urgent need to understand the spatial distribution of permafrost and its potential changes with climate. This study developed a long-term (1901–2100), 1-km resolution daily meteorological dataset (Met1km) for modeling and mapping permafrost at high spatial resolutions in Canada. Met1km includes eight climate variables (daily minimum, maximum, and mean air temperatures, precipitation, vapor pressure, wind speed, solar radiation, and downward longwave radiation) and is suitable to drive process-based permafrost and other land-surface models. Met1km was developed based on four coarser gridded meteorological datasets for the historical period. Future values were developed using the output of a new Canadian regional climate model under medium-low and high emission scenarios. These datasets were downscaled to 1-km resolution using the re-baselining method based on the WorldClim2 dataset as spatial templates. We assessed Met1km by comparing it to climate station observations across Canada and a gridded monthly anomaly time-series dataset. The accuracy of Met1km is similar to or better than the four coarser gridded datasets. The errors in long-term averages and average seasonal patterns are small. The error occurs mainly in day-to-day fluctuations, thus the error decreases significantly when averaged over 5 to 10 days. Met1km, as a data generating system, is relatively small in data volume, flexible to use, and easy to update when new or improved source datasets are available. The method can also be used to generate similar datasets for other regions, even for the entire global landmass. Full article
(This article belongs to the Special Issue Climate Data for Agricultural Applications: Downscaling and Scenarios)
Show Figures

Figure 1

23 pages, 4857 KiB  
Article
Climate Scenarios and Agricultural Indices: A Case Study for Switzerland
by Flavian Tschurr, Iris Feigenwinter, Andreas M. Fischer and Sven Kotlarski
Atmosphere 2020, 11(5), 535; https://doi.org/10.3390/atmos11050535 - 21 May 2020
Cited by 8 | Viewed by 3986
Abstract
The CH2018 Climate Scenarios for Switzerland are evaluated with respect to the representation of 24 indices with agricultural relevance. Furthermore, future projections of the considered indices until the end of the 21st century are analyzed for two greenhouse gas scenarios (Representative Concentrations Pathways [...] Read more.
The CH2018 Climate Scenarios for Switzerland are evaluated with respect to the representation of 24 indices with agricultural relevance. Furthermore, future projections of the considered indices until the end of the 21st century are analyzed for two greenhouse gas scenarios (Representative Concentrations Pathways RCP2.6 and RCP8.5). The validation reveals good results for indices that are based on one or two climate variables only and on simple temporal aggregations. Indices that involve multiple climate variables, complex temporal statistics or extreme conditions are less well represented. The climate projection analysis indicates an intensification of temperature-related extreme events such as heat waves. In general, climate change signals in the indices considered are subject to three main patterns: a horizontal pattern across Switzerland, a vertical pattern depending on elevation and a temporal pattern with an intensification of change in the course of the 21st century. Changes are in most cases more pronounced for the high-emission RCP8.5 scenario compared to the mitigation scenario RCP2.6. Overall, the projections indicate a challenging 21st century climate for the agricultural sector. Our findings furthermore show the value and the necessity of a robust validation of climate scenario products to enable trustworthy and valuable impact analyses, especially for more complex indices and models. Full article
(This article belongs to the Special Issue Climate Data for Agricultural Applications: Downscaling and Scenarios)
Show Figures

Figure 1

17 pages, 5035 KiB  
Article
A Markov Chain-Based Bias Correction Method for Simulating the Temporal Sequence of Daily Precipitation
by Han Liu, Jie Chen, Xun-Chang Zhang, Chong-Yu Xu and Yu Hui
Atmosphere 2020, 11(1), 109; https://doi.org/10.3390/atmos11010109 - 16 Jan 2020
Cited by 4 | Viewed by 3015
Abstract
Bias correction methods are routinely used to correct climate model outputs for hydrological and agricultural impact studies. Even though superior bias correction methods can correct the distribution of daily precipitation amounts, as well as the wet-day frequency, they usually fail to correct the [...] Read more.
Bias correction methods are routinely used to correct climate model outputs for hydrological and agricultural impact studies. Even though superior bias correction methods can correct the distribution of daily precipitation amounts, as well as the wet-day frequency, they usually fail to correct the temporal sequence or structure of precipitation occurrence. To solve this problem, we presented a hybrid bias correction method for simulating the temporal sequence of daily precipitation occurrence. We did this by combining a first-order two-state Markov chain with a quantile-mapping (QM) based bias correction method. Specifically, a QM-based method was used to correct the distributional attributes of daily precipitation amounts and the wet-day frequency simulated by climate models. Then, the sequence of precipitation occurrence was simulated using the first-order two-state Markov chain with its parameters adjusted based on linear relationships between QM-corrected mean monthly precipitation and the transition probabilities of precipitation occurrence. The proposed Markov chain-based bias correction (MCBC) method was compared with the QM-based method with respect to reproducing the temporal structure of precipitation occurrence over 10 meteorological stations across China. The results showed that the QM-based method was unable to correct the temporal sequence, with the cumulative frequency of wet- and dry-spell length being considerably underestimated for most stations. The MCBC method can could reproduce the temporal sequence of precipitation occurrence, with the generated cumulative frequency of wet- and dry-spell lengths fitting that of the observation well. The proposed method also performed reasonably well with respect to reproducing the mean, standard deviation, and the longest length of observed wet- and dry-spells. Overall, the MCBC method can simulate the temporal sequence of precipitation occurrence, along with correcting the distributional attributes of precipitation amounts. This method can be used with crop and hydrological models in climate change impact studies at the field and small watershed scales. Full article
(This article belongs to the Special Issue Climate Data for Agricultural Applications: Downscaling and Scenarios)
Show Figures

Figure 1

Back to TopTop