Climatic yield potential of Japonica???type rice in the Korean Peninsula under RCP scenarios using the ensemble of multi???GCM and multi???RCM chains

Rice production in the Korean Peninsula (KP) in the near future (2021-2050) is analysed in terms of the climatic yield potential (CYP) index for Japonica-type rice. Data obtained from the dynamically downscaled daily temperature and sunshine duration for the Historical period (1981-2010) and near future under two Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios are utilized. To reduce uncertainties that might be induced by using a Coupled General Circulation Model (CGCM)-a Regional Climate Model (RCM) chain in dynamical downscaling, two CGCM-three RCM chains are used to estimate the CYP index. The results show that the mean rice production decreases, mainly due to the increase of the temperature during the grain-filling period (40 days after the heading date). According to multi model ensemble, the optimum heading date in the near future will be approximately 12 days later and the maximum CYP will be even higher than in the Historical. This implies that the rice production is projected to decrease if the heading date is selected based on the optimum heading date of Historical, but to increase if based on that of near future. The mean rice production during the period of ripening is projected to decrease (to about 95% (RCP4.5) and 93% (RCP8.5) of the Historical) in the western and southern regions of the KP, but to increase (to about 104% (RCP4.5) and 106% (RCP8.5) of the Historical) in the northeastern coastal regions of the KP. However, if the optimum heading date is selected in the near future climate, the peak rice production is projected to increase (to about 105% (RCP4.5) and 104% (RCP8.5) of the Historical) in the western, southern and northeastern coastal regions of the KP, but to decrease (to about 98% (RCP4.5) and 96% (RCP8.5) of the Historical) in the southeastern coastal regions of the KP

Sensitivity of Summer Precipitation over Korea to Convective Parameterizations in the RegCM4: An Updated Assessment

This study investigates the performance of the latest version of RegCM4 in simulating summer precipitation over South Korea, comparing nine sensitivity experiments with different combinations of convective parameterization schemes (CPSs) between land and ocean. In addition to the gross pattern of seasonal and monthly mean precipitation, the northward propagation of the intense precipitation band and statistics from extreme daily precipitation are thoroughly evaluated against gridded and in situ station observations. The comparative analysis of 10-year simulations demonstrates that no CPS shows superiority in both quantitative and qualitative aspects. Furthermore, a nontrivial discrepancy among the different observation datasets makes a robust assessment of model performance difficult. Regardless of the CPS over the ocean, the simulations with the Kain–Fritsch scheme over land show a severe dry bias, whereas the simulations with the Tiedtke scheme over land suffer from a limited accuracy in reproducing spatial distributions due to the excessive orographic precipitation. In general, the simulations with the Emanuel scheme over land are better at capturing the major characteristics of summer precipitation over South Korea, despite not all statistical metrics showing the best performance. When applying the Emanuel scheme to both land and the ocean, precipitation tends to be slightly overestimated. This deficiency can be alleviated by using either the Tiedtke or Kain–Fritsch schemes over the ocean instead. As few studies have applied and evaluated the Tiedtke and Kain–Fritsch schemes to the Korean region within the RegCM framework, and this study introduces the potential of these new CPSs compared with the more frequently selected Emanuel scheme, which is particularly beneficial to RegCM users

Improvement of daily precipitation estimations using PRISM with inverse-distance weighting

Improved daily precipitation estimations were attempted using the parameter-elevation regressions on a parameter-elevation regression on independent slopes model (PRISM) with inverse-distance weighting (IDW) and a precipitation-masking algorithm for precipitation areas. The PRISM (PRISM_ORG) suffers two overestimation problems when the daily precipitation is estimated: overestimation of the precipitation intensity in mountainous regions and overestimation of the local precipitation areas. In order to solve the problem of overestimating the precipitation intensity, we used the IDW technique that employs the same input stations as those used in the PRISM regression (PRISM_IDW). A precipitation-masking algorithm that selectively masks the precipitation estimation grid points was additionally applied to the PRISM_IDW results (PRISM_MSK). For 6 months from March to August 2012, daily precipitation data were produced in a horizontal resolution of 1 km based on the above two experiments and PRISM_ORG. Afterwards, each experiment was evaluated for improvements. The monthly root mean squared errors (RMSEs) of PRISM_IDW and PRISM_MSK were reduced by 0.83 mm/day and 0.86 mm/day, respectively, compared to PRISM_ORG

Projections of suitable cultivation area for major fruit trees and climate-type in South Korea under representative concentration pathway scenarios using the ensemble of high-resolution regional climate models

This study projected the future changes in the climate-type distribution in South Korea according to the Koppen-Trewartha climate classification (KTCC) under the representative concentration pathway (RCP) 4.5/8.5 scenarios and the future change of cultivation area of apple (Malus domestica Borkh.) and mandarin (Citrus unshiu Marc.), which are major fruit crops in South Korea, using five regional climate models with a 12.5 km horizontal resolution. According to KTCC, type temperate (D)s is dominant in most of South Korea during the reference period (1981-2005). On the other hand, it is projected that the area of Type D and Type subtropical (C) will decrease and increase, respectively, towards higher latitudes and elevations in the future under RCP4.5/8.5 scenarios. Accordingly, the cultivation areas of major fruit crops in South Korea are projected to change significantly. The cultivation area of apple (mandarin), which is a major current fruit crop in Type D (C), is projected to be reduced (expanded) as it moves towards higher latitudes and elevations in the future. Apples grown throughout South Korea in the present climate (reference period) are not expected to be cultivated in the late-21C due to climate change. On the other hand, the cultivation area of mandarins is projected to increase steadily in the future. At present, mandarins are cultivated only in Jeju Island, which is located in the south of the South Korea. However, the cultivation area is expected to increase by 1323% in late-21C under the RCP8.5 scenario compared to the reference period. Moreover, mandarin cultivation is projected to be possible anywhere in South Korea. Nevertheless, in late-21C, excessive increases in temperature that exceeds the appropriate temperature for mandarin in Jeju Island and the southern part of South Korea will eventually decrease the cultivation area of mandarins

Random Forests for Global and Regional Crop Yield Predictions.

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data

Random Forests for Global and Regional Crop Yield Predictions - Fig 1

<p><b>Study regions: global wheat mega-environments (A), US maize producing counties (B), and northeastern seaboard region (NESR) (C).</b> All 12 wheat mega-environments are shown with different colors (A). Maize grain yield by the US counties in 2013 surveyed by USDA-NASS is visualized using different shades with darker shades representing higher yields (B). The NESR includes 433 counties of Connecticut, Delaware, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, Virginia, and West Virginia. The red dots indicate the location of the data points, where weather stations exist. Point type data was used for this region (C).</p

Random Forest (RF) and multiple linear regression (MLR) model performance evaluation statistics.

<p>See text for detailed description and equation of each statistic shown in the table.</p

The predictors used for random forests (RF) and multiple linear regression models.

<p>Rank corresponds to a variable importance measure determined by RF models for each dataset.</p