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Adaptation policies are being increasingly implemented to cope with the increased frequency of natural disasters due to climate change. However, it is crucial to quantitatively evaluate the mitigating effects of these policies, as this allows for their assessment and prioritization. Therefore, this study sought to develop a nonlinear flood damage function for each administrative region of South Korea to quantify the potential damage caused by extreme rainfall events. Afterward, a power function was nested to model the coefficient of the flood damage function with regional factors characterizing each region's flood mitigation capacity. Here, regional factors were selected based on their linkage to two potential adaptation policies: enhancing retention reservoir and pumping capacity and enhancing river dike construction. The proposed evaluation framework was tested in 15 administrative districts in South Korea and the flood damage reduction effects of the two policy scenarios were estimated. The enhancement of retention reservoir and pumping capacity showed a 6.7% reduction effect, whereas enhancing river dike construction showed a 29.2% reduction effect. The implementation of both policies yielded a 34.3% reduction effect. Despite the limitations of this study, our findings still provide a quantitative comparison of the long‐term effects of national climate change adaptation measures. Additionally, accumulating more damage records would allow for the development of damage functions at a finer scale. [ABSTRACT FROM AUTHOR]

Acer pictum and Quercus acuta are native species recommended for restoration. To restore ecosystem functions and maintain natural ecosystems, it is suggested to deploy well-adapted and locally adapted plant material, and this notion is gaining interest. Studying how species change in response to climate change is an important part of forest restoration planning. Our method uses climate data to define the habitat range of species and to identify regions with relatively similar climates through Seed Transfer Zones (STZs). Potential habitat suitability changes of A. pictum and Q. acuta were identified under various environmental scenarios using seven climatic factors and five topographical factors. The MaxEnt algorithm was used to predict potential habitat suitability in current and future (1980–2100) climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). STZs are maps of areas with comparable climates and have been used to determine the climates of potentially habitable areas. This helps to minimize the maladaptation of seed movement within the same area. As a result, A. pictum growth increased along the southern coastal area and drainage was the paramount factor influencing A. pictum distribution. By checking the climate of regions with high habitability in STZs (Winter Minimum Temperature (WMT) 15–20 °F, Annual Heat: Moisture (AHM) 16–19 °C/m zone, WMT 20–25 °F, AHM 16–19 °C/m located in the zone), Q. acuta was shown to move northward with climate change centering on the southern coastal area. Additionally, Annual Precipitation (Bio12) was the most significant factor influencing Q. acuta distribution. In confirming the climate of areas with high habitability in STZs, we verified that habitat density was high in the WMT 10–15 °F, AHM 19–21 °C/m zone and WMT 20–25 °F, AHM 19–21 °C/m zone. This study establishes that the potential distributions of A. pictum and Q. acuta are affected by climate change. It supplies evidence for ecological restoration and sustainable development, and can formulate future conservation and management plans for economically valuable species. [ABSTRACT FROM AUTHOR]

This study evaluated the rainfall historical simulations of 15 Global Climate Models (GCMs) of the Coupled Model Intercomparison Project phase 6 (CMIP6) in replicating annual and seasonal rainfall climatology, their temporal variability and trends in Bangladesh for the period 1979–2014, considering ERA5 (ECMWF Reanalysis 5th Generation) reanalysis as the reference dataset. Shannon's Entropy decision‐analysis was employed for GCMs' rating based on eight statistical indicators and a comprehensive rating metric for the final grading of the GCMs. The majority of the CMIP6 GCMs accurately reproduced the spatial feature of ERA5 rainfall. However, the GCMs underestimated annual rainfall by an average of 190.5 mm, with the highest underestimation in monsoon (131.76 mm) and least in winter (3.52 mm) seasons. Most GCMs also underestimated rainfall variability for all seasons except winter. Besides, the GCMs showed an increasing trend in pre‐monsoon and a decreasing trend in post‐monsoon rainfall like ERA5, but an opposite (negative) to ERA5 trend (positive) in monsoon season rainfall. The ensemble mean of the GCMs showed higher skill in reconstructing rainfall climatology, temporal variability and trends than the individual GCMs. The study identified MPI‐ESM1‐2‐LR, MPI‐ESM1‐2‐HR, and GFDL‐ESM4 as the most effective GCMs in reproducing precipitation over Bangladesh. The selected models' simulation can be used for climate change impact assessment in Bangladesh after bias minimization. [ABSTRACT FROM AUTHOR]

Global climate models (GCMs) serve as essential tools for projecting future climate trends, but their coarse resolution limits localized impact assessments in sectors like hydrology, agriculture, and biodiversity. Observation data with a spatial resolution of a few kilometers are crucial for downscaling and bias-correcting GCMs at finer resolutions. However, Nepal's extreme topography and organizational challenges have led to uneven distribution of meteorological stations and inconsistent data quality. Moreover, CMIP6-based climate extremes projections for the entire country are currently unavailable. To tackle these challenges, we developed a comprehensive national database for Nepal, offering high-resolution historical and projected precipitation and temperature data analyzed through 25 climate extreme indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). Initially, observation grid data were prepared at a daily timescale with a spatial resolution of 0.05° × 0.05° for baseline period (1981–2010) using the Asian Precipitation High-Resolved Observational Data Integration Toward Evaluation (APHRODITE), the fifth generation of the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), and available good quality observed climate data. This data was then utilized to downscale and bias-correct 18 CMIP6 GCMs for 2015–2100 under four SSPs (1–2.6, 2–4.5, 3–7.0, 5–8.5). Quantile mapping was employed for the bias correction of the CMIP6 GCMs. The performance of the multimodal ensemble (MME) indicated better Nash-Sutcliffe Efficiency (NSE), root mean square error ratio (RSR), and Percent Bias (PBIAS) of climate extreme indices for the historical period. A comparative analysis was conducted across Nepal's major geographic regions to account for spatial variability in regional climate systems. The finer-resolution dataset can be crucial to deepen our understanding of climate impacts, and climate change, and eventually informing the policy-making in Nepal. Moreover, the methodology can be effectively replicated in data-scarce developing nations to promote climate research and adaptation efforts. • Development of baseline (1981-2010) daily observation grid data at ∼5 km resolution for Nepal. • Establishment of a high-resolution national climate database encompassing historical and future climate projections from 18 GCMs. • Comparative analysis of climate change impacts on climate extreme indices from ETCCDI across Nepal''s geographic regions. [ABSTRACT FROM AUTHOR]

In this study, an ensemble of statistically downscaled 14 multi‐global climate models for RCP4.5 and RCP8.5 emission scenarios was employed to implement a comprehensive assessment of climate change impacts over Pakistan in order to identify the future hotspots cities in terms of changes in temperature and precipitation. The analyses focused on the minimum, maximum and average temperature and precipitation in three time‐slices: 2006–2035, 2041–2070, and 2071–2,100. Average temperature is projected to increase by 2.6°C under RCP4.5 while 5.1°C under RCP8.5 by the end of this century with the north side of Pakistan (mainly over North Pakistan—NP, Monsoon Region—MR and Khyber Pakhtunkhwa—KP) presenting the highest changes in the temperatures. Wetter conditions are expected in the future over Pakistan, mainly over the MR. In general, air temperature and precipitation showed linear positive correlation over Pakistan in both RCP scenarios. Hotspot cities where extreme climate, that is, the hottest, dryer and wetter, exists were also identified. Hyderabad will likely become the hottest city of Pakistan by end century with the highest average temperature reaching 29.9°C under RCP4.5 and 32.0°C under RCP8.5 followed by Jacobabad, Bahawalnagar, and Bahawalpur. Most of the hottest cities are detected in areas on the southern side of Pakistan. On the other hand, the wettest cities, Murree, Balakot and Muzaffarabad, are located in the MR. Dry conditions are likely to be prevalent in Dalbandin followed by Khanpur and Jacobabad under both RCPs. The uncertainties of the projections were also evaluated. For precipitation, for example, there are a large number of outliers indicating the high variability/uncertainties of the projections. These uncertainties are clearer when the probability density functions are analysed for individual sub‐domains in Pakistan. [ABSTRACT FROM AUTHOR]

Rainfall events largely control hydrological processes occurring on and in the ground, but the performance of climate models in reproducing rainfall events has not been investigated enough to guide selection among the models when making hydrological projections. We proposed to compare the durations, intensities, and pause periods, as well as depths of rainfall events when assessing the accuracy of general circulation models (GCMs) in reproducing the hydrological characteristics of observed rainfall. We also compared the sizes of design storm events and the frequency and severity of drought to demonstrate the consequences of GCM selection. The results show that rainfall and extreme hydrological event projections could significantly vary depending on climate model selection and weather stations, suggesting the need for a careful and comprehensive evaluation of GCM in the hydrological analysis of climate change. The proposed methods are expected to help to improve the accuracy of future hydrological projections for water resources planning. [ABSTRACT FROM AUTHOR]

Climate change may accelerate the water cycle at a global scale, resulting in more frequent extreme climate events. This study analyzed changes in extreme precipitation events employing climate projections statistically downscaled at a station-space scale in South Korea. Among the CMIP5 climate projections, based on spatial resolution, this study selected 26 climate projections that provide daily precipitation under the representative concentration pathway (RCP) 4.5. The results show that a 20-year return period of precipitation event during a reference period (1980∼2005) corresponds to a 16.6 yr for 2011 to 2040, 14.1 yr for 2041 to 2070, and 12.8 yr for 2071 to 2100, indicating more frequent extreme maximum daily precipitation may occur in the future. In addition, we found that the probability density functions of the future periods are located out of the 10% confidence interval of the PDF for the reference period. The result indicates that the design standard under the reference climate is not managed to cope with climate change, and accordingly the revision of the design standard is required to improve sustainability in infrastructures. [ABSTRACT FROM AUTHOR]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is highly anticipated that meteorological changes resulting from global climate change will affect the pattern of rice disease epidemics worldwide. Here, we evaluated the potential impacts of climate change on two representative rice diseases, leaf blast and sheath blight, in Korea. This study involves analyses of disease simulation using an epidemiological model, EPIRICE, which was validated for Korean rice paddy fields. The goal of our study was to assess likely changes in national disease probabilities using individual climate scenarios across different models and multi-model ensemble scenarios constructed by running 11 global climate models. In this way, the results from this study emphasize the uncertainties in climate change scenarios resulting from the variations in initial conditions as well as the structural differences in the global climate models. Observed and simulated epidemics for both diseases were compared using the area under the disease progress curve from EPIRICE model runs. Overall, the simulated incidence of epidemics for both diseases gradually decreased towards 2100 both from individual global climate models and multi-model ensembles. It was noted that while each individual model resulted in different magnitudes of impact, the multi-model ensemble gave the most reliable result that accounts for uncertainty compared to the individual models. In conclusion, we found that in modeling climate impacts on rice diseases, ensembles account for uncertainty better than individual climate models and can lead to better decision making. [ABSTRACT FROM AUTHOR]

Rice diseases, responsible for about 8% of annual yield losses of rice production in South Korea, are likely to be affected by meteorological changes resulting from global climate change. No critical evaluation has yet been made of the potential impacts of climate change on rice diseases in South Korea. This study involved a quantitative analysis of two rice diseases that result in the greatest damages, leaf blast and sheath blight, using the generic epidemiological model, EPIRICE. The goals of the study were to evaluate the EPIRICE model using historical rice disease incidence data and fine-scale weather data for 2002–2010 in South Korea, and then to assess likely changes in national disease probabilities under climate change scenarios to allow for more robust planning. EPIRICE was calibrated and validated against observed disease incidence data for leaf blast and sheath blight. Observed and simulated epidemics for both diseases were compared using disease progress curves and the area under the disease progress curve. Statistical equivalence and quantitative envelope of acceptance tests were applied on the deviations of the model outputs to evaluate whether EPIRICE was sufficiently accurate for its intended purpose. The level of agreement between the observed and simulated epidemics was high and the model was found to be valid according to the performance criteria. Predicted daily climate data based on the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCP) 8.5 and 4.5 scenarios were used as inputs into the EPIRICE model. Outputs from the model runs were displayed using geographic information systems (GIS) to show future changes in potential epidemics for both rice diseases. Overall, the incidence of epidemics for both diseases was simulated to gradually decrease toward 2100. These results can be used to interpret the likely magnitude of changes in disease risk in regions of South Korea and to estimate climate change impacts on disease losses and disease control. [ABSTRACT FROM AUTHOR]

Soybean sprout is an important food ingredient in East Asian cuisine. Soybean growth is highly sensitive to temperature and photoperiod. Thus, it is important to determine the optimal base temperature for an accurate yield prediction. The optimal base temperature can be varied by cultivars. In this study, six soybean sprout cultivars that are commonly grown in Korea were planted in South Jeolla province, South Korea between 2003 and 2018. Data on phenology were collected from the field and used to determine the optimal base temperature for each cultivar. As a result, variations of optimal base temperatures of cultivars ranged from 0 °C to 15 °C. In simulation, three plant parameter sets, including Soy15, Soy6, and Soy0, were created. Soy15, Soy6, and Soy0 represented soybean cultivars with base temperatures of 15 °C, 6 °C, and 0 °C, respectively. In simulation results, the values of percent bias were under 15%, indicating that the Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) could reasonably simulate soybean yields. Among these three cultivars, Soy15 had the smallest yield, while Soy6 had the highest yield. In climate change scenarios (SSP245 and SSP585), both maximum and minimum temperatures were increased by 1–3.3 °C. With increasing temperatures in the future period, grain yields for all cultivars decreased. The yield reduction might be because the high temperature shortened the length of growth period of the soybeans. Among the three cultivars, Soy6 was a promising cultivar that could have a high yield under climate change scenarios. [ABSTRACT FROM AUTHOR]

Evaluating the impact of climate change on water resources is necessary for improving water resource management and adaptation measures at the watershed level. This study evaluates the impact of climate change on streamflow in South Korea using downscaled climate change information based on the global climate model (GCM) and hydrological simulation program–FORTRAN model. Representative concentration pathway (RCP) scenarios 4.5 and 8.5 W/m2 were employed in this study. During the distant future (2071–2099), the flow increased by 15.11% and 24.40% for RCP scenarios 4.5 and 8.5 W/m2, respectively. The flow is highly dependent on precipitation and evapotranspiration. Both precipitation and evapotranspiration increased, but the relative change of precipitation was greater than the relative change of evapotranspiration. For this reason, the flow would show a significant increase. Additionally, for RCP 8.5 W/m2, the variability of the flow according to the GCM also increased because the variability of precipitation increased. Moreover, for RCP 8.5 W/m2, the summer and autumn flow increased significantly, and the winter flow decreased in both scenarios. The variability in autumn and winter was so great that the occurrence of extreme flow could intensify further. These projections indicated the possibility of future flooding and drought in summer and winter. Regionally, the flow was expected to show a significant increase in the southeastern region. The findings presented for South Korea could be used as primary data in establishing national climate change adaptation measures. [ABSTRACT FROM AUTHOR]

Switchgrass (Panicum virgatum L.) is a C4, warm season, perennial native grass that has been strongly recommended as an ideal biofuel feedstock. Accurate forecasting of switchgrass yield across a geographically diverse region and under future climate conditions is essential for determining realistic future ethanol production from switchgrass. This study compiled a switchgrass database through reviewing the existing literature from field trials across the U.S. Using observed switchgrass data, a process-based model (ALMANAC) was developed. The ALMANAC simulation results showed that crop management had more effect on yield than location. The ALMANAC model consists of functional relationships that provide a better understanding of interactions among plant physiological processes and environmental factors (water, soil, climate, and nutrients) giving realistic predictions in different climate conditions. This model was used to quantify the impacts of climate change on switchgrass yields. Simulated lowland switchgrass would have more yield increases between Illinois and Ohio in future (2021–2050) under both Representative Concentration Pathway (RCP) 4.5 and 8.5 pathways with low N fertilizer inputs than high N fertilizer inputs. There was no significant effect of climate variability on upland simulated yields, which means that N fertilization is a key factor in controlling upland switchgrass yields under future climate conditions. [ABSTRACT FROM AUTHOR]

This research aims to assess the impact of climate change on water balance components in irrigated paddy cultivation. The APEX-Paddy model, which is the modified version of the APEX (Agricultural Policy/Environmental eXtender) model for paddy ecosystems, was used to evaluate the paddy water balance components considering future climate scenarios. The bias-corrected future projections of climate data from 29 GCMs (General Circulation Models) were applied to the APEX-Paddy model simulation. The study area (Jeonju station) forecasts generally show increasing patterns in rainfall, maximum temperature, and minimum temperature with a rate of up to 23%, 27%, and 45%, respectively. The hydrological simulations suggest over-proportional runoff–rainfall and under-proportional percolation and deep-percolation–rainfall relationships for the modeled climate scenarios. Climate change scenarios showed that the evapotranspiration amount was estimated to decrease compared to the baseline period (1976–2005). The evaporation was likely to increase by 0.12%, 2.21%, and 7.81% during the 2010s, 2040s, and 2070s, respectively under Representative Concentration Pathway (RCP)8.5, due to the increase in temperature. The change in evaporation was more pronounced in RCP8.5 than the RCP4.5 scenario. The transpiration is expected to reduce by 2.30% and 12.62% by the end of the century (the 2070s) under RCP4.5 and RCP8.5, respectively, due to increased CO2 concentration. The irrigation water demand is generally expected to increase over time in the future under both climate scenarios. Compared to the baseline, the most significant change is expected to increase in the 2040s by 3.21% under RCP8.5, while the lowest increase was found by 0.36% in 2010s under RCP4.5. The increment of irrigation does not show a significant difference; the rate of increase in the irrigation was found to be greater RCP8.5 than RCP4.5 except in the 2070s. The findings of this study can play a significant role as the basis for evaluating the vulnerability of rice production concerning water management against climate change. [ABSTRACT FROM AUTHOR]

The impacts of climate change on precipitation and drought characteristics over Bangladesh were examined by using the daily precipitation outputs from 29 bias-corrected general circulation models (GCMs) under the representative concentration pathway (RCP) 4.5 and 8.5 scenarios. A precipitation-based drought estimator, namely, the Effective Drought Index (EDI), was applied to quantify the characteristics of drought events in terms of the severity and duration. The changes in drought characteristics were assessed for the beginning (2010–2039), middle (2040–2069), and end of this century (2070–2099) relative to the 1976–2005 baseline. The GCMs were limited in regard to forecasting the occurrence of future extreme droughts. Overall, the findings showed that the annual precipitation will increase in the 21st century over Bangladesh; the increasing rate was comparatively higher under the RCP8.5 scenario. The highest increase in rainfall is expected to happen over the drought-prone northern region. The general trends of drought frequency, duration, and intensity are likely to decrease in the 21st century over Bangladesh under both RCP scenarios, except for the maximum drought intensity during the beginning of the century, which is projected to increase over the country. The extreme and medium-term drought events did not show any significant changes in the future under both scenarios except for the medium-term droughts, which decreased by 55% compared to the base period during the 2070s under RCP8.5. However, extreme drought days will likely increase in most of the cropping seasons for the different future periods under both scenarios. The spatial distribution of changes in drought characteristics indicates that the drought-vulnerable areas are expected to shift from the northwestern region to the central and the southern region in the future under both scenarios due to the effects of climate change. [ABSTRACT FROM AUTHOR]

Climate change has altered ecohydroclimatological variables and the phenology of crops and thus has threatened the ecosystem and food security. This study aims to quantify the impacts of climate change on the length and timing of growing seasons, crop yields, and nitrogen transport from paddy fields. A calibrated APEX-Paddy model was used to simulate the paddy rice fields in Gimje district of South Korea. A combination of sixteen general circulation models (GCMs) with two greenhouse gas emission scenarios (Shared Socioeconomic Pathways, SSPs) were used in order to evaluate the uncertainty of the impacts. The results indicated that shortened and unseasonable growing seasons are expected based on simulated leaf area index and biomass, accounting for a reduction in rice yields of about 10–13% and 10–27% under SSP2–4.5 and SSP5–8.5, respectively. This shorter growing season, in addition to projected higher precipitation and less efficient application of nitrogen, causes an increase of 7.2–19.8% and 12.0–35.3% in nitrogen loss under SSP2–4.5 and SSP5–8.5, respectively. The findings demonstrated that the projected shortened and unseasonable crop growing season caused by climate change is a threat to global food security and the ecosystem due to the excessive pollutants loads into the environment. • An actual growing season is introduced based on leaf area index and biomass. • Shortened growing seasons are projected under a changing climate. • A reduction in rice yields is expected in the future. • This shorter projected growing season causes an increase in nitrogen loss. [ABSTRACT FROM AUTHOR]

• The APEX-Paddy model was used to evaluate the effects of BMPs on discharge and mineral nitrogen yields from paddy fields. • The differences among the effects of BMPs under future climate conditions were investigated by future time spans. • Overall, the precipitation, maximum, and minimum temperatures likely to increase in the 21st century. • The surface discharge and mineral nitrogen yields were generally expected to increase in future. • The combination of DOR and STF are the most effective BMPs in reducing the surface discharge and mineral nitrogen yield. This study is to evaluate the BMPs in the reduction of surface discharge and mineral nitrogen yield from paddy cultivation for three future time slices (e.g., the 2010s, 2040s, and 2070s) using APEX-Paddy (Agricultural Policy/Environmental eXtender-Paddy) model. The model was calibrated and validated for surface discharge and mineral nitrogen yield using 3-year monitoring data (2013–2015) from the conventional paddy management field (CMP-1). For surface discharge and mineral nitrogen yield estimates, the future projections of 29 GCMs (General Circulation Model) were bias-corrected and applied to the calibrated APEX-Paddy model. We investigated five specific management strategies related to paddy drainage outlet regulation and new fertilization methods, as the BMPs minimize the mineral nitrogen yield and surface discharges due to climate change. The modeling results indicated that the effects of BMPs would vary by future climate scenarios (i.e., RCP4.5, RCP8.5) and periods (i.e., the 2010s, 2040s, 2070s). It was generally expected that the surface discharge and mineral nitrogen yields would increase in the future. The combination of raising drainage outlets and soil test-based fertilization (DOR-STF) showed a substantial reduction in surface discharge in both scenarios (RCP4.5 and 8.5); the highest reduction rate was observed in the 2010s and was estimated at 21.9 % under RCP4.5. Soil test-based fertilization (STF) showed a substantial reduction in mineral nitrogen yield by 31.0 and 28.3 % during the 2010s under RCP8.5 and RCP4.5, respectively followed by DOR-STF, as compared to conventional management practice (CMP-1). However, the combination of drainage outlet raising, and fertilizer application before outlet weir installation (DOR-FABWI) management resulted in increased mineral nitrogen yield of up to 31.0 % under RCP4.5 and 36.7 % under RCP8.5. The study findings indicate that climate change will increase exports of mineral nitrogen from paddy fields. Nevertheless, appropriate BMPs may play a vital role in reducing the mineral nitrogen yields for the production of paddy rice in future climates, and these effects may vary according to future climate conditions. [ABSTRACT FROM AUTHOR]