Regional Climate Change Assessment Research Articles

Hourly Downscaling of Daily Climate Projection Data for the Nakdong River Basin using the Nearest Neighbor Search Method

Hydrological and water quality models have been continuously improved for assessing the impacts of climate change. Accordingly, meteorological data with high spatial and temporal resolutions are required. Current climate change scenario-based projections provide meteorological data on monthly and daily scales, which are too coarse for regional climate change assessments. In this study, we propose a temporal downscaling method based on the nearest neighbor search methodology for temporal downscaling from daily time-step meteorological data to hourly time-step data. To verify the temporal downscaling method, historical meteorological data from weather stations located in the Nakdong River basin were used, and the normalized root mean square error (NRMSE) between observational and simulated data was calculated. Consequently, the simulation errors were approximately 2–4% for temperature and precipitation, and approximately 7–16% for wind speed, relative humidity, and solar radiation. Next, using the temporal downscaling method, hourly future climate projection data were derived from the daily SSP5-8.5 climate scenario projections retrieved from weather stations in the target area. According to the downscaled hourly climate projection results, under the SSP5-8.5 climate change scenario, the future annual maximum temperature is projected to increase by up to 7.0 ℃ and 5.6 ℃ at Daegu and Busan weather stations, compared to the reference period maximum temperatures of 36.2 ℃ and 33 ℃, respectively. The annual precipitation duration is projected to decrease by up to 103.3 h (21.25%) and 157.2 h (27.57%), compared to the reference period durations of 486.2 h and 570.2 h, respectively. Annual precipitation intensity is projected to increase by up to 0.71 mm/h (33.5%) and 0.83 mm/h (31.2%), compared to the reference period precipitation intensities of 2.12 mm/h and 2.66 mm/h, respectively. The temporal downscaling method presented in this study is expected to contribute to the preparation of meteorological input data to effectively simulate detailed hourly rainfall runoff and the behavior of water quality factors, thereby improving the assessment of climate change impacts.

Reconstructing 10-km-resolution direct normal irradiance dataset through a hybrid algorithm

Evaluating, mapping, and monitoring high-quality Direct Normal Irradiance (DNI) is crucial for the design, financing, and operation of solar power plants, especially those utilizing concentrating technologies. In China, limited number of first-class meteorological stations provide DNI observations. To bridge this gap, this study has reconstructed a comprehensive 41-year (1982–2022) daily DNI dataset for China (CHDNI). This dataset integrated CMA ground-based observations with ERA5 and MERRA-2 reanalysis data, employing a spatial resolution of 10 km. The data was processed using the REST2 radiation transfer model combined with Stacking ensemble machine learning. Validation with ground-based measurements indicated that the Stacking model exhibited high and consistent performance. This was evidenced by its sample-based cross-validation results, showing a correlation coefficient (R) of 0.92, a root-mean-square error (RMSE) of 32.04 W/m2, a mean absolute error (MAE) of 23.68 W/m2, and a global performance indicator of 3.86. Among the four analyzed DNI products (CERES, ERA5, SWGDN, and CHDNI), CHDNI demonstrated the closet alignment with ground observations, evidenced by the least deviation (MAE = 21.67 W/m2 and RMSE = 31.00 W/m2) and the highest R value (0.92). Aerosol optical depth and cloud cover emerged as the primary factors influencing the quality and accuracy of DNI products under clear-sky and total-sky conditions. The spatial distribution of DNI across China is complex, showcasing DNI values ranging from 25.82 W/m2 to 194.22 W/m2, with an average value of 98.08 W/m2 over 41-year. This dataset is a valuable resource for analyzing regional climate change, photovoltaic applications, and solar energy resources assessment.

Robust changes to the wettest and driest days of the year are hidden within annual rainfall projections: a New Zealand case study

Understanding how the statistical properties of daily rainfall will respond to a warming climate requires ensembles of climate model data which are much larger than those typically available from existing centennial-scale modelling experiments. While such centennial-scale experiments are very useful to explore scenario uncertainty in twenty-first century climate, ensemble size constraints often result in regional climate change assessments restricting their focus to annual- or season-mean rainfall projections without providing robust information about changes to the most extreme events. Here, we make use of multi-thousand member ensembles of regional climate model output from the Weather@Home project to explicitly resolve how the wettest and driest days of the year over New Zealand will respond to simulations of a 3 °C world, relative to simulations of the climate of the recent past (2006–15). Using a novel framework to disentangle changes during the wettest and driest days of the year, we show that many regions which show negligible change in annual mean rainfall are in fact experiencing significant changes in the amount of rain falling during both the wettest and driest spells. Exploring these changes through the lens of drought risk, we find many agricultural regions in New Zealand will face significant changes in the frequency of low-rainfall extremes in a warmer world.

Evaluating the Effectiveness of Best Management Practices in Adapting the Impacts of Climate Change-Induced Urban Flooding

Floods are amongst the most destructive and costly natural disasters impacting communities around the globe. The severity and reoccurrence of flooding events have been more common in recent years as a result of the changing climate and urbanization. Best Management Practices (BMPs) are commonly used flood management techniques that aim to alleviate flooding and its impacts by capturing surface runoff and promoting infiltration. Recent studies have examined the effectiveness of BMPs in countering the effects of flooding; however, the performance of such strategies still needs to be analyzed for possible future climate change. In this context, this research employs climate model-driven datasets from the North American Regional Climate Change Assessment Program to evaluate the effects of climate change on urban hydrology within a study region by calculating historical and projected 6 h 100-year storm depths. Finally, the climate-induced design storms are simulated in the PCSWMM model, and the three BMP options (i.e., porous pavement, infiltration trench, and green roof) are evaluated to alleviate the impact of flooding events. This study quantifies the impact of changing climate on flood severity based on future climate models. The results indicate that peak discharge and peak volume are projected to increase by a range of 5% to 43% and 8% to 94%, respectively. In addition, the results demonstrated that green roofs, Permeable Pavement, and infiltration trenches help to reduce peak discharge by up to 7%, 14%, and 15% and reduce flood volume by up to 19%, 24%, and 29%, respectively, thereby presenting a promising solution to address the challenges posed by climate change-induced flooding events.

A CMIP6-based assessment of regional climate change in the Chinese Tianshan Mountains

A CMIP6-based assessment of regional climate change in the Chinese Tianshan Mountains

Evaluating the Effectiveness of Low Impact Development Practices against Climate Induced Extreme Floods

Short but extreme flooding events have been frequent and severe globally due to climate change and urbanization in recent years. Similarly, researchers, scientists, and water managers are suggesting the application of sustainable flood management strategies such as Low Impact Development (LID) to mitigate the impacts of such extreme flooding events. However, most of these strategies have primarily been evaluated using historical precipitation events, which may not accurately represent the impact of climate-induced flooding events, which are projected to become more extreme. In this context, this study assesses the effectiveness of LIDs in combating climate change-induced flooding events. The North American Regional Climate Change Assessment Program (NARCCAP) climate model was applied in this study to quantify the magnitude of future projected storm depths, which are expected to increase due to climate change. Similarly, Personal Computer Storm Water Management Model (PCSWMM) was used to develop a rainfall-runoff simulation model and to assess the effectiveness of three LID techniques (Permeable Pavement, Green Roof, and Bio-Retention Cell) in reducing surface runoff under various climate scenarios. The results revealed that under the climate change scenarios the future projected design depths are expected to increase by up to 104%. Similarly, peak discharge, and total flooding volume were found to increase by 37.72% and 88.73%, respectively under the most extreme climate change scenario. Furthermore, the study demonstrated that applying LID strategies decreases peak discharge, offering a viable solution to tackle flooding events induced by climate change. The results illustrated the performance of permeable pavement was superior in reducing the peak discharge by up to 28.57%. Similarly, applying green roofs and bioretention cells reduced the peak discharge by up to 19.93% and 14.25%, respectively.

Investigating Hydroclimatic Variables Trends on the Natural Lakes of Western Greece Using Earth Observation Data.

Expected global climate change is allegedly becoming more intense, and the impacts on water resources are being tracked in various hydroclimatic regimes. The present research investigates a hydrologically important area of Greece, where four natural lakes are concentrated. It aims to quantify any potential long-term trends in lake water area, precipitation, and temperature timeseries. Water area timeseries spanning four decades are estimated by the mNDWI from Landsat satellite imagery and used as an index of each lake's water storage. Precipitation and temperature measurements are obtained from the open access datasets Hydroscope and ERA5-Land, respectively. All of the timeseries were tested seasonally and annually with the Pettitt and Mann-Kendal tests for statistically significant breakpoints and trends detection. No timeseries analysis resulted in a statistically significant (at 0.05 or 0.1 levels) annual or seasonal trend. The hydroclimatic regime over the past forty years in western Greece is found to have been relatively stable. Land use was also assessed to have been relatively unchanging, converging to the overall stability of the local water regime. However, the findings of this research should not be interpreted as a reassurance against climate change, but as a call to further research for the detailed regional and local assessment of climate change and hydroclimatic variability with acknowledged statistical approaches.

Impact Assessment of climate change on water resources of the Amu Darya River basin

Water consumption and water use have been continuously increasing, and the impact of economic activities on the hydrological regime of water flow, especially in irrigation, population and industry, has increased significantly. In particular, it is significantly increasing in the countries of Central Asia, including the Republic of Uzbekistan. Today's global problem is the severity, scale and unusualness of anthropogenic climate change, especially in developing countries, creates the need for action with an urgent scientific approach. Undoubtedly, the assessment of the observed climate change and the current state of water resources will require many years of in-depth scientific research. One of the factors limiting the development of the countries of the Amu Darya basin is the lack of water resources. The world is experiencing intensive climate warming, which leads to the melting of glaciers, which are the main sources of fresh water for the river basins that make up the water resources of the countries of Central Asia. The article gives an assessment of regional and local climate change and gives recommendations for reducing water shortages in the countries of the province

The Worldwide C3S CORDEX Grand Ensemble: A Major Contribution to Assess Regional Climate Change in the IPCC AR6 Atlas

Abstract The collaboration between the Coordinated Regional Climate Downscaling Experiment (CORDEX) and the Earth System Grid Federation (ESGF) provides open access to an unprecedented ensemble of regional climate model (RCM) simulations, across the 14 CORDEX continental-scale domains, with global coverage. These simulations have been used as a new line of evidence to assess regional climate projections in the latest contribution of the Working Group I (WGI) to the IPCC Sixth Assessment Report (AR6), particularly in the regional chapters and the Atlas. Here, we present the work done in the framework of the Copernicus Climate Change Service (C3S) to ­assemble a consistent worldwide CORDEX grand ensemble, aligned with the deadlines and ­activities of IPCC AR6. This work addressed the uneven and heterogeneous availability of CORDEX ESGF data by supporting publication in CORDEX domains with few archived simulations and performing quality control. It also addressed the lack of comprehensive documentation by compiling information from all contributing regional models, allowing for an informed use of data. In addition to presenting the worldwide CORDEX dataset, we assess here its consistency for precipitation and temperature by comparing climate change signals in regions with overlapping CORDEX domains, obtaining overall coincident regional climate change signals. The C3S CORDEX dataset has been used for the assessment of regional climate change in the IPCC AR6 (and for the interactive Atlas) and is available through the Copernicus Climate Data Store (CDS).

Drivers of uncertainty in precipitation frequency under current and future climate – application to Maryland, USA

The intensity duration frequency (IDF) curves that inform engineering design must reflect the effects of changing climate on extreme precipitation events. These changes can be addressed, in part, by statistically assessing synthetic data/outputs from high-resolution climate model projections to develop IDF curves. The estimation of IDF curves is associated with multiple sources of uncertainty. Most studies that characterize uncertainty in IDF curves under climate change only address the uncertainty due to the choice and processing of climate model outputs, but neglect uncertainty from statistical modeling choices. This study assesses the uncertainty introduced by developing IDF curves for Maryland, USA from model simulations of current and future climate, using statistical methods that are used in U.S. practice. The study analyzes output time series from the North American Regional Climate Change Assessment Program (NARCCAP) suite, which consists of 6 Atmosphere-Ocean General Circulation Models, each spatially downscaled with two different Regional Climate Models. In a separate study, the 3-hour NARCCAP model output was temporally downscaled to 15 min using two different machine learning models. Uncertainty is assessed both across and within models. Across-model uncertainty arises from the differences among synthetic time-series for precipitation and other meteorological variables produced by the 12 NARCCAP climate model projections. Within-model uncertainty arises from the modeling choices used to develop statistical IDF curves from a single climate model time series. These choices include: temporal downscaling method, time-series type, distribution, and parameter estimation method. The choice of climate model (across-model uncertainty) is the dominant contributor under both current and future climate conditions. On the within-model level, the other sources of uncertainty contribute differently for different climate models. The development and application of future-climate IDF curves should acknowledge the uncertainty introduced by statistical modeling choices, as well as by variation among climate model projections.

Conceptual hydrological model-guided SVR approach for monthly lake level reconstruction in the Tibetan Plateau

Study regionTibetan Plateau (TP) Study focusLakes in the TP that are subject to low human activity serve as an important indicator for quantitative assessment of regional climate change. However, previous studies have mainly focused on annual changes in lake area, level, and storage because of limited monitoring stations, and long-term lake level reconstruction at monthly resolution remains challenging. We propose a conceptual hydrological model-guided monthly lake level reconstruction (WBM-SVR) approach that incorporates water balance models (WBMs) and support vector regression (SVR), to improve the training and testing sets compared with SVR alone through consideration of the hydrological process. The WBM-SVR approach integrates WBMs to select input factors, sub-process control equations to quantify the contributions of input factors, empirical parameters to characterize catchment uniqueness, and SVR for water level modelling. New hydrological insights for the regionPhysically guided WBM-SVR is more accurate than SVR in reconstructing monthly lake water levels. WBMs can quantify the hydrological process in the lake catchment area with efficient quasi-physical mechanisms and refine the input–output factors within lake hydrometeorology. The reconstructability, generalization capability, and transferability of WBM-SVR are validated for three different types of lakes (glacier-free inflow lake, glacier-free outflow lake, and glacier-fed inflow lake), and the reconstruction results indicate significant improvements in WBM-SVR compared with SVR. The WBM-SVR approach shows great promise for achieving monthly lake level reconstruction.

Climatic–Environmental Effects of Aerosols and Their Sensitivity to Aerosol Mixing States in East Asia in Winter

To establish the direct climatic and environmental effect of anthropogenic aerosols in East Asia in winter under external, internal, and partial internal mixing (EM, IM and PIM) states, a well-developed regional climate–chemical model RegCCMS is used by carrying out sensitive numerical simulations. Different aerosol mixing states yield different aerosol optical and radiative properties. The regional averaged EM aerosol single scattering albedo is approximately 1.4 times that of IM. The average aerosol effective radiative forcing in the atmosphere ranges from −0.35 to +1.40 W/m2 with increasing internal mixed aerosols. Due to the absorption of black carbon aerosol, lower air temperatures are increased, which likely weakens the EAWM circulations and makes the atmospheric boundary more stable. Consequently, substantial accumulations of aerosols further appear in most regions of China. This type of interaction will be intensified when more aerosols are internally mixed. Overall, the aerosol mixing states may be important for regional air pollution and climate change assessments. The different aerosol mixing states in East Asia in winter will result in a variation from 0.04 to 0.11 K for the averaged lower air temperature anomaly and from approximately 0.45 to 2.98 μg/m3 for the aerosol loading anomaly, respectively, due to the different mixing aerosols.

Impacts of Climatic Variation and Human Activity on Runoff in Western China

Hydrological cycle is sensitively affected by climatic variation and human activity. Taking the upper- and middle-stream of the Weihe River in western China as an example, using multiple meteorological and hydrological elements, as well as land-use/land-cover change (LUCC) data, we constructed a sensitivity model of runoff to climatic elements and human activities based on the hydro-thermal coupling equilibrium equation, while a cumulative slope was used to establish a comprehensive estimation model for the contributions of climatic variation and human activities to the changes of runoff. The results showed that the above function model established could be well applied to quantitatively study the elasticity of runoff’s response to climatic variation and human activities. It was found that the annual average precipitation, evaporation, wind velocity, sunshine hours, relative humidity and runoff showed decreasing trends and that temperature increased. While in the hydrological cycle, precipitation and relative humidity had a non-linear positive driving effect on runoff, while temperature, evaporation, sunshine hours, wind velocity, and land-use/land-cover change (LUCC) have non-linearly negatively driven the variation of runoff. Moreover, runoff has a strong sensitive response to precipitation, evaporation and LUCC. In areas with strong human activities, the sensitivity of runoff to climatic change was decreasing, and runoff has a greater elastic response to underlying surface parameters. In addition, the analysis showed that the abrupt years of climate and runoff changes in the Weihe River Basin were 1970, 1985 and 1993. Before 1985, the contribution rate of climatic variation to runoff was 68.3%, being greater than that of human activities to runoff, and then the contribution rates of human activities to runoff reached 75.1%. The impact of natural climate on runoff was weakened, and the effect of human activities on runoff reduction increased. Under 30 hypothetical climatic scenarios, the evaluation of runoff in the future showed that the runoff in the Weihe River Basin will be greatly reduced, and the reduction will be more significant during the flood season. Comparing the geographically fragile environments and intense human activities, it was believed that climatic variation had a dramatic effect on driving the water cycle of precipitation and evaporation and affected regional water balance and water distribution, while human activities had driven the hydrological processes of the underlying surface, thus becoming the main factors in the reduction of runoff. This study provided scientific tools for regional climate change and water resources assessment.

Spatial and temporal variability and risk assessment of regional climate change in northern China: a case study in Shandong Province

Spatial and temporal variability and risk assessment of regional climate change in northern China: a case study in Shandong Province

Exploring the Gender-Specific Adaptive Responses to Climate Variability: Application of Grazing Game in the Semi-Arid Region of Ghana

Regional climate change assessments show a likely temperature increase that is higher than the global average for all seasons in Africa, which would have extreme negative implications for ecosystem health and productivity. Most extreme climate change effects in West Africa are predicted to occur in desert and grassland areas. It is important for smallholder farmers in this region to understand the implications of these projections to their livelihood and to identify appropriate adaptation strategies. A grazing game was used to explore gender-specific adaptive responses to climate variability in the semiarid region of Ghana. The game was designed to understand the decision-making processes that result in the overgrazing of animals, leading to desertification based on the players’ interactions with the environment. A total of 44 grazing games comprising 22 games for male-headed households (HH) and 22 games for female-HH were played from August to December 2014 from 14 communities within the Bolgatanga Municipality and the Bongo district. The study revealed that males migrate to the southern part of the country to work on other people’s farms during the dry season as an adaptation strategy, while females engage in off-farm activities such as shea-butter production and basketry. Results of the game showed that males produced the highest number of cattle but created the largest desert patches. Females, on the other hand, were more conscious about the environment (long-term condition of the rangeland) than the short-term income benefits from the sale of cattle; hence, they created fewer desert patches. Strategies such as reducing the number of cattle to allow for the re-growth of vegetation in periods of feed scarcity, ploughing for one another using bulls, and family support using income from the sale of livestock were employed by both gender groups. The involvement of female farmers in decision-making is crucial to improve natural resource management.

Evaluation of land-use, climate change, and low-impact development practices on urban flooding

ABSTRACT We investigated the potential influence of land-use and climate change on urban hydrology for an urbanized watershed located in Columbia, South Carolina (USA). The Personal Computer Storm Water Management Model (PCSWMM) was used as an urban hydrology model to formulate the low-impact development (LID) controls on runoff for flood mitigation. We used ensemble projections provided by the North American Regional Climate Change Assessment Program (NARCCAP) for the climate change assessment. The results for future periods (2038–2069) show an increase in mean annual runoff from 40% to 70%. The LID mitigation strategies were compared based on the rain barrel, the rain garden, and a combination of the two approaches to evaluate the potential reduction in urban flooding. It is recommended that LID practices should be used synergistically with improved drainage facilities for successful stormwater management. This analysis will help stakeholders to develop strategies to minimize the socio-economic impacts due to urban flooding.

An improved daily weather generator for the assessment of regional climate change impacts

An improved daily weather generator for the assessment of regional climate change impacts

A climate service for ecologists: sharing pre-processed EURO-CORDEX regional climate scenario data using the eLTER Information System

Abstract. eLTER was a “Horizon 2020” project with the aim of advancing the development of long-term ecosystem research infrastructure in Europe. This paper describes how eLTER Information System infrastructure has been expanded by a climate service data product providing access to specifically pre-processed regional climate change scenario data from a state-of-the-art regional climate model ensemble of the Coordinated Regional Downscaling Experiment (CORDEX) for 702 registered ecological research sites across Europe. This tailored, expandable, easily accessible dataset follows FAIR principles and allows researchers to describe the climate at these sites, explore future projections for different climate change scenarios and make regional climate change assessments and impact studies. The data for each site are available for download from the EUDAT collaborative data infrastructure B2SHARE service and can be easily accessed and visualised through the Dynamic Ecological Information Management System – Site and Dataset Registry (DEIMS-SDR), a web-based information management system which shares detailed information and metadata on ecological research sites around the globe. This paper describes these data and how they can be accessed by users through the extended eLTER Information System architecture. The data and supporting information are available from B2SHARE. Each individual site (702 sites are available) dataset has its own DOI. To aid data discovery, a persistent B2SHARE lookup table has been created which matches the DOIs of the individual B2SHARE record with each DEIMS site ID. This lookup table is available at https://doi.org/10.23728/b2share.bf41278d91b445bda4505d5b1eaac26c (eLTER EURO-CORDEX Climate Service, 2020).

Climate effects on health in Small Islands Developing States

Climate effects on health in Small Islands Developing States

Future Crop Yield Projections Using a Multi-model Set of Regional Climate Models and a Plausible Adaptation Practice in the Southeast United States

Since maize, peanut, and cotton are economically valuable crops in the southeast United States, their yield amount changes in a future climate are attention-grabbing statistics demanded by associated stakeholders and policymakers. The Crop System Modeling—Decision Support System for Agrotechnology Transfer (CSM-DSSAT) models of maize, peanut, and cotton are, respectively, driven by the North American Regional Climate Change Assessment Program (NARCCAP) Phase II regional climate models to estimate current (1971–2000) and future (2041–2070) crop yield amounts. In particular, the future weather/climate data are based on the Special Report on Emission Scenarios (SRES) A2 emissions scenario. The NARCCAP realizations show on average that there will be large temperature increases (~2.7 °C) and minor rainfall decreases (~−0.10 mm/day) with pattern shifts in the southeast United States. With these future climate projections, the overall future crop yield amounts appear to be reduced under rainfed conditions. A better estimate of future crop yield amounts might be achievable by utilizing the so-called weighted ensemble method. It is proposed that the reduced crop yield amounts in the future could be mitigated by altering the currently adopted local planting dates without any irrigation support.