Bias-corrected Global Climate Models Research Articles

Continental-scale bias-corrected climate and hydrological projections for Australia

Abstract. The Australian Bureau of Meteorology has developed a national hydrological projections (NHP) service for Australia. The NHP aimed to provide nationally consistent hydrological projections across jurisdictional boundaries to support planning of water-dependent industries. NHP is complementary to those previously produced by federal and state governments, universities, and other organisations for limited geographical domains. The projections comprise an ensemble of application-ready bias-corrected climate model data, derived hydrological projections at daily temporal and 0.05° × 0.05° spatial resolution for the period 1960–2099, and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP8.5). The spatial resolution of the projections matches that of gridded historical reference data used to perform the bias correction and the Bureau of Meteorology's operational gridded hydrological model. Three bias correction techniques were applied to four CMIP5 global climate models (GCMs), and one method was applied to a regional climate model (RCM) forced by the same four GCMs, resulting in a 16-member ensemble of bias-corrected GCM data for each emission scenario. The bias correction was applied to fields of precipitation, minimum and maximum temperature, downwelling shortwave radiation, and surface winds. These variables are required inputs to the Bureau of Meteorology's landscape water balance hydrological model (AWRA-L), which was forced using the bias-corrected GCM and RCM data to produce a 16-member ensemble of hydrological output. The hydrological output variables include root zone soil moisture (moisture in the top 1 m soil layer), potential evapotranspiration, and runoff. Here we present an overview of the production of the hydrological projections, including GCM selection, bias correction methods and their evaluation, technical aspects of their implementation, and examples of analysis performed to construct the NHP service. The data are publicly available on the National Computing Infrastructure (https://doi.org/10.25914/6130680dc5a51, Bureau of Meteorology, 2021), and a user interface is accessible at https://awo.bom.gov.au/products/projection/ (last access: 24 November 2023).

Response of Matching Degree between Precipitation and Maize Water Requirement to Climate Change in China

The synchronicity of rain and heat in the summer of China’s monsoon region provides sufficient water and heat resources for maize growth. However, the intra-annual distribution of precipitation and the probability of extreme precipitation have been inevitably altered by the ongoing climate change, thus affecting the matching degree between precipitation and crop water requirements (MDPCWR). Evaluating the extent to which the MDPCWR will change in the future is of great importance for food security and the sustainable management of water resources. In this study, considering that different growth stages of crops have different sensitivities to water stress, the AquaCrop model was used to calculate the MDPCWR more accurately. In addition, a cumulative distribution function-transform (CDF-t) method was used to remove the bias of 11 global climate models (GCMs) under two typical emission scenarios (SSP2-4.5 and SSP5-8.5) from phase six of the Coupled Model Intercomparison Project (CMIP6). A comprehensive investigation was conducted on how maize growth, water consumption, and the MDPCWR will respond to future climate change with CO2 concentration enrichment in the Huang–Huai–Hai (3H) region in China by driving a well-tested AquaCrop model with the bias-corrected GCMs outputs. The results indicate the following: (1) The CDF-t method can effectively remove seasonal bias, and it also performs well in eliminating the bias of extreme climate events. (2) Under the SSP2-4.5 scenario, the average maximum temperature will increase by 1.31 °C and 2.44 °C in 2021–2050 and 2051–2080, respectively. The average annual precipitation will increase up to 96.8 mm/year, but it will mainly occur in the form of heavy rain. (3) The increased maize evapotranspiration rate does not compensate for the decreased crop water requirement (up to −32 mm/year), due to a shorter growth cycle. (4) The farmland cultivation layer is not able to hold a significant amount of precipitation, due to the increased frequency of heavy rains, resulting in increased irrigation water requirements for maize over the next two periods, with the maximum value of 12 mm/year. (5) Under different scenarios, the projected future MDPCWR will decrease by 9.3–11.6% due to changes in precipitation patterns and crop water requirements, indicating that it will be more difficult for precipitation to meet the water demand of maize growing in the 3H region. The results can provide comprehensive information to understand the impact of climate change on the agricultural water balance and improve the regional strategy for water resource utilization in the 3H region.

Assessment of Climate Change Effects on the Hydrological Regime of Bagmati River Basin using SWAT Model

Developing economies have been particularly vulnerable to the consequences of climate change due to their quickly growing populations and underdeveloped social and economic infrastructure. The Bagmati River Basin (BRB) belonging to such an economy is susceptible to the effects of climate change, including droughts and floods brought upon by changes in discharge. This study assessed the potential effects of climate change on discharge in BRB using the Soil and Water Assessment Tool (SWAT). The model was calibrated and validated based on observed flow data from 2000-2010 at two outlets: Khokana and Padhera Dovan. Using R2, NSE, PBIAS, RSR, and p-factor, the goodness of fit between the final simulated values and the observed values were evaluated. Historical data from three meteorological stations and bias-corrected global climate model (GCM) outputs from the ACCESS-CM2 model were used to drive the calibrated SWAT model under two scenarios (SSP 2-4.5 and SSP 5-8.5) from the IPCC Sixth Assessment Report (AR6). The study demonstrated long-term spatial and temporal variations in hydrologic responses to future climate changes, providing insights for water resources managers and those involved in mitigating natural hazards in the region.

Multi-scenario simulation of runoff and nutrient loads in a rapidly urbanizing watershed during China's Dual Carbon periods

Apprehending the hydrological and nutrient variations in rapidly urbanizing watersheds under changing environments is crucial for pollution control and water resource management. However, existing studies have primarily focused on hydrological processes, neglecting water quality aspects, and comprehensive assessment of future runoff and nutrient loads in these watersheds during China's Dual Carbon periods is limited. This study firstly bridges these gaps by constructing multi-scenario with different levels of “Urban Development - Ecological Conservation” and utilizing latest bias-corrected General Circulation Models or Global Climate Models (GCMs) projections to evaluate future runoff and nutrient loads in the Shenzhen River. The calibrated and validated models display satisfactory performance in simulating runoff, nutrient loads, and land use types. The bias-corrected GCMs projections exhibit enhanced accuracy for temperature variables, particularly during the wet season. Implementing effective ecological protection measures is paramount in mitigating water quantity fluctuations and controlling total nitrogen pollution, which is closely associated with urban development and human activities. Conversely, total phosphorus loads demonstrate greater simulation uncertainty, particularly during the dry season of the Carbon Neutrality period, requiring further exploration. Compared to the baseline period, runoff changes minimally, with notable seasonal variations. The findings highlight the escalating uncertainty in load predictions as time progresses. Additionally, addressing uncertainties in precipitation projections driven by GCMs is imperative, given their substantial influence on runoff and nutrient load simulations, particularly during challenging dry seasons. While further research is needed to reduce simulation uncertainty, our study provides valuable insights into nitrogen-phosphorus pollution control and sustainable water resource management in rapidly urbanizing watersheds, especially during the near-term period.

Future Projections of Precipitation and Temperature in Northeast, Thailand using Bias-Corrected Global Climate Models

The increase in anthropogenic greenhouse gases (GHGs) has resulted in global climate change, with the Northeast region of Thailand experiencing the highest rate of change. This poses signifi cant risks of drought and its impact on crop yield production. To better understand the potential consequences of climate change and devise suitable adaptation strategies, this study aims to project precipitation and temperature data for Northeast Thailand from 2015 to 2055, using bias-corrected global climate models under Representative Concentration Pathways (RCPs) 4.5 and 8.5. The results showed that the annual precipitation and temperature increased from 2015 to 2055 under both RCPs. The annual average (minimum-maximum) of precipitation from the CanESM, GFDL, MIROC5, NorESM, and ensemble mean from four models under RCP4.5 were 1,238.83 (904.28-1,629.70), 1,227.82 (865.49- 1,721.31), 1,312.78 (876.60-1,616.38), 1,350.21 (985.55-1,625.00), and 1,282.41 (1,088.43-1,461.49) mm and RCP8.5 were 1,267.96 (864.24-1,712.86), 1,222.20 (863.79-1,835.43), 1,294.07 (843.04-1,752.41), 1,353.14 (1,059.50-1,827.23), and 1,284.34 (1,116.55-1,541.63) mm, respectively. While, those of temperature under RCP4.5 were 29.27 (28.15-30.35), 29.59 (27.98-31.20), 29.12 (27.65-30.70), 28.09 (26.96-29.14), and 29.02 (28.08-29.90) °C and RCP8.5 were 29.50 (28.12-31.40), 29.68 (28.23-31.50), 29.11 (27.51-30.26), 28.37 (27.11-29.88), and 29.17 (28.08-30.66) °C, respectively. These fi ndings suggest that the annual precipitation is lower under RCP4.5 compared to RCP8.5, while the temperature shows an increasing trend under both RCPs. Therefore, it is evident that climate change will manifest differently in Northeast Thailand, depending on local contexts and the measures implemented today. Understanding the impacts and risks of future climate change at a local scale and identifying adaptive solutions pose signifi cant challenges for the future.

Evaluation of global climate models for the simulation of precipitation and maximum and minimum temperatures at coarser and finer resolutions based on temporal and spatial assessment metrics in mainland of China

Abstract Many studies have evaluated the performance of multiple global climate models (GCMs) from a temporal or spatial perspective at finer resolution, but no study has evaluated the performance of individual GCMs at different resolutions and before and after bias correction from both temporal and spatial perspectives. The goal of this study is to evaluate the performance of 21 Coupled Model Inter-comparison Project 6 (CMIP6) GCMs at the raw (coarser) and downscaled (finer) resolutions and after bias correction in relation to their skills in the simulation of daily precipitation and maximum and minimum temperatures over China for the period 1961–2014 using state-of-the-art temporal and spatial metrics, Kolmogorov–Smirnov statistic and SPAtial EFficiency. The results indicated some differences in the ranks of GCMs between temporal and spatial metrics at different resolutions. The overall ranking shows that the simulations at the raw resolution of GCMs are more similar to the observations than the simulations after inverse distance weighted interpolation in SPAtial EFficiency. Three variables from bias-corrected GCMs ranked from 1 to 21 show similar good performance in spatial patterns but the poorest trend in empirical Cumulative Distribution Functions (ECDFs) except daily precipitation.

Effects of heatwave features on machine-learning-based heat-related ambulance calls prediction models in Japan

Researchers agree that there is substantial evidence of an increasing trend in both the frequency and duration of extreme temperature events. Increasing extreme temperature events will place more pressure on public health and emergency medical resources, and societies will need to find effective and reliable solutions to adapt to hotter summers. This study developed an effective method to predict the number of daily heat-related ambulance calls. Both national- and regional-level models were developed to evaluate the performance of machine-learning–based methods on heat-related ambulance call prediction. The national model showed a high prediction accuracy and can be applied over most regions, while the regional model showed extremely high prediction accuracy in each corresponding region and reliable accuracy in special cases. We found that the introduction of heatwave features, including accumulated heat stress, heat acclimatization, and optimal temperature, significantly improved prediction accuracy. The adjusted coefficient of determination (adjusted R2) of the national model improved from 0.9061 to 0.9659 by including these features, and the adjusted R2 of the regional model also improved from 0.9102 to 0.9860. Furthermore, we used five bias-corrected global climate models (GCMs) to forecast the total number of summer heat-related ambulance calls under three different future climate scenarios nationally and regionally. Our analysis demonstrated that, at the end of the 21st century, the total number of heat-related ambulance calls in Japan will reach approximately 250,000 per year (nearly four times the current amount) under SSP-5.85. Our results suggest that disaster management agencies can use this highly accurate model to forecast potential high emergency medical resource burden caused by extreme heat events, allowing them to raise and improve public awareness and prepare countermeasures in advance. The method proposed in Japan in this paper can be applied to other countries that have relevant data and weather information systems.

Impacts of climate warming on global floods and their implication to current flood defense standards

Floods usually threaten human lives and cause serious economic losses, which can be more severe with global warming. Therefore, it is a salient challenge to find out how global flood characteristic changes and whether current flood protection standards will face more pressures. This study aims to characterize changes in global floods and explicit flood defense pressures in warming climates of 1.5–3.0 °C above pre-industrial levels by running four well-calibrated lumped hydrological models using bias-corrected Global Climate Model (GCM) simulations for 9045 watersheds worldwide. The results show that global warming from 1.5 to 3.0 °C has increasingly dominated all continents, with amplification effects on changes of flood frequency and magnitude. Southeast Eurasia, Africa, and South America are hotspots of changes for significant proportions of watersheds with larger flood patterns and greater changing extents than others. For example, for the 3.0 °C warming period under the combination of shared socioeconomic pathway 2 and representative concentration pathway 4.5 (SSP245) scenario, the regionally averaged 50-year flood magnitude will increase by 25.6 %, 30.6 %, and 16.4 % for these regions, respectively. The increases in occurrence and magnitude indicate that current flood protection standards will face increasing pressures in future warming climates. The design-level flood frequency is projected to increase for about 47 %, 55 %, 70 %, and 74 % of watersheds in 1.5, 2.0, 2.5, and 3.0 °C warming periods under the SSP245 scenario. However, large uncertainty are observed for the change of flood characteristics dominated by GCMs and their interactions with SSP scenarios and hydrological models. This study implies that the current flood defense standards should be enhanced and climate adaptation and mitigation strategies should be proposed to cope the change of future flood. Plain language summaryFloods usually threaten human lives and cause serious economic losses, which can be more severe in the context of global warming. It is a salient challenge to find out how global flood risk changes and whether current flood protection standards will face more pressures. This study aims to characterize changes in global floods and explicit flood defense pressures in warming climates of 1.5, 2.0, 2.5, and 3.0 °C above pre-industrial levels. Here we show that amplification effects of higher air temperature on the range of changes in flood frequency and magnitude are projected. Southeast Eurasia, Africa, and South America are hotspots of changes for significant proportions of watersheds with larger flood patterns and greater changing extents than others. Most watersheds worldwide is likely to face increasing flood defense pressures in warming climates. Our findings could improve the understanding of future flood conditions under the warming climates and provide information to mitigation and adaptation policymaking.

Revealing historical observations and future projections of precipitation over Northwest China based on dynamic downscaled CMIP6 simulations

The warming climate driven by global change has great potential in altering regional and global hydrologic cycles, thus leading to considerable changes in spatial variability and temporal pattern of precipitation. Northwest China (NW) has witnessed a significant wetting trend over the past decades, while the persistence of this wetting trend and potential changes in precipitation under future climate impacts remains elusive. In this study, long-term meteorological observations were used to probe historical variations of precipitation from 1951 to 2020, and the WRF model was employed as a regional climate model to examine future precipitation patterns over NW. Two 9-year downscaled WRF simulations were conducted comprising of historical (WRF-HIST; 2012–2020) and future climate change scenarios (WRF-SSP585; 2047–2055) using bias-corrected global climate model outputs from Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared with ground observations, the WRF model exhibited strong capability in capturing the spatial pattern and temporal variations of precipitation across the NW. Intense precipitation was mainly found in stations located at northern NW and southeastern NW. Summertime precipitation substantially contributed to annual precipitation over the study region. Future precipitation projections suggest significant decreases of precipitation across the southern and eastern NW, with a stronger reduction magnitude in summer. Further, extreme precipitation events were projected to decrease in spring and summer, suggesting that the NW may become drier and the wetting trend may shift to another pattern in the 2050s under the SSP585 climate scenario. Overall, this study reveals historical and future potential changes in precipitation over NW through a high-resolution, dynamically downscaled dataset from WRF modeling, which in turn will help inform regional mitigation and adaption on potential impacts of future climate change on NW.

Tracking the impacts of precipitation phase changes through the hydrologic cycle in snowy regions: From precipitation to reservoir storage

Cool season precipitation plays a critical role in regional water resource management in the western United States. Throughout the twenty-first century, regional precipitation will be impacted by rising temperatures and changing circulation patterns. Changes to precipitation magnitude remain challenging to project; however, precipitation phase is largely dependent on temperature, and temperature predictions from global climate models are generally in agreement. To understand the implications of this dependence, we investigate projected patterns in changing precipitation phase for mountain areas of the western United States over the twenty-first century and how shifts from snow to rain may impact runoff. We downscale two bias-corrected global climate models for historical and end-century decades with the Weather Research and Forecasting (WRF) regional climate model to estimate precipitation phase and spatial patterns at high spatial resolution (9 km). For future decades, we use the RCP 8.5 scenario, which may be considered a very high baseline emissions scenario to quantify snow season differences over major mountain chains in the western U.S. Under this scenario, the average annual snowfall fraction over the Sierra Nevada decreases by >45% by the end of the century. In contrast, for the colder Rocky Mountains, the snowfall fraction decreases by 29%. Streamflow peaks in basins draining the Sierra Nevada are projected to arrive nearly a month earlier by the end of the century. By coupling WRF with a water resources model, we estimate that California reservoirs will shift towards earlier maximum storage by 1–2 months, suggesting that water management strategies will need to adapt to changes in streamflow magnitude and timing.

Assessment of CMIP5 and CORDEX-SA experiments in representing multiscale temperature climatology over central India

Abstract The central India region has been seriously affected by repeated droughts in recent decades due to climate change, which is the main reason for conducting this research. It is still uncertain how the numerous climate models could precisely estimate the future climate for central India. The study mainly focuses on the forcing global climate models (GCMs) and the regional climate models (RCMs). The models have been checked using the coefficient of correlation (r2), Nash-Sutcliffe efficiency (NSE) and an improved method, skill score (SS). The performance is also spatially checked on ArcGIS using the kriging interpolation. The bias-corrected GCMs performed more authentically than the CORDEX RCMs in signifying maximum and minimum temperatures for the Bundelkhand region in central India. Bias-corrected GCMs, EC-EARTH, CCSM4 and GFDL-ESM-2M affirmed the best models on multiple time scales for maximum and minimum temperature in the study region. Maximum NSE and r2 have been observed for seasonal minimum temperature. GCM-EC-EARTH has shown 97% to 98% accuracy, while GCM-GFDL-ESM-2M has demonstrated 84% to 97% accuracy among other selected models. The research outcomes will also assist policymakers in developing strategies and policies for the future climate of central India with the help of more precise projected climatic data.

HCPD-CA: high-resolution climate projection dataset in central Asia

Abstract. Central Asia (referred to as CA) is one of the climate change hot spots due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need of high-resolution climate projection datasets for application to vulnerability, impacts, and adaption assessments in this region. In this study, a high-resolution (9 km) climate projection dataset over CA (the HCPD-CA dataset) is derived from dynamically downscaled results based on multiple bias-corrected global climate models and contains four geostatic variables and 10 meteorological elements that are widely used to drive ecological and hydrological models. The reference and future periods are 1986–2005 and 2031–2050, respectively. The carbon emission scenario is Representative Concentration Pathway (RCP) 4.5. The evaluation shows that the data product has good quality in describing the climatology of all the elements in CA despite some systematic biases, which ensures the suitability of the dataset for future research. Main features of projected climate changes over CA in the near-term future are strong warming (annual mean temperature increasing by 1.62–2.02 ∘C) and a significant increase in downward shortwave and longwave flux at the surface, with minor changes in other elements (e.g., precipitation, relative humidity at 2 m, and wind speed at 10 m). The HCPD-CA dataset presented here serves as a scientific basis for assessing the potential impacts of projected climate changes over CA on many sectors, especially on ecological and hydrological systems. It has the DOI https://doi.org/10.11888/Meteoro.tpdc.271759 (Qiu, 2021).

Modelling the Impacts of Climate Change on the Yield of Crops

This study aims to improve the understanding of the impact changes being experienced in our climate system will have on the level of crop productivity in the immediate period as well as in the nearest future. Nigeria was used as a case study and an observed climatic dataset was obtained and used alongside collected 20 year cassava, rice and soybean yield data to develop models that were applied to estimate future crop yield. Four statistically downscaled and bias-corrected Global Climate Models (GCMs): NOAA, MIROC5, ICHEC, and NCC performed simulations for the period 1985–2100 under the Representative Concentration Pathway RCP8.5. These were used to predict how the yields of cassava, rice and soybean will be in the years 2020-2050 and 2070-2100 for the 36 states in Nigeria and the FCT. 89 Empirical models were developed to estimate the yields of the three crops earlier mentioned across Nigeria with their coefficient of determination (R2) ranging between 15% - 99%. The result showed an increase of 3.91% (P<0.001), 0.08, 1.79 (P<0.1) and a decrease of 0.93% for cassava yield for ICHEC, MIROC, NOAA and NCC respectively. It also projected an increase in yield of 8.88% (P<0.001), 7.77% (P<0.001), 6.62% (P<0.001) and 8.85% (P<0.001) for Rice yield using climatic data from ICHEC, MIROC, NOAA and NCC respectively. Soybean, increase in yield are 2.81% (P<0.01), 5.84% (P<0.001), 11.38 (P<0.001) and 9.06% (P<0.001) for ICHEC, MIROC, NOAA and NCC respectively.

High-resolution dynamical downscaling for regional climate projection in Central Asia based on bias-corrected multiple GCMs

Central Asia (CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need to achieve robust projection of regional climate change. In this study, we applied three bias-corrected global climate models (GCMs) to conduct 9 km-resolution regional climate simulations in CA for the reference (1986–2005) and future (2031–2050) periods. The regional climate model (RCM) and GCM simulated daily temperature and precipitation are evaluated and the results show that both the bias-correction technique and dynamical downscaling method obtain numerous added values in reproducing the historical climate in CA, respect to the original GCMs. The former contributes more to reducing the biases of the climatology and the latter contributes more to capturing the spatial pattern. The RCM simulations indicate significant warming over CA in the near-term future, with the regional mean increase of annual mean temperature in a range of 1.63–2.01 ℃, relative to the reference period. Pronounced warming is detected north of ~ 45° N in CA from autumn to spring, which can be explained by the snow-albedo feedback. Enhanced warming projected in many mountains in the world is not found in CA, which is consistent with the study based on the reanalysis datasets during the past. Heatwave day frequency, number and maximum duration are expected to become more severe by 2031–2050. Changes in precipitation and Standard Precipitation Index (SPI) shows a wetter condition in CA in the coming decades. However, a fairer assessment of the wet/dry change with Standard Precipitation Evapotranspiration Index (SPEI) which takes into account of both precipitation and potential evapotranspiration reveals a drier condition. The climate change projections presented here serve as a robust scientific basis for assessment of future risk from climate change in CA.

Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part II: Climate Change Impact Assessment)

This study aims at analysing the impact of climate change (CC) on the river hydrology of a complex mountainous river basin—the Budhigandaki River Basin (BRB)—using the Soil and Water Assessment Tool (SWAT) hydrological model that was calibrated and validated in Part I of this research. A relatively new approach of selecting global climate models (GCMs) for each of the two selected RCPs, 4.5 (stabilization scenario) and 8.5 (high emission scenario), representing four extreme cases (warm-wet, cold-wet, warm-dry, and cold-dry conditions), was applied. Future climate data was bias corrected using a quantile mapping method. The bias-corrected GCM data were forced into the SWAT model one at a time to simulate the future flows of BRB for three 30-year time windows: Immediate Future (2021–2050), Mid Future (2046–2075), and Far Future (2070–2099). The projected flows were compared with the corresponding monthly, seasonal, annual, and fractional differences of extreme flows of the simulated baseline period (1983–2012). The results showed that future long-term average annual flows are expected to increase in all climatic conditions for both RCPs compared to the baseline. The range of predicted changes in future monthly, seasonal, and annual flows shows high uncertainty. The comparative frequency analysis of the annual one-day-maximum and -minimum flows shows increased high flows and decreased low flows in the future. These results imply the necessity for design modifications in hydraulic structures as well as the preference of storage over run-of-river water resources development projects in the study basin from the perspective of climate resilience.

Population exposure to precipitation extremes in the Indus River Basin at 1.5 °C, 2.0 °C and 3.0 °C warming levels

Adverse effects of extreme events are the major focus of climate change impact studies. Precipitation-related extremes has substantial socioeconomic impacts under the changing climate. Quantifying population exposure to precipitation extreme is the fundamental aspect of population risk assessments in the climate hotspot of Indus River Basin. This study investigates the population exposure to precipitation extremes at 1.5 °C, 2.0 °C, and 3.0 °C global warmings in the Indus River Basin using daily precipitation data, and projected population under shared socioeconomic pathways (SSPs). The Intensity–Area–Duration method was applied to detect the extreme precipitation event by tracing the rainstorm process, calculated based on five downscaled and bias-corrected Global Climate Model (GCM) outputs from Coupled Model Intercomparison Project Phase 5 (CMIP5) under four Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). The exposure of the population is finally estimated by combing SSP1 with 1.5 °C, SSP2 with 2.0 °C, and SSP5 with 3.0 °C warming levels. Results show that warming over the Indus River Basin is projected to be higher than that of the global average. Both the extreme precipitation events and population exposure are projected to increase with warming level. With regard to the reference period (1986–2005), the frequency, duration, and impacted area of extreme precipitation are projected to increase by 13.2%, 7.4%, and 1.6% annually under 1.5 °C in the Indus River Basin, respectively. Whereas, an additional 0.5 °C and 1.5 °C warming can lead to further increase in the frequency of 16.6%, 17.3%, as well as the duration of 8.6%, 12%, and areal coverage of 2.1%, 5.3%, respectively. The population exposure to extreme precipitation is projected to increase by 72.4%, 122.7%, and 87.6%, respectively, at SSP1 with 1.5 °C, SSP2 with 2.0 °C and SSP5 with 3.0 °C warming levels compare to the reference period. The demographic change is responsible more for the tremendous increment of population exposure in the Indus River Basin. If the population was held constant to the level of 2010, the increase of population exposure would be 4.4%, 8.8%, and 17.6%, respectively, at 1.5 °C, 2.0 °C, and 3.0 °C warming levels. Spatially, the prominent increment of population exposure is projected in the central and southwestern Indus River Basin. This study highlights that limiting the increase of temperature to 1.5 °C can substantially reduce population exposure to extreme precipitation events in the Indus River Basin, compared to an additional warming. Simultaneously, urge paid to formulate policies on population growth to reduce future exposure.

Intensification characteristics of hydroclimatic extremes in the Asian monsoon region under 1.5 and 2.0 °C of global warming

Abstract. Understanding the influence of global warming on regional hydroclimatic extremes is challenging. To reduce the potential risk of extremes under future climate states, assessing the change in extreme climate events is important, especially in Asia, due to spatial variability of climate and its seasonal variability. Here, the changes in hydroclimatic extremes are assessed over the Asian monsoon region under global mean temperature warming targets of 1.5 and 2.0 ∘C above preindustrial levels based on representative concentration pathways (RCPs) 4.5 and 8.5. Analyses of the subregions classified using regional climate characteristics are performed based on the multimodel ensemble mean (MME) of five bias-corrected global climate models (GCMs). For runoff extremes, the hydrologic responses to 1.5 and 2.0 ∘C global warming targets are simulated based on the variable infiltration capacity (VIC) model. Changes in temperature extremes show increasing warm extremes and decreasing cold extremes in all climate zones with strong robustness under global warming conditions. However, the hottest extreme temperatures occur more frequently in low-latitude regions with tropical climates. Changes in mean annual precipitation and mean annual runoff and low-runoff extremes represent the large spatial variations with weak robustness based on intermodel agreements. Global warming is expected to consistently intensify maximum extreme precipitation events (usually exceeding a 10 % increase in intensity under 2.0 ∘C of warming) in all climate zones. The precipitation change patterns directly contribute to the spatial extent and magnitude of the high-runoff extremes. Regardless of regional climate characteristics and RCPs, this behavior is expected to be enhanced under the 2.0 ∘C (compared with the 1.5 ∘C) warming scenario and increase the likelihood of flood risk (up to 10 %). More importantly, an extra 0.5 ∘C of global warming under two RCPs will amplify the change in hydroclimatic extremes on temperature, precipitation, and runoff with strong robustness, especially in cold (and polar) climate zones. The results of this study clearly show the consistent changes in regional hydroclimatic extremes related to temperature and high precipitation and suggest that hydroclimatic sensitivities can differ based on regional climate characteristics and type of extreme variables under warmer conditions over Asia.

Comprehensive evaluation of hydrological models for climate change impact assessment in the Upper Yangtze River Basin, China

Climate change has substantial impacts on regional hydrology in the major river basins. To figure out such latent hydrological impacts of changing climate, more reliable hydrological simulations are imperative. In this study, we evaluated the impacts of climate change on hydrological regime in the Upper Yangtze River Basin based on four downscaled and bias-corrected Global Climate Model outputs from Coupled Model Intercomparison Project Phase 5 under four Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) driving three hydrological models. Two model evaluation approaches were applied: simple and comprehensive. The comprehensive approach was used to evaluate models in the historical period, optimizing objective function at four gauges, and hydrological models were weighted for impact assessment based on their performance. In such a way, projected streamflow time series are obtained under different emission scenarios. Results show that the annual average discharge is projected to increase by 4.1–10.5% under the RCP scenarios at the end of twenty-first century relative to the reference period (1970–1999). Moreover, the high flow is projected to increase and the low flow to decrease indicating a higher probability of flood and drought occurrence in the basin. The severity of floods and droughts may increase. In comparison with the simple one-site model evaluation approach, the comprehensive method reveals that the anticipated extreme flow events would be less severe, and annual mean discharge slightly lower. The projected results imply that application of the comprehensive model evaluation approach could narrow the simulated spreads of projections significantly, and might provide more credible results.

Levee Fragility Behavior under Projected Future Flooding in a Warming Climate

Adaptation to climate change requires careful evaluation of infrastructure performance under future climatic extremes. This study demonstrates how a multidisciplinary approach integrating geotechnical engineering, hydrology, and climate science can be employed to quantify site-specific impacts of climate change on geotechnical infrastructure. Specifically, this paper quantifies the effects of changes in future streamflow on the performance of an earthen levee in Sacramento, California, considering multiple modes of failure. The streamflows for historical (1950–2000) and projected (2049–2099) scenarios with different recurrence intervals were derived from routed hydrological simulations driven by bias-corrected global climate models. The historical and future flood levels were then applied in a set of transient coupled finite-element seepage and limit equilibrium slope stability analyses to simulate the levee subjected to extreme streamflow. Variability in hydraulic and mechanical properties of soils was addressed using a Monte Carlo sampling method to evaluate and compare the probability of failure of the levee under different historical and future climate scenarios. Three individual modes (underseepage, uplift, and slope stability) along with lower and upper bounds for the combined mode of failure were examined. The results showed that incorporating future floods into levee failure analysis led to considerable reductions in the mean factor of safety and increases in the levee’s probability of failure, suggesting that risk assessment based on historical records can significantly underestimate the levee’s failure probability in a warming climate. Despite inherent uncertainties in future projections and substantial variability across climate models, evaluating infrastructure against projected extremes offers insights into their likely performance for the future.

GCM selection and temperature projection of Nigeria under different RCPs of the CMIP5 GCMS

The possible future changes in temperature over Nigeria were projected in this study. Using Climate Research Unit (CRU) temperature as the reference data, gain ratio (GR), entropy gain (EG), and symmetrical uncertainty (SU) feature selection methods and a multi-criteria decision-making (MCDM) approach were used in selecting the most suitable GCMs for Nigeria from 20 Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models (GCMs). The biases in selected GCMs were corrected using power transformation (PT) method. Multi-model ensembles (MMEs) were generated for the selected GCMs for the different temperature classes’ maximum, average, and minimum for all RCPs. The MMEs were used for the projection of temperatures over the country during 2010–2039, 2040–2069, and 2070–2099. The GCMs HadGEM2-ES, CESM1-CAM5, CSIRO-Mk3.6.0, and MRI-CGCM3 were the best performing in replicating temperature characteristics of the observed temperature in Nigeria. The MME mean projections of bias-corrected (BC) GCMs using PT revealed that there will be an increase in temperature of 4.0 °C at the semi-arid and 5.0 °C at the arid regions during dry and wet seasons respectively under RCP 4.5. In the same regions, the maximum temperature is expected to increase up to 5.5 °C under RCP 8.5 during 2070–2099 in the dry season. In the wet season, temperatures are expected to be higher under RCP 8.5, with an increase of 0.0–4.0 °C in the southern region and 3.0–6.9 °C in the northern region.