Climate Change Impact Assessment Research Articles

Correcting streamflow bias considering its spatial structure for impact assessment of climate change on floods using d4PDF in the Chao Phraya River Basin, Thailand

Study regionChao Phraya River Basin (CPRB), the predominant basin located in Thailand. Study focusThis study aims to ascertain the difference between spatial bias heterogeneity of streamflow in large river basins such as CPRB for a robust analysis. The upstream major dams and the outlet of the basin were examined with two-step bias correction and compared with a more practical bias correction only at the outlet of the basin. The former clarified that, due to the large effect of downstream bias, the upstream bias effect was considered negligible thus the two approaches resulted in similar future projections in the CPRB. Through this comparison, streamflow bias in the past and future climate experiments was corrected considering its spatial characteristics for robust assessments of quantitative impacts of climate change. New hydrological insights for the regionA + 4 K warmer climate will increase the frequency of the 2011 flood in CPRB and enhance 100-year flood peak discharge by 1.1–1.6 times than the past climate (1961–2010). The future flood in the basin, which starts predominantly in September in the present climate, is likely to begin in September and August equally with a prolonged duration of floods around 10–50 days. The study region is likely expected to experience elevated flood volume, earlier flood occurrence, and longer flood duration which indicates that forthcoming floods will be more rigorous.

Climate Change Impact Assessment on Stream flow of Ganjal River, India

Hydrological models often predict a changing situation, necessitating further research into models to make water resource management more realistic. This study uses soil and water assessment tool (SWAT) model to analyse the possible effect of climate change on the future streamflow of the Ganjal river watershed. It is located in middle sub-basin of the Narmada River, India. The model was calibrated for 1988-2007 and validated for 2008-2015 using monthly discharge data at the watershed outlet. The calibrated model was then run for the future (2025-52) using climate model output. The study of climate change is completed using the Representative Concentration Pathway scenarios, RCP 4.5 and 8.5 of three different General Circulation Models (GCM). The downscaled output of these GCM from the Coordinated Regional Downscaling Experiment (CORDEX) has been used in this study after bias correction. The findings demonstrate the significance of climate change’s effect on streamflow.

Gridded value-added of primary, secondary and tertiary industries in China under Shard Socioeconomic Pathways

Gridded distribution of future economy plays an important role in climate change impact assessment. The trend of the output values of different industries is crucial for a variety of planning and design processes. Under the Shared Socioeconomic Pathways (SSPs) global framework, the multidimensional model and Cobb-Douglas production model with localized population and economic parameters are used to develop the annual provincial population and value-added of primary, secondary and tertiary industries in China from 2020 to 2100. The most recently implemented fertility-promoting and industrial planning policies in China are considered in our projections. We build multiple models to evaluate the impact of different types of land use on the value-added of primary, secondary and tertiary industries and then gridded the projected value-added to a 5′ × 5′ resolution, based on recorded county-level economic statistics and gridded land use. The reliability of estimations is verified against 2011–2019 statistical data and multiple published datasets. The high-resolution economic dataset is expected to contribute greatly to national and regional climate change impact, adaptation, and vulnerability studies.

Potential impact of diversion canals and retention areas as climate change adaptation measures on flood risk reduction: A hydrological modelling case study from the Chao Phraya River Basin, Thailand

The countries of Southeast Asia are projected to experience severe flood damage and economic impacts from climate change, compared with the global average. Hence adaptation by incorporating infrastructures is essential, but it has been seldom explicitly included in the simulations projecting climate change impacts on flood risk in these countries. Quantifying the effects of infrastructure is the key to climate change impact and adaptation assessment. Therefore, this study was conducted in the Chao Phraya River Basin (CPRB) in Thailand to examine the adaptation potential of (i) existing structural and non-structural measures that include reservoir and diversion dams, diversion canals, and water retention areas, and (ii) the combined adaptation measures, a combination of alterations made to the existing diversion canals and retention areas, on reducing future floods using the H08 global hydrological model (GHM).The results revealed that the impact of existing measures on the future flood reduction was smaller than the increase caused by warming in the CPRB. Conversely, the combined adaptation measures successfully mitigated the effect of warming by redirecting nearly 50 % of the diverted river flow to the ocean and storing 30 % of the diverted flow in the retention areas. Although a remarkable reduction was noted in the basin-wide flood risk, the effect of adaptation measures greatly varied across the basin. The combined adaptation measures largely reduced the number of flooding days by close to 100 at many of the considered stations within the basin, except for extreme flood events (historical 1-percentile flood events). This further reveals that the feasibility of adaptation measures in alleviating the extreme future floods will be limited in flood-vulnerable basins and thus require area-based prioritization for flood management. The modelling framework implemented in this study can be easily adapted to different GHMs and regions and should be examined for their applicability.

Climate monitoring as an indicator of the hydrological condition of the Siversky Donets river basin

Formulation of the problem. Today, in changing climate conditions, it is very relevant to study the impact of regional climate change on the regime of hydrological indicators and ecological status of the Siversky Donets river basin within Kharkiv region. Analysis of recent research and publications. Hydrometeorological studies are complex and large-scale. In the late 20th - early 21st centuries, a number of articles studied annual runoff of the rivers of Ukraine under the influence of atmospheric processes. Present-day changes in temperature and humidity of the territory affect the hydrological conditions of the rivers. The aim of the work is to assess the relationship between climatic and hydrological indicators (environmental dynamics) of the Siversky Donets basin against the background of regional climate change. In these conditions, monitoring of climate, hydrological and environmental indicators, allows us to make further management decisions on water resources management. Research methods are presented by statistical and cartographic analysis. The source data are a number of climatological, hydrological and environmental observations within the state network of the Ukrainian Hydrometeorological Center of the SES of Ukraine. Problems of further research. Modern changes in climatic conditions in Ukraine are characterized by locality and rapidity. Considering the volume of water use from the river Siversky Donets, the question arises about the water supply of the region, optimization of its use and further rational management. Presentation of the main research material. The dates of the ice cover have changed in recent years, and often ice phenomena may not occur at all. We can see significant warming in winter on the example of January air temperatures. Summer temperatures are growing the fastest (by 0.37°C every 10 years), autumn temperatures are in the second place in terms of growth rate. which means that stable ice cover on most rivers of the Donets basin has been absent in recent decades, which clearly indicates a warming trend. Practical value. Based on the main provisions of the national environmental policy of Ukraine on the use of water resources the study of changes in hydrological regime of rivers is of practical importance for sustainable management. Calculation of the IWP has revealed that most rivers belong to the third and fourth categories - "moderately polluted" and "polluted", but there are also absolutely catastrophic cases. Research results. Heavy economic burden on the waterway will increase its over-regulation. In future, comprehensive assessment of climate change impact on the hydrological conditions of the rivers will determine the degree of change in the ecological state of the waterways, their rational use and protect.

Integrated Hydrologic-Hydrodynamic Inundation Modeling in a Groundwater Dependent Tropical Floodplain

The rapid development of free and open-access hydrological models and coupling framework tools continues to present more opportunities for coupled model development for improved assessment of floodplain hydrology. In this study, we set up an Upper Zambezi hydrological model and a fully spatially hydrological-hydrodynamic coupled model for the Barotse Floodplain using GLOFRIM (GLObally applicable computational FRamework for Integrated hydrological–hydrodynamic Modelling). The hydrological and hydrodynamic models used are WFLOW and LISFLOOD-FP, respectively. The simulated flows generated by the wflow model for the upstream gauge stations before the Barotse Floodplain were quite similar and closely matched the observed flow as indicated by the evaluation statistics; Chavuma, nse = 0.738; kge = 0.738; pbias = 2.561 and RSR = 0.511; Watopa, nse = 0.684; kge = 0.816; pbias = 10.577 and RSR = 0.557; and Lukulu, nse = 0.736; kge = 0.795; pbias = 10.437 and RSR = 0.509. However, even though the wflow hydrological model was able to simulate the upstream hydrology very well, the results at the floodplain outlet gauge stations did not quite match the observed monthly flows at Senanga gauge station as indicated by the evaluation statistics: nse = 0.132; kge = 0.509; pbias = 37.740 and RSR = 0.9233. This is mainly because the representation of both floodplain channel hydrodynamics and vertical hydrological processes is necessary to correctly capture floodplain dynamics. Thus, the need for an approach that saves as a basis for developing fully spatially distributed coupled hydrodynamic and hydraulic models’ assessments for groundwater dependent tropical floodplains such as the Barotse floodplain, in closing the gap between hydrology and hydrodynamics in floodplain assessments. A fully coupled model has the potential to be used in implementing adaptive wetland management strategies for water resources allocation, environmental flow (eflows), flood control, land use and climate change impact assessments. Doi: 10.28991/HEF-2022-03-02-09 Full Text: PDF

Bias Correction of Climate Model Outputs Using IMD Srinagar Data for Climate Change Impact Assessment

Regional climate models (RCMs) give more credible findings for a regional climate change impact assessment, but they still have a bias that must be rectified. Two correction functions using two methods, the modified difference approach and linear scaling method, were utilized for local bias correction of Tmax, Tmin, and precipitation data at monthly scales and validated to minimize the bias between modelled (HAD GEM2-ES-GCM) and observed climate data at IMD Srinagar Station, J&K. Linear scaling technique at monthly time scale for Tmax, Tmin, and precipitation was superior to modified difference approach for bias correction of modelled data to close it to observed data.

Distributed Hydrological Model Based on Machine Learning Algorithm: Assessment of Climate Change Impact on Floods

Rapid population growth, economic development, land-use modifications, and climate change are the major driving forces of growing hydrological disasters like floods and water stress. Reliable flood modelling is challenging due to the spatiotemporal changes in precipitation intensity, duration and frequency, heterogeneity in temperature rise and land-use changes. Reliable high-resolution precipitation data and distributed hydrological model can solve the problem. This study aims to develop a distributed hydrological model using Machine Learning (ML) algorithms to simulate streamflow extremes from satellite-based high-resolution climate data. Four widely used bias correction methods were compared to select the best method for downscaling coupled model intercomparison project (CMIP6) global climate model (GCMs) simulations. A novel ML-based distributed hydrological model was developed for modelling runoff from the corrected satellite rainfall data. Finally, the model was used to project future changes in runoff and streamflow extremes from the downscaled GCM projected climate. The Johor River Basin (JRB) in Malaysia was considered as the case study area. The distributed hydrological model developed using ML showed Nash–Sutcliffe efficiency (NSE) values of 0.96 and 0.78 and Root Mean Square Error (RMSE) of 4.01 and 5.64 during calibration and validation. The simulated flow analysis using the model showed that the river discharge would increase in the near future (2020–2059) and the far future (2060–2099) for different Shared Socioeconomic Pathways (SSPs). The largest change in river discharge would be for SSP-585. The extreme rainfall indices, such as Total Rainfall above 95th Percentile (R95TOT), Total Rainfall above 99th Percentile (R99TOT), One day Max Rainfall (R × 1day), Five-day Max Rainfall (R × 5day), and Rainfall Intensity (RI), were projected to increase from 5% for SSP-119 to 37% for SSP-585 in the future compared to the base period. The results showed that climate change and socio-economic development would cause an increase in the frequency of streamflow extremes, causing larger flood events.

Potential climate change impact assessment on the hydrology of the Lake Tana Basin, Upper Blue Nile River Basin, Ethiopia

The recent climate change is manifested by human activities, such as increasing global temperature, anomalous rainfall, and extreme fluctuations in the hydrological cycle noticed in different regions of the earth. This study assesses the hydrological changes of Lake Tana Basin under future climatic changes. The study used Statistical Downscaling Model (SDSM) to project future climate change. The basin's hydrology was assessed using the Soil and Water Assessment Tool (SWAT). The SWAT was driven by the outputs of SDSM and calibrated against observed river flow. The results indicate that the maximum and minimum temperature trends are generally in line with the global trends, whereas the precipitation shows an inconsistent pattern. The predictive results from the RCP8.5 radiative forcing scenarios indicate that during the wet season (dry season), the evapotranspiration, streamflow, and base flow will tend to increase by 14.9%, 9.7%, and 20.9% (70.1%, 2.8%, and 16.7%) respectively by the year 2090s. The streamflow curve for the Lake Tana basin's under future climate change shows a steep slope, and stream discharge is highly variable, particularly the surface runoff. This study indicates that although climate change seems uncertain for the study area, preparedness is warranted to avoid the aggravation of soil erosion, water stress and scarcity, and often floods/droughts.

Hydrological and Environmental Conditions and Implications of the Operation of a Thermal Power Plant with an Open Cooling System—An Example from Poland

In Poland, coal-based thermal energy for cooling power plant installations uses a large amount of surface water. Historically, there have been cases of limitations in electricity supply due to low water levels and high temperature of water in rivers. Moreover, environmental requirements limit the possibility of using water resources for cooling purposes, pointing to the necessity to leave inviolable flows in rivers. This raises questions about the future of the operation of thermal power plants with open cooling systems and hence the research undertaken by the authors. The research consisted of a questionnaire survey, hydrological analyses, the impact assessment of climate change on the operation of power plants, and a discussion of technical solutions for water abstraction and power loss analysis in a particular power plant. The results indicate that there are power plants that are more sensitive to hydrological and environmental conditions and the temperature of the water required for cooling. In one case, keeping a power plant in operation requires the maintenance of periodic artificial damming of water. The conclusions from the research indicate that in Poland, regardless of the source of thermal energy, it is necessary to implement only closed cooling circuits.

Climate Change Impact Assessment of Geopolymer Mortars

The awareness of environmental protection, with the conservation of resources and the efficient use of industrial waste, has attracted the attention in recent decades as both the overexploitation of natural resources and the disposal of industrial waste have a negative impact on the environment and sustainability [1]. Under such circumstances, replacing ordinary Portland cement (OPC) with industrial waste has been shown as a sustainable and practical way to reduce the use of natural resources, as well as landfill waste and pollution [2]. The discussion of this issue is part of a path, which sees as its starting point the design of a hydraulic pipeline prototype (Figure 1) made of geopolymer mortar instead of conventional concrete pipes. The environmental sustainability of geopolymer mortars was demonstrated through the Life Cycle Assessment (LCA) methodology. Analysis results indicate that the use of eco-friendly materials contributes to minimizing the environmental impact of new technologies for engineering sector.

Climate Change Impact Assessment for Future Wind and Solar Energy Installations in India

Solar and wind assets are climate-dependent and changes in climate will result in variations in their generation and intermittency. Developers of solar and wind parks in India have observed changes in climate conditions and variability in solar irradiation and wind profiles at the seasonal and year-to-year timescales. Future climate change perturbations, including monsoon shifts, could lead to lower-than-predicted wind and solar energy production and affect the economics of solar and wind assets. Regional climate models (RCMs) are the basis of climate impact assessments and the most trusted source of information to extract knowledge about future trends in climate variables. However, RCM projections are tainted with variability and uncertainty about the future trends. For India as a case study, we use the RCMs generated by the Coordinated Regional Climate Downscaling Experiment West Asia project (CORDEX WAS) to calculate individual wind, radiation, and temperature trends at selected sites; estimate wind and solar PV energy time series; and embed them in portfolio methods to test the impact of combining wind and solar assets on the variability of the total production and the uncertainty about the predicted production. We include a comparison of CORDEX RCMs with the ERA5 reanalysis dataset and conclude that all available RCMs reasonably simulate the main annual and seasonality features of wind speed, surface solar radiation, and temperature in India. The analysis demonstrates that the uncertainty about the portfolio return can be reduced by optimizing the combination of wind and solar assets in a producer portfolio, thus mitigating the economic impact of climate change. We find that the reduction obtained with a mixed portfolio ranges from 33 to 50% compared to a wind only portfolio, and from 30 to 96% compared to a solar only portfolio.

Assessment of climate change impact on surface water: a case study—Karoun River Basin, Iran

Assessment of climate change impact on surface water: a case study—Karoun River Basin, Iran

Application of CSM-CANEGRO Model for Climate Change Impact Assessment and Adaptation for Sugarcane in Semi-arid Environment of Southern Punjab, Pakistan

Application of CSM-CANEGRO Model for Climate Change Impact Assessment and Adaptation for Sugarcane in Semi-arid Environment of Southern Punjab, Pakistan

Evaluation and joint projection of temperature and precipitation extremes across Canada based on hierarchical Bayesian modelling and large ensembles of regional climate simulations

Individual and joint variations of extreme temperature and precipitation are assessed across Canada using the large ensemble of Canadian Regional Climate Model simulations (CanRCM4-LE) and two corresponding multivariate bias-corrected datasets (Canadian Large Ensembles Adjusted Datasets, CanLEAD-E & S). The overall performance of the three 50-member ensembles is evaluated against the NRCANmet gridded observation for 1951–2000. A hierarchical Bayesian framework is then applied to analyze the biases of each ensemble member, characterize the ensemble uncertainties associated with internal climate variability, and assess the trends and the joint distributions of temperature and precipitation. Further, projected changes of extreme climate indices are assessed across the three ensembles in the historical period and four future scenarios corresponding to +1.5 °C to +4.0 °C warming above the pre-industrial level of 1850–1900. Results show that the CanLEAD products have lower warm and wet biases compared to CanRCM4-LE over most regions and in all seasons except in winter. CanLEAD-S significantly reduces precipitation biases and represents the behaviour of extreme climate indices better than the other two ensembles. All ensembles consistently project strong warming and wetting trends over most parts of southern Canada excluding the Canadian Prairies in summer, which shows a drying trend towards the end of the 21st century. The ensembles show increases in hot extremes in central and southeastern Canada and increases in wet extremes in western coastal regions. The results of the study suggest that CanLEAD-E&S can be used as reliable products for climate change impact assessments at regional scales particularly for the analysis of nonstationary compound events.

Temporal downscaling of daily precipitation to 10 min data for assessment of climate change impact on floods in small-size watersheds applied to Jinju, South Korea

Temporal downscaling of daily precipitation to 10 min data for assessment of climate change impact on floods in small-size watersheds applied to Jinju, South Korea

Assessment of Climate Change Impact on the Annual Maximum Flood in an Urban River in Dublin, Ireland

Hydrological modelling to address the problem of flood risk corresponding to climate change can play an important role in water resources management. This paper describes the potential impact of climate change on an urban river catchment using a physically based hydrological model called Soil Water Assessment Tool (SWAT). The study area considered is the Dodder River basin located in the southern part of Dublin, the capital city of Ireland. Climate projections from three regional climate models and two representative concentration pathways (RPC 4.5 and RCP 8.5) were used to evaluate the impact of flooding corresponding to different climate change scenarios. Annual maximum flow (AMF) is generated by combining the bias-corrected climate projections with the calibrated and validated SWAT model to understand the projected changes in flood patterns for the year 2021–2100. The expected changes in flood quantiles were estimated using a generalised extreme value distribution. The results predicted up to 12% and 16% increase in flood quantiles corresponding to 50 years and 100 years return periods. Based on the flood quantiles, flood inundation maps were developed for the study area.

Dominant sources of uncertainty in simulating maize adaptation under future climate scenarios in China

CONTEXTThe potential of climate adaptation has been widely investigated with a climate-crop modeling approach. Although different sources of uncertainty in projected crop yields have been quantified in climate change impact assessments, uncertainty in simulating the crop adaptation to future climate has not been fully assessed. OBJECTIVEThe objective of this study was to determine the uncertainty in simulating maize adaptation to future climate change with two adaptation options (adjusting planting date and shifting cultivars) at four contrasting sites across China's Maize Belt. METHODSMaize yield with adaptation was simulated using three crop models (APSIM, DSSAT-CERES, and STICS) driven by 22 global climate models (GCMs) under four emission scenarios of future societal development pathway (SSP126, SSP245, SSP370, and SSP585) during two periods (2040–2069 and 2070–2099). RESULTS AND CONCLUSIONSWe found that late planting had a greater potential to cope with climate change at most study sites. However, all sites required new cultivars with increased thermal time requirements. Under optimum management options at the four study sites, rainfed maize yields were likely to increase by 1.9%–68.3% compared with yields obtained without adaptation. For the adaptation simulation using adjusted planting date alone, GCM was the major source of uncertainty, accounting for 22.9%–36.7% of the total uncertainty at all sites except a high-altitude site where changing planting time was the major source of uncertainty (32.4%). For the adaptation simulation using shifting cultivar alone, crop model was the dominant source of uncertainty, accounting for 24.0%–38.0% of the total uncertainty at all sites except a high-latitude site where shifting cultivar was the major source of uncertainty (34.0%). These findings demonstrated that adaptation options have great potential for increasing maize yields, and the major source of uncertainty depends on study sites and adaptation type used. SIGNIFICANCEThe results of this study advance the understanding of the dominant sources of uncertainty in crop yield under different climate adaptations, thereby improving our confidence in assessments of future climate impact on maize yields determined by different adaptation strategies.

Climate change impacts on phenology and ripening of cv. Touriga Nacional in the Dão wine region, Portugal

AbstractThe present study is devoted to climate change impact assessment on the phenological development and ripening of cv. Touriga Nacional in the Dão Wine Region, Portugal. For this purpose, the dates of the three main phenological stages (budbreak, flowering and veraison) and two maturity stages are projected for two future periods (2041–2070 and 2071–2100), under two anthropogenic radiative forcing scenarios (RCP4.5 and RCP8.5), and compared against a baseline period (1991–2020). The phenological and maturity stages are simulated using phenological development models (PDMs) and a temperature‐based ripeness model (TRM), respectively. An overall advancement in both phenology and ripening stages are identified under future warmer climates, though site‐dependent. Furthermore, the advancements in phenology are more pronounced (a) for stages between the beginning of veraison and end of ripening than for the earlier stages, (b) for the long‐term future period (2071–2100) under RCP8.5 and (c) for the vineyard site “Viseu.” These changes are due to the combination of budbreak advancement with a shortening of some phenophases. The strongest shortening is found in the ripening period, while no significant changes in flowering timings and duration of the berry development period are projected. The advancement and the shortening of the grapevine growing season will shift ripening to the warmest part of the year. This twofold climate change impact of the air temperature on ripening may affect the sugar and organic acid balance, as well as the colour of the must. The current findings can be used by the regional winemaking sector in planning and implementing suitable climate change adaptation to enhance its climate resiliency and sustainability. Subsequent studies for this wine region should be carried out to assess the climate change impacts on late frost risk, on climatic viticultural zoning, on yield and berry quality at harvest.

Climate Change Impacts and Extinction Risk Assessment of Nepeta Representatives (Lamiaceae) in Greece

The ongoing climate change has already left its imprint on species distributions, with rare, endemic species being more threatened. These changes are more prominent in regional biodiversity hotspots, such as Greece, which is already facing the short term impacts of human induced climate change. Greek flora hosts numerous endemic medicinal and aromatic plant taxa (MAPs), which are economically important and provide integral ecosystem services. The genus Nepeta is one of the largest Lamiaceae genera, containing several MAPs, yet, despite its taxonomical and economical significance, it remains vastly understudied in Greece. We explore the effects of climate change on the range of the Greek endemic Nepeta MAPs, via a species distribution models (SDMs) approach in an ensemble modeling framework, using soil, topographical and bioclimatic variables as predictors in three different time steps. By doing so, we attempt to estimate the current and future extinction risk of these taxa and to locate their current and future species richness hotspots in Greece. The taxa analyzed are expected to experience severe range retractions, with minor intraspecific variation across all time steps (p > 0.05), driven mainly by soil- and aridity-related variables. The extinction risk status of only one taxon is predicted to worsen in the future, while all other taxa will remain threatened. Current species richness hotspots are mainly located in southern Greece and are projected to shift both altitudinally and latitudinally over time (p < 0.01).