Climate Change Impact Assessment Research Articles

Assessment of climate change impact on irrigation water demand for food security in rice-fallow cropland areas using an integrated modeling technique

Assessment of climate change impact on irrigation water demand for food security in rice-fallow cropland areas using an integrated modeling technique

Assessing Climate Change Impact on Rainfall Patterns in Northeastern India and Its Consequences on Water Resources and Rainfed Agriculture

Assessing Climate Change Impact on Rainfall Patterns in Northeastern India and Its Consequences on Water Resources and Rainfed Agriculture

Validation of the Ensemble Generalized Analog Regression Downscaling (En-GARD) Model to Downscale Near-Surface Wind Speed for the Northeast United States

Abstract Wind, often overlooked in climate change impact assessments, is now a crucial atmospheric variable due to our imminent reliance on renewable energy sources like wind energy. Therefore, establishing a wind speed database in higher temporal and spatial resolution is of great importance to assess wind power in historical periods and future projections. This study focuses on enhancing wind data representation and reliability of statistically downscaled model outputs by refining grid spacing from coarser to a desirable finer scale. Employing the Ensemble Generalized Analog Regression Downscaling (En-GARD) technique, we downscaled near-surface wind speed across a northeast (NE) U.S. domain that combines onshore and offshore wind farm areas. We tested multiple atmospheric variable combinations and identified near-surface wind speed (wind speed at 10 m), longitudinal and latitudinal wind at 10 m, and atmospheric temperature at 2 m, as the set that effectively guides the selection of analog days for the analog regression downscaling approach. Validation involved using ERA5 atmospheric reanalysis products (31 km) and high-resolution (4 km) Weather Research and Forecasting (WRF) Model simulations. Downscaling was conducted using a leave-one-year-out approach over 12 years (2001–12). Statistical analysis of the downscaled output over this period demonstrated a significant correlation and low error metrics (less than 20% for normalized RMSE and 16% for normalized MAE), indicating the reliability of the method and paving the way for its future application to downscale climate projection outputs. Significance Statement Wind speed has become a significant atmospheric variable due to our society’s imminent reliance on renewable energy resources like wind power. Often overlooked in climate change assessments, wind speed at a local scale over wind farm locations plays a crucial role in assessing and planning wind power resources in a future climate. We are providing the successful validation of a statistical downscaling methodology for near-surface wind speed at 4-km grid spacing using reanalysis data. The validity of the methodology gives confidence to downscale climate projection models and assess changes in wind speed for future climate scenarios in a follow-up study.

An improved empirical quantile mapping approach for bias correction of extreme values in climate model simulations

Abstract Quantifying and correcting biases in modeling simulations is crucial for deriving meaningful findings across various scientific disciplines. Climate model simulations, in particular, often exhibit systemic biases when compared to observations. These biases may persist in future climate simulations, affecting the results of many climate change impact assessment studies. Empirical Quantile Mapping (QM) is a widely used method to correct such biases, mapping quantiles between observed and simulated Cumulative Distribution Functions (CDFs). However, empirical QM faces a fundamental challenge when the CDF of future simulations differs from historical simulations, potentially leading to extreme values falling outside the historical CDF range. To address this issue, our study introduces a novel approach to extrapolate future extreme values for bias correction, preserving the rank order of simulated future extremes. By construction, our approach ensures that bias corrected values are not exaggerated and retain the rank structure of the original simulated data, while preserving climate change signals in the bias corrected outputs. Additionally, our approach includes a technique to adjust the wet-day frequency for precipitation by preserving the ratio of wet-day frequency between observations and historical model simulations.

In‐situ and ex‐situ conservation priorities and distribution of lentil wild relatives under climate change: A modelling approach

Abstract Lentil wild relatives are an important source of desirable traits that can be used for improving the productivity and resilience of cultivated lentil. Yet, our understanding of their habitat suitability and associated environmental factors remains limited. This study aimed to (i) assess climate change's impact on the potential distribution of six wild lentil species (Lens culinaris subsp. orientalis, L. culinaris subsp. tomentosus, L. culinaris subsp. odemensis, Lens ervoides, Lens lamotte and Lens nigricans) under various climate scenarios and (ii) assess their risk of extinction and determine their in‐situ and ex‐situ conservation status. We used a species distribution modelling approach with MaxEnt to assess the present and future potential distribution of wild lentil species. Extinction risk was evaluated based on International Union of Conservation of Nature criterion B, and the conservation status was assessed using the GapAnalysis method. The precipitation of the coldest quarter (bio19) and the minimum temperature of the coldest month (bio6) were found as the most important variables influencing the distribution of wild lentil species. Final Conservation Score (FCS) ranged from 17.85 and 37.55, highlighting three wild lentil species (L. ervoides, L. nigricans, and L. culinaris subsp. tomentosus) with high priority for conservation and medium priority for the remaining species. L. culinaris subsp. tomentosus is categorized as a vulnerable species, while the other five species are of least concern or near threatened. Synthesis and applications: This study underscores the urgent need for policy development to safeguard the diversity of lentil wild relatives in the face of climate change. The identified vulnerability of Lens culinaris subsp. tomentosus, among others, needs prompt and proactive conservation actions. Key management practices include the establishment and expansion of protected areas, habitat restoration, and the promotion of sustainable land use practices. The integration of effective in‐situ and ex‐situ conservation strategies, along with ecological management practices, is essential. These measures, not only, enhance biodiversity conservation but also improve the resilience of agricultural ecosystems. Such an approach is pivotal in shaping effective conservation management practices for lentil wild relatives, promoting a sustainable agricultural system and ensuring food security in an evolving climate scenario.

Assessment of Climate Change Impact on Wheat Water Demand and Yield in Setif, Algeria

Assessment of Climate Change Impact on Wheat Water Demand and Yield in Setif, Algeria

Assessing Annual and Monthly Precipitation Anomalies in Ecuador Bioregions Using WorldClim CMIP6 GCM Ensemble Projections and Dynamic Time Warping

ABSTRACTThe Coupled Model Inter‐comparison Project phase 6 (CMIP6) provides a suite of general circulation models (GCMs) and Socioeconomic Shared Pathways (SSPs) primarily for continental‐scale climate assessments. However, adapting these models for sub‐national assessments, particularly in countries with varied geography like Ecuador, and for complex variables such as precipitation, introduces challenges, including uncertainties in selecting appropriate GCMs and SSPs. To address these issues, we adopt a biogeographical approach that integrates regional climatic variations. Our analysis explores 26 GCMs, four SSP scenarios and four 20‐year time frames from WorldClim to evaluate discrepancies between the GCM precipitation projections, historical data and national climate projections across five Ecuadorian bioregions. This approach enabled us to sort the GCMs by annual precipitation medians, classify their monthly precipitation using Dynamic Time Warping (DTW) clustering, and develop ensembles highlighting both the largest and average precipitation anomalies within and beyond the bioregions. Among the 26 models examined, 16 projected an increase in annual precipitation in Ecuador, especially during the wet seasons, with the BCC‐CSM2‐MR model showing peak values, notably in the Choco region and eastern Amazon basin. Conversely, 10 models, with CMCC‐ESM2 showing the largest decreases, projected reduced precipitation across almost all Ecuadorian territories, except the Choco region. The largest reductions were in the Amazon basin, raising concerns about reduced precipitation. Discrepancies, primarily in the Andes and Galapagos bioregions, reveal the challenges posed by their complex topography and insular environments. While the GCMs captured spatial patterns of ENSO, our research was constrained to 20‐year averages, making direct comparison with historical records infeasible, highlighting the need for further research with shorter time frames and finer spatial resolutions. The variability in precipitation was linked to geographical factors, GCM configurations and unexpected SSP outcomes. Therefore, selecting GCMs and climatic indices tailored to specific bioregions is recommended for effective climate change impact assessments.

Assessment of climate change impact on meteorological variables of Indravati River Basin using SDSM and CMIP6 models.

Climate change, one of the most pressing issues of the twenty-first century, threatens the long-term stability and short-term variability of water resources. Variations in precipitation and temperature will influence runoff and water availability, creating significant challenges as demand for potable water increases. This study addresses a critical literature gap by employing the Statistical Downscaling Model (SDSM) to downscale Global Climate Model (GCM) outputs for the Indravati River Basin, India. Maximum temperature (Tmax), minimum temperature (Tmin), and precipitation (PCP) were statistically downscaled, improving the spatial resolution of coarse GCM data. The model established strong predictor-predictand relationships, offering enhanced local-scale climate projections for the basin. This work provides critical insights into regional climate change impacts in a previously underexplored area. The study projected the meteorological variables (Tmax, Tmin, and PCP) for Chindnar, Jagdalpur, and Pathagudem stations using four GCMs, namely CanESM5, MPI-ESM1-2-HR, EC-Earth3, and NorESM2-LM for the baseline period (1990-2014). The Correlation Coefficient-values (R-values) range from 0.75 to 0.91 for maximum temperature, 0.85 to 0.96 for minimum temperature, and 0.71 to 0.83 for precipitation were achieved using SDSM. The best-performed MPI-ESM1-2-HR model was used to project maximum temperature, minimum temperature, and precipitation for 2024-2054 (2040s) and 2055-2085 (2070s) under SSP4.5 and SSP8.5 scenarios using SDSM. The downscaled results revealed significant shifts in meteorological patterns, highlighting the basin's sensitivity to different socio-economic pathways and future climate conditions. The percentage monthly, seasonal, and annual variations of Tmax, Tmin, and PCP were analysed based on each scenario and time period to suggest remedial measures for future floods and droughts.

Assessment of the CORDEX-CORE Models in Simulating the Length of Rainy Season (LRS) over Northeast India

Abstract The Length of Rainy Season (LRS) in Northeast India (NEI) is longer than that over other parts of the country and contributes to the seasonal quantum of rainfall, a hallmark of the Indian Summer Monsoon. Low-resolution global general circulation models (GCMs) underestimate rainfall annual cycle over NEI due to unresolved regional orography requiring downscaling with the Coordinated Regional Climate Downscaling Experiment (CORDEX) models for South Asia. However, the uncertainty in CORDEX simulations of the annual cycle of rainfall and its intensity of over NEI due to biases in simulating the LRS is unknown. This study evaluates performance of CORDEX climate models, focusing on their ability to simulate the LRS. Utilizing an objective metric based on the change of sign in a north-south gradient of tropospheric temperature, we define LRS and employ ERA5 reanalysis data as a proxy for observations to establish a benchmark for the historical period of 1979-2005. Our analysis reveals that the CORDEX models when forced by ERA-Interim data, exhibit a consistent positive bias of approximately 6 days, while models forced by General Circulation Models (GCMs) display a negative bias of 10 days. Additionally, both model sets indicate a reduction in bias over inter-annual timescales. Further, we have observed that RCMs forced by ERA-Interim boundary conditions tend to overestimate the thermodynamic index of the Indian summer monsoon (TISM) compared to RCMs forced by GCMs. Our findings suggest that significant discrepancies in the simulation of the upper atmospheric conditions over the South Asia domain by regional models contribute to the pronounced biases in LRS estimates over NEI forced either by observations or by GCM outputs. This study underscores the complexities of accurately simulating the LRS using regional climate models, emphasizing the need for improved methodologies to precisely forecast LRS. Such improvements are crucial for reliable climate change impact assessments over NEI.

Climate Change Impact Assessment Based on Perception and Participation in Telagah Village

Climate Change Impact Assessment Based on Perception and Participation in Telagah Village

Impact assessment of climate change on rainfall Intensity Duration Frequency curve -Swat district, Khyber Pakhtunkhwa

Impact assessment of climate change on rainfall Intensity Duration Frequency curve -Swat district, Khyber Pakhtunkhwa

Impact Assessment of Climate Change on Water Requirements for Summer Paddy Cultivation in Raipur District, Chhattisgarh

Water is a crucial and finite resource, primarily utilized for irrigation, necessitating effective distribution for long-term sustainability. This study quantifies the water requirements for summer paddy cultivation in Raipur district, Chhattisgarh, and assesses its implications under changing climatic conditions. The FAO-CROPWAT 8.0 model was applied to assess irrigation parameters using historical meteorological data from 2000 to 2020 and projected meteorological data from 2040 to 2100. Reference evapotranspiration (ET₀) was estimated using Penman-Monteith method, incorporating crop coefficient (Kc) values to determine crop evapotranspiration (ETc). Irrigation water requirements were estimated by accounting for various losses and efficiencies of crop and field. Comprehensive water balance models were developed across the century, focusing on a 20-year average time span, including the historical period as 2000-2020 and the predicted time span from 2040 to 2100 with two socio-economic scenarios: SSP2-4.5 and SSP5-8.5, along with soil, meteorological, and phenological factors. Over the baseline period, the crop water requirement (CWR) for summer paddy was calculated as 751.4 mm, with net irrigation water requirement (NIWR) and gross irrigation water requirement (GIWR) as 1094.7 mm and 1563.8 mm, respectively. Under SSP2-4.5 scenario, the minimum and maximum temperatures are projected to rise by 1.3 °C and 1 °C respectively with rainfall increasing by 378.5 mm. This scenario anticipates a CWR increase of 32.6 mm by century's end, resulting in final values of CWR as 784 mm, NIWR as 1164.7 mm, and GIWR as 1663.9 mm. In the SSP5-8.5 scenario, temperatures are expected to rise by 4.4 °C and 3.1 °C as minimum and maximum temperature, leading to a CWR of 800.4 mm, with NIWR and GIWR as 1176.5 mm and 1680.7 mm, respectively. Overall, most parameters indicate an upward trend throughout the century. This study provides critical insights for efficient irrigation management in summer paddy cultivation and highlights the implications of future climate change on irrigation needs, promoting sustainable agricultural practices.

Integrating Water Evaluation and Planning Modeling into Integrated Water Resource Management: Assessing Climate Change Impacts on Future Surface Water Supply in the Irawan Watershed of Puerto Princesa, Philippines

The Irawan Watershed in Puerto Princesa, Philippines, is an important resource that supports domestic, agricultural, and industrial water needs. This study applies the Water Evaluation and Planning (WEAP) model to project the impacts of climate change on future surface water availability, integrating the findings into an Integrated Water Resource Management (IWRM) framework. Using bias-corrected General Circulation Models (GCMs) under four shared socioeconomic pathways (SSPs), this study examines scenarios from low to high emissions (SSP126, SSP245, SSP370, and SSP585) for the assessment of potential variations in water supply. The results indicate a significant vulnerability to water availability, especially under SSP370 and SSP585, where climate warming is pronounced, leading to significant reductions in streamflow. Conversely, SSP126 suggests relatively stable conditions with less pronounced hydrological changes. The study also explores the socioeconomic drivers that affect water demand, including population growth and land use changes that influence agricultural water needs. The findings underscore the urgency of using adaptive management strategies to conserve water resources in the face of these anticipated challenges. Key recommendations include optimizing water use efficiency in all sectors, establishing protective zones around natural ecosystems, implementing climate-resilient infrastructure, and promoting community engagement in water management. These measures are critical for enhancing water security and promoting sustainable development within the watershed, contributing to the broader goals of Sustainable Development Goal (SDG) 6. This study offers decision-makers and resource managers an evidence-based framework for integrating hydrological modeling into IWRM, providing valuable insights to navigate the complexities of climate change and ensure the long- term sustainability of water resources in the Philippines.

Assessment of Climate Change Impact in Tropical Buildings: Sensitivity Analysis of Light Shelf and Building Design Parameters for Daylighting and Thermal Balance

Climate change may lead to more intense sunlight in tropical regions, potentially increasing the daylight available for buildings. While this can enhance natural lighting inside buildings, it may also exacerbate issues such as glare and overheating if not properly managed through shading devices and glazing treatments. Nevertheless, common strategies to mitigate heat gain and glare such as shades and blinds often obstruct natural light, necessitating increased reliance on artificial lighting. The conflicting interplay between daylighting and thermal performance can undermine building performance if not carefully considered. Existing research on the influence of key design parameters in tropical climates tends to focus predominantly on heat gain mitigation, neglecting other aspects. This study addresses these gaps by examining the holistic daylighting and thermal energy performance of buildings to develop optimised façade and resilient designs against climate change. The investigation encompasses an analysis of 11 design parameters of light shelves and building characteristics that are critical during the initial design stage. Global sensitivity analysis (GSA) is employed to identify the most influential design parameters affecting useful daylight illuminance, uniformity ratio, cooling energy, and solar gain energy. A case study involving double-story terrace houses in Malaysia is employed, with analyses conducted for the present and future climates of three Malaysian cities (Kuala Lumpur, Bayan Lepas, and Kota Bahru). The findings reveal that, across all three cities, glazing transmittance is the most influential parameter for daylighting performance, while room depth assumes primary significance for thermal performance. Although the relative ranking of parameters remains consistent between present and future climates, their magnitudes differ. In summary, this paper gives designers insights into the critical design parameters essential for achieving a balanced and resilient daylight-thermal design in tropical climates at the initial stages of the design process.

Hydrological Response to Climate Change: McGAN for Multi-Site Scenario Weather Series Generation and LSTM for Streamflow Modeling

This study focuses on the impacts of climate change on hydrological processes in watersheds and proposes an integrated approach combining a weather generator with a multi-site conditional generative adversarial network (McGAN) model. The weather generator incorporates ensemble GCM predictions to generate regional average synthetic weather series, while McGAN transforms these regional averages into spatially consistent multi-site data. By addressing the spatial consistency problem in generating multi-site synthetic weather series, this approach tackles a key challenge in site-scale climate change impact assessment. Applied to the Jinghe River Basin in west-central China, the approach generated synthetic daily temperature and precipitation data for four stations under different shared socioeconomic pathways (SSP1-26, SSP2-45, SSP3-70, SSP5-85) up to 2100. These data were then used with a long short-term memory (LSTM) network, trained on historical data, to simulate daily river flow from 2021 to 2100. The results show that (1) the approach effectively addresses the spatial correlation problem in multi-site weather data generation; (2) future climate change is likely to increase river flow, particularly under high-emission scenarios; and (3) while the frequency of extreme events may increase, proactive climate policies can mitigate flood and drought risks. This approach offers a new tool for hydrologic–climatic impact assessment in climate change studies.

Combining a Multi-Lake Model Ensemble and a Multi-Domain CORDEX Climate Data Ensemble for Assessing Climate Change Impacts on Lake Sevan.

Global warming is shifting the thermal dynamics of lakes, with resulting climatic variability heavily affecting their mixing dynamics. We present a dual ensemble workflow coupling climate models with lake models. We used a large set of simulations across multiple domains, multi-scenario, and multi GCM- RCM combinations from CORDEX data. We forced a set of multiple hydrodynamic lake models by these multiple climate simulations to explore climate change impacts on lakes. We also quantified the contributions from the different models to the overall uncertainty. We employed this workflow to investigate the effects of climate change on Lake Sevan (Armenia). We predicted for the end of the 21st century, under RCP 8.5, a sharp increase in surface temperature and substantial bottom warming , longer stratification periods (+55days) and disappearance of ice cover leading to a shift in mixing regime. Increased insufficient cooling during warmer winters points to the vulnerability of Lake Sevan to climate change. Our workflow leverages the strengths of multiple models at several levels of the model chain to provide a more robust projection and at the same time a better uncertainty estimate that accounts for the contributions of the different model levels to overall uncertainty. Although for specific variables, for example, summer bottom temperature, single lake models may perform better, the full ensemble provides a robust estimate of thermal dynamics that has a high transferability so that our workflow can be a blueprint for climate impact studies in other systems.

Suggestions for utilizing climate change impact assessment information for the in-situ conservation of subalpine coniferous forest in Republic of Korea

Suggestions for utilizing climate change impact assessment information for the in-situ conservation of subalpine coniferous forest in Republic of Korea

Climate change impact assessment of variations in temperature trends across India: a case study

ABSTRACT Extreme weather events are becoming more severe and frequent worldwide. This study employs the Mann–Kendall test and Thein-Sen estimator for analysing the mean temperature trend in India’s four densely populated metropolitan cities of New Delhi, Kolkata, Mumbai, and Chennai under representative concentration pathways (RCP) 4.5 and 8.5 scenarios. The trend reveals rising temperatures across four Indian regions with the change being more dominant under RCP 8.5 scenario. By the end of the twenty-first century, RCP 4.5 and 8.5 anticipate a significant rise in mean temperature of 6.0°C and 9.0°C in the Western Himalayas, 5.0°C and 7.5°C in East India and 4.0°C and 7.0°C in the Thar Desert’s south-eastern region. Spatio-temporal variation also indicated the influence of heat waves across different regions of India in the late twenty-first century. There should be relevant long-term and short-term policies to mitigate the effect of climate change and optimise the mortality rate caused by heat stress.

Reducing the Uncertainty in the Tropical Precipitation through a Multi‐Criteria Decision‐Making Approach

ABSTRACTThe inherent model uncertainty in precipitation projections is found to be more dominant over tropical regions thereby reducing the reliability of using them in climate change impact assessment studies. To address such issues, a subset of well performing global climate models (GCMs) can provide narrow range of possible future outcomes, which can be helpful in formulating mitigation and adaptation strategies that are more targeted and efficient. In this study, climate models are selected based on their performance in simulating relative humidity and vertical velocity since these variables play an important role in precipitation simulation and significantly contribute toward the intermodel spread. The models are evaluated by using various statistical performance measures and ranked using multi‐criteria decision‐making approaches. Finally, based on Jenks natural breaks optimization algorithm, subset of GCMs consisting of ACCESS1.0, ACCESS1.3 and INM‐CM4 models, are considered as the best possible subset for precipitation simulation over tropical land regions. Two observational precipitation datasets are further considered to investigate the effectiveness of the proposed framework. The proposed methodology is validated to be effective in identifying the best climate models since the resulting subset is capable of both capturing observed precipitation and minimizing the uncertainty in future projections. Hence, this methodology can be utilized further for performance evaluation of GCMs focusing different geography and climatic drivers.

Capturing the health and well-being impact of climate change: a health impact assessment in Wales

Abstract Climate change is one of the biggest threats to global health in the 21st century and is likely to impact on health through a range of factors (societal, economic or environmental). The need to take action on climate and nature emergencies in order to protect population health and well-being and prevent negative impacts are becoming more urgent. Public Health Wales carried out a comprehensive mixed-method Health Impact Assessment (HIA) of climate change in Wales. HIA is a systematic process which considers the impact of a policy on the health and well-being of a population. It appraised the potential impacts of climate change on health and inequalities in Wales. This involved participatory workshops, engagement with stakeholders, statistical data, systematic literature reviews and case studies. The HIA findings indicate significant potential impacts across the determinants of health and mental well-being. For example, air quality, flooding, economic productivity, working conditions, access to services and community resilience. Impacts were identified across population groups, settings, and areas including urban and rural contexts, outdoor workers, children and young people, older people, schools, hospitals and workplaces. These findings have been beneficial to inform decision-makers to prepare for climate change plans and policies using a preventative evidence-informed approach. The work has demonstrated the value of HIA for significant, complex policies by mobilising a range of evidence through a transparent process, resulting in government and organizational action and transferrable learning for others. Key messages • HIA is a beneficial tool to identify at a granular level the health impacts of climate change. • The HIA has been used by the government of Wales and organisations to respond to and plan for climate change.