Climate Change Impact Assessment Research Articles

Streamflow prediction in ungauged basins: How dissimilar are drainage basins?

Hydrological processes leading to flow in river channels are extremely complex, which is why we cannot use any fundamental law to fully explain river flow dynamics at basin scale. The general approach for hydrological modelling considers that every catchment’s response to precipitation input is unique, which means different catchments respond differently to the same precipitation input. In other words, river basins are sufficiently dissimilar to warrant having hydrological models with multiple free-parameters for predicting discharge. It is assumed that free-parameters are related to static catchment characteristics. However, past efforts to relate model parameters with observable catchment characteristics have not been very successful. We thus need a framework to quantify catchment dissimilarities exhibited by catchment characteristics because there is no need of free-parameters if we decide to ignore dissimilarities. Thus, relative role of climate and catchment characteristics in streamflow response of the basin needs to be quantified. In this study, we have considered daily data from 3534 gauging stations spread across the globe to compare HBV, a widely used hydrological model, with Dynamic Budyko (DB) model, a recently proposed calibration-free model. The HBV model assumes that both climate and catchment characteristics are important in modeling streamflow response, whereas a calibration-free DB model essentially assumes that streamflow response is largely governed by climate. The performance of HBV run with regionalized parameters and DB in terms of median coefficient of determination (R2) and weighted R2 (WR2) are (0.43, 0.45) and (0.24, 0.27), respectively. The corresponding values of R2 and WR2 from quantile-quantile comparison are (0.94, 0.94) and (0.60, 0.59). Results here seem to support of idea of developing calibration-free models by appreciating similarities between basins, particularly if the objectives are prediction in ungauged basins and assessment of climate change impact on streamflow.

Debris‐flow entrainment modelling under climate change: Considering antecedent moisture conditions along the flow path

AbstractDebris‐flow volumes can increase along their flow path by entraining sediment stored in the channel bed and banks, thus also increasing hazard potential. Theoretical considerations, laboratory experiments and field investigations all indicate that the saturation conditions of the sediment along the flow path can greatly influence the amount of sediment entrained. However, this process is usually not considered for practical applications. This study aims to close this gap by combining runout and hydrological models into a predictive framework that is calibrated and tested using unique observations of sediment erosion and debris‐flow properties available at a Swiss debris‐flow observation station (Illgraben). To this end, hourly water input to the erodible channel is predicted using a simple, process‐based hydrological model, and the resulting water saturation level in the upper sediment layer of the channel is modelled based on a Hortonian infiltration concept. Debris‐flow entrainment is then predicted using the RAMMS debris‐flow runout model. We find a strong correlation between the modelled saturation level of the sediment on the flow path and the channel‐bed erodibility for single‐surge debris‐flow events with distinct fronts, indicating that the modelled water content is a good predictor for erosion simulated in RAMMS. Debris‐flow properties with more complex flow behaviour (e.g., multiple surges or roll waves) are not as well predicted using this procedure, indicating that more physically complete models are necessary. Finally, we demonstrate how this modelling framework can be used for climate change impact assessment and show that earlier snowmelt may shift the peak of the debris‐flow season to earlier in the year. Our novel modelling framework provides a plausible approach to reproduce saturation‐dependent entrainment and thus better constrain event volumes for current and future hazard assessment.

Bayesian Framework for Uncertainty Quantification and Bias Correction of Projected Streamflow in Climate Change Impact Assessment

Bayesian Framework for Uncertainty Quantification and Bias Correction of Projected Streamflow in Climate Change Impact Assessment

False springs in the Netherlands: climate change impact assessment with the false spring damage indicator model

Strong night frosts in spring have the potential to cause severe damage to fruit trees, causing up to €3.3 billion of damage in Europe in 2017. If temperatures drop below − 2.2 °C after the growing season starts, this phenomenon is referred to as a false spring. For the Netherlands, the risk of false spring damage increased during the past 70 years, due to the decreasing time between the last strong night frost and the flower budburst. This raises the question of whether this trend will continue under future influences of climate change. To analyse the development of false spring damage risks, we developed the False Spring Damage Indicator (FSDI) model. Applying this model to the Dutch KNMI’14 climate change scenarios for 2050 and 2085 showed a decrease in false spring damage risk. The historical risk for 60% bud damage was reduced to around 40% for the moderate scenario in 2050 and to less than 15% for the severe scenario in 2050 and all scenarios in 2080. False spring damage varied spatially throughout the Netherlands. Currently, more severe damage can be expected in the east, while coastal regions in the north and west are less prone to false spring damage. In the future, this is expected to become more uniform over the Netherlands, as the risk in the east is predicted to decrease strongly, becoming similar to the low damage risk in the coastal regions.

Stingless bee foragers experience more thermally stressful microclimates and have wider thermal tolerance breadths than other worker subcastes

IntroductionThe current state of anthropogenic climate change is particularly concerning for tropical insects, species predicted to be the most negatively affected. Researching climatic tolerance in social insects is challenging because adaptations exist at both individual and societal levels. Division of labor research helps to bridge the gap between our understanding of these adaptations at different scales, which is important because social insects comprise a tremendous portion of global animal biomass, biodiversity, and ecosystem services. Considering how individual physiologies construct group-level adaptations can improve climate change impact assessments for social species. Tetragonisca angustula is a neotropical stingless bee species that exhibits high worker subcaste specialization with a morphologically distinct soldier caste.MethodsWe used this species to investigate 1) whether age- and size-differentiated subcastes differ in thermal tolerance, 2) which worker subcaste operates closest to their thermal limits, and 3) the extent to which this species selects active foraging times to offset thermal stress. We measured the thermal tolerance (CTmax and CTmin) of small-bodied foragers and two soldier subcastes (hovering guards and standing guards) in T. angustula.Results and discussionDespite body size differences between foragers and guards, no differences in the upper or lower thermal limits were observed. However, the average thermal tolerance breadth of foragers was significantly larger than that of guards, and foraging sites were more thermally variable than nest sites, supporting the Climatic Variability Hypothesis at a microclimate scale and in the context of division of labor. Warming tolerance was significantly lower among small-bodied foragers compared to hovering and standing guards. The magnitude of warming tolerances indicated low risk of imminent climate change impacts in this environment but suggests that increasing temperatures and heatwave prevalence may cause foragers to meet their upper thermal limits before other subcastes. Foraging occurred at a narrower range of temperatures than would challenge critical temperatures, with higher morning activity. Directionally increasing temperatures will likely confine these preferred foraging temperatures to a narrower time window. Further study is needed to elucidate how foragers may shift times of activity in response to anthropogenic warming, but changing climates may impact plant pollination rates in natural and agricultural systems.

ANN and regression based quantification framework for climate change impact assessment on a weak transmission grid of a developing country across Horizon 2050 plus

ANN and regression based quantification framework for climate change impact assessment on a weak transmission grid of a developing country across Horizon 2050 plus

Assessment of climate change impact on inflows to Amandara headwork using HEC-HMS and ANNs

AbstractThis research has assessed the impact of climate change on temperature, precipitation, and inflows to the Amandara headwork in Pakistan. Trend Analysis using the Mann–Kendall test and Innovative Trend Analysis has been performed. Rainfall-runoff modeling is executed using the Hydrological Engineering Centre-Hydrological Modeling System (HEC-HMS) and Artificial Neural Networks including Feed Forward Neural Network, Conjugate Gradient, Two-layer Backpropagation Neural Network, and Broyden Fletcher-Goldfarb-Shanno. Mean daily hydro-meteorological data (1992 to 2023) was utilized for this study in which 70% was employed for calibration while the remaining 30% was used for validation of the model. Two GCMs namely CSIROMk3-6–0 and HadGEM2-ES with four Representative Concentration Pathways; RCP 2.6, 4.5, 6.0, and 8.5, were employed for future forecasting of temperature and precipitation. This future predicted data was then used to forecast flows up to 2050 by HEC-HMS. The performance of the models was assessed using correlation coefficient (R), Root Mean Square Error, Mean Bias Error, and Nash Sutcliffe Efficiency. Significant patterns in the runoff and temperature with no trend in precipitation were found. GCMs showed an increase in the range of 3–9 °C in temperature, 300 to 500 mm in precipitation, and 45 to 54% in peak flows.

Determining the Public Health Impact of Climate Change: A National Study Using a Health Impact Assessment Approach in Wales.

Objective: Climate change is recognised as the biggest threat to global health of the 21st century and impacts on health and wellbeing through a range of factors. Due to this, the need to take action in order to protect population health and wellbeing is becoming ever more urgent. Methods: In 2019, Public Health Wales carried out a comprehensive mixed-method Health Impact Assessment (HIA) of climate change. Unlike other risk assessments, it appraised the potential impact of climate change on health and inequalities in Wales through participatory workshops, stakeholder consultations, systematic literature reviews and case studies. Results: The HIA findings indicate potential impacts across the wider determinants of health and wellbeing. For example, air quality, excess heat/cold, flooding, economic productivity, infrastructure, and community resilience. A range of impacts were identified across population groups, settings, and geographical areas. Conclusion: These findings can inform decision-makers to prepare for climate change plans and policies using an evidence-informed approach. The work has demonstrated the value of a HIA approach by mobilising a range of evidence through a transparent process, resulting in transferrable learning for others.

Long-term species-level observations indicate the critical role of soil moisture in regulating China's grassland productivity relative to phenological and climatic factors

As a sensitive indicator of climate change and a key variable in ecosystem surface-atmosphere interaction, vegetation phenology, and the growing season length, as well as climatic factors (i.e., temperature, precipitation, and sunshine duration) are widely recognized as key factors influencing vegetation productivity. Recent studies have highlighted the importance of soil moisture in regulating grassland productivity. However, the relative importance of phenology, climatic factors, and soil moisture to plant species-level productivity across China's grasslands remains poorly understood. Here, we use nearly four decades (1981 to 2018) of in situ species-level observations from 17 stations distributed across grasslands in China to examine the key mechanisms that control grassland productivity. The results reveal that soil moisture is the strongest determinant of the interannual variability in grassland productivity. In contrast, the spring/autumn phenology, the length of vegetation growing season, and climate factors have relatively minor impacts. Generally, annual aboveground biomass increases by 3.9 to 25.3 g∙m2 (dry weight) with a 1 % increase in growing season mean soil moisture across the stations. Specifically, the sensitivity of productivity to moisture in wetter and colder environments (e.g., alpine meadows) is significantly higher than that in drier and warmer environments (e.g., temperate desert steppes). In contrast, the sensitivity to the precipitation of the latter is greater than the former. The effect of soil moisture is the most pronounced during summer. Dominant herb productivity is more sensitive to soil moisture than the others. Moreover, multivariate regression analyses show that the primary climatic factors and their attributions to variations in soil moisture differ among the stations, indicating the interaction between climate and soil moisture is very complex. Our study highlights the interspecific difference in the soil moisture dependence of grassland productivity and provides guidance to climate change impact assessments in grassland ecosystems.

Climate change impact assessments on the Andean genus Menonvillea (Brassicaceae) reveal uneven vulnerability among major phylogenetic and biogeographic groups

Climate change impact on species can be heterogeneous depending on their environments, exposure, and intrinsic characteristics. Likewise, global warming may have an uneven effect on lineages, depending on whether phylogenetic conservatism or divergence of ecological niches predominates during clade diversification, imposing a higher risk to species groups from certain regions, habitats and lineages. This study evaluates the impact of future climate change on Menonvillea, a genus with 24 species distributed along the Andes and contiguous regions of the Southern Cone. The impact on the main phylogenetic, ecological and biogeographic groups is evaluated, also analyzing the effect on its richness and phylogenetic diversity. Results show a strongly negative impact on most species of the genus. However, the greatest pressure seems to be recovered for high Andean species, mainly from the southern portion of the Southern Andes (between 34°S–53S°), and mostly included in Menonvillea sect. Cuneata. Richness appears to be more impacted in high Andean regions, and the loss of phylogenetic diversity is greater than expected at random. These results highlight the strong negative impact that climate change can induce on lineages distributed in the Andean-Patagonian region, and that show patterns of phylogenetic niche conservatism.

Assessment of climate change impact and comparison of downscaling approaches: a case study in a semi-arid river basin

Assessment of climate change impact and comparison of downscaling approaches: a case study in a semi-arid river basin

Navigating climate change complexity and deep uncertainty: approach for building socio-ecological resilience using qualitative dynamic simulation

The consequences of climate change on different sectors of society are interrelated. The threats posed by rising global temperatures, intensifying extreme weather events, and shifting climatic patterns are becoming increasingly evident all around the world. Policymakers face the daunting task of assessing climate change risks, encompassing impacts and response strategies, to guide sustainability transformations. In this study, we introduce a four-step qualitative Decision Making Under Deep Uncertainty (DMDU) approach in the context of Climate Change Impact Assessments (CCIA). Our goal is to enhance the integration of CCIA into spatial planning, particularly in the Global South, using qualitative system dynamics simulation. Emphasizing the value of qualitative DMDU, we explore vulnerability and resilience through a lens of multi-sectoral and multi-scalar socio-ecological processes. We exemplify our approach by applying CCIA to the coastal zone of Yucatán, Mexico, accounting for social and environmental heterogeneity across the four Regions in which it is administered. Results identify the optimal allocation of climate change mitigation and adaptation policies to address specified resilience in each Region, all of which are required to achieve the overall resilience of the coastal zone. We argue that our qualitative DMDU approach provides an analytical platform to address the trade-offs inherent in the ranking of multiple vulnerabilities related to achieving general resilience.

Assessing Climate Change Impacts on Streamflow and Baseflow in the Karnali River Basin, Nepal: A CMIP6 Multi-Model Ensemble Approach Using SWAT and Web-Based Hydrograph Analysis Tool

Our study aims to understand how the hydrological cycle is affected by climate change in river basins. This study focused on the Karnali River Basin (KRB) to examine the impact of extreme weather events like floods and heat waves on water security and sustainable environmental management. Our research incorporates precipitation and temperature projections from ten Global Circulation Models (GCMs) under the Coupled Model Intercomparison Project Phase 6 (CMIP6). We applied thirteen statistical bias correction methods for precipitation and nine for temperatures to make future precipitation and temperature trend projections. The research study also utilized the Soil and Water Assessment Tool (SWAT) model at multi-sites to estimate future streamflow under the Shared Socioeconomic Pathway (SSP) scenarios of SSP245 and SSP585. Additionally, the Web-based Hydrograph Analysis Tool (WHAT) was used to distinguish between baseflow and streamflow. Our findings, based on the Multi-Model Ensemble (MME), indicate that precipitation will increase by 7.79–16.25% under SSP245 (9.43–27.47% under SSP585) and maximum temperatures will rise at rates of 0.018, 0.048, and 0.064 °C/yr under SSP245 (0.022, 0.066, and 0.119 °C/yr under SSP585). We also anticipate that minimum temperatures will increase at rates of 0.049, 0.08, and 0.97 °C/yr under SSP245 (0.057, 0.115, and 0.187 °C/yr under SSP585) for near, mid, and far future periods, respectively. Our research predicts an increase in river discharge in the KRB by 27.12% to 54.88% under SSP245 and 45.4% to 93.3% under SSP585 in different future periods. Our finding also showed that the expected minimum monthly baseflow in future periods will occur earlier than in the historical period. Our study emphasizes the need for sustainable and adaptive management strategies to address the effects of climate change on water security in the KRB. By providing detailed insights into future hydrological conditions, this research serves as a critical resource for policymakers and stakeholders, facilitating informed decision-making for the sustainable management of water resources in the face of climate change.

Impact of Climate Change on Tourism Sector in Nepal

Climatic variations might have a negative impact on the activities and services provided by the tourism industry. However, this idea has not yet been investigated in the context of Nepal. This study examines the monetary impact that climate change has had and will continue to have on Nepal's tourism industry and makes projections on such impacts. The economic impact assessment of climate change on Nepal's tourism sector is the first study of its sort to be conducted anywhere in the nation. This research is predicated on a conceptual model that was established on the theoretical foundation, a mathematical model that generated the tourism demand function, and an economic effect analysis that was performed on the tourism sector using secondary data. The findings of the study's analysis have been broken down into three distinct sections: a trend analysis of tourism and its contribution; regression results based on the models that have been established; and a projection of GDP for the years 2020-2030 and 2100. It is abundantly clear from the trend analysis, the analysis of loss and damage from climate-induced hazards, the econometric modeling for tourism function analysis and correlation on tourism GDP, and other research that the tourism sector is extremely vulnerable to the effects of climate change and that it will have significant repercussions. The projection of tourism's contribution to the national GDP shows that this sector has the potential to make a larger contribution, provided that the conditions that could prevent this from happening do not change. On the other hand, the rising trend of climatic factors and climate-induced dangers could potentially lead to a greater overall loss and damage in the tourism industry. As a result, it will have an immediate bearing on the contribution made to the national economy.

The Impact of Climate Change on Health Services in Low- and Middle-Income Countries: A Systematised Review and Thematic Analysis.

Aim: The aim of this study was to assess the impact of climate change on health services as categorized by the WHO's Building Blocks for creating Climate-Resilient Health Systems. Objective: The objective was to conduct a systematized review of the published literature concerning the impact of climate change, using a thematic analysis approach to address our aim and identify areas for further research. Design: A search was conducted on 8 February 2022 using the Embase and PubMed research databases. Peer-reviewed scientific studies that were published in English from 2012 to 2022, which described at least one report concerning the impact of climate change on health services in LMICs, were included. Studies were organized based on their key characteristics, which included the date of publication, objective, method, limitations, participants, and geographical focus. The Mixed-Methods Appraisal Tool (MMAT) was used to assess the risk of bias in the included studies. Results: Twenty-three studies were included in this review. Five areas of health services which align with the WHO building blocks framework were impacted by climate change. These health service areas included: (1) Service Delivery, (2) Human Resources, (3) Health Finance, (4) Healthcare Products and Technology, and (5) Leadership and Governance. However, research concerning the impact of climate change on health information systems, which is part of the WHO building blocks framework, did not feature in our study. The climatic effects were divided into three themes: meteorological effects, extreme weather events, and general. The research in this study found that climate change had a detrimental impact on a variety of health services, with service delivery being the most frequently reported. The risk of bias varied greatly between studies. Conclusions: Climate change has negatively impacted health services in a variety of different ways, and without further actions, this problem is likely to worsen. The WHO building blocks have provided a useful lens through which to review health services. We built an aligned framework to describe our findings and to support future climate change impact assessments in this area. We propose that further research concerning the impact of climate change on health information systems would be valuable, as well as further education and responsible policy changes to help build resilience in health services affected by climate change.

Spatio-temporal distribution of groundwater recharge under climate change in the Namngum++ river basin in lower Mekong region

Groundwater meets the water needs of one third of global population. The erratic nature of rainfall, likely to be exacerbated by climate change further compounded by population growth and urbanization, underscore the crucial role of groundwater in water resources management in Namngum++ river basin in Lower Mekong. Climate change affects groundwater by altering different hydrological processes such as evapotranspiration, infiltration and most importantly the ground water recharge. This study attempts to quantify groundwater recharge in the basin and how climate change is likely to impact it in three stress periods (2030s, 2060s and 2090s) under socio-economic pathways (SSP 2-4.5 and SSP 5-8.5) scenarios. Precipitation, maximum temperature and minimun temperature datasets from four global climate models (GCMs) under coupled model inter comparison project phase 6 (CMIP6) were linearly bias corrected. The Soil and Water Assessment Tool (SWAT), a semi-distributed model, was then employed to assess climate change impact on groundwater recharge. The model performance was very good with coefficient of determination and Nash Sutcliffe Efficiency of 0.72 and 0.70 for calibration period (2000–2010) and 0.80 and 0.77 for validation period (2011–2015) respectively. The hydrological response unit (HRU) level groundwater recharge for four GCMs were individually computed then ensembled by averaging across stress periods. Precipitation, minimum temperature and maximum temperature are likely to increase by 8.20%, 1.40 °C and 1.60 °C for SSP2-4.5 and 11.15%, 1.85 °C and 1.96 °C for SSP5-8.5 respectively. Groundwater recharge is likely to increase for 2060s and 2090s by 9.31% and 8.31% respectively in SSP2-4.5 and decrease by −3.25% in 2030s while increase up to 2.23% in 2060s and finally decreasing by −1.10% in 2090s in SSP5-8.5. The differences in the direction of change for two scenarios are attributed to increase in evapotranspiration due to elevated temperature, as well as increased stream flow yielded by increased precipitation in SSP5-8.5 compared to SSP2-4.5. While this study acknowledges uncertainties that permeate every step, it still presents a potential future scenario for water management.

Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China

AbstractNortheast China (NEC) is the most prominent grain‐producing region in China. However, it is currently facing significant impacts from climate change. Since the climate‐related impacts on crop yield in this region are a major concern for society in the future, quantifying climate change impacts on crop yields in NEC is essential to ensure future food security. This study aimed to quantify the effects of future climate change on crop yields in NEC and explore adaptation strategies using the Crop Growth Model (PCSE) driven by downscaled CMIP6 climate projections under four Shared Socioeconomic Pathways (SSPs) scenarios during 2015–2100. Results showed that there could be average reductions in crop yields of 21.4% for maize and 4.2% for soybean by the year 2100 under SSP585 compared to the 2015 baseline. The increasing temperature was the dominant factor in reducing yields, although elevated CO2 and precipitation offered partial compensation. The optimized planting date brought noticeable benefits for rice and soybean but had limited effects on maize due to heat stress. Relocating rice expansion eastward and implementing earlier planting increased yields by up to 50% but adversely decreased soybean and maize due to competition. This study enriches our comprehension of climate change impacts on NEC agriculture, while also quantifying potential benefits and constraints of evaluated adaptations. The proposed adaptations may help mitigate projected yield declines in other key agricultural regions across the globe. Adjusting crop management practices to capitalize on changing climate factors shows promise as a strategy for sustaining production globally.

Assessment of Climate Change Impact on Reservoir Inflow

Abstract: Evaluating the effects of climate change is crucial in regions grappling with water scarcity. The Ujjani dam, a significant structure in Maharashtra, India, erected on the Bhima river in 1980, serves as a vital water source for the agricultural lands downstream in Solapur and Pune districts. Assessing climate change's impact involves analysing data from General Circulation Models (GCMs), which project climate parameters under various emission scenarios but often at a broader scale. Since hydrological models necessitate finer-scale climate data, a downscaling technique is employed to derive localized variables from GCM outputs. This study employs Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) techniques for downscaling. By utilizing projected temperature and rainfall data, three distinct models are developed to predict reservoir inflow: multiple linear regression, artificial neural network, and wavelet neural network. The findings indicate significant alterations in rainfall distribution patterns, with a decline during the monsoon season and an increase post-monsoon

Regulatory effects of non-growing season precipitation on the community structure, biomass allocation, and water-carbon utilization in a temperate desert steppe

Under the influence of global change, precipitation amounts and extreme precipitation frequency during non-growing seasons in mid-high latitude grasslands have been increasing. However, the ecological effects of non-growing season precipitation in the desert steppe have long been overlooked due to an insufficient understanding of the correlative mechanisms linking non-growing season precipitation to plant growth. Therefore, a 3-year non-growing season precipitation manipulation experiment was conducted to reveal the response of desert steppe plants to non-growing season precipitation changes. Our study indicates that, by influencing water budget and availability, non-growing season precipitation directly or indirectly impacted community structure, plant biomass allocation, and water-carbon utilization intensity. Adaptive strategies of communities and plants included: ① Dominant species enhanced their dominance in the community to adapt to non-growing season precipitation changes. ② Stipa krylovii exhibited different biomass allocation strategies in response to non-growing season precipitation variations. Plants in the precipitation shading plots tended to allocate biomass to the roots, while those in the precipitation increase plots favored aboveground development. ③ Persistent drought during the growing season intensified early insufficient development of plants in the precipitation shading plots. Upon entering the wet period, plants in the precipitation shading plots shifted into a compensatory growth mode with high water-carbon activity intensity, while those in the precipitation increase plots entered a moderate growth mode with relatively low water-carbon activity intensity. Additionally, our study found that the regulatory effects of non-growing season precipitation were more pronounced in the growing seasons with less precipitation in the early to middle stage. Moreover, increased non-growing season precipitation enhanced plant water use efficiency (WUE) and strengthened their resilience to drought conditions. Our study suggests that the ecological role of non-growing season precipitation may be further highlighted in the future climate change pattern. Given the worldwide increase in frequency of extreme precipitation events, particular vigilance should be paid to the underlying long-term adverse effects of severe droughts during the non-growing season. Our findings provide new insights and valuable experimental observational evidence for the climate change impact assessment and response in xerophytic grassland ecosystems.

Evaluation of temporal trends of growing degree days index for major crops in Türkiye

AbstractClimate change, previously considered an environmental problem, has begun to be defined as a climate crisis since its effects intensify faster than previously thought, cause severe economic losses, and threaten food security. Climate indexes have been defined as the primary descriptive indicator to acquire a consistent perspective on observed and projected changes resulting from weather and climatic extremes. In this study, the growing degree days (GDD) index, one of the essential indicators in planning agricultural activities, was examined for wheat (Triticum aestivum), maize (Zea mays), and sunflowers (Helianthus anuus) across Türkiye. The GDD index was examined between 1971 and 2020 for the observed change analysis and between 2023 and 2098 for future projections. Three global climate models and two representative concentration pathways (RCPs), that is, 4.5 and 8.5, were selected to assess climate change impact. The analysis of the entire observed period (1971–2020) indicated a considerable increasing trend in the annual total GDD index for wheat, except in the southeast of Türkiye, and maize and sunflower across the country. Assessment results of the climate projection analysis revealed that there would be increases over 1400 and 2300°C day/year for the RCP4.5 and RCP8.5 scenarios, respectively, for all three crops considered. Additionally, the findings indicated further increases in the southern latitudes of Türkiye. Projection results also confirmed that there will be longer suitable periods for agricultural production, and the crop vegetation periods will be shorter and shift earlier in Türkiyein the future periods.