Impact Of Projected Climate Change Research Articles

Projected climate change impact on cyanobacterial bloom phenology in temperate rivers based on temperature dependency

Climate warming is linked to earlier onset and extended duration of cyanobacterial blooms in temperate rivers. This causes an unpredictable extent of harm to the functioning of the ecosystem and public health. We used Microcystis spp. cell density data monitored for seven years (2016–2022) in ten sites across four temperate rivers of the Republic of Korea to define the phenology of cyanobacterial blooms and elucidate the climatic effect on their pattern. The day of year marking the onset, peak, and end of Microcystis growth were estimated using a Weibull function, and linear mixed-effect models were employed to analyze their relationships with environmental variables. These models identified river-specific temperatures at the beginning and end dates of cyanobacterial blooms. Furthermore, the most realistic models were employed to project future Microcystis bloom phenology, considering downscaled and quantile-mapped regional air temperatures from a general circulation model. Daily minimum and daily maximum air temperatures (mintemp and maxtemp) primarily drove the timing of the beginning and end of the bloom, respectively. The models successfully captured the spatiotemporal variations of the beginning and end dates, with mintemp and maxtemp predicted to be 24℃ (R2 = 0.68) and 16℃ (R2 = 0.35), respectively. The beginning and end dates were projected to advance considerably in the future under the Representative Concentration Pathway 2.6, 4.5, and 8.5. The simulations suggested that the largest uncertainty lies in the timing of when the bloom ends, whereas the timing of when blooming begins has less variation. Our study highlights the dependency of cyanobacterial bloom phenology on temperatures and earlier and prolonged bloom development.

Assessment of projected climate change impact on agro‐climatic indicators in Poland

AbstractThis study presents a long‐term assessment of the impact of projected climate change on selected agro‐climatic indicators in Poland. The analysis was based on an ensemble of six bias‐corrected EURO‐CORDEX projections covering the period from 2006 to 2100 under Representative Concentration Pathways: RCP4.5 and 8.5, and partly on simulations of an agro‐hydrological Soil and Water Assessment Tool model. The indicators included growing season (length, beginning, and ending date), growing degree days, frost days, accumulated frost, and planting and harvest timing. The study focused on four major crops cultivated in Poland, namely spring barley, rapeseed, maize silage, and potato. The analysis revealed a significant future increase in precipitation and mean temperature in Poland reaching +10.3% and 3.4°C by the end of century, respectively. Warmer conditions will significantly accelerate the beginning of the growing season (by up to 24 days) and delay its ending (by up to 18 days), consequently significantly extending its length throughout the country (by up to 42 days). A decrease is projected in potential soil moisture deficit by up to 26 mm by the end of century in the high‐end emission scenario. A significant advancement (by up to 30 days) in planting timing is projected for the future for all crops under consideration. A significant increase in growing degree days (by up to 760°C‐days) will also advance the harvest timing (by up to 54 days). The projected temperature increase over the winter season will result in a significant decrease in frost days (by 56 days) and accumulated frost (by 375°C‐days), which may lead to an increased presence of pests. This study can provide a scientific basis for assessing the future risks to the local agriculture from climate change and will be beneficial in planning adaption and mitigation actions for food security at the national and local scale.

Climate change impact on streamflow and suspended sediment load in the flood-prone river basin

Abstract Understanding the relationship between climate change and changes in streamflow and suspended sediment load (SSL) at the regional scale is critical for future decisions on water resource management. Hence, this study assessed the projected climate change impact on streamflow and SSL based on CMIP6 projections in the flood-prone Mahanadi River Basin (MRB) using the SWAT model. Bias-corrected climate data from five global climate models under medium and extreme shared socioeconomic pathways (SSPs) were utilized as an input to the SWAT model for the baseline (1985–2014), near future (NF, 2020–2046), and mid future (MF, 2047–2073) periods. Rainfall is expected to increase by 2.86% (2.19%) and 13.35% (16.98%) in the NF and MF, respectively, under SSP245 (SSP585). Streamflow and SSL may increase by 6.38% (6.06%) and 4.43% (7.89%) in the NF and by 29.78% (36.90%) and 37% (46.43%) in the MF, respectively, under SSP245 (SSP585) scenario. Also, streamflow and SSL exhibited an increasing trend in most months as well as in most seasons. The findings may be helpful to decision-makers in identifying watershed management plans by offering more information on how climate change affects the hydro-sedimentological processes in a flood-prone river basin.

Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe

Abstract. High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications, including water resource management. However, many of the existing global- to continental-scale hydrological models are applied at coarse resolution and neglect more complex processes such as lateral surface and groundwater flow, thereby not capturing smaller-scale hydrologic processes. Applications of high-resolution and physically based integrated hydrological models are often limited to watershed scales, neglecting the mesoscale climate effects on the water cycle. We implemented an integrated, physically based coupled land surface groundwater model, ParFlow-CLM version 3.6.0, over a pan-European model domain at 0.0275∘ (∼3 km) resolution. The model simulates a three-dimensional variably saturated groundwater-flow-solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, which is fully integrated with land surface exchange processes. A comprehensive evaluation of multiple hydrologic variables including discharge, surface soil moisture (SM), evapotranspiration (ET), snow water equivalent (SWE), total water storage (TWS), and water table depth (WTD) resulting from a 10-year (1997–2006) model simulation was performed using in situ and remote sensing (RS) observations. Overall, the uncalibrated ParFlow-CLM model showed good agreement in simulating river discharge for 176 gauging stations across Europe (average Spearman's rank correlation (R) of 0.77). At the local scale, ParFlow-CLM model performed well for ET (R>0.94) against eddy covariance observations but showed relatively large differences for SM and WTD (median R values of 0.7 and 0.50, respectively) when compared with soil moisture networks and groundwater-monitoring-well data. However, model performance varied between hydroclimate regions, with the best agreement to RS datasets being shown in semi-arid and arid regions for most variables. Conversely, the largest differences between modeled and RS datasets (e.g., for SM, SWE, and TWS) are shown in humid and cold regions. Our findings highlight the importance of including multiple variables using both local-scale and large-scale RS datasets in model evaluations for a better understanding of physically based fully distributed hydrologic model performance and uncertainties in water and energy fluxes over continental scales and across different hydroclimate regions. The large-scale, high-resolution setup also forms a basis for future studies and provides an evaluation reference for climate change impact projections and a climatology for hydrological forecasting considering the effects of lateral surface and groundwater flows.

Assessment of the benefits of climate model weights for ensemble analysis in three urban precipitation frequency studies

AbstractIn hydrology, projected climate change impact assessment studies typically rely on ensembles of downscaled climate model outputs. Due to large modeling uncertainties, the ensembles are often averaged to provide a basis for studying the effects of climate change. A key issue when analyzing averages of a climate model ensemble is whether to weight all models in the ensemble equally, often referred to as the equal‐weights or unweighted approach, or to use a weighted approach, where, in general, each model would have a different weight. Many studies have advocated for the latter, based on the assumption that models that are better at simulating the past, that is, the models with higher hindcast accuracy, will give more accurate forecasts for the future and thus should receive higher weights. To examine this issue, observed and modeled daily precipitation frequency (PF) estimates for three urban areas in the United States, namely Boston, Massachusetts; Houston, Texas; and Chicago, Illinois, were analyzed. The comparison used the raw output of 24 Coupled Model Intercomparison Project Phase 5 (CMIP5) models. The PFs from these models were compared with the observed PFs for a specific historical training period to determine model weights for each area. The unweighted and weighted averaged model PFs from a more recent testing period were then compared with their corresponding observed PFs to determine if weights improved the estimates. These comparisons indeed showed that the weighted averages were closer to the observed values than the unweighted averages in nearly all cases. The study also demonstrated how weights can help reduce model spread in future climate projections by comparing the unweighted and weighted ensemble standard deviations in these projections. In all studied scenarios, the weights actually reduced the standard deviations compared to the equal‐weights approach. Finally, an analysis of the results' sensitivity to the areal reduction factor used to allow comparisons between point station measurements and grid‐box averages is provided.

Assessment of Projected Climate Change Impact on Green Gram Productivity through DSSAT Model under South Gujarat Environmental Condition

An investigation on climate change impact on green gram was conducted at Agronomy farm, NAU, Navsari, Gujarat during the rabi seasons of 2021-22. The calibrated and validated DSSAT CROPGRO simulation model was used to determine the productivity of green grams under various climate change scenarios. In this experiment, the effects of CO2 concentration, solar radiation and maximum and minimum temperatures on green gram productivity were examined individually and in combination. According to the sensitivity of the DSSAT CROPGRO model, under the conditions of projected climate change, the best case scenario for its favourable impact on green gram yield was a rise in CO2 concentration (600 ppm), along with solar radiation up to a certain level (1 unit), which may nullified the adverse effect of increases in temperature on green gram productivity.

Projected climate change impact on a coastal sea-As significant as all current pressures combined.

Climate change influences the ocean's physical and biogeochemical conditions, causing additional pressures on marine environments and ecosystems, now and in the future. Such changes occur in environments that already today suffer under pressures from, for example, eutrophication, pollution, shipping, and more. We demonstrate how to implement climate change into regional marine spatial planning by introducing data of future temperature, salinity, and sea ice cover from regional ocean climate model projections to an existing cumulative impact model. This makes it possible to assess climate change impact in relation to pre‐existing cumulative impact from current human activities. Results indicate that end‐of‐century projected climate change alone is a threat of the same magnitude as the combination of all current pressures to the marine environment. These findings give marine planners and policymakers forewarning on how future climate change may impact marine ecosystems, across space, emission scenarios, and in relation to other pressures.

Assessment of Projected Climate Change Impact on Wheat (Triticum aestivum L.) Production with Coping Strategies at Jamma Wereda, Ethiopia

Assessment of Projected Climate Change Impact on Wheat (Triticum aestivum L.) Production with Coping Strategies at Jamma Wereda, Ethiopia

Simulation and Assessment of Projected Climate Change Impacts on Urban Flood Events: Insights From Flooding Characteristic Metrics

AbstractUrban flooding is a global water disaster resulting from the expansion of urban impervious surfaces and the strengthening of extreme precipitation events, especially in China. Nonetheless, few studies have focused on the spatial distributions of urban flooding characteristics and their variations in the context of climate change. In this study, eight critical metrics (i.e., maximum flooding volume, total overloaded manholes with different flooding volumes or durations, total flooding volume, mean and maximum flooding durations, maximum inundation area, and depth) are adopted to characterize the urban flood events. The impacts of climate change on these metrics are assessed for two periods, the 2030s (2020–2049) and 2070s (2060–2089), and compared with those in the baseline period (1976–2005). The Future Science City Park in Beijing, China, is selected as our study area. The results show that all four flood events are well simulated, with both efficiency coefficients and correlation coefficients being over 0.8. The number of overloaded manholes and the total flooding volume are projected to increase 19.3%–44.8% and 171%–716% under 20‐year rainfall events due to climate change in the two future periods. The spatial distribution of overloaded manholes with different increased flooding volumes is projected to expand to almost the whole area from the region with lowland and limited drainage capacity. Furthermore, the maximum inundation area and depth are projected to increase obviously. This study will be helpful for designing and improving the drainage system, controlling urban flooding, and adapting to climate change.

Equilibrium in soil respiration across a climosequence indicates its resilience to climate change in a glaciated valley, western Himalaya

Soil respiration (SR), a natural phenomenon, emits ten times more CO2 from land than anthropogenic sources. It is predicted that climate warming would increase SR in most ecosystems and give rise to positive feedback. However, there are uncertainties associated with this prediction primarily due to variability in the relationship of SR with its two significant drivers, soil temperature and moisture. Accounting for the variabilities, we use a climosequence in Himalaya with a temperature gradient of ~ 2.1 °C to understand the variations in the response of SR and its temperature sensitivity to climate change. Results indicate an equilibrium in SR ranging from 1.92 to 2.42 µmol m−2 s−1 across an elevation gradient (3300–3900 m) despite its increased sensitivity to temperature (Q10) from 0.47 to 4.97. Additionally, moisture reduction towards lower elevation weakens the temperature-SR relationship. Finally, soil organic carbon shows similarities at all the elevations, indicating a net-zero CO2 flux across the climosequence. The findings suggest that as the climate warms in this region, the temperature sensitivity of SR reduces drastically due to moisture reduction, limiting any change in SR and soil organic carbon to rising temperature. We introduce an equilibrium mechanism in this study which indicates the resilient nature of SR to climate change and will aid in enhancing the accuracy of climate change impact projections.

Social Dimensions of Projected Climate Change Impacts on Ecosystem Services in the Coastal-Rural Area of Nemunas River Reaches and Curonian Lagoon (Lithuania)

The capacity of ecosystems to provide ecosystem services (hereinafter referred to as ES) depends on the state of their structure, processes, and functions, which is determined by interactions with other systems. These interactions are complex and take place in different climatic areas, and have different impacts on ecosystems and the use of ES. As the most sensitive part of the Lithuanian shore zone to climate change is the seaside zone or the coastal zone, which includes the Curonian Lagoon and the sea coast, the said area was therefore chosen for research. The case study examined those ES that were specific to the study area, without attempting to account for absolutely all ES. With an emphasis on the representation of different perspectives (in the process of assessing and preserving the potential of ES), the empirical study involved representatives of different (public and private) sectors. The public sector was represented by elders and eldership employees, the private sector by farmers and businesspeople. Respondents were selected purposefully to reflect the entire existing totality of the existing area. The evaluation of the obtained theoretical and practical results of the research identified the potential of the existing area ecosystem services and perspectives for the implementation of services by assessing their use according to respondents’ opinions, regional climate change, and national scale in EU environmental policy.

The climate change impact projections on seasonal residential sector CO<SUB align="right">2 emissions and energy demand forecasting for Turkish provinces

The main aim of this study was to determine the seasonal Turkish climate change maps by using long-term temperature trends for the first time and add it to published literature. Residential energy demand and CO2 emissions for Turkish cities were predicted from a current and future perspective by using seasonal climate change maps. There was a significant rise in outdoor temperature according to the results of daily, monthly and seasonal temperature distribution analyses. The temperature increase for each province in Turkey was calculated to reach a maximum of 6.09°C, but it was expected to be 2.65°C on average. Household energy demand and emission changes for each province over the next 100 years were predicted depending on the level of global climate change. Analysis results demonstrated that in the near future, Turkey will get hotter, more arid and unstable in terms of precipitation patterns, and the increase in the average temperatures in Turkey will lead to an increase in energy needs.

The climate change impact projections on seasonal residential sector CO<SUB align="right">2 emissions and energy demand forecasting for Turkish provinces

The climate change impact projections on seasonal residential sector CO<SUB align="right">2 emissions and energy demand forecasting for Turkish provinces

Projected Climate Change Impacts on Hurricane Storm Surge Inundation in the Coastal United States

The properties of hurricanes directly influence storm surges; however, the implications of projected changes to the climate are unclear. Here, we simulate the storm surges of historical storms under present day and end of century climate scenarios to assess the impact of climate change on storm surge inundation. We simulate 21 storms that impacted the Gulf of Mexico and Atlantic Coasts of the continental U.S. from 2000 to 2013. We find that the volume of inundation increases for 14 storms and the average change for all storms is +36%. The extent of inundation increases for 13 storms, and the average change for all storms is +25%. Notable increases in inundation occur near Texas, Louisiana, Mississippi, the west coast of Florida, the Carolinas, and New Jersey. Our calculations of inundation volume and extent suggest that at the end of the century, we can expect hurricanes to produce larger storm surge magnitudes in concentrated areas, as opposed to surges with lower magnitudes that are widespread. We examine changes in maximum wind speed, minimum central pressure, translation speed, and radius of the 33 ms−1wind to assess the impacts of individual storm characteristics on storm surge. We find that there is no single storm characteristic that directly relates to storm surge inundation or its climate induced changes. Even when all the characteristics are considered together, the resulting influences are difficult to anticipate. This is likely due to the complexity of the hydrodynamics and interactions with local geography. This illustrates that even as climate change research advances and more is known about projected impacts to hurricanes, implications for storm surge will be difficult to predict without explicit numerical simulation.

Application of Machine Learning Techniques to Delineate Homogeneous Climate Zones in River Basins of Pakistan for Hydro-Climatic Change Impact Studies

Climatic data archives, including grid-based remote-sensing and general circulation model (GCM) data, are used to identify future climate change trends. The performances of climate models vary in regions with spatio-temporal climatic heterogeneities because of uncertainties in model equations, anthropogenic forcing or climate variability. Hence, GCMs should be selected from climatically homogeneous zones. This study presents a framework for selecting GCMs and detecting future climate change trends after regionalizing the Indus river sub-basins in three basic steps: (1) regionalization of large river basins, based on spatial climate homogeneities, for four seasons using different machine learning algorithms and daily gridded precipitation data for 1975–2004; (2) selection of GCMs in each homogeneous climate region based on performance to simulate past climate and its temporal distribution pattern; (3) detecting future precipitation change trends using projected data (2006–2099) from the selected model for two future scenarios. The comprehensive framework, subject to some limitations and assumptions, provides divisional boundaries for the climatic zones in the study area, suitable GCMs for climate change impact projections for adaptation studies and spatially mapped precipitation change trend projections for four seasons. Thus, the importance of machine learning techniques for different types of analyses and managing long-term data is highlighted.

Global resource consumption effects of borderless climate change: EU’s indirect vulnerability

Abstract We investigate how global climate change impacts affect the consumption of resources, namely water, labor, malaria risk and crops, across world regions. We quantify both impact channels, direct comprising domestic effects and indirect, subsuming cross-border effects. Methodologically we combine MRIO methodology, illustrating virtual flows of resources, with global climate change impact projections, showing climate induced changes of global resource availability. Our results underscore the importance of analyzing both, the direct and indirect climate change impact channels. Europe is predominantly indirectly affected, via imports from abroad, while for Asia-Pacific and for North America -in case of most resources-direct impacts are much stronger. The significance of borderless climate change impacts also depends on the resource investigated. Results show that, in the median scenario, crop consumption of all regions is mainly affected by domestic impacts, while in case of labor and water consumption the indirect impact channel is decisive. Worldwide and on a country level, this high influence of the indirect impact channel is emphasized: depending on the investigated resource, in a third up to a half of all countries the indirect impacts exceed the domestic ones. This dominance is particularly pronounced for the EU28 member states, where in most cases the imported impacts are five to ten times higher than the domestic ones.

Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream Flows

Hydrologic models driven by downscaled meteorologic data from general circulation models (GCM) should be evaluated using long-term simulations over a historical period. However, simulations driven by GCM data cannot be directly evaluated using observed flows, and the confidence in the results can be relatively low. The objectives of this paper were to bias correct simulated stream flows from calibrated hydrologic models for two basins in New Jersey, USA, and evaluate model performance in comparison to uncorrected simulations. Then, we used stream flow bias correction and flow duration curves (FDCs) to evaluate and assess simulations driven by statistically downscaled GCMs for the historical period and the future time slices 2041–2070 and 2071–2099. Bias correction of stream flow from simulations increased confidence in the performance of two previously calibrated hydrologic models. Results indicated there was no difference in projected FDCs for uncorrected and bias-corrected flows in one basin, while this was not the case in the second basin. This result provided greater confidence in projected stream flow changes in the former basin and implied more uncertainty in projected stream flows in the latter. Applications in water resources can use the methods described to evaluate the performance of GCM-driven simulations and assess the potential impacts of climate change with an appropriate level of confidence in the model results.

Modelling climate sensitivity of agriculture in Trans- and Upper Gangetic Plains of India

The present study assesses the response of farm income to climatic variations and projects the impact of climate change on agriculture in the Trans- and Upper Gangetic Plains Region of India. Panel data estimation of crop response function has been carried out by pooling long time series (1980–2009) and cross-section data of 84 districts. Climate change impact projections have been made with Representative Concentration Pathway (RCP) 4.5 climate scenario for the time slice 2010–2039 and two sub periods, viz. 2020–2029 and 2030–2039. The negative effect of rise in temperature on gross margin per hectare ranges from 10.5% in monsoon season (June to September) to 6.5% in winter season (October to February). The marginal effect of precipitation change is positive albeit of small magnitude, 0.04% and 0.03% , in the monsoon and winter season, respectively. Loss in annual gross margin per hectare in Trans- and Upper Gangetic Plains is projected to be 14.47% for the time slice 2010–2039. The losses are likely to escalate in 2030–2039 (17.06%) over 2020–2029 (15.86%). The northern part of the region will be affected adversely during the monsoon season, with the exception of a few districts which are likely to gain marginally due to winter warming.

Parametric Sensitivity of Vegetation Dynamics in the TRIFFID Model and the Associated Uncertainty in Projected Climate Change Impacts on Western U.S. Forests

AbstractChanging climate conditions impact ecosystem dynamics and have local to global impacts on water and carbon cycles. Many processes in dynamic vegetation models (DVMs) are parameterized, and the unknown/unknowable parameter values introduce uncertainty that has rarely been quantified in projections of forced changes. In this study, we identify processes and parameters that introduce the largest uncertainties in the vegetation state simulated by the DVM Top‐down Representation of Interactive Foliage and Flora Including Dynamics (TRIFFID) coupled to a regional climate model. We adjust parameters simultaneously in an ensemble of equilibrium vegetation simulations and use statistical emulation to explore sensitivities to, and interactions among, parameters. We find that vegetation distribution is most sensitive to parameters related to carbon allocation and competition. Using a suite of statistical emulators, we identify regions of parameter space that reduce the error in modeled forest cover by 31±9%. We then generate large initial atmospheric condition ensembles with 10 improved DVM parameterizations under preindustrial, contemporary, and future climate conditions to assess uncertainty in the forced response due to parameterization. We find that while most parameterizations agree on the direction of future vegetation transitions in the western United States, the magnitude varies considerably: for example, in the northwest coast the expansion of broadleaf trees and corresponding decline of needleleaf trees ranges from 4 to 28% across 10 DVM parameterizations under projected future climate conditions. We demonstrate that model parameterization contributes to uncertainty in vegetation transition and carbon cycle feedback under nonstationary climate conditions, which has important implications for carbon stocks, ecosystem services, and climate feedback.

Investigation of Low- and High-Flow Characteristics of Karst Catchments under Climate Change

It is not clear how projected climate change will impact the hydrological functioning of complex catchments that have significant karst characteristics. Therefore, in this paper we focused on the investigation of the low- and high-flow characteristics of the karst Ljubljanica River catchment. One smaller (51 km2) and one larger (1135 km2) catchment were selected in order to investigate the projected climate change impact on the hydrological conditions. For the investigation of the hydrological situation in the future, we used a lumped conceptual hydrological model. The model was calibrated using past measured daily data. Using the calibrated model, we investigated the impact of five different climate models outputs for the moderately optimistic scenario (RCP4.5). We investigated the situation in next 30-years periods: 2011–2040, 2041–2070, and 2071–2100. Several low and high-flow indices were calculated and compared. The results indicate that a summer precipitation decrease (i.e., 2011–2070) could lead to lower low-flow values for the investigated areas, which could increase the vulnerability of karst areas. Thus, additional focus should be given to water resource management in karst areas. On the other hand, mean flow could increase in the future. The same also applies for the high-flows where flood frequency analysis results indicate that a climate adaptation factor could be used for the hydrotechnical engineering design. However, differences among investigated models are large and show large variability among investigated cases.