Climate Change Impact Assessment Research Articles

Impact assessment of climate change on energy performance and thermal load of residential buildings in New Zealand

While it is evident that climate change will have an impact on the energy demand for heating and cooling in buildings, the exact extent of this impact is not yet fully understood. Quantification of future cooling and heating need in buildings provides a basis for taking appropriate measures for building climate change adaptation. The focus of this study is to examine how future climate change scenarios will impact the heating and cooling of residential buildings across different climatic regions in New Zealand. The future weather data under changing climate were generated for six climatic zones of New Zealand employing the statistical downscaling method. The study used various climate change scenarios, which represent concentration pathways (RCPs), to generate weather data. Specifically, the RCP8.5 and RCP4.5 scenarios were employed in the building performance simulations for different prototypes of residential buildings. The results showed there would be a significant change in the thermal performance of residential buildings, with a noticeable increase in cooling load and a decrease in heating load. These changes include a maximum thermal load change of 3 kWh/m2 in Auckland by 2090, 2.7 kWh/m2 in Hamilton, 8.3 kWh/m2 in Wellington, 4.2 kWh/m2 in Rotorua, 11 kWh/m2 in Christchurch, and 11.6 kWh/m2 in Queenstown. The warmer climatic zones are expected to change from a heating dominated to a cooling-dominated zone. The results indicated the importance of considering present and future climatic conditions in design and establishing a foundation for actions for the resilience of buildings to climate change.

Coupled hydrological and hydrodynamic modelling application for climate change impact assessment in the Nemunas river watershed–Curonian Lagoon–southeastern Baltic Sea continuum

Abstract. We analyse the cumulative impacts of climate change in a complex basin–lagoon–sea system continuum, which covers the Nemunas river basin, Curonian Lagoon, and the southeastern part of the Baltic Sea. A unique, state-of-the-art coupled modelling system was developed using hydrological and hydrodynamic models. The results of four regional downscaled models from the Rossby Centre high-resolution regional atmospheric climate model have been bias-corrected using in situ measurements and were used as forcing to assess the changes that the continuum will undergo until the end of this century. Results show that the Curonian Lagoon will be subjected to higher river discharges that in turn increase the outgoing fluxes into the Baltic Sea. Through these higher fluxes, both the water residence time and saltwater intrusion into the lagoon event frequency will decrease. Most of these changes will be more pronounced in the northern part of the lagoon, which is more likely to be influenced by the variations in the Nemunas river discharge. Its delta area may be susceptible to flooding as a result of the elevated discharge during winter. The southern part of the lagoon will experience lesser changes. While water temperatures in the entire lagoon and the southeastern Baltic Sea will steadily increase and salinity will decrease, the foreseen changes in the physical characteristics will not cause significant shifts in the ecosystem functioning but may affect the nutrient retention capacity. However, some ecosystem services such as ice fishing are expected to vanish completely due to the loss of ice cover.

Making virtual species less virtual by reverse engineering of spatiotemporal ecological models

AbstractThe virtual species (VS) and virtual ecologist (VE) approaches are useful tools that allow testing different methodological aspects of species distribution modelling. However, methods used to generate VS so far lack solutions that can ensure a high degree of biological realism, taking into account spatial and temporal variability of population densities.We have developed a method for generating dynamic VS that can reconstruct their living prototypes in a realistic way. The framework consists of fitting a spatiotemporal model to real abundance data, generating a VS population from that model over the entire study area and spanning the whole study period, calibrating the VS, and obtaining the VE data by sampling from the VS. The effectiveness of the developed approach has been illustrated by data from large‐scale and long‐term bird abundance monitoring, using the whinchatSaxicola rubetraas a study system. We evaluated how well the spatiotemporal model can reconstruct the ‘true’ system by comparing response curves and population trends between those used to generate the VS (i.e. what constitutes the ‘truth’) and those estimated from the replicated instances of VE data. In addition, we performed a sensitivity analysis to test how the varying sampling effort affects the accuracy of trend estimation.The synthetic VE data thoroughly reconstructed the real monitoring data. Response curves from generalized additive mixed models (GAMMs), fitted to these two types of data, showed high concordance, as indicated by the 95% confidence intervals of coverage probability of 87.7%–99.8% (mean 96.9%). The population trend estimated from the VE data accurately reconstructed the ‘true’ trend calculated from VS (coverage probability: 82.3%).The proposed method for generating VS and VE data by reverse engineering of the spatiotemporal ecological model reproduces well the properties of the original system, substantially increasing the ecological realism of simulation results. The method may have further applications in evaluating various modelling techniques used to study species range dynamics, where real‐world properties are of particular importance, like conservation and invasion biology or climate change impact assessment.

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

Abstract. As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.

Do Derived Drought Indices Better Characterize Future Drought Change?

AbstractCurrent methods for climate change assessment ignore the significant differences in uncertainty in model projections of the two key constituents of drought, precipitation, and evapotranspiration. We present here a new basis for assessing future drought using climate model simulations that addresses this limitation. The new method estimates the Standardized Precipitation Evapotranspiration Index (SPEI) in a two‐stage process. The first stage of our proposed approach is to derive the Standardized Precipitation Index (SPI) using reliable atmospheric variables, which are filtered with a wavelet‐based spectral transformation. This derived SPI is then converted to an equivalent SPEI by combining it with climate model evapotranspiration simulations. We assess the performance of our proposed approach across Australia. The consistency of general circulation model (GCM) drought projections, in terms of both frequency and severity, is improved using the derived SPI. Incorporating evapotranspiration further improves the consistency of the multiple GCMs and drought time scales. The proposed framework can also be generalized to other water resources applications, where the differences in GCM uncertainty for precipitation and evapotranspiration affect climate change impact assessments.

Modelling the climate change impact of mitigation (RCP 2.6) and high emission (RCP 8.5) scenario on maize yield and possible adaptation measures in different agroclimatic zones of Punjab, India.

A simulation study was performed for assessing climate change impact on maize under Representative Concentration Pathways (RCPs 2.6 and 8.5) for Punjab, India. The study area comprised of five agroclimatic zones (AZs) including seven locations. The bias corrected temperature and rainfall data from four models (CSIRO-Mk-3-6-0, FIO-ESM, IPSL-CM5A-MR and Ensemble) were used as input in CERES-Maize model which was run with constant management practices for two Punjab maize hybrids (PMH1 and PMH2). The maize yield for upcoming 70 years (2025-2095) was simulated and its deviations from the baseline (2010-2021) yield were computed under optimized sowing (early May to early July) and current sowing (end May to end June) period. With current sowing dates, the maize yield declined under both RCP2.6 and RCP8.5 scenarios, respectively in all the AZs, i.e. by 4-23 and 60-80% in AZII, by 5-60 and 60-90% in AZIII, by 9-30 and 50-90% in AZIV and by 13-40 and 30-90% in AZV. Though yield decline was lesser under RCP2.6 as compared to RCP8.5, but still it indicates that adaptive strategy such as shifting of sowing dates may be helpful in stabilizing the maize yield. The results for iterative combinations of sowing period revealed that early June sowing in AZ II for both the hybrids, mid to end June (Ludhiana and Amritsar) and end May to mid June (Patiala) sowings for PMH1 were able to nullify the negative impact of climate change. Maize cultivation in AZIV and AZV would not be a suitable venture for farmers of the region. This article is protected by copyright. All rights reserved.

Correcting systematic bias in derived hydrologic simulations – Implications for climate change assessments

Abstract Quantifying climate change impact on water resources systems at regional or catchment scales is important in water resources planning and management. General circulation models (GCMs) represent our main source of knowledge about future climate change. However, several key limitations restrict the direct use of GCM simulations for water resource assessments. In particular, the presence of systematic bias and the need for its correction is an essential pre-processing step that improves the quality of GCM simulations, making climate change impact assessments more robust and believable. What exactly is systematic bias? Can systematic bias be quantified if the model is asynchronous with observations or other model simulations? Should model bias be sub-categorized to focus on individual attributes of interest or aggregated to focus on lower moments alone? How would one address bias in multiple attributes without making the correction model complex? How could one be confident that corrected simulations for the yet-to-be-seen future bear a closer resemblance to the truth? How can one meaningfully extrapolate correction to multiple dimensions, without being impacted by the ‘Curse of Dimensionality’? These are some of the questions we attempt to address in the paper.

Future climate change projections in the agroecological, bioclimatic, biogeographical and altitudinal vegetation zones of Morocco

AbstractMorocco is located in a region vulnerable to the impacts of climate change, which can have profound effects on its social, economic and environmental systems. This makes studies aimed at forecasting these impacts in future using climate models particularly important. However, the generally coarse spatial resolution of models, combined with a large number of models, imposes a limitation on the models, allowing the selection of the most appropriate ones for climate change impact assessments in a specific region. In this study, 38 GCMs and GCM‐RCMs from CMIP5 ensemble and CORDEX project were downscaled and bias‐corrected for use in projecting climate change over Morocco under the RCP4.5 and RCP8.5 scenarios. A three‐step sequential process was adopted, involving in that order the selection of models based on: (i) projection of climate means; (ii) projection of climate extremes; and (iii) ability of the models to simulate the baseline climate. Climate projections show precipitation decreases of up to 10% by the beginning of the century, with decreases of more than 20% under RCP8.5 projected by 2100, with the central and northern mountainous regions of the country being the most affected. Seasonal projections showed autumn months likely to experience the greatest decline in precipitation, up to 36.56% by the end of the century. Temperature projections revealed an upward trend in mean, maximum and minimum temperatures, with increases of up to 3°C predicted by mid‐century over most of the country, particularly in the winter months. Our results point to a concerning future, with impacts related to decreased precipitation and increased temperatures expected to be many and varied across the country. Nevertheless, they can help provide a knowledge base for efforts to mitigate and adapt to expected changes.

A gridded multi-site precipitation generator for complex terrain: an evaluation in the Austrian Alps

Abstract. For climate change impact assessment, many applications require very high-resolution, spatiotemporally consistent precipitation data on current or future climate. In this regard, stochastic weather generators are designed as a statistical downscaling tool that can provide such data. Here, we adopt the precipitation generator framework of Kleiber et al. (2012), which is based on latent and transformed Gaussian processes, and propose an extension of that framework for a mountainous region with complex topography by allowing elevation dependence in the model. The model is used to generate two-dimensional fields of precipitation with a 1 km spatial resolution and a daily temporal resolution in a small region with highly complex terrain in the Austrian Alps. This study aims to evaluate the model with respect to its ability to simulate realistic precipitation fields over the region using historical observations from a network of 29 meteorological stations as input. The model's added value over the original setup and its limitations are also discussed. The results show that the model generates realistic fields of precipitation with good spatial and temporal variability. The model is able to generate some of the difficult areal statistics useful for impact assessment, such as the areal dry and wet spells of different lengths and the areal monthly mean of precipitation, with great accuracy. The model also captures the inter-seasonal and intra-seasonal variability very well, while the inter-annual variability is well captured in summer but largely underestimated in autumn and winter. The proposed model adds substantial value over the original modeling framework, specifically with respect to the precipitation amount. The model is unable to reproduce the realistic spatiotemporal characteristics of precipitation in autumn. We conclude that, with further development, the model is a promising tool for downscaling precipitation in complex terrain for a wide range of applications in impact assessment studies.

Climate change impact assessment on groundwater level changes: A study of hybrid model techniques

AbstractOne of the most important sources of water supply is groundwater. However, the groundwater level (GWL) is significantly impacted by the global climate change. Therefore, under these more severe climate change conditions, the accurate and simple forecast of farmland GWL is a crucial component of agricultural water management. A hybrid model (HM) of Bayesian random forest (BRF), Bayesian support vector machine (BSVM), and Bayesian artificial neural network (BANN) is built in this study. The HM is made up of a Bayesian model averaging (BMA) and three machine learning models: random forest (RF), support vector machine (SVM), and artificial neural network. These three HMs are employed to help automate logical inference and decision‐making in business intelligence for groundwater management. For this purpose, data on 8 separate climatic factors that impact GWL changes in the study area were acquired. Nine distinct farming communities' GWL change data were utilised as the dependent variables for each model fit (community data). The effectiveness of the HM techniques was assessed using the evaluation metrics of mean absolute error (MAE), coefficient of determination (R2), mean absolute percent error (MAPE), and root mean square error (RMSE). The model fit in Suhum had the greatest performance with the highest accuracy (R2 varied from 0.9051 to 0.9679) and the lowest error scores (RMSE ranged from 0.0653 to 0.0727, and MAE ranged from 0.0121 to 0.0541), according to the models' evaluation results. The BRF delivered the greatest results when compared to the two independent HMs, the BSVM and BANN. Future GWL and climatic variable data may be trained using the trained HM techniques to determine the effects of climate change. Farmers, businesses, and civil society organisations might benefit from continuous monitoring of GWL data and education on climate change to help control and prevent excessive deteriorations of global climate change on GWL.

Climate change impact assessment on the water resources of the Upper Yamuna River Basin in India

Climate change impact assessment on the water resources of the Upper Yamuna River Basin in India

UNCERTAINTIES IN CLIMATE CHANGE SCENARIOS FOR DETERMINING TEMPERATURE AND RAINFALL PATTERNS IN REGIONS WITH MIXED CLIMATE CONDITIONS

Aim of the study: This study aims to quantify uncertainty in climate change impact assessment on crop production by using all available climate models (GCMs) under both harsh and mild emission scenarios from 2020 to 2095, which has not yet been done in the study region. Material and methods: In this regard, a comparative study was carried out for Ludhiana district, Punjab, India, in which Global Climate Model (GCM) outputs for daily maximum (Tmax) and minimum temperature (Tmin) and rainfall under A1B scenario concerning Mid Century (MC) (2020-2050) and End Century (EC) (2070-2095) was extracted from ECHam5-GCM and PRECIS model. DSSAT v.4.6.1 model and Papadakis method were used to study the climate change behavior under these two-time slices. In addition, climate data from RCP scenarios for the future were extracted from five randomly selected GCMs under scenarios RCP 4.5 and RCP 8.5 using the marksim DSSAT weather generator. Results and conclusions: The results showed that the annual minimum temperature would increase by 2.4 °C and 2.45 °C during EC using ECHAM5 and PRECIS models. In contrast, under RCPs 4.5 and 8.5 scenarios, the mean annual temperature would increase by 1.56°C in MC and 3.11°C in EC compared to that of the baseline period and 2.75°C in MC and 5.46°C in EC compared to that of the baseline period, respectively. The corresponding likely decrease in annual RF under RCP 4.5 is 98 mm and 90 mm during MC and EC, respectively. The corresponding increase in annual RF under RCP 8.5 is 153 mm and 251 mm, respectively.

IMPACT OF CLIMATE CHANGE ON SOIL HEALTH: EMERGING ISSUES

Climate change is the single biggest threat facing humanity. Soil health is impacted by climate change. Climate change effects soil formation, soil structure and soil health which are complex processes affecting each other. Impact assessment of climate change on agriculture, horticulture and forests did not consider for a long time the soil quality issues. In this review, impacts of climate change on soil texture, soil structure, porosity, soil physico- chemical-biological components are reviewed. Role of soil microbes, soil carbon and crop diversication on soil health are also discussed.

Assessment of Climate Change Impact on Water Productivity and Yield of Wheat Cultivated Using Developed Seasonal Schedule Irrigation in the Nineveh Province

The agricultural lands that depend on supplementary irrigation methods for winter wheat cultivating in wide areas of the Nineveh province are most vulnerable to climate change concerns. Due to frequent rainfall shortages and the temperature increase recently noticed and predicted by the climate scenarios. Hence important to assess the climate effect on the crop response in terms of water consumption during the periods (2021-2040) and (2041-2060) by using high-resolution data extracted from 6 global climate data GCMs under SSP5-8.5 fossil fuel emission scenarios in changing and fixed CO2 concentration. And validate the Aqua-Crop model to estimate the yield and water productivity. And gives the RRSME of 7.1- 4.1 for the calibration and verification, respectively, d and R2 equal 1, indicating good model performance. From findings, the predicted response to the temperature increase and variability in rainfall between increase and decrease represents an increase in irrigation water productivity to 28% in 2060 related to the reference period in the developed schedule under changing CO2 scenario and a reduction by 13% in the near term related to the mid-term under the fixed CO2 concentration scenario. And the simulation of yield production increased by 30 % under the scenario of changing CO2 concentration. While a slight increase of 13 % under the fixed CO2 concentration scenario. These findings help realize the future uncertain resilience of agriculture in Iraq to create efficient adaptation measures to benefit from climate change opportunities.

A regional scale impact and uncertainty assessment of climate change in the Western Ghats in India.

The general circulation models (GCMs) and emission scenarios (RCP 4.5 and 8.5) have proven to be significantly functional in evaluating the impacts of climate change (CC) on hydrology, although their performance and accuracy varies on a regional scale. The objective of the present study is to evaluate the performance of five CMIP5 GCMs (CanESM2, BNU-ESM, CNRM-CM5, MPI-ESM-LR and MPI-ESM-MR) on a regional scale in the West Flowing River Basins-2 (WFRB-2) in India to model the impact of CC and its scenario uncertainty using reliability ensemble average (REA) method. For quantifying the results, the upper, middle and lower regions of WFRB-2 are separately analysed. The MPIMR and MPILR GCM model shows highest reliability factor range (0.3-0.6) in predicting the annual mean and annual maximum rainfall for most of the grids in the region. The GCM-simulated runoff using VIC (variable infiltration capacity) model is evaluated using statistical parameters such as root mean square error (RMSE), percentage bias (Pbias) and standard deviation (Std). The annual mean (maximum) runoff obtained using REA ensemble shows least RMSE, Pbias and Std values, i.e. 21.08%, 9.10mm and 8.9mm (6%, 39.1mm, 39.1mm), respectively for the middle region, which demonstrates higher reliability of GCM outputs in the flood-prone regions of WFRB-2. Furthermore, the future projection of annual maximum rainfall/runoff shows an increase of 50mm/15mm in the near future (2011-2040) for lower and 20mm/6mm for middle regions, which may cause flooding activities in the lower and middle region of WFRB-2.

Impact assessment of climate change on water resources of the Kokcha watershed: a sub-basin of the Amu Darya river basin in Afghanistan

Abstract Climate change is one of the most concerning issues which mostly impacts water resources. This study aimed to investigate the Kokcha watershed under the effects of climate change. The study was performed utilizing the Soil and Water Assessment Tool (SWAT) considering the Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios in the periods of 2050–2059 and 2090–2099. The Model for Interdisciplinary Research on Climate (MIROC5) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) was used to prepare future climate data. The temperature indicated a rising of +2.47 and +2.85 °C in 2050–2069 considering RCP4.5, and +3.38 and +5.51 °C based on the RCP8.5 scenario through 2080–2099. Precipitation showed a −30 and −17.17% decrease based on RCP4.5, and a decrease of −9.28 and −4.52% considering RCP8.5 in the mentioned periods, respectively. The historical runoff peak shifted a month earlier with a −54.56 and −25.98% decrease considering RCP4.5 and a −29.18 and −6.45% based on the RCP8.5 scenario in the mid and end of the century accordingly. Alternatively, a second river flow peak takes shape due to rainfall in July. This study's result can be used to adapt water management to climate change in the Kokcha watershed and similar regions.

Groundwater Modeling to Assess Climate Change Impacts and Sustainability in the Tana Basin, Upper Blue Nile, Ethiopia

Climate change effects on long-term groundwater (GW) resource developments in the Tana Basin, Ethiopia, are a growing concern. Efforts to provide estimates under various climatic uncertainties are lacking in the region. To address this need, we deployed a fine-resolution (500 m) GW model using MODFLOW-NWT for the Tana Basin, Upper the Blue Nile region. The GW model was calibrated based on 98 historical instantaneous well-level measurements (RMSE = 16.36 m, 1.6% of range), and 38 years of monthly lake level data (RMSE = 0.2 m, 6.7% of range). We used the model to simulate long-term climate change impacts by considering two representative concentration pathways, (RCPs) 4.5 and 8.5, from the two extreme global circulation models (MIROC5 for wetter conditions and CSIRO-Mk3 for drier conditions) available in the region. While the MIROC5 simulated GW table (GWT) was found to be stable, the CSIRO-Mk3 simulated GWT exhibited large fluctuations within +2 m to −4 m by 2100 due to climate change. More critical impacts were predicted for the lake, where total lake releases from the baseline scenario were foreseen to be changed by +50% (MIROC5) or −22% (CSIRO-Mk3) by the end of 2100.

Challenges and prospects of climate change impact assessment on mangrove environments through mathematical models

Challenges and prospects of climate change impact assessment on mangrove environments through mathematical models

Predicting the Habitat Suitability for Angelica gigas Medicinal Herb Using an Ensemble Species Distribution Model

The distribution shift of forest species due to the fact of climate change may negatively affect ecosystem services including the production of medicinal resources. Climate change impact assessments of habitat range changes are essential to sustainably manage forest resources. A change in the habitat suitability due to the fact of climate change was predicted for Angelica gigas, which has high economic value among forest medicinal resources in South Korea. The habitat suitability was predicted by an ensemble species distribution model that combined the results of nine single algorithm models using the committee averaging method. A total 168 occurrence data and 10 environmental variables were used for the single algorithm models. The area under the receiver operation characteristic curve (AUC) and true skill statistic (TSS) were applied to evaluate the models’ performance, and the contribution of the environmental variables was calculated as an important value for each single algorithm model. Climate change scenarios were projected to predict future habitat suitability. The future suitable habitat for A. gigas was gradually reduced to the high mountain regions of the eastern part of South Korea regardless of the climate change scenarios. The main environmental variable was the annual mean temperature, and the rise in temperature due to the fact of climate change was found to have a negative effect on the habitat suitability for A. gigas. The decline in the habitat suitability for A. gigas, a major forest medicinal resource, is expected to result in the reduction in its production. Therefore, it is required to establish adaptation measures to mitigate the negative impact of this decrease, such as protecting the natural habitats of A. gigas.

Advancements in Modeling Protocols for Assessing Climate Change Impacts on Water Resources: A Review

The scientific community has dedicated significant attention to climate change and climate variability in the past two decades, with numerous investigations focused on these topics. The Intergovernmental Panel on Climate Change's third and fourth assessment reports have provided clear evidence that the planet's climate has undergone significant changes since the pre-industrial era, resulting in a warmer phase. These changes have had severe effects on hydrological processes and the availability of water resources due to shifts in temperature and precipitation patterns. A better comprehension of climate change's impact on water resources can aid in developing sustainable strategies for their management and development. Hydrological models combined with climate models can offer a framework to comprehend and explore the interplay between climate, human activities, and water resources.