Climate Change Impact Studies Research Articles

Comparison of bias correction methods to regional climate model simulations for climate change projection in Muger Subbasin, Upper Blue Nile Basin, Ethiopia

ABSTRACT The objective of this study was to evaluate the best performed bias correction methods to simulate the regional climate models for future climate change projections in Muger Subbasin. Delta change methods perform very good with a coefficient of correlation of 0.99 and a percent of bias –3. When we compare its corrected simulation result with observed data, delta change method seems to be with no biases for maximum temperature, but it increases by 1.67 °C from the mean for minimum temperature of 0.39 and 38.41 mm for monthly and annual precipitation, respectively. Delta change methods underestimate the model result for both temperature and precipitation. Linear scaling and variance scaling methods overestimate the maximum temperature of the simulation by 0.002 and 0.004 °C amount from the mean of the observed data, but it underestimates 1.59 and 1.56 °C the minimum temperature, respectively. The long-term temperature projection values (2060–2090) are higher than the near-term projections (2030–2060) for both RCP2.6 and RCP8.5 scenarios. Similarly, the change in annual precipitation for the long-term is higher than the near-term projections. As a conclusion, the results draw attention to the fact that bias-adjusted regional climate models data are crucial for the provision of local climate change impact studies in the Muger Subbasin.

Investigation of rainfall disaggregation with flexible timescales based on point process models

Analyzing high-temporal-resolution rainfall data with disaggregation tools is vital for understanding precipitation variability, especially in climate change studies with limited access to such data. To facilitate such study for tropical region, we proposed a Point-Process Modelling and Rainfall Disaggregation (PPMRD) framework for rainfall disaggregation with flexible time scales. The framework was tested on two tropical sites in Singapore, using six point-process models and four disaggregation schemes (24 h to 5 min, 3 h to 5 min, 24 h to 1 h, and 6 h to 1 h). Most models excelled fundamental statistics and extreme value analysis. The Bartlett-Lewis Rectangular Pulse Seven parameter (BLRP7) model was found to be the best performer for simulating and disaggregating tropical rainfall. The study findings emphasized the utility of disaggregation models in generating fine-resolution time series for analyzing rainfall extremes. However, it is crucial to underscore the importance of employing ensembles to comprehensively address uncertainties arising from the stochastic nature of rainfall. The study also emphasized the importance of model selection in the proposed framework, as the disaggregation method depends on the generator’s performance. The PPMRD surpasses traditional methods by handling subdaily upper-level series disaggregation to minute-level rainfall. This advancement offers greater capabilities and flexibility, promising benefits for generating high-temporal-resolution rainfall data in hydrological modeling and climate change impact studies.

Improving performance of bucket-type hydrological models in high latitudes with multi-model combination methods: Can we wring water from a stone?

Multi-model combination (averaging) methods (MMCMs) are used to improve the accuracy of hydrological (precipitation-runoff) outputs in simulation or forecasting/prediction modes. In this paper, we examined if the application of MMCMs can improve model performance in reproducing distributions of hydrological signatures, such as annual maxima or minima of varying durations. To this end, ten MMCMs were applied to 29 bucket-type models to simulate runoff in 50 high-latitude catchments. The MMCMs were evaluated by comparing the resulting simulated flows to the reference (i.e., best-performing) individual model, considering various commonly used performance indicators, as well as model performance in reproducing the distributions of signatures. Additionally, we analysed whether (1) the selection of the candidate models, or (2) targeting specific signatures, such as annual maxima or minima, can improve performance of the model combinations. The results suggest that the application of MMCMs can improve accuracy of runoff simulations in terms of traditional performance indicators, but fails to improve performance in reproducing the distributions of signatures. Neither excluding poor-performing models nor applying the MMCMs with the targeted signatures, improves this aspect of model performance. These findings clearly reveal the need for further research aiming at enhancing model performance in reproducing the distributions of hydrological signatures, which is essential for climate-change impact studies.

Identifying and ranking of CMIP6-global climate models for projected changes in temperature over Indian subcontinent

Selecting the best region-specific climate models is a precursor information for quantifying the climate change impact studies on hydraulic/hydrological projects and extreme heat events. A crucial step in lowering GCMs simulation-related uncertainty is identifying skilled GCMs based on their ranking. This research performed a critical assessment of 30 general circulation models (GCMs) from CMIP6 (IPCC’s sixth assessment report) for maximum and minimum temperature over Indian subcontinent. The daily temperature data from 1965 to 2014 were considered to quantify maximum and minimum temperatures using a gridded spatial resolution of 1°. The Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC), Perkins skill score (PSS), normalized root mean square error (NRMSE), and absolute normalized mean bias error (ANMBE) were employed as performance indicators for two different scenarios, S1 and S2. The entropy approach was used to allocate weights to each performance indicator for relative ranking. Individual ranking at each grid was achieved using a multicriteria decision-making technique, VIKOR. The combined ranking was accomplished by integrating group decision-making, average ranking perspective, and cumulative percentage coverage of India. The outcome reveals that for S1 and S2, NRMSE and NSE are the most significant indicators, respectively whereas CC is the least significant indicator in both cases. This study identifies ensemble of KIOST-ESM, MRI-ESM2-0, MIROC6, NESM3, and CanESM5 for maximum temperature and E3SM-1-0, NESM3, CanESM5, GFDL-CM4, INM-CM5-0, and CMCC-ESM2 for minimum temperature.

Statistically downscaled CMIP6 ocean variables for European waters

Climate change impact studies need climate projections for different scenarios and at scales relevant to planning and management, preferably for a variety of models and realizations to capture the uncertainty in these models. To address current gaps, we statistically downscaled (SD) 3–7 CMIP6 models for five key indicators of marine habitat conditions: temperature, salinity, pH, oxygen, and chlorophyll across European waters for three climate scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5. Results provide ensemble averages and uncertainty estimates that can serve as input data for projecting the potential success of a range of Nature-based Solutions, including the restoration of habitat-forming species such as seagrass in the Mediterranean and kelp in coastal areas of Portugal and Norway. Evaluation of the ensemble with observations from four European regions (North Sea, Baltic Sea, Bay of Biscay, and Mediterranean Sea) indicates that the SD projections realistically capture the climatological conditions of the historical period 1993–2020. Model skill (Liu-mean efficiency, Pearson correlation) clearly improves for both surface temperature and oxygen across all regions with respect to the original ESMs demonstrating a higher skill for temperature compared to oxygen. Warming is evident across all areas and large differences among scenarios fully emerge from the background uncertainties related to internal variability and model differences in the second half of the century. Scenario-specific differences in acidification significantly emerge from model uncertainty and internal variability leading to distinct trajectories in surface pH starting before mid-century (in some cases starting from present day). Deoxygenation is also present across all domains, but the climate signal was significantly weaker compared to the other two indicators when compared to model uncertainty and internal variability, and the impact of different greenhouse gas trajectories is less distinct. The substantial regional and local heterogeneity in these three abiotic indicators underscores the need for highly spatially resolved physical and biogeochemical projections to understand how climate change may impact marine ecosystems.

An Imputing Technique for Surface Water Extent Timeseries with Streamflow Discharges

A continuous and multi-decadal surface water extent (SWE) record is vital for water resources management, flood risk assessment, and comprehensive climate change impact studies. The advancements in remote sensing technologies offer a valuable tool for monitoring surface water with high temporal and spatial resolution. However, challenges persist due to image gaps resulting from sensor issues and adverse weather conditions during data collection. To address this issue, one way to fill the gaps is by leveraging in situ measurements such as streamflow discharges (SFDs). We investigate the relationship between SFDs and Landsat-derived SWE in the New England region watersheds (eight-digit hydrological unit code (HUC)) on a monthly scale. While previous studies indicate the relationship exists, it remains elusive for larger domains. Recent research suggests using monthly average SFD data from a single stream gage to fill the gaps in SWE. However, as SWE represents a monthly maximum value, relying on a single gage with average values may not capture the complex dynamics of surface water. Our study introduces a novel approach by replacing the monthly average SFD with the maximum day streamflow discharge anomaly (SFDA) within a month. This adjustment aims to better reflect extreme scenarios, and we explore the relationship using ridge regression, incorporating data from all stream gages in the study domain. The SWE and SFDA are both transformed to stabilize the variance. We found that there is no discernible correlation between the magnitude of the correlation and the size of the basins. The correlations vary based on HUC and display a wide range, indicating the variances of the importance of stream gages to each HUC. The maximum correlation is found when the stream gage is located outside of the target HUC, further verifying the complex relationship between SWE and SFDA. Covering over 30 years of data across 45 HUCs, the imputing technique using ridge regression shows satisfactory performance for most of the HUCs analyzed. The results show that 41 out of 45 HUCs achieve a root-mean-square error (RMSE) of less than 10, and 44 out of 45 HUCs exhibit a normalized root-mean-square error (NRMSE) of less than 0.1. Of 45 HUCs, 42 have an R-squared (R2) score higher than 0.7. The Nash–Sutcliffe efficiency index (Ef) shows consistent results with R2, with the relative bias ranging from –0.02 to 0.03. The established relationship serves as an effective imputing technique, filling gaps in the time series of SWE. Moreover, our approach facilitates the identification and visualization of the most significant gages for each HUC, contributing to a more refined understanding of surface water dynamics.

Seamlessly combined historical and projected daily meteorological datasets for impact studies in Central Europe: The FORESEE v4.0 and the FORESEE-HUN v1.0

The FORESEE is an open access, climatological database for Central Europe containing observed and projected meteorological data for the 1951–2100 period. As a climate service, FORESEE disseminates basic meteorological variables at a daily time step with a 0.1° × 0.1° spatial resolution including maximum/minimum temperature, precipitation, incoming shortwave solar radiation and daylight vapour pressure deficit. The future climate in FORESEE v4.0 and FORESEE-HUN v1.0 is projected by 14 regional climate models from the EURO-CORDEX database using the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. Based on RCP4.5 the country-specific results indicate similar projected mean changes in annual mean temperature (1.5–1.7 °C) but considerable differences in precipitation (from −1.6 to 6.9%) in the region for 2071–2100 relative to 1991–2020. We present two case studies to demonstrate the applicability of FORESEE in climate change impact studies using the ensemble approach. Climate change induced negative weather effect (15.4% and 28.9% mean loss for 2071–2100 according to RCP4.5 and RCP8.5, respectively) might dominate the future winter wheat yields in Hungary that is superimposed to the overall trend determined by other factors. The projections provide consistent results about the mean advance in the start of the growing season for forests in Hungary up to 2100 with ensemble mean of 9.1 days (RCP4.5) and 19.8 days (RCP8.5). We also demonstrate that the representative model selection method might lead to misleading results in impact studies that should be considered. The updated FORESEE is a way forward in the dissemination of policy-relevant essential climate data in Central Europe.

The application of a plant community model to evaluate adaptation strategies for alleviating climate change impacts on grassland productivity, biodiversity and forage quality

Grasslands are environments characterized by an elevated biodiversity in plant species, which dynamically evolves over time as a function of management practices, soil properties, and climate conditions. Climate change can locally affect grassland growth and floristic composition and, in turn, the quality and quantity of provided ecosystem services. We show how the use of the plant community model CoSMo allows to explicitly account for the dynamics of floristic composition as response to environmental variables. This, in turn, allows quantifying climate change impacts on grasslands growth and composition and evaluating alternative strategies to improve adaptation in the mid-term. As a case study, we focused on mountain areas in the northern Apennines (Italy), where temporary alfalfa (Medicago sativa L.)-dominated grasslands are sown for the production of Parmesan cheese. Five sites and four alternative climate scenarios (RCP4.5 and RCP8.5 projections as provided by the HadGEM2 and GISS general circulation models) were considered, to explore a wide range of agro-environmental conditions. Results showed that the observed dynamics of floristic composition and biomass accumulation were successfully simulated, with a mean absolute error lower than 10% for floristic composition and less than 1 t ha−1 for total biomass. Climate change impacts were globally negative, with a clear decrease of forage production as compared to the baseline (from -3.6% to -14.3% according to the climate scenario). The biodiversity of the plant community also declined (12.5% average decrease in the inverse Simpson index) due to the increase in alfalfa dominance. The latter, however, led to preserve the forage crude protein content (+0.9% on average). Guidelines for optimizing grassland productivity and forage protein under future climate were defined, mainly focused on reducing the alfalfa field duration, sowing grass-legume mixtures, and delaying the last cut of the season. These practices can be easily adopted under operational farming conditions to support the adaptation of temporary grasslands to climate change. According to our knowledge, this is one of the first study of climate change impact that uses a plant community model to explicitly considers phytocoenosis dynamics, biodiversity, and their effect on forage production (quantity and quality). Considering the role of grasslands as providers of key ecosystem services ‒ especially in marginal areas ‒ we believe that our modelling approach can support the identification of effective adaptation strategies to address future climate challenges.

Examining current bias and future projection consistency of globally downscaled climate projections commonly used in climate impact studies

The associated uncertainties of future climate projections are one of the biggest obstacles to overcome in studies exploring the potential regional impacts of future climate shifts. In remote and climatically complex regions, the limited number of available downscaled projections may not provide an accurate representation of the underlying uncertainty in future climate or the possible range of potential scenarios. Consequently, global downscaled projections are now some of the most widely used climate datasets in the world. However, they are rarely examined for representativeness of local climate or the plausibility of their projected changes. Here we explore the utility of two such global datasets (CHELSA and WorldClim2) in providing plausible future climate scenarios for regional climate change impact studies. Our analysis was based on three steps: (1) standardizing a baseline period to compare available global downscaled projections with regional observation-based datasets and regional downscaled datasets; (2) bias correcting projections using a single observation-based baseline; and (3) having controlled differences in baselines between datasets, exploring the patterns and magnitude of projected climate shifts from these datasets to determine their plausibility as future climate scenarios, using Hawaiʻi as an example region. Focusing on mean annual temperature and precipitation, we show projected climate shifts from these commonly used global datasets not only may vary significantly from one another but may also fall well outside the range of future scenarios derived from regional downscaling efforts. As species distribution models are commonly created from these datasets, we further illustrate how a substantial portion of variability in future species distribution shifts can arise from the choice of global dataset used. Hence, projected shifts between baseline and future scenarios from these global downscaled projections warrant careful evaluation before use in climate impact studies, something rarely done in the existing literature.

Evaluation of Baseflow Modeling with BlueM.Sim for Long-Term Hydrological Studies in the German Low Mountain Range of Hesse, Germany

So far, research with the hydrological model BlueM.Sim has been focused on reservoir management and integrated river basin modeling. BlueM.Sim is part of the official toolset for estimating immissions into rivers in Hesse (Germany) via long-term continuous modeling. Dynamic runoff modeling from rural catchments is permitted within the Hessian guidelines, but in practice, a constant flow or low flow is used. However, due to increasing water stress in the region caused by climate change, the dynamic modeling of runoff from rural catchments will become necessary. Therefore, dynamic baseflow modeling with BlueM.Sim is of the greatest importance. This study evaluated baseflow modeling with BlueM.Sim in a representative hard-rock aquifer in the German Low Mountain range. Two model setups (Factor Approach (FA): CN method + monthly baseflow; Soil Moisture Approach (SMA): physical soil moisture simulation) were calibrated (validated) for a 9-year (5-year) period. The FA achieved an NSE of 0.62 (0.44) and an LnNSE of 0.64 (0.60) for the calibration and validation periods. The selection of a solution for the successful validation of the FA was challenging and required a selection that overestimated baseflow in the calibration period. This is due to the major disadvantage of the FA, namely, that baseflow can only vary according to an estimated yearly pattern of monthly baseflow factors. However, the data requirements are low, and the estimation of monthly baseflow factors is simple and could potentially be regionalized for Hesse, leading to a better representation of baseflow than in current practice. The SMA achieved better results with an NSE of 0.78 (0.75) and an LnNSE of 0.72 (0.78). The data requirements and model setup are extensive and require the estimation of many parameters, which are limitations to its application in practice. Furthermore, a literature review has shown that a single linear reservoir, as in BlueM.Sim, is not optimal for modeling baseflow in hard-rock aquifers. However, for detailed climate change impact studies in the region with BlueM.Sim, the SMA should be preferred over the FA. It is expected that BlueM.Sim would benefit from implementing a more suitable model structure for baseflow in hard-rock aquifers, resulting in improved water balance and water quality outcomes.

Impacts of climate change on semi-natural alpine pastures productivity and floristic composition

Climate change impact on pasture floristic composition needs to be carefully assessed, given its key role for the resilience of pastoral systems and related ecosystem services. Nevertheless, variations in floristic composition are rarely taken into account in climate change impact studies. Here, we used the plant community model CoSMo to simulate future dynamics of biomass accumulation and floristic composition for high-altitude semi-natural alpine pastures. Dedicated multi-site field activities were conducted to collect data for model calibration. Simulations were run for four 20-year climate scenarios centered on 2040, resulting from the combination of two general circulation models (GISS-ES and HadGEM2) and two representative concentration pathways (RCP4.5 and RCP8.5). Results highlighted the capability of CoSMo to successfully reproduce the productivity and floristic composition of semi-natural pastures, modeling efficiency and R2 being higher than 0.90 for aboveground biomass accumulation and relative abundance of species. CoSMo simulated an overall positive effect of increasing temperatures on pasture productivity (+ 10.7% on average), due to higher biomass accumulation rates and longer growing seasons. However, these benefits were highly heterogeneous among the monitored pastures (ranging from − 2.5 to + 16.2%), because of differences in floristic composition and in species-specific thermal requirements that led to complex, non-linear reactions to climate variations. A negative impact of climate change was simulated for grazing value (− 11.1% on average), due to the higher suitability to future conditions of Nardus stricta, which has low grazing value compared to other species. Our results highlight that floristic composition should be explicitly considered while assessing climate change impacts on semi-natural pasture productivity and connected ecosystem services.

Impact of diverse configuration in multivariate bias correction methods on large-scale hydrological modelling under climate change

For climate change impact studies, bias correction methods are widely utilized to improve climate model outputs by correcting the representation of climate variables. In addition to the univariate bias correction methods, many multivariate bias correction methods have been proposed, using diverse mathematical concepts in their individual designs. Although several attempts have been made to evaluate the performance of the multivariate bias correction methods, there is no exploration in the impact of diverse configurations to target multivariate dependencies in the multivariate bias correction methods particularly for hydrological modeling. This study tackles this research gap by developing an inter-comparison framework that assesses how the different configurations affect the climate and hydrological simulations. To achieve the research goal, the impact of three configurations (inter-variable, inter-spatial, and full-dimensional dependence configurations) is evaluated on multiple basins in a national-scale domain over South Korea. Results show that although most methods in the three different configurations capably correct the statistics, some instabilities are also observed when a high-dimensional dependence configuration is used, informing that some multivariate bias correction methods may not be compatible with high-dimensional configurations. For hydrological modeling, the configuration definitions bring about distinct differences in streamflow realizations. Only some of the inter-variable and full-dimensional configurations perform better or similarly to univariate bias correction methods. Finally, we demonstrate the importance of selecting bias correction methods while addressing the necessity for further improvement.

Improving the quantification of climate change hazards by hydrological models: a simple ensemble approach for considering the uncertain effect of vegetation response to climate change on potential evapotranspiration

Abstract. Almost no hydrological model takes into account that changes in evapotranspiration are affected by how vegetation responds to changing CO2 and climate. This severely limits their ability to quantify the impact of climate change on evapotranspiration and, thus, water resources. As the simulation of vegetation responses is both complex and very uncertain, we recommend a simple approach to considering (in climate change impact studies with hydrological models) the uncertainty that the vegetation response causes with respect to the estimation of future potential evapotranspiration (PET). To quantify this uncertainty in a simple manner, we propose running the hydrological model in two variants: with its standard PET approach and with a modified approach to compute PET. In the case of PET equations containing stomatal conductance, the modified approach can be implemented by adjusting the conductance. We introduce a modified approach for hydrological models that computes PET as a function of net radiation and temperature only, i.e., with the Priestley–Taylor (PT) equation. The new PT-MA approach is based on the work of Milly and Dunne (2016) (MD), who compared the change in non-water-stressed actual evapotranspiration (NWSAET) as computed by an ensemble of global climate models (GCMs), which simulate vegetation response as well as interactions between the atmosphere and the land surface, with various methods to compute PET change. Based on this comparison, MD proposed estimating the impact of climate change on PET as a function of only the change in net energy input at the land surface. PT-MA retains the impact of temperature on daily to interannual as well as spatial PET variations but removes the impact of the long-term temperature trend on PET such that long-term changes in future PET are driven by changes in net radiation only. We implemented PT-MA in the global hydrological model WaterGAP 2.2d and computed daily time series of PET between 1901 and 2099 using the bias-adjusted output of four GCMs. Increases in GCM-derived NWSAET between the end of the 20th and the end of the 21st century for Representative Concentration Pathway 8.5 (RCP8.5) are simulated well by WaterGAP if PT-MA is applied but are severely overestimated with the standard PT method. Application of PT-MA in WaterGAP results in smaller future decreases or larger future increases in renewable water resources (expressed as the variable RWR) compared with the standard PT method, except in a small number of grid cells where increased inflow from upstream areas due to increased upstream runoff leads to enhanced evapotranspiration from surface water bodies or irrigated fields. On about 20 % of the global land area, PT-MA leads to an increase in RWR that is more than 20 % higher than in the case of standard PT, while on more than 10 % of the global land area, the projected RWR decrease is reduced by more than 20 %. While the modified approach to compute PET is likely to avoid the overestimation of future drying in many if not most regions, the vegetation response in other regions may be such that the application of the standard PET leads to more likely changes in PET. As these regions cannot be identified with certainty, the proposed ensemble approach with two hydrological model variants serves to represent the uncertainty in hydrological changes due to the vegetation response to climate change that is not represented in the model.

Farmers’ Perceptions of Climate Change and Its Socio-Ecological Consequences in Bhutan’s Biological Corridor Network

Sandwiched between two highly populated countries in the world, Bhutan is not an exception to the impacts of climate change. In the dearth of comprehensive climate data and limited research endeavors, Bhutan’s 69% of the total population, depending on agriculture and livestock farming, is apparently vulnerable to climate change adversities. Notably, those dwelling within the precincts of the Biological Corridors (BCs), recognized as custodians and conservation partners, have been relatively neglected in aspects of climate change impact studies. In the year 2019, our pioneering study in the northern biological corridors of Bhutan aimed to (i) comprehend people’s sensitivity to climate change; (ii) discern people’s perceptions on the severity of climate change impacts on livelihood, natural resources, and wildlife patterns; and (iii) help formulate appropriate strategies and policy transformation in climate adaptation interventions. We conducted a semi-structured questionnaire survey, applying random sampling (n = 136) of 435 households from western, central, and eastern Bhutan. Study revealed that farmers’ sensitivity and perception of the impact of climate change vary spatiotemporally, demographically, and occupationally. Not more than 76.7% of respondents noticed the changing climate patterns over the last ten years, while landless households (3%) noticed no discernible impacts. A rise in temperature is the most sensitive variable reported, followed by erratic precipitation, a change in plant phenology, and a shift in wildlife occupancy patterns. A total of 48% of respondents reported drought, scanty rain, floods, and diseases that moderately affected agriculture crops and livestock, while 42% of respondents perceive that climate change is a driving factor for human–wildlife conflict because of increasing wild animal movement towards their region. About 51% of respondents indicated adopting new adaptation strategies, which involved transitioning to different cattle breeds and altering agricultural cropping patterns and crop varieties. We recommend political support for inducing climate-smart agriculture and livestock practices. Urgent research is recommended for the shift of habitats and wildlife due to climate change to better understand and help formulate pragmatic climate adaptation measures in the face of rising human–wildlife conflict.

21st Century climate change projections of temperature and precipitation in Central Kashmir Valley under RCP 4.5 and RCP 8.5

Regional climate models (RCMs) give more reliable results for a regional impact study of climate change, but they still have a significant bias that has to be corrected before they can be utilised in climate change research. In this study, two methods for local bias correction of Tmax, Tmin and precipitation data at monthly scales, namely the modified difference approach (MDA) and the linear scaling (LS) method, were applied and validated to minimize the bias between the modelled (HAD GEM2-ES-GCM) and observed climate data in Central Kashmir Valley. Tmax, Tmin and precipitation correction functions generated using the LS method on a monthly time scale were shown to be excellent than MDA for bias correction of weather data to make it close to observed data in both scenarios (RCP 4.5 & 8.5). Comparison between two scenarios was done to determine the climate change extent in Central Kashmir Valley using LS method. The past 30 years observed average temperature and precipitation was 14.17 °C and 734.06 mm, respectively considered as a baseline for comparison purpose. Annual Taverage (°C) showed increase in all the three time slices and maximum increase by 3.09 and 5.72 °C during far future (FF) (2071-2095) under RCP 4.5 & 8.5, respectively. Whereas, the results of average annual precipitation also showed increase in future scenario and maximum increase by 29.25 mm (3.98%) during mid future (2046-2070) and 215.98 mm (29.42%) during end future (2071-2095), under RCP 4.5 & 8.5 respectively. It was concluded that under RCP 8.5 scenario climate change was quite significant than RCP 4.5.

Validation and Selection of a Representative Subset from the Ensemble of EURO-CORDEX EUR11 Regional Climate Model Outputs for the Czech Republic

To better understand the impact of climate change at a given location, it is crucial to consider a wide range of climate models that are representative of the area. In this study, we emphasize the importance of the careful validation and selection of climate models most suitable for a particular region. This step is critical to enhance the relevance of climate change impact studies and consequently design appropriate and robust adaptation measures, particularly in agriculture, forestry and water resources management. We propose validation and selection methods for regional climate models that can help identify a smaller group of well-performing models using the Central European area and Czech Republic as examples. In the validation process, 7 out of 19 regional climate models performed poorly. Of the 12 well-performing models, a subset of 7 models was selected to represent the uncertainty in the entire ensemble, which could be used in subsequent studies. The methodology is sufficiently general and may be applied to other climate model ensembles.

On model selection criteria for climate change impact studies

Climate change impact studies inform policymakers on the estimated damages of future climate change on economic, health and other outcomes. In most studies, an annual outcome variable is observed, e.g. agricultural yield, along with a higher-frequency regressor, e.g. daily temperature. Applied researchers then face a problem of selecting a model to characterize the nonlinear relationship between the outcome and the high-frequency regressor to make a policy recommendation based on the model-implied damage function. We show that existing model selection criteria are only suitable for the policy objective if one of the models under consideration nests the true model. If all models are seen as imperfect approximations of the true nonlinear relationship, the model that performs well in the historical climate conditions is not guaranteed to perform well at the projected climate. We therefore propose a new criterion, the proximity-weighted mean squared error (PWMSE) that directly targets precision of the damage function at the projected future climate. To make this criterion feasible, we assign higher weights to historical years that can serve as “weather analogs” to the projected future climate when evaluating competing models using the PWMSE. We show that our approach selects the best approximate regression model that has the smallest weighted squared error of predicted impacts for a projected future climate. A simulation study and an application revisiting the impact of climate change on agricultural production illustrate the empirical relevance of our theoretical analysis.

Hydrological models weighting for hydrological projections: The impacts on future peak flows

Reliable hydrological projections are of great importance for climate change impact studies, especially knowing that these analyses can allow identifying regional adaptation and mitigation strategies for the future. However, the literature has highlighted that hydrological models used under climate conditions that are contrasting to those used during their calibrations can lower their performance and reliability, an issue that lowers the confidence in hydrological projections and adds uncertainty to their analyses. More studies are needed to explore this issue and evaluate potential strategies that might improve hydrological models’ reliability in climate change impact studies. Thus, the present study evaluates the robustness of hydrological models under contrasting climatic conditions and investigates the use of weighting techniques to improve their combined performance and reliability. The robustness of five lumped hydrological models is analysed using a Differential Split-Sample Testing (DSST) that evaluates their performance under cold, warm, humid and dry historical contrasting conditions over 77 basins covering different hydroclimatic conditions (two domains, one located in Quebec, Canada, and one in Mexico). Additionally, four basins were selected from the study area to evaluate the robustness of a more complex semi-distributed and more physically-based hydrological model and compare its simulations against the simpler lumped hydrological models. Based on the resulting performance of each hydrological model, five different weighting methods were applied to evaluate the potential improvements in the multi-model ensemble performance and quantify their effects on hydrological projections, particularly on future peak flows. For each basin, these streamflow projections were produced using two regional climate simulations (one per studied domain) issued from the Canadian Regional Climate Model version 5 (CRCM5) under the Representative Concentration Pathway (RCP) 8.5 for the 1976–2005, 2041–2070 and 2070–2099 periods. The results showed that weighting hydrological models, even with the most simplistic methods, showed better performances over historical contrasting conditions than the best-performing lumped hydrological model. Between the different weighting methods, the Granger-Ramanathan type A showed the overall best performance among the different basins and climate conditions, particularly in peak streamflows. Over the CRCM5-driven peak flow projections, the weighting methods Granger-Ramanathan types A and B produced the largest impacts on the projected floods magnitudes and climate change signals. On the other hand, the additional tests using the semi-distributed and more physically-based hydrological model revealed that this model showed more robust simulations than the weighted lumped hydrological models on low flows over the four selected basins. Additionally, more robust high-flow simulations were observed over a small snow-dominated basin, suggesting a potential added value in adding more complex hydrological models to simulate conditions under a changed climate.

Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain

AbstractHigh spatio‐temporal resolution near‐surface projected data is vital for climate change impact studies and adaptation. We derived the highest statistically downscaled resolution multivariate ensemble currently available: daily 1 km until the end of the century. Deep learning models were employed to develop transfer functions for precipitation, water vapor pressure, radiation, wind speed, and, maximum, mean and minimum temperature. Perfect prognosis is the particular statistical downscaling methodology applied, using a subset of the ReKIS data set for Saxony as predictands, the ERA5 reanalysis as during‐training predictors and the CORDEX‐EUR11 ensemble as projected predictors. The performance of the transfer functions was validated with the VALUE framework, yielding highly satisfactory results. Particular attention was given to the three major perfect prognosis assumptions, for which several tests were carried out and thoroughly discussed. From the latter, we corroborated their fulfillment to a high degree, thus, the derived projections are considered adequate and relevant for impact modelers. In total, 18 runs for RCP85, 1 for RCP45, and 4 for RCP26 were downscaled under both stochastic and deterministic approaches. This multivariate ensemble could drive more accurate and diverse impact studies in the region. Generally, the projected climatologies are in agreement with coarser resolution projections. Nevertheless, statistical particularities were observed for some projections, thus, a list of caveats for potential users is given. Due to the scalability of the presented methodology, further possible applications with additional datasets are proposed. Lastly, several potential improvement prospects are discussed toward the ideal subsequent iteration of the perfect prognosis statistical downscaling methodology.

Investigating the knowledge gap in research on climate and land use change impacts on water resources, with a focus on groundwater resources in South Africa: a bibliometric analysis

Climate and land use change (CLUC) impact studies on water and groundwater resources have evolved in recent years. To determine whether all research gaps have been or are being addressed through the current intellectual structure, a bibliometric analysis, as well as a record review, was enacted to determine the intellectual structure of CLUC impacts on water resources, with a particular focus on the implications for groundwater resources research in the Breede Gouritz Water Management Area (BGWMA) in South Africa. Methods applied included publication-related trends and science mapping. This study found that CLUC impact research being published has increased by 600% between 2014 and 2021, localised research is being done in 95 countries, and land use change (LUC), specifically urbanisation, is being considered more often as a variable. However, a few gaps in the research remain, including smaller spatiotemporal scales in more locations, a stronger focus on LUC in all its forms, LUC versus climate change (CC) impact studies, and multi-modal approaches to related research. CLUC impacts on water and groundwater resources research have made significant progress over the years, but more research is necessary to make this a robust area of research.