Climate Change Impact Studies Research Articles

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.

Evaluation of DSSAT-CROPGRO Module for Spatial Yield Estimation of Chickpea in Vidisha (Madhya Pradesh) and Nagaur (Rajasthan), India

Crop simulation models are often used to characterize, develop and assess field crop production practices. The present study was carried out for chickpea spatial yield estimation at Vidisha of Madhya Pradesh and Nagaur district of Rajasthan employing the DSSAT model. In this study, the DSSAT-CROPGRO module was used to estimate chickpea yield during rabi 2022. To simulate the yield, DSSAT required datasets of crop growth and management, daily weather data, and soil data were provided. The simulated yield was validated using the observed yield through CCEs from farmers’ fields. When the observed and simulated yields were compared, their deviation was found to be less than 20 percent for all varieties at experimental locations of the Vidisha and Nagaur districts. The observed yield of Chickpea matched well after calibration which showed that model could simulate the yield with high accuracy as it showed R2, d, and MAPE of 0.87, 0.92, and 7.30 for calibration and 0.88, 0.90, and 7.60 for the validation, respectively. The model has been successfully calibrated and validated for the chickpea at spatial level and it can be taken for further applications in natural resources management and climate change impact studies.

How to assess climate change impact models: uncertainty analysis of streamflow statistics via approximate Bayesian computation (ABC)

ABSTRACT Climate change impact models (CCIMs) suffer from inherent bias, uncertainty, and asynchronous observations in the baseline period. To overcome these challenges, this study introduces a methodology to assess CCIMs in the baseline period using the uncertainty analysis of streamflow statistics via the approximate Bayesian computation (ABC) post-processor, which infers the residual error model parameters based on summary statistics (signatures). As an illustrative case study, we analyzed the climate change projections of the fifth assessment report of the United Nations intergovernmental panel on climate change (AR5 - IPCC) of the monthly streamflow in the upper Oria catchment (Spain) with deterministic and probabilistic verification frameworks to assess the ABC post-processor outputs. In addition, the ABC post-processor is evaluated against the ensemble (reference method). The results show that the ABC post-processor outperformed the ensemble method in all verification metrics, and the ensemble method has reasonable reliability but exhibited poor sharpness. We suggest that the ensemble method should be complemented with the ABC post-processor for climate change impact studies.

Deep learning for stochastic precipitation generation – deep SPG v1.0

Abstract. We present a deep-neural-network-based single-site stochastic precipitation generator (SPG), capable of producing realistic time series of daily and hourly precipitation. The neural network outputs a wet-day probability and precipitation distributions in the form of a mixture model. The SPG was tested in four different locations in New Zealand, and we found it accurately reproduced the precipitation depth, the autocorrelations seen in the original data, the observed dry-spell lengths, and the seasonality in precipitation. We present two versions of the hourly and daily SPGs: (i) a stationary version of the SPG that assumes that the statistics of the precipitation are time independent and (ii) a non-stationary version that captures the secular drift in precipitation statistics resulting from climate change. The latter was developed to be applicable to climate change impact studies, especially studies reliant on SPG projections of future precipitation. We highlight many of the pitfalls associated with the training of a non-stationary SPG on observations alone and offer an alternative method that replicates the secular drift in precipitation seen in a large-ensemble regional climate model. The SPG runs several orders of magnitude faster than a typical regional climate model and permits the generation of very large ensembles of realistic precipitation time series under many climate change scenarios. These ensembles will also contain many extreme events not seen in the historical record.

Bias correction and projection of temperature over the altitudes of the Upper Indus Basin under CMIP6 climate scenarios from 1985 to 2100

AbstractThe identification of projected changes in temperature caused by global warming at a fine-scale spatial resolution is of great importance for the high-altitude glacier and snow covered Upper Indus Basin. This study used a multimodel ensemble mean bias-correction technique which uses the ensemble empirical mode decomposition method to correct the bias of ensemble mean of seven CMIP6 GCMs outputs with reference to the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5). The bias-corrected data have a nonlinear trend of seven GCMs but interannual variance and mean climate of ERA5 dataset. The dataset spans from 1985 to 2100 for historical and future climate scenarios (SSP126, SSP245, SSP370, and SSP585) at daily time intervals with a 1 km grid resolution. The result of different scenarios indicates that the increase in maximum (Tmax) and minimum temperature (Tmin) ranging from 1.5 to 5.4 °C and 1.8 to 6.8 °C from 2015 to 2100, respectively. Similarly, elevation-dependent warming is identified in Tmin from 1.7 to 7.0 °C at elevations <2,000 to 6,000 m asl, while the contrary relationship in Tmax is projected under different scenarios from 2015 to 2100. This study provides an insight into how to improve the GCMs projections and can be helpful for further climate change impact studies.

Extent of aerosol effect on the precipitation of squall lines: A case study in South China

It remains unclear to what extent squall lines respond to aerosol particles serving as cloud condensation nuclei (CCN). To solve this problem, dozens of simulated cases formed by adding balanced perturbations to initial meteorological fields have been conducted to quantify the effect of the aerosol on hydrometeors, precipitation, and their related microphysical processes in this study. The results showed that the changes in total surface precipitation of squall lines to aerosol perturbations ranged from −14% to 4% in South China. Even if there was no significant change in total precipitation in some cases, the precipitation over partial area still showed obvious changes which were related to the degree of convective organization. Through cluster analysis, the sign of the precipitation response to increasing aerosol loading depended on whether the increase in graupel growth compensated for the loss of snow and rain growth under polluted conditions. With increased aerosol loading, more cloud droplets suppressed rain formation and subsequently rain growth due to lower collision-coalescence efficiency, and the snow growth was also suppressed by the decrease in the riming process. However, there was more graupel melting into rainwater due to an increase in graupel growth by accreting cloud droplets. Through composite analysis of meteorological fields, surface precipitation suppressed by aerosols was shown to be more likely to appear in poor water vapor conditions and weak convective intensities, and vice versa. Plain language summaryAnthropogenic aerosol, which is one of uncertain factors in global climate system and has increased with the development of industry, can serve as cloud condensation nuclei to influence the radiative budget of climate system by affecting cloud albedo, cloud microphysical structure, and precipitation. Quantified the impact of aerosol perturbation on precipitation is urgent for climate change impact study in China. Here, dozens of simulated squall lines with similar synoptic situations have been used to evaluate the sensitivity of squall line precipitation to aerosol particles, especially focused on microphysical processes. This will help to quantitatively investigate to what extent surface precipitation of squall line can be induced by aerosols and how it relates to meteorological elements.

CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies

Abstract. Current changes in the world's climate increasingly impact a wide variety of sectors globally, from agriculture and ecosystems to water and energy supply or human health. Many impacts of climate on these sectors happen at high spatio-temporal resolutions that are not covered by current global climate datasets. Here we present CHELSA-W5E5 (https://doi.org/10.48364/ISIMIP.836809.3, Karger et al., 2022): a climate forcing dataset at daily temporal resolution and 30 arcsec spatial resolution for air temperatures, precipitation rates, and downwelling shortwave solar radiation. This dataset is a spatially downscaled version of the 0.5∘ W5E5 dataset using the CHELSA V2 topographic downscaling algorithm. We show that the downscaling generally increases the accuracy of climate data by decreasing the bias and increasing the correlation with measurements from meteorological stations. Bias reductions are largest in topographically complex terrain. Limitations arise for minimum near-surface air temperatures in regions that are prone to cold-air pooling or at the upper extreme end of surface downwelling shortwave radiation. We further show that our topographically downscaled climate data compare well with the results of dynamical downscaling using the Weather Research and Forecasting (WRF) regional climate model, as time series from both sources are similarly well correlated to station observations. This is remarkable given the lower computational cost of the CHELSA V2 algorithm compared to WRF and similar models. Overall, we conclude that the downscaling can provide higher-resolution climate data with increased accuracy. Hence, the dataset will be of value for a wide range of climate change impact studies both at global level and for applications that cover more than one region and benefit from using a consistent dataset across these regions.

An evaluation framework for downscaling and bias correction in climate change impact studies

Climate change impact studies commonly use impact models (such as hydrological or crop models) forced with corrected climate input data from global climate models. A range of downscaling and bias correction methods have been developed to increase the spatial resolution and remove systematic biases in climate model outputs to be applied before use in impact models. Many studies have focused on evaluating such approaches for the climate variables they aim to correct. However, due to nonlinear error propagation there can be large remaining biases in model outputs, even when ingesting bias corrected climate forcings.Here we propose an impact-centric evaluation framework for downscaling and bias correction methods to be used in climate change risk assessments. This framework evaluates and compares the strengths and limitations of downscaling and bias correction in the impact domain, highlighting approaches that lead to reduced biases in impact variables of interest. We demonstrate the evaluation framework in the context of assessing downscaling and bias correction methods for hydrological projections in Australia. Our results show that although all downscaling and bias correction methods evaluated perform adequately for the input climate variables, their errors vary markedly when the impact is modelled. Our proposed evaluation framework involves selecting a number of key performance metrics, and ranking the four downscaling and bias correction methods to compute an overall ranking, highlighting the best-performing methods for each statistical metric and the overall best-performing approach. We present an application of this approach using performance metrics relevant to hydrological applications, relating to mean biases, variability, heavy precipitation and peak runoff days, and dry conditions.For impact studies related to hydrological applications, we find that multi-variate bias correction that considers cross-correlations, temporal auto-correlations and biases at multiple time scales (daily to annual) performs best in reducing biases in hydrological output variables for Australia. Our proposed evaluation approach can be applied to a wide range of climate change applications where downscaling and bias correction are required, including impacts on agricultural production, wildfires, energy generation, human health, ecosystem functioning, and water resource management.

Climate variability, trends, projections and their impact on different crops: A case study of Gujarat, India

Gujarat, being a coastal state, is likely to be impacted by global warming and climate change not only due to sea level rise and salinity ingress but also due to an increase in the frequency of cyclonic storms and other extreme weather events, causing uncertainty in crop production. An attempt has therefore been made to understand the climate of Gujarat in the past, present, and future based on the works done at the Department of Agricultural Meteorology, Anand. Analysis carried out on climatic trends and climatic extremes using past available data from different stations in Gujarat has been highlighted. Crop simulation models validated with experimental data collected for different crops across Gujarat state were used to understand the response of crops to climatic variability. The climate change impact studies and adaptation strategies carried out under the NPCC project have also been highlighted. And lastly, the work done by the author as an emeritus scientist on climate projections under RCP 4.5 and RCP 8.5 for all the districts of Gujarat and their likely impact on selected crops is presented. The results revealed that in the past, temperatures have shown increasing trends but not reached significant levels except at certain locations at night. Rainfall has also increased, but marginally. Future temperatures have been projected to increase in different parts of Gujarat under RCP4.5 and RCP8.5 with varying magnitudes. Similarly, the rainfall has also been projected to increase, while the sunshine hours are projected to decrease. The ultimate impact would be a drastic reduction in yields in spite of the increase in CO2 level, suggesting that the present-day crop varieties would not be able to sustain crop production levels under a changing climatic scenario.

Snow and avalanche climates in the French Alps using avalanche problem frequencies

AbstractAvalanches result from an interaction of weather and terrain, where past weather and internal snow cover processes play important roles. So far, climatology was mainly based on weather data, as regional snow instability information, such as avalanche activity, is scarce on climatological time scales. We present a new approach to create a snow avalanche climatology from simulations of avalanche problem types based on snow cover simulations of reanalysis data and a cluster analysis. Analyzing the winters between 1958 and 2020 in the French Alps, wet-snow situations dominated natural release. Dry-snow situations with non-persistent and persistent weak layers occurred each on at least one third of the days. Four typical patterns of avalanche problem types were identified. They follow the main orography with more new snow situations in the northern regions and more cases of persistent weak layers in inner-Alpine regions. In the front-ranges and in southern regions wet-snow situations occurred early in winter – typical for coastal snow climates. Agreement with the standard snow climate classification and the geography of the French Alps suggests that mountain regions with similar conditions can now be outlined. This method for snow avalanche climatology will inform avalanche forecasting and facilitate climate change impact studies.

Impact of climate change on shumbrite small scale irrigation project, South Gojjam subbasin, Ethiopia

Climate change has the potential to affect climate parameters like rainfall and temperature which lead to a change in the irrigation water requirement of the irrigation system. As irrigation water requirement is highly dependent on precipitation and potential evapotranspiration, climate change impact studies are necessary. Therefore, this study aims to assess the impact of climate change on the irrigation water requirement of the Shumbrite irrigation project. For this study, climate variables of precipitation and temperature were generated from CORDEX-Africa simulations downscaled from MPI Global Circulation Model (GCM) under three emission scenarios (RCP2.6, RCP4.5, and RCP8.5). The climate data covers from 1981 to 2005 for the baseline period and 2021–2045 for the future period for all scenarios. Future precipitation shows a decrease for all scenarios with a maximum decrease under RCP2.6 (4.2%) and temperature show an increase in the future as compared to the baseline period. The reference evapotranspiration and Irrigation Water Requirements (IWR) were calculated by using CROPWAT 8.0 software. Results showed that the mean annual reference evapotranspiration is expected to increase in the future by 2.7%, 2.6%, and 3.3% for RCP2.6, RCP4.5, and RCP8.5 respectively as compared to the baseline period. Mean annual irrigation water requirement shows an increase of 2.58%, 0.74%, and 8.4% for the future under RCP2.6, RCP4.5, and RCP8.5 respectively. The Crop Water Requirement (CWR) also increases for the future period under all RCP scenarios, with maximum CWR for tomato, potato, and pepper crops. To ensure the sustainability of the project, crops with high irrigation water requirements should be replaced by other crops with low water requirements.

Chelsa‐cmip6 1.0: a python package to create high resolution bioclimatic variables based on CHELSA ver. 2.1 and CMIP6 data

Data on projected climate conditions for the future is essential for many applications in climate change impact studies. Yet, with the release of the CMIP6, the increasing amount of data poses challenges for users to access and process the data. Here we present the chelsa_cmip6 package to create bioclimatic variables and climatological normals based on CHELSA ver. 2.1 and cloud based CMIP6 data for user defined geographical extents. The package offers simple access to CMIP6 data on cloud servers. It aggregates the CMIP6 data to climatological normals and downscales it from its native resolution to a 30 arcsec grid resolution using the delta change method for user defined future periods. Based on the climatological normals, it creates a set of bioclimatic variables that represent long term climatic means or variability in air temperatures and precipitation. The package offers users a simple way of creating climate change projections for user defined geographical extents and time periods.