Tag: Climate Change Downscaling Techniques

Chasing parts in quadrillion: applications of dynamical downscaling in atmospheric pollutant transport modelling during field campaigns

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Abstract

Atmospheric transport and dispersion models (ATDMs) are widely used to study and forecast pollution events. In the frame of the “Effect of Megacities on the transport and transformation of pollutants on the regional to global scales” (EMeRGe) project, ATDM forecasts were carried out to identify potential airborne sampling areas of perfluorocarbons (PFCs) emanating from controlled PFC releases. The forecasts involved short-distance transport over small-scale topographic maxima (Manilla; Philippines), short-distance transport over large-scale topographic maxima (Taipei, Taiwan) and long-distance transport over mixed topography (Nanjing, China, sampled over Taiwan). In situ aircraft measurements of PFC mixing ratios down to a few parts per quadrillion (ppqv) provide us with a unique dataset to explore the added benefits of dynamical downscaling. Transport simulations were repeated using FLEXPART driven by ERA5 and IFS meteorological data and FLEXPART-WRF with dynamically downscaled IFS data down to 1.1 km and four PBL parametrisations. Of the three cases studied, dynamical downscaling led to significant differences for the Manilla and Taipei releases that can be interpreted through changes in the modelled orographic flow regimes. The choice of PBL scheme also significantly impacted accuracy, but there was no systematically better-performing option, highlighting the benefits of ensemble forecasting. Results show how convergence and divergence between ensemble members can be utilised to help decision-making during field campaigns. This study highlights the role that dynamical downscaling can play as an important component in campaign planning when dealing with observations over orographically complex areas.

Temporal and spatial evolution of net primary productivity in the Three-River Headwaters Region under phenological changes and anthropogenic influence

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Understanding the spatiotemporal changes in net primary productivity (NPP) and the driving factors behind these changes in climate-vulnerable regions is crucial for ecological conservation. This study simulates the actual NPP (NPPA) and climate potential NPP (NPPC) in the Three-River Headwaters Region from 2000 to 2020. The Theil–Sen Median method and Mann–Kendall mutation analyses are employed to explore their spatiotemporal variation patterns, while geographic weighted regression and machine learning are used to investigate the influence of anthropogenic activities and climatic factors on NPPA, the results indicate that the average NPPA across the entire region over multiple years is 382.506 \(gC{m}^{-2}{yr}^{-1}\) , which is 0.132 times the average annual NPPC over the past 21 years, showing an overall distribution pattern of low in the northwest and high in the southeast. The annual increase in NPPA from 2000 to 2020 is approximately 1.034 \(gC{m}^{-2}{yr}^{-1}\) . The source region of the Yangtze River shows the largest improvement in vegetation, with 74.1% of the area showing improvement. Between 2002 and 2003, the annual NPPA in the Three-River Headwaters Region experienced a sudden change, lagging behind the NPPC change by 1 year, and after 2005, the upward trend in NPPA became more pronounced. The impact of anthropogenic activities on NPPA shifted from positive to negative to positive from 2000 to 2020, with significant impact areas mainly concentrated in the northeast and a few areas in the central and southern parts. The proportion of areas with extremely significant impact increased from 1.9% in 2000 to 3.7% in 2020. Over the past 21 years, the main factors influencing NPPA changes in the Three-River Headwaters Region have been soil moisture and precipitation, with the influence of different climate factors on NPP changing over time. Additionally, NPP is more sensitive to changes in altitude in low-altitude areas. This study can provide more accurate theoretical support for ecological environment assessment and subsequent protection efforts in the Three-River Headwaters Region.

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

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Abstract

Climate change has ability to intensify the magnitude of flood and drought episodes, as well as their amplitude; also it has the potential to exacerbate hydrological extremes. It is crucial to forecast changes to hydrological regimes and determine the level of uncertainty around them to increase resilience and prepare for future changes. In order to enlighten long-term estimates, an attempt has been made to sustain the available water resources through Calibration and Validation of river discharge data using SWAT model for Upper Yamuna River Basin. Spatial climatic data were further crystallized to forecast climatic projection scenarios for Base line period, Mid-Century and End Century considering RCPs 2.6, 4.5 and 8.5. Result reveals that the average annual minimum temperature is estimated to be increased 1.4 °C in Mid-Century and 2.2 °C in End Century from the Base line Scenario while the average annual maximum temperature is found to be increased 1.5 °C in Mid-Century and 2.1 °C in End Century from the Base line Scenario. Further, while analyzing the hydrological components, Soil water percentage is expected to be increased in Mid-Century, whereas Percolation rate is found to be increased for all scenarios other than BL-MC (4.5) which is an indication of rise in Ground water. In addition to it, Surface flow is observed as a considerable increase from 4.33 to 72.69% in all scenarios. Also the Surface flow is more in case of End Century as compared to the Mid-Century. The estimated Ground water flow is found to be increased except BL-MC (4.5 & 8.5). Overall water yield has been estimated as a relative change from 7.06 to 18.70% based upon the specified conditions. The prediction for Evapotranspiration values is found as decreased in all scenarios except BL-MC (4.5 & 8.5). The outcome of the present study is very useful for planning of development strategies in the project area.

Combining genetic and environmental data to map and model regions of provenance for silver fir (Abies alba Mill.) in Italy

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Abstract

Regions of provenance for forest reproductive materials are the basis for wise use of forest resources in a changing climate. In this work a modelling framework is proposed for silver fir (Abies alba Mill.) in Italy where genetic clusters described by nuclear microsatellites were combined with high-resolution climatic data. When the genetic clusters were too large or had an uncertain ecological niche expression, an additional subregion division-was evaluated according to a climatic assessment. Subsequently each genecological group (Region of Provenance, RoP) was projected in geographic space separately using species distribution modelling (SDM) procedure under current (1991–2020) and a future climate scenario derived from the 6th assessment report for the period 2041–2070. The final division into nine RoPs was able to explain 77.41% of the total climatic variance, a good trade-off between statistical significance and practical usability. The modelling steps then showed a large degree of ecological overlap between RoPs with some of them occurring in similar ecological environments but characterized by a different genetic structure. When projected at the continental scale, the Italian RoPs were found to be suitable for almost all the current European range of silver fir, with potential expansion in Nordic countries in the future, beyond the current distribution range. The study showed that the combination of genetic and ecological data can be a robust way to proceed in areas where a strong genetic differentiation between populations occurs, such as in Italy. New markers such as SNPs can then be used to detect adaptive traits and drive the selection of provenances for common garden experiments in areas where the SDM modelscurrently extrapolate potential sites outside the current natural range.

Performance ranking of global precipitation estimates over data scarce Western Himalayan Region of India

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Abstract

With the advent of numerous global precipitation estimates (GPEs) in the recent decades, dependability of hydrologists has lessened on the station data as the GPEs can be readily availed and utilized. Since the skills of GPEs may differ from region-to-region, it is vital to analyse their ability in resolving the regional precipitation climatology using appropriate statistical methods. In this study, a total of five GPEs, viz., APHRODITE, PERSIANN-CDR, CHIRPS, CMORPH, and IMERG were evaluated for their abilities in resolving regional precipitation climatology of WHR with respect to gridded precipitation product of India Meteorological Department (IMD). Different performance indicators i.e., Probability of Detection (POD), False Alarm Ratio (FAR), Normalised Root Mean Square Deviation (NRMSD), Pearson Correlation Coefficient (CC) and Skill Score (SS) were used for evaluating the GPEs. Multicriterion Decision Making (MCDM)approaches i.e., Compromise Programming (CP), Cooperative Game Theory (CGT), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Weighted Average Technique (WAT), and Fuzzy TOPSIS were used for ranking the GPEs across different grids in WHR. Entropy based weight assignment to NRMSD, CC, and SS were performed while applying them in MCDM methods. Group Decision Making (GDM) approach utilizing spearman correlation coefficient and additive ranking rule was employed to obtain the final ranking of GPEs from multiple rankings assigned through different MCDM methods. Across 115 grids, APHRODITE exhibits superior performance compared to other GPEs in 89 grids. Conversely, CHIRPS and CMORPH emerge as the least favorable products among the five GPEs across more than 70 grids, being consistently ranked either 4th or 5th. Notably, IMERG was identified as the best-performing product in 14 grids and as the second-best product in 63 grids, positioning it as the second most suitable option after APHRODITE for monthly rainfall time series analysis. Similar results, as detailed in the paper, were also obtained for month-wise rainfall time series analysis.

Comparative analysis of bias correction techniques for future climate assessment using CMIP6 hydrological variables for the Indian subcontinent

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The study focuses on the bias correction of Coupled Model Intercomparison Project Phase 6 (CMIP6) hydrologic variables for the Indian region. The performance of two widely accepted bias correction methodologies, namely Quantile Mapping (QM) and Bias Correction Spatial Disaggregation (BCSD), is compared. The study undertakes to evaluate the application of these popular bias correction methodologies on four important hydrologic variables viz. precipitation, temperature, and surface wind. The QM methodology is employed and compared with BCSD based bias corrected variables obtained from NEX-GDDP-CMIP6 dataset. The selected GCM historical bias corrected climate variables using QM are compared with the NCEP reanalysis variables. The objective is to improve the reliability and accuracy of climate projections by minimizing biases present in the GCM outputs. Through a comprehensive comparative analysis, it is determined that QM exhibits superior performance in reducing biases when compared to BCSD. Thus, use of QM demonstrates higher efficacy by effectively capturing the statistical distribution characteristics of observed data and transferring them to the GCM outputs. The future climate change over the Indian region is observed for both QM and BCSD algorithms for SSP5-8.5, SSP2-4.5, and SSP1-2.6. The result emphasizes the importance of selecting an appropriate bias correction methodology to enhance the reliability of climate projections in the Indian region. Ultimately, the findings of this study contribute to the broader field of climate modeling and impact assessment, providing valuable insights into the selection and application of bias correction techniques for CMIP6 datasets in the Indian subcontinent region.

Graphical abstract

Downscaling the MODIS land surface temperature using a trapezial concept applied to the MODIS and sentinel 2 images

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Abstract  

Downscaling methods are crucial for accessing high-resolution thermal data simultaneously. The DisTRAD model is commonly used for downscaling thermal images, but changes in soil moisture, such as those caused by irrigation operations, can lead to errors in the process. This study investigated the potential use of TOTRAM and OPTRAM models to reduce errors in LST downscaling in irrigated fields. Sentinel satellite imagery was utilised to enhance the resolution of MODIS Land Surface Temperature (LST) from 1000 to 20 m in the fields of Megsal and Hezarjolfa agro-industrial company in Qazvin province. Soil moisture was estimated using the OPTRAM model, and the results were compared with observational data. The findings indicated that on days with NDVI greater than 0.6, the R2 value exceeded 0.88 and the RMSE value was less than 0.06 cm3/cm3. Then, MODIS LST images were downscaled to 20 m using codes in Google Earth Engine (GEE). Evaluation was conducted using observational data from collected land surface temperature data for 36 points. Comparison of the downscaled LST data with observational data on days with irrigation revealed a decrease in MAE and RMSE error indices by approximately 0.4 and 1.2 degrees Celsius, respectively, in the OPTRAM-TPTRAM model compared to the DisTRAD model. Consequently, the OPTRAM-TOTRAM model generally outperforms the DisTRAD model in LST downscaling. Lastly, it is recommended to assess the TOTARM and OPTRAM models for downscaling MODIS sensor LST in other irrigated fields.

A CMIP6-ensemble-based evaluation of precipitation and temperature projections

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Abstract

Understanding climate change’s effects on dam basins is very important for water resource management because of their important role in providing essential functions such as water storage, irrigation, and energy production. This study aims to investigate the impact of climate change on temperature and precipitation variables in the Altınkaya Dam Basin, which holds significant potential for hydroelectric power generation in Türkiye. These potential impacts were investigated by using ERA5 reanalysis data, six GCMs from the current CMIP6 archive, and two Shared Socioeconomic Pathways (SSP2 − 4.5 and SSP5 − 8.5) scenario data. Four Multi-Model Ensemble (MME) models were developed by using an Artificial Neural Network (ANN) approach (ENS1), simple averaging (ENS2), weighted correlation coefficients (ENS3), and the MARS algorithm (ENS4), and the results were compared to each other. Moreover, quantile delta mapping (QDM) bias correction was used. The 35-year period (1980–2014) was chosen as the reference period, and further evaluations were conducted by dividing it into three future periods (near (2025–2054), mid-far (2055–2084), and far (2085–2100)). Considering the results achieved from the MMEs, variations are expected in the monthly, seasonal, and annual assessments. Projections until the year 2100 indicate that under optimistic and pessimistic scenarios, temperature increases could reach up to 3.11 °C and 5.64 °C, respectively, while precipitation could decrease by as much as 19% and 43%, respectively. These results suggest that the potential changes in temperature and precipitation within the dam basin could significantly impact critical elements such as future water flow and energy production.

Assessing potential impacts of climate change on China’s ski season length: a data-constrained approach

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Faced with the challenges presented by climate change, the necessity to navigate the sustainable development of China’s skiing industry emerges as a pivotal and pressing concern, especially considering the region’s vulnerability to climate variations and its burgeoning status as an emerging skiing destination. This study develops a methodology to assess the impact of climate change on ski resorts that is especially applicable in situations with limited climate station data and can be employed by ski industry stakeholders. A multiple linear regression (MLR) based on climate parameters from 1981 to 2010 is coupled with climate change projections under RCP4.5 and RCP8.5 scenarios for the 2020s, 2050s, and 2080s. To validate the precision of the MLR model assessment, the study compares the results with those of the SkiSim 2.0 model — a model widely applied in various countries and regions for evaluating the impact of climate change on the ski industry. Results from the MLR model reveal that there are comparatively modest decreases in skiing days in the northeast and northwest regions, contrasting with significant declines in the eastern, central, and southwestern areas. The findings of the MLR model are largely consistent with SkiSim 2.0, thereby broadly validating this approach. A series of implications and recommendations for further studies and industry applications are provided.

Mapping risk of heat stress for dairy cattle in Tigray Regional State, Northern Ethiopia

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This study aimed to assess the risk of heat stress conditions for dairy cattle in the Tigray regional state of Ethiopia under historical and future climatic conditions. The daily thermal heat index (THI) was computed for each of the 14 weather stations after quality control of the maximum and minimum temperature datasets. The calculations were performed for the historical period (1980–2023) and two future climate periods (mid-term: 2040–2069 and end-term: 2070–2099) using an ensemble of 20 global circulation models under two representative concentration pathways (RCP 4.5 and 8.5). During the historical period, the frequency of severe heat stress was 3.4% (13 days/year), predominantly occurring in the western corner of the region (39.5% of days/year). The frequency of projected severe heat stress days across the region is expected to increase to 5.4% (mid-term) and 6% (end-term) under the RCP 4.5 emission scenario. Under the RCP 8.5 scenario, the frequency is expected to rise to 6.2% (mid-term) and 9.4% (end-term). On average, there were 6–9 consecutive severe heat stress days in both the historical and future climate periods. It is crucial to emphasize that the mapping of heat stress risk in dairy cattle was carried out using THI thresholds developed elsewhere. However, it is imperative to underscore the significance of conducting local experiments to determine context-specific thresholds.