Tag: Climate Change Downscaling Techniques

Abstract

High-quality satellite precipitation products (SPPs) are needed for hydrological simulations and water resource management, especially in remote regions where rain gauges are scarce, therefore the comprehensive evaluation of SPPs is critical. In this study, an improved rank score (RS) method was used to comprehensively and quantitatively evaluate the performance of 14 SPPs at the site and basin scales on the Chinese mainland based on gauge-observed precipitation data from 2000 to 2018 in terms of continuous statistics, detection of precipitation events, and extreme metrics. The results demonstrate the following: (1) The performance of the SPPs varied significantly when evaluated using different metrics and at spatial and temporal scales. However, the Climate Prediction Center morphing technique gauge blended product (CMORPH-BLD), Multi-Source Weighted-Ensemble Precipitation (MSWEP), and Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG)-final run generally showed the best performance. (2) MSWEP performed best in the Yellow River basin (YeRB), the Tibetan Plateau's endorheic basins (TPEBs), and the Northwest endorheic basins (NWEBs), whereas CMORPH-BLD showed the best performance in the other basins of the comprehensive quantitative assessment. (3) The spatial and temporal performances of SPPs varied considerably, and the performance of SPPs was generally better in the summer (rainy season) and in the southeastern coastal basins than in the winter (dry season) and in the northwestern basins. Overall, this study provides a reference for selecting appropriate SPPs for precipitation analysis over mainland China.

Abstract

High-quality satellite precipitation products (SPPs) are needed for hydrological simulations and water resource management, especially in remote regions where rain gauges are scarce, therefore the comprehensive evaluation of SPPs is critical. In this study, an improved rank score (RS) method was used to comprehensively and quantitatively evaluate the performance of 14 SPPs at the site and basin scales on the Chinese mainland based on gauge-observed precipitation data from 2000 to 2018 in terms of continuous statistics, detection of precipitation events, and extreme metrics. The results demonstrate the following: (1) The performance of the SPPs varied significantly when evaluated using different metrics and at spatial and temporal scales. However, the Climate Prediction Center morphing technique gauge blended product (CMORPH-BLD), Multi-Source Weighted-Ensemble Precipitation (MSWEP), and Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG)-final run generally showed the best performance. (2) MSWEP performed best in the Yellow River basin (YeRB), the Tibetan Plateau's endorheic basins (TPEBs), and the Northwest endorheic basins (NWEBs), whereas CMORPH-BLD showed the best performance in the other basins of the comprehensive quantitative assessment. (3) The spatial and temporal performances of SPPs varied considerably, and the performance of SPPs was generally better in the summer (rainy season) and in the southeastern coastal basins than in the winter (dry season) and in the northwestern basins. Overall, this study provides a reference for selecting appropriate SPPs for precipitation analysis over mainland China.

Abstract

Food production, particularly cattle husbandry, contributes significantly to air pollution and its associated health hazards. However, making changes in dietary habits, such as reducing red meat consumption and minimizing food waste, can lead to substantial improvements in both air quality and human health. In this study, we explored the impact of dietary changes on future air quality and human wellbeing. We also assessed the influence of dietary transformation policies in the context of climate change mitigation, with the objective of understanding how policies can effectively complement each other. We used a chemical transport model and an integrated assessment model to determine changes in fine particulate matter (PM_2.5) and ozone (O₃) concentrations. Then, an exposure model was applied to estimate premature deaths as a consequence of air pollution. Our results showed that dietary changes could play a crucial role in mitigating air pollution, particularly in regions where agricultural activities emit significant quantities of ammonia. In the European Union, for example, dietary changes could lead to a reduction of 5.34% in PM_2.5 by 2050. Similarly, in Asia, the models projected a reduction of 6.23% in PM_2.5 by 2100. Ground surface O₃ levels in Southeast Asia were projected to drop by as much as 12.93% by 2100. Our results further showed that dietary changes could lead to significant reductions in global mortality associated with PM_2.5 and O₃, with 187,500 and 131,110 avoided deaths per year expected by 2100. A combined approach that integrates dietary changes with climate change mitigation measures could lead to more comprehensive air quality improvements in specific regions. However, careful consideration is needed to address any potential adverse effects on O₃ concentrations in some areas.

Abstract

Flooding represents the greatest natural threat to the UK, presenting severe risk to populations along coastlines and floodplains through extreme tidal surge and hydrometeorological events. Climate change is projected to significantly elevate flood risk through increased severity and frequency of occurrences, which will be exacerbated by external drivers of risk such as property development and population growth throughout floodplains. This investigation explores the entire flood hazard modelling chain, utilising the nonparametric bias correction of UKCP18 regional climate projections, the distributed HBV-TYN hydrological model and HEC-RAS hydraulic model to assess future manifestation of flood hazard within the Broadland Catchment, UK. When assessing the independent impact of extreme river discharge and storm surge events as well as the impact of a compound event of the two along a high emission scenario, exponential increases in hazard extent over time were observed. The flood extent increases from 197 km² in 1990 to 200 km² in 2030, and 208 km² in 2070. In parallel, exponential population exposure increases were found from 13,917 (1990) to 14,088 (2030) to 18,785 (2070). This methodology could see integration into policy-based flood risk management by use of the developed hazard modelling tool for future planning and suitability of existing infrastructure at a catchment scale.

Abstract

This review summarized the past and current studies on forest nutrient export and existing watershed water quality models that are capable of predicting nutrient loadings from forest-dominated watersheds. Emphasis was given to the watershed models used under cold climate conditions and their capacities and limitations in assessing the impacts of forest best management practices (BMPs) and climate change scenarios on nutrient loadings at a watershed scale. The nutrient export rates in forest-dominated watersheds were found to vary significantly controlled by local climate and landscape conditions. Some watershed water quality models can estimate nutrient loadings from forests either with a simplified forest growth function or without a forest growth component. No existing watershed water quality models have explicit representation forest BMP functions. Combining or coupling with a forest growth model is required for a realistic simulation of nutrient dynamics and assessing the impact of forest BMPs in a forest-dominated watershed. The review also considered the suitability of models for exploring the potential effects of climate change on hydrologic and nutrient processes relevant to forest management. Discussions on the challenges and limitations of forested watershed water quality models and recommendations for future development were made following the review. The findings of this study can provide valuable references for water quality modeling studies in forest-dominated watersheds under cold climate conditions.

Abstract

Drought projection is used to evaluate the risk of drought in future. Drought projection has been performed using the bias corrected General Circulation Models. However, it is necessary to have a reliable future projection, but the future is uncertain, and the bias correction method can affect the projection. This study evaluated the past and future drought projection over Pakistan and showed spatial distribution of frequency, duration, and severity over a region by using parametric and non-parametric transformation methods for bias correction. Drought was quantified using Standardized Precipitation Evapotranspiration Index (SPEI). Reliability-Resilience-Vulnerability (RRV) approach was used to analyze the drought for the historical (1981–2014), near future (2026–2059, NF) and far future (2066–2099, FF) periods. RRV value represents which region has the most sustainable water resources system. In NF of SSP2-4.5, the severity increases abruptly but duration of drought decreases while both severity and duration decrease in FF. Drought severity for SSP2-4.5 is higher than SSP5-8.5 in NF while the opposite was revealed in FF. The drought frequency didn’t undergo much of changes except for Balochistan which has a frequent drought in NF and FF for both SSP scenarios. NF of SSP2-4.5 showed reduction in RRV values implementing the increase in water availability while the opposite was revealed in FF. RRV values for SSP5-8.5 indicate that droughts become more severe in FF. The parametric transformation method showed more severe droughts for future than the non-parametric transformation. The finding of this study could help water resources managers and farmers to plan and adapt to changes in climate.

Abstract

Drought projection is used to evaluate the risk of drought in future. Drought projection has been performed using the bias corrected General Circulation Models. However, it is necessary to have a reliable future projection, but the future is uncertain, and the bias correction method can affect the projection. This study evaluated the past and future drought projection over Pakistan and showed spatial distribution of frequency, duration, and severity over a region by using parametric and non-parametric transformation methods for bias correction. Drought was quantified using Standardized Precipitation Evapotranspiration Index (SPEI). Reliability-Resilience-Vulnerability (RRV) approach was used to analyze the drought for the historical (1981–2014), near future (2026–2059, NF) and far future (2066–2099, FF) periods. RRV value represents which region has the most sustainable water resources system. In NF of SSP2-4.5, the severity increases abruptly but duration of drought decreases while both severity and duration decrease in FF. Drought severity for SSP2-4.5 is higher than SSP5-8.5 in NF while the opposite was revealed in FF. The drought frequency didn’t undergo much of changes except for Balochistan which has a frequent drought in NF and FF for both SSP scenarios. NF of SSP2-4.5 showed reduction in RRV values implementing the increase in water availability while the opposite was revealed in FF. RRV values for SSP5-8.5 indicate that droughts become more severe in FF. The parametric transformation method showed more severe droughts for future than the non-parametric transformation. The finding of this study could help water resources managers and farmers to plan and adapt to changes in climate.

Abstract

Urban areas are becoming more susceptible to severe storms, flash floods, and drainage system failures due to climate change, population growth, and urbanization. Flood modeling is a useful method for managing storm water drainage networks, predicting behavior, and evaluating effective solutions to structural and operational problems. This research describes the application of the Stormwater Management Model (SWMM) to evaluate the performance and effectiveness of the rainwater network, identify flood-prone locations, and determine the extent of floods in the center of Kerbala Governorate, Iraq. Saif Saad neighborhood was chosen as a case study. The model's validity was confirmed using the occurrence of actual rainfall by the coefficient of determination (R² = 0.8952), normalized mean square error (NMSE = 0.0964), and Nash–Sutcliffe efficiency (NSE = 0.7152), and the model's performance was reasonably good. Simulation results indicated that the system works well under near-term rainfall events, except for some sites that require maintenance and the diversion of surplus water to nearby green spaces. Over time, in periods of medium and far future until the year 2100, the system showed an increase in manhole floods, exceeding 0.1 m³/s. The percentage of flooding in manholes was more than 13% in the worst case, and continued floods for longer periods could potentially negatively affect the current drainage infrastructure. The study provides technical support for decision-makers to address these issues. By providing a comprehensive view of flood-prone areas and sites, as well as the flood percentage for each under different climate change scenarios, with the help of the Geographic Information System (GIS) software to represent future rain events. It suggests increasing the depth of manholes most vulnerable (especially R18, R98, and R101A manholes) to flooding and correcting slopes to achieve sustainability and a good service rate for the storm drainage system.

Abstract

Geospatial datasets are very much useful in carrying out hydrological modeling in distributed and semi-distributed models. Through modeling approach, this study is performed to study the impact of climate change on reservoir inflows in the upper Krishna sub-basin, India using the Coupled Model Intercomparison Project (CMIP5) climate dataset for the hydrological year (2021–2055) considering the base period as 1985–2020. Bias corrected Centre National de Recherches Meterolgiques (CNRM5) with Representative Concentration Pathway (RCP4.5) climate data are used in this study. Variable Infiltration Capacity (VIC) hydrological model is used for the study because of its ability to simulate hydrological processes by incorporating storage structures. The water storage structures in terms of Major reservoirs are incorporated using the VIC-Reservoir Representation module. The cascade effect of reservoirs is captured by considering the upstream reservoir's operational strategies and hydraulic particulars. The model performance in terms of R², Nash Sutcliffe Efficiency (NSE) and Kling Gupta Efficiency (KGE) during the calibration period is found to be 0.91, 0.74, 0.74 and 0.95, 0.84, 0.86 during the validation period respectively in the baseline period. The future study period is divided into near (2021–2032), mid (2033–2044) and far (2045–2055) decade. The projected runoff coefficient varies 0.15–0.54 and the Evapotranspiration coefficient lies 0.32–0.72. It is found that the reservoir Almatti and Narayanpur receives a maximum inflow of 1926 m³/s and 2057 m³/s against the Long Term Average of 1577 m³/s and 2319 m³/s in the mid and far decadal periods respectively.

Abstract

Ever since the first image of a coral reef was captured in 1885, people worldwide have been accumulating images of coral reefscapes that document the historic conditions of reefs. However, these innumerable reefscape images suffer from perspective distortion, which reduces the apparent size of distant taxa, rendering the images unusable for quantitative analysis of reef conditions. Here we solve this century-long distortion problem by developing a novel computer-vision algorithm, ReScape, which removes the perspective distortion from reefscape images by transforming them into top-down views, making them usable for quantitative analysis of reef conditions. In doing so, we demonstrate the first-ever ecological application and extension of inverse-perspective mapping—a foundational technique used in the autonomous-driving industry. The ReScape algorithm is composed of seven functions that (1) calibrate the camera lens, (2) remove the inherent lens-induced image distortions, (3) detect the scene’s horizon line, (4) remove the camera-roll angle, (5) detect the transformable reef area, (6) detect the scene’s perspective geometry, and (7) apply brute-force inverse-perspective mapping. The performance of the ReScape algorithm was evaluated by transforming the perspective of 125 reefscape images. Eighty-five percent of the images had no processing errors and of those, 95% were successfully transformed into top-down views. ReScape was validated by demonstrating that same-length transects, placed increasingly further from the camera, became the same length after transformation. The mission of the ReScape algorithm is to (i) unlock historical information about coral-reef conditions from previously unquantified periods and localities, (ii) enable citizen scientists and recreational photographers to contribute reefscape images to the scientific process, and (iii) provide a new survey technique that can rigorously assess relatively large areas of coral reefs, and other marine and even terrestrial ecosystems, worldwide. To facilitate this mission, we compiled the ReScape algorithm into a free, user-friendly App that does not require any coding experience. Equipped with the ReScape App, scientists can improve the management and prediction of the future of coral reefs by uncovering historical information from reefscape-image archives and by using reefscape images as a new, rapid survey method, opening a new era of coral-reef monitoring.