Tag: Climate Change Downscaling Techniques

Abstract

Based on numerical weather prediction model Weather Research and Forecasting (WRF) and Hydrologic Modeling System (HEC-HMS), a coupling model is constructed in Taihang Piedmont basin. The WRF model parameter scheme combinations composed of microphysics, planetary boundary layers, and cumulus parameterizations suitable for the study area are optimized. In both time and space, we tested the effects of the WRF model by a multi-index evaluation system composed of relative error, root meantime square error, probability of detection, false alarm ratio, and critical success index and established this system in two stages. A multi-attribute decision-making model based on Technique for Order Preference by Similarity to an Ideal Solution and grey correlation degree is proposed to optimize each parameter scheme. Among 18 parameter scheme combinations, Mellor-Yamada-Janjic, Grell-Devinji, Purdue-Lin, Betts-Miller-Janjić, and Single-Moment6 are ideal choices according to the simulation performance in both time and space. Using the unidirectional coupling method, the rolling rainfall forecast results of the WRF model in the 24 h and 48 h forecast periods are input to HEC-HMS hydrological model to simulate three typical floods. The coupling simulation results are better than the traditional forecast method, and it prolongs the flood forecast period of the Taihang Piedmont basin.

Abstract

This study aimed to create Greenhouse Gas - Air Pollution Interactions and Synergies (GAINS)-Korea, an integrated model for evaluating climate and air quality policies in Korea, modeled after the international GAINS model. GAINS-Korea incorporates specific Korean data and enhances granularity for enabling local government-level analysis. The model includes source-receptor matrices used to simulate pollutant dispersion in Korea, generated through CAMx air quality modeling. GAINS-Korea's performance was evaluated by examining different scenarios for South Korea. The business as usual scenario projected emissions from 2010 to 2030, while the air quality scenario included policies to reduce air pollutants in line with air quality and greenhouse gas control plans. The maximum feasible reduction scenario incorporated more aggressive reduction technologies along with air quality measures. The developed model enabled the assessment of emission reduction effects by both greenhouse gas and air pollutant emission reduction policies across 17 local governments in Korea, including changes in PM_2.5 (particulate matter less than 2.5 μm) concentration and associated benefits, such as reduced premature deaths. The model also provides a range of visualization tools for comparative analysis among different scenarios, making it a valuable resource for policy planning and evaluation, and supporting decision-making processes.

Abstract

The Indo-Gangetic Plain (IGP), which is the site of India's Green Revolution, covers almost 15% of the country's landmass and is among the most extensively fertile lands across the world. The densely populated IGP region bears great importance for the socioeconomic facets of India and contributes to a major share of the GDP of the country. The present study demonstrates the regional-specific assessment of summer monsoon precipitation and associated extremes with dynamical and thermodynamical aspects over the IGP region using high-resolution regional climate models (RCMs) under the CORDEX-CORE framework. The analysis reveals that the eastern parts of the IGP receive low-to-moderate precipitation with a higher tail than the western parts, which is due to the direction of the monsoon low-level flow. The observed mean precipitation characteristics are well represented by the RCMs. Further, the research identifies extreme precipitation events over the IGP and conducts comprehensive analysis to understand their underlying mechanisms. It has been observed that extreme precipitation events are linked with the moisture transport associated with trough activity and instability, and RCMs are capable in representing the observed precipitation extremes and underlying mechanisms at localized scales. Overall, this study represents a significant step forward in understanding the evolution of spatio-temporal variability of precipitation over the IGP region, where agriculture is a major economic activity and millions of people depend on rainfed agriculture.

Abstract

The forecasting of future streamflow aids researchers and policymakers to understand how changes in climate affect hydrological systems. However, traditional computational approaches demand intensive data specifically for the basin, and it is costly. The shift towards more contemporary and data-driven approaches known as support vector regression (SVR) in hydrological modeling utilizing only the hydro-climate data from Coupled Model Intercomparison Project Phase 6 (CMIP6) provides rapid input–output data processing with accurate future projection. CMIP6 is an updated and improved Global Climate Models (GCMs) for the exploration of the specific impacts of changing streamflow patterns for improved water management in agricultural areas. The delta change factor method was used to generate climate sequences, fed into the SVR model to project streamflow from 2021 to 2080. The SVR model fitted reasonably well, demonstrated by several statistical indicators, including Kling-Gupta Efficiency (KGE), Nash–Sutcliffe Efficiency (NSE), Percent Bias (PBias), and Root Mean Squared Error (RMSE), with the training phase performance surpassing the testing phase. Future projections indicated increased rainfall during the dry season for most months, excluding April to June. The rise in precipitation was particularly pronounced during the wet season. Maximum and minimum temperature projections increased for all SSPs, with SSP5-8.5 predicted a substantial increase. The projection revealed that seasonal streamflow changes would range between  – 19.1% to – 1.2% and – 7.5% to – 3.1% in the dry and wet seasons, respectively. A considerable streamflow reduction is anticipated for all SSPs in April and May due to increased temperatures, with the most pronounced impact in the SSP5-8.5. Assessing the effects of climate variations on water resource availability is crucial for identifying effective adaptation strategies to address the anticipated rise in irrigation demands due to global warming. The projected streamflow changes due to potential climate impacts are significant for Bukit Merah Reservoir, aiding the formulation of appropriate operational strategies for irrigation releases.

Abstract

The forecasting of future streamflow aids researchers and policymakers to understand how changes in climate affect hydrological systems. However, traditional computational approaches demand intensive data specifically for the basin, and it is costly. The shift towards more contemporary and data-driven approaches known as support vector regression (SVR) in hydrological modeling utilizing only the hydro-climate data from Coupled Model Intercomparison Project Phase 6 (CMIP6) provides rapid input–output data processing with accurate future projection. CMIP6 is an updated and improved Global Climate Models (GCMs) for the exploration of the specific impacts of changing streamflow patterns for improved water management in agricultural areas. The delta change factor method was used to generate climate sequences, fed into the SVR model to project streamflow from 2021 to 2080. The SVR model fitted reasonably well, demonstrated by several statistical indicators, including Kling-Gupta Efficiency (KGE), Nash–Sutcliffe Efficiency (NSE), Percent Bias (PBias), and Root Mean Squared Error (RMSE), with the training phase performance surpassing the testing phase. Future projections indicated increased rainfall during the dry season for most months, excluding April to June. The rise in precipitation was particularly pronounced during the wet season. Maximum and minimum temperature projections increased for all SSPs, with SSP5-8.5 predicted a substantial increase. The projection revealed that seasonal streamflow changes would range between  – 19.1% to – 1.2% and – 7.5% to – 3.1% in the dry and wet seasons, respectively. A considerable streamflow reduction is anticipated for all SSPs in April and May due to increased temperatures, with the most pronounced impact in the SSP5-8.5. Assessing the effects of climate variations on water resource availability is crucial for identifying effective adaptation strategies to address the anticipated rise in irrigation demands due to global warming. The projected streamflow changes due to potential climate impacts are significant for Bukit Merah Reservoir, aiding the formulation of appropriate operational strategies for irrigation releases.

Abstract

It is crucial to investigate the characteristics of fire danger in the Beijing-Tianjin-Hebei (BTH) region to improve the accuracy of local fire danger monitoring, forecasting, and management. With the use of instrumental observation data from 173 national meteorological stations in the BTH region from 1991 to 2020, the fire weather index (FWI) is first calculated in this study, and its spatiotemporal characteristics are analyzed. The high- and low-fire danger periods based on the FWI occur in April and August, respectively, with significant decreasing and increasing trends throughout the BTH region over the past 30 years. Next, the contributions of different meteorological factors to the FWI are quantified via a detrending technique. Most regions are affected by precipitation during the high-fire danger period. Both the maximum surface air temperature (Tmax) and precipitation, however, notably contribute to the FWI trend changes during the low-fire danger period. Then, we assess the linkage with atmospheric circulation. Abundant water vapor from the Northwest Pacific and local upward motion jointly lead to increased precipitation and, as a consequence, a decreased FWI during the high-fire danger period. A lack of water vapor from the boreal zone and local downward movement could cause adiabatic subsidence and hence, amplify the temperature and FWI during the low-fire danger period. In contrast to shared socioeconomic pathway (SSP) 585, in which the FWI in the BTH region exhibits a north–south dipole during the low-fire danger period, SSP245 yields an east–west dipole during the low-fire danger period. This study reveals that there is a higher-than-expected probability of fire danger during the low-fire danger period. Therefore, it is essential to intensify research on the fire danger during the low-fire danger period to improve our ability to predict summer fire danger.

Abstract

It is crucial to investigate the characteristics of fire danger in the Beijing-Tianjin-Hebei (BTH) region to improve the accuracy of local fire danger monitoring, forecasting, and management. With the use of instrumental observation data from 173 national meteorological stations in the BTH region from 1991 to 2020, the fire weather index (FWI) is first calculated in this study, and its spatiotemporal characteristics are analyzed. The high- and low-fire danger periods based on the FWI occur in April and August, respectively, with significant decreasing and increasing trends throughout the BTH region over the past 30 years. Next, the contributions of different meteorological factors to the FWI are quantified via a detrending technique. Most regions are affected by precipitation during the high-fire danger period. Both the maximum surface air temperature (Tmax) and precipitation, however, notably contribute to the FWI trend changes during the low-fire danger period. Then, we assess the linkage with atmospheric circulation. Abundant water vapor from the Northwest Pacific and local upward motion jointly lead to increased precipitation and, as a consequence, a decreased FWI during the high-fire danger period. A lack of water vapor from the boreal zone and local downward movement could cause adiabatic subsidence and hence, amplify the temperature and FWI during the low-fire danger period. In contrast to shared socioeconomic pathway (SSP) 585, in which the FWI in the BTH region exhibits a north–south dipole during the low-fire danger period, SSP245 yields an east–west dipole during the low-fire danger period. This study reveals that there is a higher-than-expected probability of fire danger during the low-fire danger period. Therefore, it is essential to intensify research on the fire danger during the low-fire danger period to improve our ability to predict summer fire danger.

Abstract

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.

Abstract

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.

Abstract

The fast human climate change we are witnessing in the early twenty-first century is inextricably linked to the health and function of the biosphere. Climate change is affecting ecosystems through changes in mean conditions and variability, as well as other related changes such as increased ocean acidification and atmospheric CO₂ concentrations. It also interacts with other ecological stresses like as degradation, defaunation, and fragmentation.Ecology and climate monitoring are critical to understanding the complicated interactions between ecosystems and changing climate trends. This review paper dives into the issues of ecological and climate monitoring, emphasizing the complications caused by technical limits, data integration, scale differences, and the critical requirement for accurate and timely information. Understanding the ecological dynamics of these climatic impacts, identifying hotspots of susceptibility and resistance, and identifying management measures that may aid biosphere resilience to climate change are all necessary. At the same time, ecosystems can help with climate change mitigation and adaptation. The processes, possibilities, and constraints of such nature-based climate change solutions must be investigated and assessed. Addressing these issues is critical for developing successful policies and strategies for mitigating the effects of climate change and promoting sustainable ecosystem management. Human actions inscribe their stamp in the big narrative of our planet’s story, affecting the very substance of the global atmosphere. This transformation goes beyond chemistry, casting a spell on the physical characteristics that choreograph Earth’s brilliant dance. These qualities, like heavenly notes, create a song that echoes deep into the biosphere. We go on a journey via recorded tales of ecological transformation as they respond to the ever-shifting environment in this text. We peek into the rich fabric of change, drawing insight from interconnected observatories. Nonetheless, this growing symphony is set to unleash additional transformational stories - narratives of natural riches and rhythms that are both economically and environmentally essential. Understanding these stories is essential for navigating this developing epic. A roadmap for sustainable development necessitates the ability to comprehend these stories, a problem that resonates across the breadth of monitoring programs, particularly in the infancy of integrated sites.