Tag: Climate Change Downscaling Techniques

Abstract

Older adults are generally amongst the most vulnerable to heat and cold. While temperature-related health impacts are projected to increase with global warming, the influence of population aging on these trends remains unclear. Here we show that at 1.5 °C, 2 °C, and 3 °C of global warming, heat-related mortality in 800 locations across 50 countries/areas will increase by 0.5%, 1.0%, and 2.5%, respectively; among which 1 in 5 to 1 in 4 heat-related deaths can be attributed to population aging. Despite a projected decrease in cold-related mortality due to progressive warming alone, population aging will mostly counteract this trend, leading to a net increase in cold-related mortality by 0.1%–0.4% at 1.5–3 °C global warming. Our findings indicate that population aging constitutes a crucial driver for future heat- and cold-related deaths, with increasing mortality burden for both heat and cold due to the aging population.

Abstract

This research assesses the efficacy of thirteen bias correction methods, including traditional and machine learning-based approaches, in downscaling four chosen GCMs of Coupled Model Intercomparison Project 6 (CMIP6) in Nigeria. The 0.5° resolution gridded rainfall, maximum temperature (Tmx), and minimum temperature (Tmn) of the Climate Research Unit (CRU) for the period 1975 − 2014 was used as the reference. The Compromise Programming Index (CPI) was used to assess the performance of bias correction methods based on three statistical metrics. The optimal bias-correction technique was employed to rectify bias to project the spatiotemporal variations in rainfall, Tmx, and Tmn over Nigeria for two distinct future timeframes: the near future (2021–2059) and the distant future (2060–2099). The study's findings indicate that the Random Forest (RF) machine learning technique better corrects the bias of all three climate variables for the chosen GCMs. The CPI of RF for rainfall, Tmx, and Tmn were 0.62, 0.0, and 0.0, followed by the Power Transformation approach with CPI of 0.74, 0.36, and 0.29, respectively. The geographic distribution of rainfall and temperatures significantly improved compared to the original GCMs using RF. The mean bias-corrected projections from the multimodel ensemble of the GCMs indicated a rainfall increase in the near future, particularly in the north by 2.7–12.7%, while a reduction in the south in the far future by -3.3% to -10% for different SSPs. The temperature projections indicated a rise in the Tmx and Tm from 0.71 °C and 0.63 °C for SSP126 to 2.71 °C and 3.13 °C for SSP585. This work highlights the significance of comparing bias correction approaches to determine the most suitable approach for adjusting biases in GCM estimations for climate change research.

Abstract

This study analyzed systematic biases in surface air temperature (SAT) within Far East Asia during the boreal winter using the SNURCM and WRF regional climate models (RCMs) from the Coordinated Regional Downscaling Experiment (CORDEX)-East Asia phase II. The SAT biases were examined in relation to the East Asian winter monsoon (EAWM). The models consistently simulated lower winter temperatures over East Asia, particularly in the Manchuria (MC) region, compared to the observation, showing a positive correlation with the EAWM. This study assessed the models' ability to capture EAWM variability and revealed relationships between SAT biases and discrepancies in low-level and near-surface EAWM conditions. The findings emphasized the value of analyzing extreme monsoon years, with the RCMs exhibiting larger cold SAT biases during strong EAWM years. Systematic biases in sea-level pressure contrast and lower-level winds over the MC region were evident during years with a robust monsoon. The overestimation of low-level winds during strong EAWM years contributed to increased cold advection, affecting the MC region. These systematic errors are influenced by the internal factors of the model, such as the physics parameterization schemes, rather than large-scale circulation forced by the reanalysis data (perfect boundary condition). These results provide insights for model improvements, understanding EAWM dynamics, and call for investigation of processes in the planetary boundary layer and coupled air-sea interaction.

Abstract

Coastal urban areas like New York City (NYC) are more vulnerable to urban pluvial flooding particularly because the rapid runoff from extreme rainfall events can be further compounded by the co-occurrence of high sea-level conditions either from tide or storm surge leading to compound flooding events. Present-day urban pluvial flooding is a significant challenge for NYC and this challenge is expected to become more severe with the greater frequency and intensity of storms and sea-level rise (SLR) in the future. In this study, we advance NYC’s assessment of present and future exposure to urban pluvial flooding through simulating various storm scenarios using a citywide hydrologic and hydraulic model. This is the first citywide analysis using NYC’s drainage models focusing on rainfall-induced flooding. We showed that the city’s stormwater system is highly vulnerable to high-intensity short-duration “cloudburst” events, with the extent and volume of flooding being the largest during these events. We further showed that rainfall events coupled with higher sea-level conditions, either from SLR or storm surge, could significantly increase the volume and extent of flooding in the city. We also assessed flood exposure in terms of the number of buildings and length of roads exposed to flooding as well as the number of the affected population. This study informs NYC’s residents of their current and future flood risk and enables the development of tailored solutions to manage increasing flood risk in the city.

Abstract

The present study has shown the changes in climatic variables and land use/land cover to observe the long-term changes in the hydrological characteristics of the Kulsi River. Mann–Kendall test, Expert Team on Climate Change Detection and Indices Index, t tests, and Analysis of variation were used to analyze the change and variation. The Mann–Kendall test shows a significant declining trend of rainfall at Chamaria (− 26.76 mm/year) annually, the seasonally significant declining trend was noticed in the winter season at the rate of (− 1.47 mm/year) at Ukium, whereas Chamaria showed a declining trend of rainfall in the post-monsoon season at the rate of (− 5.62 mm/year). Among all stations, maximum temperature showed a significant increasing trend at the rate of (0.03 °C/year) at Chaygaon, while minimum temperature showed a significant increasing trend at the rate of (0.02 °C/year) at Ukium and Chamaria, respectively. Consecutive dry days have shown significant positive trends at the rate of (0.85 days/year), (0.67 days/year) and (1.35 days/year) at Chaygaon, Ukium, and Chamaria. The areas under forest cover and water body have decreased, whereas cropland and transition areas have increased. In the case of the hydrological variables, only the water level has shown significant annual variation (p value is 0.002). However, both water level and discharge show seasonal variation significantly. The water level has shown a significant negative relation with maximum temperature (R and p values are − 0.78 and 0.00 respectively) and a significant positive relation with forest cover (R and p values are 0.6 and 0.01 respectively). On the other hand, water discharge has a significant positive relation with annual rainfall (R and p values are 0.5 and 0.03, respectively). The findings of the research show that increasing maximum temperature and decreasing forest cover are responsible for the decline of water level in the Kulsi River. Whereas, a decrease in rainfall leads to a decrease in discharge and vice versa. The findings of the present research will be helpful to the policymakers and government in preparing a suitable sustainable river management plan to address the declining water resources to mitigate water scarcity issues.

Abstract

Flood is one of the most frightening dangers in the world, which can cause a lot of human and financial losses. In this study, an attempt has been made to create a flood risk map with higher accuracy by using the combination of SWAT, SDSM, AHP, and TOPSIS models. The flood risk map helps to identify areas that have flood potential. Managers and officials can control and reduce human and financial losses caused by floods by using such maps and adopting correct policies. In this study, using the SWAT and SDSM models, the future runoff of the Kashkan basin of Lorestan Province in Iran was simulated for the period from 2049 to 2073. Simulated runoff with different return periods of 2, 5, 10, 25, 50, and 100 years was investigated. According to the obtained results, RCP2.6 was introduced as the most dangerous scenario of this watershed with a runoff forecast of 7715 cubic meters per second. With the help of the obtained flood risk map, sub-basins 22, 24, 28, and 32 representing Khorram Abad and Poldakhter cities were introduced as flood-prone areas of the study area. The simulation of the precipitation, maximum and minimum temperature of the studied basin in the period from 2006 to 2100 showed that the maximum and minimum temperatures can get warmer by 1.3–3  C, and 1 to 2  C can get colder. On the other hand, the rainfall of the entire basin will be able to decrease between 54 and 120 mm. The methods used in this study can also be used to detect flood-prone areas for other parts of the world that have been exposed to sudden floods due to climate change.

Graphical abstract

Abstract

In the context of river basins, the threat of climate change has been extensively studied. However, many of these studies centred on hazard analysis while neglecting the need for comprehensive risk assessments that account for exposure and vulnerability. Hazard analysis alone is not adequate for making adaptive decisions. Thus, to effectively manage flood risk, it is essential to understand the elements that contribute to vulnerability and exposure in addition to hazard analysis. This study aims to assess flood risk (in space and time until the year 2100) for the agricultural system, in the White Volta Basin in northern Ghana. Employing the impact chain methodology, a mix of quantitative and qualitative data and techniques were used to assess hazard, exposure, and vulnerability. Multi-model climate change data (RCP 8.5) from CORDEX and observation data from the Ghana Meteorological Agency were used for hazard analysis. Data on exposure, vulnerability, and adaptation were collected through structured interviews. Results indicate that flood hazard will increase by 79.1% with high spatial variability of wet periods but the flood risk of the catchment will increase by 19.3% by the end of the twenty-first century. The highest flood risk is found in the Upper East region, followed by North East, Northern, Savannah, and Upper West for all four analysed periods. Adaptive capacity, sensitivity, and exposure factors are driven by poverty, ineffective institutional governance, and a lack of livelihood alternatives. We conclude that the region is highly susceptible and vulnerable to floods, and that shifting from isolated hazard analysis to a comprehensive assessment that considers exposure and vulnerability reveals the underlying root causes of the risk. Also, the impact chain is useful in generating insight into flood risk for policymakers and researchers. We recommend the need to enhance local capacity and foster social transformation in the region.

Abstract

Precipitation is a key component in hydrologic processes. It plays an important role in hydrological modeling and water resource management. However, many regions suffer from limited and data scarcity due to the lack of ground-based rain gauge networks. The main objective of this study is to evaluate other source of rainfall data such as remote sensing data (three different satellite-based precipitation products (CHIRPS, PERSIANN, and GPM) and a reanalysis (ERA5) against ground-based data, which could provide complementary rainfall information in semiarid catchment of Tunisia (Haffouz catchment), for the period between September 2000 and August 2018. These remotely sensed-data are compared for the first time with observations in a semiarid catchment in Tunisia.

Twelve rain gauges and two different interpolation methods (inverse distance weight and ordinary kriging) were used to compute a set of interpolated precipitation reference fields. The evaluation was performed at daily, monthly, and yearly time scales and at different spatial scales, using several statistical metrics. The results showed that the two interpolation methods give similar precipitation estimates at the catchment scale. According to the different statistical metrics, CHIRPS showed the most satisfactory results followed by PERSIANN which performed well in terms of correlation but overestimated precipitations spatially over the catchment. GPM underestimates the precipitation considerably, but it gives a satisfactory performance temporally. ERA5 shows a very good performance at daily, monthly, and yearly timescale, but it is unable to represent the spatial variability distribution of precipitation for this catchment. This study concluded that satellite-based precipitation products or reanalysis data can be useful in semiarid regions and data-scarce catchments, and it may provide less costly alternatives for data-poor regions.

Abstract

Precipitation is a key component in hydrologic processes. It plays an important role in hydrological modeling and water resource management. However, many regions suffer from limited and data scarcity due to the lack of ground-based rain gauge networks. The main objective of this study is to evaluate other source of rainfall data such as remote sensing data (three different satellite-based precipitation products (CHIRPS, PERSIANN, and GPM) and a reanalysis (ERA5) against ground-based data, which could provide complementary rainfall information in semiarid catchment of Tunisia (Haffouz catchment), for the period between September 2000 and August 2018. These remotely sensed-data are compared for the first time with observations in a semiarid catchment in Tunisia.

Twelve rain gauges and two different interpolation methods (inverse distance weight and ordinary kriging) were used to compute a set of interpolated precipitation reference fields. The evaluation was performed at daily, monthly, and yearly time scales and at different spatial scales, using several statistical metrics. The results showed that the two interpolation methods give similar precipitation estimates at the catchment scale. According to the different statistical metrics, CHIRPS showed the most satisfactory results followed by PERSIANN which performed well in terms of correlation but overestimated precipitations spatially over the catchment. GPM underestimates the precipitation considerably, but it gives a satisfactory performance temporally. ERA5 shows a very good performance at daily, monthly, and yearly timescale, but it is unable to represent the spatial variability distribution of precipitation for this catchment. This study concluded that satellite-based precipitation products or reanalysis data can be useful in semiarid regions and data-scarce catchments, and it may provide less costly alternatives for data-poor regions.

Abstract

Sea level projections for the future indicate a likely increase, raising concerns in the community due to its detrimental consequences. It has been reported by several researchers that there is considerable uncertainty in the future climate projections of Global Climate Models (GCMs), the primary tools for projecting future climate. In this study, the support vector machine (SVM) was employed to downscale sea level projections for the future from the projections of CMIP5 (Coupled Model Intercomparison Project Phase 5) GCMs. Quantile regression was employed to examine the predictors’ uncertainty, and it was found that sea surface salinity (halosteric component of sea level change) is the highly uncertain variable among the three predictors, followed by sea surface temperature and mean sea level pressure. The uncertainty associated with downscaled future sea level projections under Representative Concentration Pathways (RCPs) 4.5 and 8.5, stemming from GCM structure, was investigated using Normal distribution and non-parametric kernel density estimation. Kolmogorov–Smirnov (KS) test was performed to assess the goodness of fit and found that both normal distribution and kernel density estimation satisfactorily represent the probability density function (PDF) of sea level projections for the future. The uncertainty bounds in the sea level projections under both RCPs were estimated using the bias-corrected and accelerated bootstrap algorithm and found that the lower and upper bounds of sea level projection during December 2050 are 0.529 m and 0.604 m under RCP 4.5 and 0.535 m and 0.700 m under RCP 8.5. Results of the study revealed that uncertainty is comparatively high under RCP 8.5.