Tag: Climate Change Downscaling Techniques

Abstract

The study investigated the use of a Hardware-in-the-Loop (HiL) technique applied in model ice experiments to enable the analysis of offshore structures with low natural frequencies under dynamic ice loading. Traditional approaches were limited by facility capacities and ineffective downscaling of the geometry of the offshore structures. The goal of the present study was to overcome these challenges and to enhance the understanding and explore the applicability of a hybrid testing technique in model ice experiments. To achieve the objective, 204 Hardware-in-the-Loop simulations in model Ice (HiLI) were analyzed. Results showed robust behavior and good performance of the HiLI due to minimal variation in measured delay, normalized root mean square error, and peak tracking error and low magnitudes of such parameters despite alterations in factors such as the choice of the numerical structural model, physical prototype, measurement system, and ice type. Notably, the performance of the HiLI was affected when testing with warm model ice or scaling for harsh ice conditions, attributed to a reduced signal-to-noise ratio and instability of the system, respectively. Experimental identification of the critical delay, along with the application of an analytical stability criterion, revealed that the instability observed, was likely induced by reducing the structural stiffness of the numerical structural model to fulfil the scaling requirements when testing for harsh ice conditions. Additionally, the study showed improved HiLI performance when the physical prototype was in contact with the model ice. This observation was further analyzed and is assumed to be caused by the coupling between the ice and physical prototype, causing a coupled and thus increased eigenfrequency of the physical prototype-ice system.

Abstract

When a hydrological drought occurs due to a decrease in water storage, there is no choice but to supply limited water. Because this has a devastating impact on the community, it is necessary to identify causes and make predictions for emergency planning. The state of change in dam inflow can be used to confirm hydrological drought conditions using the Standardized Runoff Index (SRI), and meteorological drought and climate variability are used to identify causal relationships. Multiple Linear Regression (MLR) and Generalized Additive Model (GAM) models are developed to predict accumulated hydrological drought for 6, 12, and 24 months in the Boryeong Dam basin, and the Nash-Sutcliffe model efficiency coefficient (NSE) exceeded 0.4, satisfying the suitability criteria. The estimation ability is highest when predicting a 12-month annual drought, and reliability can be further increased by reflecting some climate fluctuations in a non-linear form. The droughts of 6 month and 24 month cumulative scales are significantly influenced by the Western Hemisphere Warm Pool (WHWP) extending from the eastern North Pacific to the North Atlantic and by the Nino 3.4 region in the tropical Pacific. Furthermore, it is anticipated that the drought conditions of the inflow volume to the Boryeong Dam will worsen with increasing sea surface temperatures in both regions.

Abstract

Data from the EURO-CORDEX ensemble of regional climate model simulations and the CORDEX-Adjust dataset were evaluated over the European Alps using multiple gridded observational datasets. Biases, which are here defined as the difference between models and observations, were assessed as a function of the elevation for different climate indices that span average and extreme conditions. Moreover, we assessed the impact of different observational datasets on the evaluation, including E-OBS, APGD, and high-resolution national datasets. Furthermore, we assessed the bi-variate dependency of temperature and precipitation biases, their temporal evolution, and the impact of different bias adjustment methods and bias adjustment reference datasets. Biases in seasonal temperature, seasonal precipitation, and wet-day frequency were found to increase with elevation. Differences in temporal trends between RCMs and observations caused a temporal dependency of biases, which could be removed by detrending both observations and RCMs. The choice of the reference observation datasets used for bias adjustment turned out to be more relevant than the choice of the bias adjustment method itself. Consequently, climate change assessments in mountain regions need to pay particular attention to the choice of observational dataset and, furthermore, to the elevation dependence of biases and the increasing observational uncertainty with elevation in order to provide robust information on future climate.

Abstract

Selecting proper parameterization scheme combinations for a particular application is of great interest to Weather Research and Forecasting (WRF) model users. The goal of this research is to create an objective method for identifying a set of scheme combinations to form a Multi-Physics Ensemble (MPE) suitable for short-term precipitation forecasting over Odisha, India’s east coast state. In this study, five member ensembles for Cloud Microphysics (CMP) and Land Surface Model (LSM, conventional ensemble) are created, as well as an ensemble of the top five performing members (optimized ensemble) for 13 Monsoon Depressions (MD) and 8 Deep Depression (DD) cases. There are a total of 30 combinations (5 PBL * 5 CMP, 5 LSM with best PBL and CMP, and one with ISRO Land Use Land Cover data). WRF 4.1 is used to carry out simulations, which are initialized with ERA5 reanalysis data and have a 72-h lead time. Rainfall verification skill scores indicate that ensemble members perform significantly better than any deterministic model. Rainfall characteristics such as location, intensity, and time of occurrence are well predicted in ensemble members as measured by a higher correlation coefficient and a lower RMSE. Neighbourhood ensemble probability also demonstrates that ensemble members have a higher chance of detecting heavy to very heavy rainfall events with more spatial accuracy. The study also concludes that choice of parameterization also affects large-scale dynamical parameters (temperature, humidity, wind, hydrometeors) and thus associated rainfall. Ensemble members exhibited less bias in the composite analysis of large-scale parameters. Furthermore, a composite analysis of moisture budget components revealed that the convergence term is the most important component of moisture accumulation, resulting in rainfall during the monsoon low-pressure system. These findings indicate that the proposed method is an effective method for reducing bias in rainfall forecasts.

Abstract

Selecting proper parameterization scheme combinations for a particular application is of great interest to Weather Research and Forecasting (WRF) model users. The goal of this research is to create an objective method for identifying a set of scheme combinations to form a Multi-Physics Ensemble (MPE) suitable for short-term precipitation forecasting over Odisha, India’s east coast state. In this study, five member ensembles for Cloud Microphysics (CMP) and Land Surface Model (LSM, conventional ensemble) are created, as well as an ensemble of the top five performing members (optimized ensemble) for 13 Monsoon Depressions (MD) and 8 Deep Depression (DD) cases. There are a total of 30 combinations (5 PBL * 5 CMP, 5 LSM with best PBL and CMP, and one with ISRO Land Use Land Cover data). WRF 4.1 is used to carry out simulations, which are initialized with ERA5 reanalysis data and have a 72-h lead time. Rainfall verification skill scores indicate that ensemble members perform significantly better than any deterministic model. Rainfall characteristics such as location, intensity, and time of occurrence are well predicted in ensemble members as measured by a higher correlation coefficient and a lower RMSE. Neighbourhood ensemble probability also demonstrates that ensemble members have a higher chance of detecting heavy to very heavy rainfall events with more spatial accuracy. The study also concludes that choice of parameterization also affects large-scale dynamical parameters (temperature, humidity, wind, hydrometeors) and thus associated rainfall. Ensemble members exhibited less bias in the composite analysis of large-scale parameters. Furthermore, a composite analysis of moisture budget components revealed that the convergence term is the most important component of moisture accumulation, resulting in rainfall during the monsoon low-pressure system. These findings indicate that the proposed method is an effective method for reducing bias in rainfall forecasts.

Abstract

Tropical riverine floods have escalated their frequency and magnitude causing individual and community-level livelihood vulnerability, especially in the rural areas. Livelihood vulnerability induces social vulnerability in a community in the face of recurrent floods. Thus, while measuring livelihood vulnerability, the non-technocratic factors were emphasized. The livelihood vulnerability index (LVI) devised by the Intergovernmental Panel on Climate Change in 2007 is a widely accepted livelihood vulnerability framework that is applied in the present study to reveal the nature of exposure, sensitivity, and adaptive capacity of rural communities. The study measures 36 parameters based mainly on the primary field survey of 2382 households in the Mayurakshi River Basin (India) along with district census reports, annual flood reports, satellite images and topographical maps. The result depicts that Kandi is the most exposed community development block (score: 0.591) owing to low-lying topography and drainage congestion, with a greater adaptive capacity (score: 0.480) on account of the receipt of higher foreign remittances. Thus, floods could not escalate the livelihood vulnerability due to the rural communities’ higher adaptive capacity. However, the nature of the LVI is primarily determined by the flood hazards, as shown by the close clustering of LVI and exposure using principal component analysis. The hierarchical cluster analysis depicts that the northern part of the study area, characterized by the lower flood hazards, is distinctly separated from the southern part in terms of the LVI. The one-way ANOVA also found significant differences (p < 0.05) among the villages based on exposure and LVI. These findings help various stakeholders to prepare flood management plans.

Abstract

Tropical riverine floods have escalated their frequency and magnitude causing individual and community-level livelihood vulnerability, especially in the rural areas. Livelihood vulnerability induces social vulnerability in a community in the face of recurrent floods. Thus, while measuring livelihood vulnerability, the non-technocratic factors were emphasized. The livelihood vulnerability index (LVI) devised by the Intergovernmental Panel on Climate Change in 2007 is a widely accepted livelihood vulnerability framework that is applied in the present study to reveal the nature of exposure, sensitivity, and adaptive capacity of rural communities. The study measures 36 parameters based mainly on the primary field survey of 2382 households in the Mayurakshi River Basin (India) along with district census reports, annual flood reports, satellite images and topographical maps. The result depicts that Kandi is the most exposed community development block (score: 0.591) owing to low-lying topography and drainage congestion, with a greater adaptive capacity (score: 0.480) on account of the receipt of higher foreign remittances. Thus, floods could not escalate the livelihood vulnerability due to the rural communities’ higher adaptive capacity. However, the nature of the LVI is primarily determined by the flood hazards, as shown by the close clustering of LVI and exposure using principal component analysis. The hierarchical cluster analysis depicts that the northern part of the study area, characterized by the lower flood hazards, is distinctly separated from the southern part in terms of the LVI. The one-way ANOVA also found significant differences (p < 0.05) among the villages based on exposure and LVI. These findings help various stakeholders to prepare flood management plans.

Abstract

The dynamic interaction between cliff, beach and shore-platform is key to assessing the sediment balance for coastal erosion risk assessments, but this is poorly understood. We present a dataset containing daily, 3D,colour LiDAR scans of a 450 m coastal section at Happisburgh, Norfolk, UK. This previously para-glaciated region comprises mixed sand-gravel sediments, which are less well-understood and well-studied than sandy beaches. From Apr-Dec 2019, 236 daily surveys were carried out. The dataset presented includes: survey areas, transects LiDAR scans, georeferenced orthophotos, meteorological- and oceanographical conditions during the Apr-Dec observation period. Full LiDAR point-clouds are available for 67 scans (Oct-Dec). Hourly time-series of offshore sea-state parameters (significant wave height, mean propagation direction, selected spectral periods) were obtained by downscaling the ERA5 global reanalysis data (global atmosphere, land surface and ocean waves) using the numerical model Simulating Waves Nearshore (SWAN). We indicate how to obtain hourly precipitation time-series by interpolating ERA5 data. This dataset is important for researchers understanding the interaction between cliff, beach and shore-platform in open-coast mixed-sand-gravel environments.

Abstract

The arid region of Northwest China (ARNC) has experienced a significantly higher warming rate than the global average and exhibits pronounced seasonal asymmetry, which has important implications for the region’s water-dependent systems. To understand the spatiotemporal patterns and driving mechanisms of seasonal asymmetric warming in the ARNC, we investigated seasonal changes in temperature rise and their underlying causes based on station and reanalysis data. We found that the dominant season of temperature increase shifted from winter to spring. The contribution of spring warming to the total temperature increase rose from −5%–7% to 58%–59%, while the contribution of winter warming decreased from 60%–75% to −4%–9%. However, the mechanisms underlying spring warming and winter cooling differ. An increase in solar radiation caused by a decrease in cloud cover (R = −0.64) was the main reason for spring warming, while a strengthening Siberian High primarily drove winter cooling.

Abstract

Nowadays, droughts and the impacts of climate change on water resources and the environment have had significant negative effects. Investigating the effects of climate change on drought indices and streamflow is crucial for water and environmental resource management. Therefore, the present study was conducted in two parts to examine the impact of climate change on drought indices and the amount of watershed streamflow. In the first part of this study, drought modeling was performed using the Standardized Precipitation Index (SPI) and emission scenarios (RCP4.5 and RCP8.5) at three temporal scales (3, 6, and 12 months) during the period of 1995–2055. Then, the climatic impacts on SPI for the period 2030–2055 under different climate scenarios were evaluated. The Karun basin in south west Iran, which is affected by droughts and the impacts of climate change, was selected as the study area. In the second part, the Adaptive Neuro-Fuzzy Inference System (ANFIS) was utilized to estimate watershed streamflow for a 20-year period. Subsequently, in this section, the Whale Optimization Algorithm (WOA) was employed to improve the results of ANFIS. Finally, streamflow prediction for the future period (2035–2055) was carried out using the hybrid model. The results indicated that analyzing precipitation through SPI under different climate scenarios could influence severe fluctuations in droughts within the study area. Frequency analysis of droughts under climate scenarios, RCP4.5 and RCP8.5, demonstrated an upward trend with diverse spatial prevalence patterns. On the other hand, the duration of droughts increased towards the RCP4.5 scenario and remained unchanged according to the RCP8.5 climate scenario. The northeastern, eastern, and southeastern regions will experience the longest and most frequent droughts compared to current conditions. Furthermore, the results of the second part showed that the developed ANFIS-WOA model provides better results (RMSE = 127, MAPE = 98.50, NSE = 0.73) compared to the ANFIS-based model with evaluation criteria of RMSE = 127, MAPE = 98.50, NSE = 0.73. Additionally, in the investigation of the impact of climate change on streamflow using ANFIS-WOA in the time range of 2030 to 2055, the flow rate in most months of the year will decrease by approximately 20 units compared to the baseline period, with a greater intensity of reduction in the RCP8.5 scenario than RCP4.5. However, there will be an increase in streamflow by approximately 20 (m³/s) only in October. The approach used in this study demonstrates the effects of climate change on the level of drought and watershed streamflow, serving as a warning for decision-makers and managers to better manage available water resources. Finally, this approach is recommended for implementation in other similar regions for water resource management and water supply assessment.