Tag: Climate Change Downscaling Techniques

Abstract

Predictions of daily maximum and minimum temperatures (T_max and T_min) are key components of operational weather forecasting. Here we show how a deep learning scheme can be used to improve their predictions based on the numerical weather prediction (NWP) output from the European Centre for Medium-Range Weather Forecasts − Integrated Forecasting System (ECMWF-IFS). Using an optimal factor set screened by a regression method, an error-correction model for T_max and T_min forecasts based on the Spatio-Temporal Stacked Residual Network (STS-ResNet) is established. We find that errors in T_max and T_min forecasts for Hunan Province, China, can be reduced by approximately 21% and 33% respectively. However, although the T_max and T_min forecasts at almost all terrain elevations have been improved, the improvement decreases with the increasing terrain elevation. To solve this problem, we designed the Residual and Spatial Attention STS-ResNet (SASTS-ResNet) based on spatial attention mechanism. In mountainous regions, the SASTS-ResNet makes up for the deficiency of STS-ResNet in improving the T_max forecasts of the ECMWF-IFS (with the improvement increasing from 1.45 to 42.53%), which has also largely improved the T_min forecasts (from 27 to 83%). Moreover, the ECMWF-IFS model, STS-ResNet and SASTS-ResNet all have some uncertainties in T_max and T_min in high-elevation areas, where the smallest uncertainty is found in the SASTS-ResNet model.

Abstract

The aim of this study is to spatially downscale the daily precipitation data from the Global Precipitation Measurement (GPM) mission, using the Integrated Multi-satellite Retrievals for GPM (IMERG), utilizing cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Cloud optical thickness (COT), cloud effective radius (CER), and cloud water path (CWP) are used to statistically downscale IMERG precipitation estimates from 0.1 to 0.01° spatial resolution, using the Multivariate Linear Regression (MLR) and residual correction methods. The downscaled precipitation estimates were subsequently validated using in situ rain gauge measurements. The residual corrected IMERG downscaled precipitation estimates were found to be more accurate than the downscaled predicted precipitation without the implementation of the residual correction algorithm (up to 37%), with a respective decrease of the Root Mean Square Error (RMSE) (up to 75%), Normalized Root Mean Square Error (NRMSE) (up to 79%), and the Percent Bias (PB) (up to 98%). In addition, the final downscaled product after the MLR method implementation with residual correction was better correlated with the rain gauge observations than the initial IMERG product (up to 20%). Thus, the implementation of the MLR method in conjunction with the residual correction algorithm is an efficient tool for downscaling remote sensing products with a coarse spatial resolution.

Abstract

Climate change accelerates the changes in the hydrological cycle, which increases or decreases the intensity and duration of the drought events. This study assesses the drought risk under future climatic projections by computing drought hazard and drought vulnerability at a sub-basin scale. A multivariate drought hazard index (MDHI) considers deficits in precipitation and streamflow to quantify droughts. MDHI was analyzed using the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Streamflow Drought Index (SDI). Soil and Water Assessment Tool (SWAT+) simulated hydrological variables under baseline and future SSP scenarios were used to estimate MDHI. Drought vulnerability assessment is estimated by combining exposure, sensitivity and adaptive capacity indicators. The results show that the Drought Hazard Index (DHI) are likely to decrease in future climate change scenarios in comparison with the baseline. The Drought Vulnerability Index (DVI) reveals more than 50% of the basin falls under the high and very high vulnerable category. Based on the drought risk assessment, during the baseline period 48.82%, and 15.25% of the total basin area fall under high and very high drought risk. In the case of the future SSP245 and SSP585 scenarios, the high drought risk area gets reduced extensively with 15.09% and 1.02% respectively. The drought risk will be lower for future scenarios compared to baseline. This study aids the policymakers and water managers in prioritizing the projects on a sub-basin level to solve drought risk in future climatic conditions.

Abstract

Coasts are settlement areas that attract human for many years. Türkiye, geographically located on Mediterranean Basin and Asian Continent passage with unique climate regime, has a great coastal zone. The mid-latitude geographic setting makes possible to experience coastal recreation and tourism activities for almost all year along. Among all activities sun, sea and sand tourism can be placed on the first stage. Recent changes in local climate of the Black Sea such as rising mean atmospheric temperatures and increasing number of sunny days have triggered touristic beach activities in the region. In the literature, there are different techniques for grading and classifying coastal beaches. In this study, as an integral part of coastal landscape formation Land Use/Land Cover (LULC) structure is linked to landscape value and regarded as cover changes that determines landscape pattern of the area under investigation. The research rationale of this study is based on an assumption of Southern Black Sea coastal blue flag beaches are under severe anthropogenic pressure. For this purpose, 15 blue flag beaches which are located on the Black Sea coast of Türkiye were selected based on their morphological dimensions. To determine landscape changes and potential human induced effects on selected beaches for the last 35–40 years period, georeferenced, atmospherically corrected Landsat 5 TM and Landsat 8 OLI-TIRS satellite images were used. Furthermore, carrying capacities for all researched beaches were calculated and documented in order to find the actual human usage frequencies during the high season. According to results of the study, it is clearly observed that all beach areas are under anthropogenic pressure. The most important evidence of this situation is the proportional increases in the IS (Impervious Surface) cover class in the classified images between 1984 and 1985 and 2021 in all 15 beaches. Moreover, these increases have reached up to 5–6 times for some beaches. As inferred, from the field surveys, beach carrying capacities were also exceeded for the most of the beaches under investigation. This means the blue flag beaches of Southern Black Sea are not only affected by construction and urbanization but also under intense pressure of overuse during the high seasons. It is revealed that these areas should be carefully managed with state-of-the-art techniques that prioritize adaptive planning that takes into account all stakeholders participation. Moreover, the activities, authorization and attitudes of municipalities regarding the coastal zones must be limited and inspected by a higher organization.

Abstract

Transpiration (T) is pivotal in the global water cycle, responding to soil moisture, atmospheric stress, climate changes, and human impacts. Therefore, establishing a reliable global transpiration dataset is essential. Collocation analysis methods have been proven effective for assessing the errors in these products, which can subsequently be used for multisource fusion. However, previous results did not consider error cross-correlation, rendering the results less reliable. In this study, we employ collocation analysis, taking error cross-correlation into account, to effectively analyze the errors in multiple transpiration products and merge them to obtain a more reliable dataset. The results demonstrate its superior reliability. The outcome is a long-term daily global transpiration dataset at 0.1°from 2000 to 2020. Using the transpiration after partitioning at FLUXNET sites as a reference, we compare the performance of the merged product with inputs. The merged dataset performs well across various vegetation types and is validated against in-situ observations. Incorporating non-zero ECC considerations represents a significant theoretical and proven enhancement over previous methodologies that neglected such conditions, highlighting its reliability in enhancing our understanding of transpiration dynamics in a changing world.

Abstract

The use of computational fluid dynamics (CFD) in the wind engineering (WE) is generally defined as computational wind engineering (CWE). Since its foundation in 2004, the use of OpenFOAM in CWE has been increasing progressively and covers nowadays a wide range of topics, from wind environment to wind structural engineering. This paper was drafted in response to the invitation from the organizers of the 18th OpenFOAM workshop held in Genoa (Italy) on 11–14 July 2023, when a technical session on Civil Engineering and Wind Engineering was organized. In this paper the author briefly reviews the history of WE and surveys the evolution, methods, and future challenges of OpenFOAM in the CWE. Topics are here regrouped into three main research areas and discussed from a physical, engineering and purely computational perspective. The study does not cover the Wind Energy and related topics, since this can be considered nowadays as a stand-alone subfield of the WE. This review confirms that OpenFOAM is a versatile tool widely used for WE applications that often require new models to be developed ad hoc by CFD users. It can be coupled easily with numerical weather prediction models for mesoscale-microscale wind and thermal studies, with building energy simulation models to determine the energy demand, with finite element method for structural engineering design. OpenFOAM represents an extraordinary opportunity for all CFD users worldwide to share codes and case studies, to explore the potential of new functionalities and strengthen the network within the CFD community.

Abstract

This study addresses the challenge of obtaining detailed wind speed data in complex terrains by employing dynamic and statistical downscaling techniques. The research utilizes diagnostic and prognostic dynamic models, along with the Decaying Average (DA) and Analog Ensemble (AE) methods, starting from ERA5 global reanalysis. The Weather Research and Forecasting (WRF) and California Meteorological (CALMET) models are evaluated for their performance in simulating 10 m wind speed in the Zhangjiakou competition zone during the Beijing 2022 Winter Olympics. The study compares the accuracy of various downscaling approaches for wind speed estimation. High-density observational data from 12 stations, covering an area of about 20 km² and collected between October and April from 2017 to 2022, were employed for model calibration and validation. The research reveals that the planetary boundary layer scheme and land surface scheme significantly impact the WRF model’s wind speed simulation. Meanwhile, the slope flow parameter emerges as a key sensitivity factor for the CALMET model. Despite inheriting biases from ERA5 and WRF, the CALMET model, driven by ERA5 and WRF at a 100 m resolution, offers a reasonable spatial distribution of wind speed which was required by the ski competition. Both the DA and AE methods prove effective in correcting biases in 10 m wind speed from the ERA5, WRF, and CALMET models, with the AE method generally outperforming the DA method. The AE method reduces the root mean square error of the CALMET model’s 10 m wind speed from 3.1 m/s to 1.5 m/s at locations with stations. In conclusion, the study identifies the combination of the CALMET model and the AE bias-corrected method as the optimal dynamic-statistical downscaling scheme for 10 m wind speed in the study area.

Abstract

Selecting appropriate Global Climate Models (GCMs) presents a significant challenge for accurate climate projections. To address this, a novel framework based on information theory based minimum redundancy and maximum relevancy (MRMR) method identifies top-performing GCMs across the entire study region using multicriteria decision analysis methodology. A subset of the ten best-performing models out of twenty-two GCMs is chosen for multi-model ensemble analysis. Five MME methods are selected to assess the ensemble performance of the ten selected GCMs, categorized into simple, regression-based, geometric-based, and machine learning ensembles. This study evaluates the effectiveness of the MME method based on a comprehensive index called the extended distance between indices of simulation and observation. An Adaptive Multimodel Standardized Drought Index (AMSDI) has been developed based on the optimal MME method. For the application of the framework and the proposed index, historical precipitation data from 1950 to 2014 were utilized from 28 grid points in the Punjab province of Pakistan as the reference dataset. Additionally, simulations from 22 models of the Coupled Model Intercomparison Project phase 6, both past and future, were employed for the estimation procedure. In AMSDI indicator, we used improved multimodel ensemble of precipitation for future drought characterization under various future scenarios. Outcome associated with this research show that AMSDI effectively have ability to effectively identifiy extreme drought events for all three future scenarios. In conclusion, the AMSDI method is shown to be effective and flexible, improving accuracy in monitoring droughts.

Abstract

Climate change and climate variation influence the occurrence of natural disasters, especially water-related disasters. Landslides are a serious hydrogeohazard in Thailand. Hence, this study aims to project the spatial changes in the landslide susceptibility area in the Nakhon Si Thammarat province of southern Thailand. The study utilizes five climate models of CMIP6 (INM-CM4-8, IITM-ESM, MPI-ESM1-2-LR, CNRM-ESM2-1 and CNRM-CM6.-1) and conducts analyses based on 2 shared socioeconomic pathways (SSPs) of tier 1, which include SSP2-4.5 and SSP5-8.5. The linear scaling bias correction technique is applied to adjust for any biases in the climate models, and the model results are combined using the ensemble mean method. To achieve the study aims, a GIS-aided physically based landslide susceptibility model is used to predict changes in spatial aspects of the landslide susceptibility area. The analysis predicted spatial changes in the landslide susceptibility area over the next 20 years (2023 to 2042). According to the analysis, the results showed that the maximum increase in landslide susceptibility areas is approximately 29%, and the maximum decrease in landslide susceptibility areas is approximately 25% under SSP2-4.5. For SSP5-8.5, the maximum increase in landslide susceptibility areas is approximately 31%, and the maximum decrease in landslide susceptibility areas is approximately 35%. Moreover, the fluctuation of rainfall strongly influences the increase or decrease in landslide susceptibility areas. However, the landslide-prone areas remain located in areas that have experienced landslides in the past.

Abstract

As fossil fuels are phased out in favour of renewable energy, electric cars and other low-carbon technologies, the future clean energy system is likely to require less overall mining than the current fossil-fuelled system. However, material extraction and waste flows, new infrastructure development, land-use change, and the provision of new types of goods and services associated with decarbonization will produce social and environmental pressures at localized to regional scales. Demand-side solutions can achieve the important outcome of reducing both the scale of the climate challenge and material resource requirements. Interdisciplinary systems modelling and analysis are needed to identify opportunities and trade-offs for demand-led mitigation strategies that explicitly consider planetary boundaries associated with Earth’s material resources.