Tag: Climate Change Downscaling Techniques

Abstract

Thermal images play a crucial role in various applications, such as environmental monitoring, energy efficiency, and food safety. However, thermal images are often affected by low spatial resolution, limited accuracy, and noise, which reduce their usefulness and effectiveness. This research paper presents a novel approach for enhancing thermal images and optimizing using Kriging Interpolation KI. The proposed KI method combines a metaheuristic optimization algorithm, Particle Swarm Optimization (PSO), with Kriging, a geostatistical method for interpolation and prediction of spatially continuous variables. The proposed KI method has been evaluated on a set of low-resolution Land surface temperature (LST) images of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and validated with higher resolution LandSat-8 LST. The use of PSO in combination with Kriging provides a powerful tool for efficient and accurate spatial enhancement of thermal images, allowing for the preservation of important thermal features and details while improving the overall quality of the images. The proposed KI algorithm demonstrated the effectiveness of the approach in enhancing the spatial resolution and accuracy of the MODIS thermal images. The results show that the proposed method outperforms traditional statistical LST image enhancement methods, such as DisTrad, TsHarp, and Regression Tree in terms of spatial resolution and accuracy. The proposed method has potential applications in agricultural, metrological, and environmental applications, where thermal images are used to continuously monitor and control temperature-sensitive data.

Abstract

Pacific islands are widely exposed to several strong wave events all year long. However, comprehensive analyses of coastal vulnerabilities to wave climates and their extremes are often lacking in those islands. In the present paper, the wave climate around the reef of New Caledonia is analyzed using a 28-year simulation performed with the Wave Watch III model, and accounting for realistic wind intensity forcing from tropical cyclones. Four mean wave regimes are defined with clustering methods, and are shown to vary along the reef depending on its main orientation. The western reef is mostly exposed to energetic south-western swells (significant height over 1.5 m, peak period of ~ 12 s) generated in the Tasman Sea that are reinforced during austral winter. The northern sector and the Loyalty Islands, are hit by shorter waves (~ 8 to 9 s period) coming from the south-east to the north-east, with height ranging on average from 0.8 m in the Loyalty Channel to 1.5 m at the northern tip of the Grande Terre reef. These waves mainly result from the south-eastern trade winds that blow over the central south-western Pacific all year long. In austral summer, additional swell remotely generated by both the extra-tropical westerlies and the north-eastern trade winds of the northern hemisphere reach the north-eastern reef of the archipelago. These wave regimes also strongly vary in response to the interannual El Niño-Southern Oscillation. El Niño events tend to increase the frequency of the south-western swell regime in austral spring and fall, and of the south-eastern trade wind waves in austral summer. In contrast, during La Niña, waves generated in the northern hemisphere are more likely to reach New Caledonia all year long. Finally, extreme wave events and their return periods were assessed. Wave amplitude reaching 7 m is estimated to occur every 100 years. On the west and southern tip of the Grande Terre reef, extreme waves are 80% of the time westerly waves generated by storms in the Tasman Sea or in the Coral Sea, while on the eastern reefs (Loyalty Islands and Channel), 70% of the extreme wave episodes are associated to tropical cyclone-induced waves. During La Niña episodes, more tropical cyclones pass by New Caledonia, increasing their contribution to extreme wave events along the western and southern coasts of the island. Conversely, in El Niño conditions, the exposure to tropical cyclone-induced waves is predominantly concentrated on the northeastern side.

Abstract

This paper summarizes recent developments made in terms of structural and non-structural solutions to manage the safety of urbanized slopes. The paper gives an overview of the pioneering effort to integrate the climate modeling chain into landslide susceptibility assessment using TRIGRS, application of virtual reality to improve the landslide risk awareness, the advancement of upstream flexible barrier system and debris flow screens to reduce the entrainment and impact on terminal barriers, and finally, internal seepage-induced progressive failure of reservoir rim slopes. These advancements are done using numerical modeling, simulations of real cases and physical modeling using small- and large-scale models.

Highlights

A new ensemble model was introduced and evaluated for projecting minimum and maximum temperatures in Iran.

Trends in minimum and maximum temperatures in the near term (2021–2040) were obtained using socio-economic scenarios of five models at 95 synoptic stations.

The ensemble technique reduced the error of the models used in projection to an optimal extent.

Abstract

Three Gorges Dam is the largest hydraulic infrastructure in the world, playing a pivotal role in flood mitigation. The hydrological responses of the Three Gorges Reservoir Region (TGRR) to climate change and human activities are unclear, yet critical for the Three Gorges Dam’s flood control and security. We simulated streamflow and water depth by coupling the Variable Infiltration Capacity model and the CaMa-Flood model. Daily discharge at the outlet of TGRR was well modeled with a relative error within 2% and a Nash-Sutcliffe efficiency coefficient of approximately 0.81. However, the flood peak was overestimated by 2.5–40.0% with a peak timing bias ranging from 5 days earlier to 2 days later. Runoff and water depth in the TGRR increased from 2015 to 2018 but decreased during flood seasons. Land use and land cover changes in 2015 (LUCC2015) and 2020 (LUCC2020) were analyzed to quantify their hydrological impacts. During the 2015–2018 period, land use conversion increased in built-up areas (+ 0.6%) and water bodies (+ 0.1%), but decreased in woodland grassland (-0.7%) and cropland (-0.1%). This led to a slight increase in runoff and inflow of less than 4‰ across the TGRR, a 7.70% decrease in average water depth, and a 15.4‰ increase in maximum water depth. Water depths in the TGRR decreased during flood seasons, and increased during non-flood seasons. Increasing water depth was identified in northern TGRR. This study clarifies the historical TGRR’s hydrological features under LUCC and climate changes, aiding regional flood mitigation in the TGRR.

Abstract

High spatio-temporal resolution estimates of electricity consumption are essential for formulating effective energy transition strategies. However, the data availability is limited by complex spatio-temporal heterogeneity and insufficient multi-source feature fusion. To address these issues, this study introduces an innovative downscaling method that combines multi-source data with machine learning and spatial interpolation techniques. The method’s accuracy showed significant improvements, with determination coefficients (R²) increasing by 30.1% and 33.4% over the baseline model in two evaluation datasets. With this advanced model, we estimated monthly electricity consumption across 1 km x 1 km grid for 280 Chinese cities from 2012 to 2019. Our dataset is highly consistent with officially released electricity consumption of different industries (Pearson correlation coefficients within 0.83 - 0.91). Moreover, our data can reflect the electricity consumption patterns of different urban land uses compared to other datasets. This study bridges a significant gap in fine-grained electricity consumption data, providing a robust foundation for the development of sustainable energy policies.

Abstract

This paper has investigated the impact of convective parameterization schemes (CPS) and land surface models (LSM) on the simulation of summer climate over the Democratic People’s Republic of Korea (DPR Korea) using the regional climate model (RegCM 4.7). The sensitivity experiments with two LSMs [Biosphere Atmosphere Transfer Scheme (BATS) and Community Land Model (CLM 3.5)] and four CPSs (Grell, Emanuel, Grell over land and Emanuel over ocean (GL_EO), Emanuel over land and Grell over ocean (EL_GO)) at 30 km horizontal resolution are carried out in summer (from June to August) for 10 years (2001–2010) for this purpose. The simulation results are compared with the available observation data provided from the State Hydro-Meteorological Administration of the DPR Korea (SHMAK). The results show that summer mean circulation patterns (SMCP) and summer averaged surface temperature (SAST) is well captured for most of the simulations, but summer rainfall is not well represented by RegCM 4.7. The performance of the CLM3.5 scheme is better in all the simulations than the BATS scheme. Among the CPSs, the EL_GO scheme shows the smallest biases in the simulation of SAST and summer rainfall. The simulations using EL_GO with CLM3.5 shows the best performance in simulating the SAST and summer rainfall over the study region among the considered CPSs and LSMs. These results will be helpful to improve the prediction of climate change over the DPR Korea.

Abstract

Near-surface air temperature is an essential climate variable for the study of many biophysical phenomena, yet is often only available as a daily mean or extrema (minimum, maximum). While many applications require sub-diurnal dynamics, temporal interpolation methods have substantial limitations and atmospheric reanalyses are complex models that typically have coarse spatial resolution and may only be periodically updated. To overcome these issues, we developed an hourly air temperature product for Australia with spatial interpolation of hourly observations from 621 stations between 1990 and 2019. The model was validated with hourly observations from 28 independent stations, compared against empirical temporal interpolation methods, and both regional (BARRA-R) and global (ERA5-Land) reanalysis outputs. We developed a time-varying (i.e., time-of-day and day-of-year) coastal distance index that corresponds to the known dynamics of sea breeze systems, improving interpolation performance by up to 22.4% during spring and summer in the afternoon and evening hours. Cross-validation and independent validation (n = 24/4 OzFlux/CosmOz field stations) statistics of our hourly output showed performance that was comparable with contemporary Australian interpolations of daily air temperature extrema (climatology/hourly/validation: R² = 0.99/0.96/0.92, RMSE = 0.75/1.56/1.78 °C, Bias = -0.00/0.00/-0.03 °C). Our analyses demonstrate the limitations of temporal interpolation of daily air temperature extrema, which can be biased due to the inability to represent frontal systems and assumptions regarding rates of temperature change and the timing of minimum and maximum air temperature. Spatially interpolated hourly air temperature compared well against both BARRA-R and ERA5-Land, and performed better than both reanalyses when evaluated against the 28 independent validation stations. Our research demonstrates that spatial interpolation of sub-diurnal meteorological fields, such as air temperature, can mitigate the limitations of alternative data sources for studies of near-surface phenomena and plays an important ongoing role in supporting numerous scientific applications.

Abstract

Decomposing the responses of ecosystem structure and function in drylands to changes in human-environmental forcing is a pressing challenge. Though trend detection studies are extensive, these studies often fail to attribute them to potential spatiotemporal drivers. Most attribution studies use a single empirical model or a causal graph that cannot be generalized or extrapolated to larger scales or account for spatial changes and multiple independent processes. Here, we proposed and tested a multi-stage, multi-model framework that detects vegetation trends and attributes them to ten independent social-environmental system (SES) drivers in Kazakhstan (KZ). The time series segmented residual trend analysis showed that 45.71% of KZ experienced vegetation degradation, with land use change as the predominant contributor (22.54%; 0.54 million km²), followed by climate change and climate variability. Pixel-wise fitted Granger Causality and random forest models revealed that sheep & goat density and snow cover had dominant negative and positive impacts on vegetation in degraded areas, respectively. Overall, we attribute vegetation changes to SES driver impacts for 19.81% of KZ (out of 2.39 million km²). The identified vegetation degradation hotspots from this study will help identify locations where restoration projects could have a greater impact and achieve land degradation neutrality in KZ.

Abstract

Context

There is a gap of knowledge on the effects of fine resolution landcover patterns on the distribution of air temperatures within neighborhoods, as well as on how these effects may differ depending on temporal (i.e., summer and winter, diurnal and nocturnal), and spatial (i.e. extent) scales.

Objectives

(1) Evaluate the effects of compositional and configurational fine-scale landcover patterns on the spatial distribution of air temperatures within neighborhoods. (2) Determine differences between winter and summer seasons and diurnal and nocturnal periods. (3) Evaluate if these effects relate to the spatial extent used for the analysis.

Methods

Relationships between four landscape metrics and air temperature within four contrasting neighborhoods located in Santiago. Landcover was classified in six classes (built up, barren, grass, evergreen, deciduous, woody) from 1.5m resolution satellite images and temperature acquired from dataloggers located within neighborhoods. Linear mixed models were used for testing the relationships at six spatial extents.

Results

Landcover composition and configuration influence temperatures within neighborhoods, but these effects can greatly differ depending on the season, time of the day and extent of analysis. Grass and evergreen trees show the highest effects on neighborhood´s temperatures among the six landcover classes. Grass reduces summer temperatures at smaller extents but may increase temperatures at larger extents. Evergreen trees play a major role during the winter season increasing coldest nocturnal temperatures at all the analyzed extents. These vegetation effects appear to be mostly associated with the average and largest size of their respective patches.

Conclusions

Fine-scale landcover patterns play a role in regulating temperatures within neighborhoods, but these effects depend on the season, time of the day and spatial extent. Researchers and decision makers must be aware that results obtained at a given scale cannot be directly translated to another scales.