Climatic Zones Research Articles

Energy Consumption and Outdoor Thermal Comfort Characteristics in High-Density Urban Areas Based on Local Climate Zone—A Case Study of Changsha, China

This study aims to investigate the characteristics of energy consumption and outdoor thermal comfort within the high-density urban fabric of Changsha. Two different types of building (residential and office), as well as three building forms (point, slab, and enclosed) were analyzed under the local climate zone scheme. Utilizing the ENVI-met 5.6.1 and EnergyPlus 23.2.0 software, simulations were conducted to assess the thermal comfort and energy consumption of 144 architectural models. Then, multiple regression and spatial regression were applied to predict the energy consumption characteristics of the study area. The results showed the following: (1) In the high-density urban area of Changsha, the central business district and historic old town adjacent to the Xiangjiang River are identified as areas with high energy use intensity. (2) Among the residential categories, the point-types LCZ-3 and LCZ-6, as well as the slab-type LCZ-4, exhibit the lowest energy use intensity. In contrast, the enclosed office buildings, LCZ-2 and LCZ-5, are characterized by the highest energy use intensity. (3) Urban form parameters such as floor area ratio and building shape coefficient have a significant impact on EUIwinter, while EUIsummer is highly related to the normalized difference vegetation index and building shape coefficient (BSC). (4) The slab-type LCZ-4 stands out with its notably lower cooling and heating energy use intensity, coupled with excellent thermal comfort, making it particularly well-suited for the climatic conditions of Changsha.

Sectoral carbon emission prediction and spatial modeling framework: A local climate zone-based case study of the Guangdong-Hong Kong-Macao Greater Bay Area

Understanding the spatio-temporal pattern of carbon emission (CE) is prerequisite for formulating carbon reduction policies. Previous studies emphasized quantitative analysis of CE inventory while ignoring sectoral spatial distribution. This study fills this gap by developing a framework for coupling the CE quantitative prediction model with the sectoral CE spatial model based on the Long-range Energy Alternatives Planning (LEAP) model, spatial proxy data and local climate zone (LCZ). The framework's sectoral CE results reveal a great varied landscape within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), one of the leading bay areas in the world with rapid urbanization and emphasis on low-carbon development, under four carbon reduction scenarios. By 2060, the CN3 scenario that considers both energy-supply and consumption sides, predicts a drastic emission cut to 35.76 million tons, just 10 % of the business as usual (BAU) scenario's forecast, mainly from transportation (29.45 million tons) and industry (9.34 million tons) sectors. Besides, compared with the common CE spatial products, the spatial simulation results of sectoral CE in our framework present detailed spatial differences at the jurisdictional level. The findings are conducive for governments to formulate accurate CE reduction and optimization strategies of the cities towards to the 2060 carbon neutrality.

LCZ-based city-wide solar radiation potential analysis by coupling physical modeling, machine learning, and 3D buildings

Addressing climate change and urban energy problems is a great challenge. Building Integrated Photovoltaics (BIPV) plays a pivotal role in energy conservation and carbon emission reduction. However, traditional approaches to assessing solar radiation on buildings with physical models are computing-intensive and time-consuming. This study presents a hybrid approach by integrating physical model-based solar radiation calculation and machine learning (ML) for city-wide building solar radiation potential (SRP) analysis. By considering urban morphology, land cover, and meteorological characteristics, local climate zones (LCZs) are classified. The SRP of representative LCZs is precisely evaluated using computing-intensive physical models integrated with 3D building models. A ML model is then developed to effectively predict the SRP of building roofs and facades throughout the city. An experiment was conducted in Shenzhen, China to validate the presented approach. The results demonstrate that Shenzhen has a total annual building solar radiation of 3.28∗1011kwh. Luohu District exhibits the highest SRP density. The LCZ-based analysis highlights that compact low-rise LCZs offer greater SRP for roofs, while compact high-rise LCZs do so for facades. Moreover, BIPV could cut CO2 emission by up to 41.85 million tons annually. Notably, solar PV installation only on rooftops in Shenzhen could meet 87.81% of the city's electricity department's carbon reduction goal. This study provides an alternative for city-wide SRP estimation by combining physical modeling and ML and offers valuable insights for data-driven and model-driven urban planning and management in low-carbon cities.

Tackling the modifiable areal unit problem: Enhancing urban sustainability through improved land surface temperature and its influencing factors analysis

Exploring the modifiable areal unit problem (MAUP) in land surface temperature (LST) and its influencing factors is crucial for understanding LST variation patterns and quantifying the factors' impact. Neglecting the MAUP could lead to an incomplete understanding of LST changes and their driving mechanisms. This study used optimal parameters-based geographical detector and gradient boosting regressor models to investigate the MAUP in LST and its influencing factors. The analysis covered 87 cities across seven climate zones in China, examining MAUP in 12 spatial scales to discern the scale and zoning effects on LST and its influencing factors. The research findings were as follows: (1) The sensitivity of LST influencing factors to spatial scales exhibited both spatial and temporal heterogeneity. Significant differences in the q-values of LST influencing factors were observed across various climate zones and periods (daytime and nighttime), with human factors, particularly those related to residents' work, buildings, life, and rest, showing higher spatial scale dependency than natural factors. (2) Zoning effects significantly impacted the q-values of LST influencing factors and were closely linked to the discretization methods and quantities used, which could alter the trends of these q-values. (3) Across the 12 spatial scales, more than 67.34 % of LST influencing factor interaction types were classified as bi-variable enhancement types. The q-values for LST influencing factor interactions were higher and more stable than those of single factors. LST influencing factor interactions in transitional climate zones exhibited high sensitivity to spatial scales. This research enhances our understanding of LST variations, providing valuable insights for urban climate adaptability planning and the development of climate-resilient cities.

Quantitative index for temporal and spatial patterns of occupant behavior based on VRF big data

The energy consumption required to cool buildings has increased significantly because of occupant behavior. The air conditioning (AC) usage in residential buildings, particularly the part-time and part-space patterns, has a considerable impact on energy consumption. However, the part-time, part-space patterns of occupant behavior, which are characterized by complexity and diversity, present significant challenges when quantitatively assessing their impact on cooling energy consumption. To accurately describe occupant behavior, this study introduces the quantitative index of the part-time, part-space ratio of AC usage. We conducted clustering analysis based on part-time and part-space ratios of AC usage to obtain the typical occupant behavior patterns, user distribution, and cooling energy consumption levels of variable refrigerant flow (VRF) AC systems in various climatic zones of China based on data from 4373 VRF systems across various climatic zones from June to October 2019. The main findings were that the typical occupant behavior pattern of AC usage for residential buildings was the part-time, part-space AC mode, with less than 500 cooling hours and a simultaneous use coefficient below 0.6. The part-time, part-space ratio of AC usage effectively correlated occupant behavior with cooling energy consumption, as evidenced by an R2 value of 0.91, compared with the traditional standard of using the duration of the AC operation. The part-time, part-space ratio of AC usage and cooling energy consumption were higher in the hot summer and warm winter regions than those in the other two climatic zones. These findings can assist in the evaluation of cooling energy consumption for various occupant behavior patterns and provide technical guidance for reducing cooling energy consumption.

Spatial effect of urban morphology on land surface tempature from the perspective of local climate zone

Urban expansion results in the intrusion of artificial surfaces into natural environments, giving rise to phenomena such as urban heat islands and heightened temperatures within inhabited areas. This study examined the dynamic evolution of urban morphology and land surface temperature through the lens of local climate zone. Furthermore, the spatial relationship between urban morphology indices and land surface temperature was analyzed within the context of loops. The investigation yielded several noteworthy findings: (1) LCZ8, LCZ6, and LCZD were the most significant transfer-in and transfer-out classes within the study area. (2) The heat contribution of residential LCZs (LCZ 2,3,5) decreased over time, ranging from 0.415 to 0.171, while that of industrial LCZs (LCZ 8,10) increased within the range of 0.348–0.553. (3) Urban heat island mitigation can be achieved by regulating building height and surface fraction. Establishing a judicious threshold for reducing LST is imperative for sky view factor and impervious surface fraction factors. Surface albedo exhibited a substantial inhibitory effect in 2020. The observation of urban morphological changes and long-term local climate zone considerations integrate the responsibility of mitigating urban heat islands into urban planning.

Competition between insulation and phase change material to intensify the resilience of envelope against heat exchange to reduce overall energy consumption in buildings

ABSTRACT In this study, the effectiveness of two techniques was investigated to reduce annual building energy demand (ABED) located in hot and desert climates. In the first technique, thermal insulation was used to boost the thermal resistance of the envelopes, and in the second one, bio-based phase change material ( BioPC M R M 27 ) was used. Using numerical methods for five hot and desert regions, the effect of some parameters such as setpoint temperature, installation location, and thermal properties of insulation/PCM on the effectiveness of both techniques were investigated. By installing PCM in the middle layer of the walls, it was found that in 90% of the cases, the first technique had better effectiveness than the second technique in reducing ABED. To enhance PCM efficiency relative to insulation, three approaches were explored: increasing thickness, altering thermal properties, and optimizing installation location. Based on the results, the first two approaches did not yield substantial enhancements in PCM efficacy compared to insulation. The location of PCM installation is very effective in its effectiveness. By installing PCM in different places, it was observed that the presence of PCM in the outermost layer maximizes its effectiveness. In this case, the second technique is more efficient than the first one by 23.63% to 40.4% depending on the climate zone weather.

Innovate, insulate, integrate: Embracing circular economy in innovative insulations for reduced air conditioning expenses and carbon mitigation in fly ash brick buildings

Global demand for energy in the construction sector has increased drastically. A substantial portion of this energy belongs to fulfilling the heating, ventilation, and air-conditioning requirements of buildings, which are essential for maintaining the well-being of people. The development of zero-energy or environmentally friendly structures is required to attain a sustainable future. Strategic design of the building’s envelopes in green energy buildings can reduce air conditioning expenses. This article focuses on several insulation-integrated fly ash brick wall envelopes that have been designed to reduce CO2 emissions, reduce air conditioning expenses, and figure out payback times in warm-temperate and hot-arid regions. The degree-hour technique for heating and cooling, as well as unsteady heat transfer characteristics, are taken into consideration in this research. This is achieved by incorporating thirteen innovative insulation materials into fly ash bricks in three distinct configurations, (i.e., insulation layer near to the outer end of the fly ash block, on the middle of the fly ash block, and near to the inside end of the fly ash block). Experimental measurements were carried out to determine the thermo-physical characteristics of insulation materials. The analytical findings are confirmed through experimental validation. In hot-arid and composite climatic zones, placing an insulation layer (cloth-40/foam-60) near the outer end of the fly ash block resulted in the highest savings on air-conditioning costs of 0.89 $/m2 and 0.90 $/m2, respectively. They also achieved the highest carbon mitigation values at 17.2 and 16.8 kg/kWh. Insulation-integrated wall envelopes represent the pinnacle of energy efficiency in green building design, seamlessly combining advanced insulation materials with cutting-edge construction techniques to minimise energy consumption and maximise sustainability.

Experimental and numerical investigation of the effects of passive radiative cooling-based cool roof on building energy consumption

Passive radiative cooling is an energy-efficient method that reduces indoor temperature and cooling load in buildings, thus contributing to significant energy savings. This study investigates the thermal performance of the passive radiative cooling-based cool roof (PRC-CR) based on experimental and numerical analysis in terms of indoor air temperature and space cooling load indicators. Two experiment rooms constructed from concrete are developed with an indoor air temperature control system. One is the PRC-CR room with the roof covered by a passive radiative cooling metamaterial and the other is the reference room with a concrete roof. Long-term testing results in Hefei show that the indoor air temperature of the PRC-CR room is 5.5 °C lower in maximal and 1.8 °C lower on average than that of the reference room in summer when the indoor air temperature control system is off. Besides, when the indoor air temperature control system is switched on, the PRC-CR room achieves a 29.1% reduction in daily cooling load in summer and a 4.1% increase in daily heating load in winter, which suggests that passive radiative cooling can indeed save energy in cooling season (i.e., hot season) but cause overcooling in heating season (i.e., cold season). In addition, a simulation building model is developed to further investigate the effect of the PRC-CR on the energy consumption of buildings in different locations. The predicted results show that PRC-CR reduces the total load of buildings in cooling load prevailing regions (e.g., tropical zones, subtropical zones, and the vast majority of warm temperate zones), while increasing the total load of buildings in heating load dominant regions (e.g., most middle temperate zones, cold temperate zones, and plateau climate zones). In summary, this work demonstrates both the positive and negative effects of PRC-CR, providing valuable insights into PRC-CR applications.

Contributions from typical sources to microplastics in surface water of a semiarid urban river

Urban regions are suggested to be the main source of microplastic pollution in rivers. Thus, we investigated the spatiotemporal distribution of microplastics in the surface water of the Lanzhou section of the Yellow River in a semiarid region and the contributions of typical sources. The average concentration of microplastics in the surface water of the river was 0.98 particles (p) L−1. The daily quantity flux and mass flux were 3.63 × 109 p d−1 and 95.38 kg d−1, respectively. Most of the microplastics in the river were fibers and fragments, composed of polyethylene terephthalate, polyamide, polypropylene and polyethylene. A large quantity and mass of microplastics were found in the high-flow period of the river. The hotspots of microplastic pollution were residential and tourist reaches. The spatial distribution of microplastics was influenced by anthropogenic factors. However, the main factor influencing the temporal distribution of microplastics was precipitation seasonality. Most of the microplastics in the surface water originated from drainage ditches. The direct contribution of microplastics from atmospheric deposition was also considerable. Our results suggest that the contribution of microplastics from atmospheric deposition to urban rivers is worthy of attention.

Use of LCZs with urban canopy modeling to evaluate urban growth effects on meteorological conditions in the Salt Lake Valley

Urban Canopy Models (UCMs) can be used to represent urban characteristics on a mesoscale without accounting for effects of individual buildings. A WRF-based single-layer urban canopy model (SLUCM) was validated for the greater Salt Lake City area (GSLA) using measurements from two weather stations for a one-week period during summer 2019. A baseline model based on current GSLA properties developed using the Noah land surface model, SLUCM, and refined urban classifications based on WUDAPT Local Climate Zones (LCZs) produced acceptable predictions and computational times. The baseline model was validated using measured meteorological data and clearly showed diurnal heating patterns. Three scenarios representing multi-decade growth were defined by modifying LCZs in different parts of the GSLA. Compared to baseline values, results showed increased urban density increased local nighttime temperatures 5–10 °C depending on the extent of growth, but did not affect daytime temperatures. This was due to changes in LCZ anthropogenic heating rates and sky view factors which were most noticeable during early nighttime hours, consistent with atmospheric heat island behavior. Predicted 2-m relative humidity and 10-m wind speeds changed minimally.

Resistance of grassland productivity to drought and heatwave over a temperate semi-arid climate zone

Drought and heatwave are the primary climate extremes for vegetation productivity loss in the global temperate semi-arid grassland, challenging the ecosystem productivity stability in these areas. Previous studies have indicated a significant decline in the resistance of global grassland productivity to drought, but we still lack a systematic understanding of the mechanisms determining the spatiotemporal variations in grassland resistance to drought and heatwave. In this study, we focused on temperate semi-arid grasslands of China (TSGC) to assess the spatiotemporal variations of grassland productivity resistance to different climate extremes: compound dry-hot events, individual drought events, and individual heatwave events that occurred during 2000–2019. Based on the explainable machine learning model, we explored the resistance to the interaction of drought and heatwave and identify the dominant factors determining the spatiotemporal variations in resistance. The results revealed that grassland resistance to climate extremes had decreased in Xilingol Grassland and Mu Us Sandy Land, and had a not significant increase in Otindag Desert during 2000–2019. Human activities and the increase in CO2 concentration causes a decline in resistance in Mu Us Sandy Land, and the increase of VPD and shift of vegetation loss event timing caused a decline in resistance in Xilingol Grassland, while the weakening of climate extremes, especially the shortening of drought duration, increase the resistance in Otindag Desert. Mean annual temperature dominates the spatial differences in resistance among different grasslands. When drought and heatwave occur simultaneously, there is an additive effect on resistance and causes lower resistance to compound dry-hot events compared to individual drought and heatwave events. Our analysis provides crucial insights into understanding the impact of climate extremes on the temperate semi-arid grasslands of China.

Shrub encroachment leads to accumulation of C, N, and P in grassland soils and alters C:N:P stoichiometry: A meta-analysis

Soil stoichiometry of carbon (C), nitrogen (N), and phosphorus (P) are indicators for nutrient balance. Shrub encroachment into grasslands could change nutrient concentrations and stoichiometry in soils, but the general patterns remain unclear. With a meta-analysis of a global dataset covering 344 observations from 68 studies, we examined the responses of grassland soil C:N:P stoichiometry to shrub encroachment under various environmental conditions. Our results show that: 1) Shrub encroachment significantly increased the concentrations of soil C (+29 %), N (+25 %), P (+20 %), C:N (+5 %), C:P (+12 %), and N:P (+6 %). The magnitude of such effects varied with climate, soil texture, and soil layer. 2) Increases in SOC and TN concentrations mainly occurred in Mediterranean and very humid climate zones. Soil C:P and N:P decreased in semi-humid climate zone after shrub encroachment. 3) The increases in SOC and TN concentrations and in the C:N, C:P, and N:P ratios after shrub encroachment were greater in the topsoil than in deeper soil layers. 4) Both finest-textured soil (clay) and coarsest-textured soil (sand) are beneficial for increase of soil nutrient concentrations following shrub encroachment. 5) The magnitude of the change in soil C:N was negatively correlated with the duration of shrub encroachment, due to greater increases in soil TN than in SOC concentrations with longer durations of encroachment. Our results indicate that soil stoichiometric shifts in shrub-encroached grasslands are relatively sensitive to environmental factors, including soil texture, soil pH, and climate. These findings help us to better understand the effects of shrub encroachment on biogeochemical cycling, functioning, and services in grasslands across a broad range of spatio-temporal scales.

Temporal dynamics of COVID-19 transmission and public health interventions: A multi-city analysis of Australian capital cities in different climate zones

Temporal dynamics of COVID-19 transmission and public health interventions: A multi-city analysis of Australian capital cities in different climate zones

Disentangling external loadings, hydrodynamics and biogeochemical controls on the fate of nitrate in a coastal embayment

Nitrogen, as an essential nutrient, largely contributes to the coastal eutrophication. However, the accurate depiction and evaluation of how external loadings, hydrodynamics, and biogeochemical reactions mediate the occurrence, transport, and transformation of nitrate (NO3-) within coastal embayment still pose ongoing challenges to date. In this study, we took advantage of dual isotopes of NO3- to track external NO3- loadings, radium and dual isotopes of H2O to characterize the influences of hydrodynamic on NO3- transport, δ18O-NO3- and δ18O-H2O along with microbial analysis to explore major NO3- biogeochemical reactions in Tolo Harbour, Hong Kong. The multiple isotopic evidence showed that NO3- in surface harbour water was predominantly contributed by precipitation in wet season and its impact was strengthened by stratification. In dry season, NO3- in the surface harbour water became largely influenced by benthic input and biogeochemical reactions due to intensified vertical mixing. Based on NO3- mass balance model, biogeochemical reaction, especially nitrification, was found to be the major process to secure the closure of NO3- budget and increase NO3- inventory from wet to dry season. Hydrodynamics redistributed the external NO3- loadings and mediated nitrogen biogeochemical reactions, both of which further synergistically regulated the fate of NO3- in the embayment.

Artificial neural network-based investigation of factors impacting faulting in rigid pavements for dryfreeze and dry no-freeze climatic zone

Artificial neural network-based investigation of factors impacting faulting in rigid pavements for dryfreeze and dry no-freeze climatic zone

Thermal environmental and energy effects of vertical greening system under the influence of localized urban climates

Vertical greening system (VGS) can cool adjacent urban space and lower building conditioning energy use without consuming valuable land area, however the influence of local microclimate on the effects is understudied. To better understand how VGS behaves environmentally in localized urban climates, the study investigated the outdoor cooling and building energy-saving potential of a VGS in five local climate zones (LCZ) in Shanghai, using integrated modeling with ENVI-met and EnergyPlus. The model verification reasonably reflects the relative differences in thermal effects of VGS due to local climates. The result reveals that, under the maximum inter-LCZ differences in air temperature (Ta) and relative humidity (RH) of 1.4 °C and 5.3 %, the greatest inter-LCZ variations in cooling and humidifying effects of the VGS are 0.16 °C and 1.1 %, respectively. The VGS creates greater adjacent Ta reduction in higher-ranked LCZs, by up to 0.27 °C, but increases RH by 1.6 %. In summer, the VGS provides energy savings of 132–278 Wh/m2 with energy saving rates of 5.7–11.7 %. The annual energy savings are partially offset by the increase in winter heating, and the annual energy saving rates are reduced to 3.2–6.3 %. Of the savings, 92 % is due to shading, 8 % to cooling, and the humidifying effect has a negative impact (- 11 %). The findings underscore the impact of localized urban climates on the VGS thermal and energy performances, and support climate-responsive and performance-optimized urban VGS planning and application.

Quantification of interactions among agricultural drought indices within Köppen–Geiger climate zones in Bangladesh

Agricultural productivity is highly correlated with climatic variations, including drought events. This study is aimed at performing a comprehensive assessment of agricultural drought conditions in the Köppen–Geiger climate zones in Bangladesh, based on the temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI). The zones are classified as follows: temperate dry winter with a hot summer zone, tropical savannah zone, and tropical monsoon zone. The range of VHI is optimized based on its correlation with TCI and VCI. The correlation among the stratified drought indices is also examined to quantify and measure the strength and direction of the linear relationships between them. Moreover, Mann–Kendall test is conducted to assess the statistical trends in the spatiotemporal propagation of droughts across different climate zones. The balanced correlation approach for VHI reveals that vegetation health is governed by the temperature conditions. Certain variations in the drought intensity, frequency, and duration are observed across climatic zones in earlier years while the recent years are noted with less droughts. Normal or no drought conditions are noticed mostly in the tropical monsoon zone through VCI and VHI. The correlations among the drought classes indicate that VHI is more strongly correlated with TCI than with VCI, while NDVI exhibits stronger correlations with VCI than with either VHI or TCI. The Mann-Kendall test revealed that VCI has significant downward trends in drought categories and an upward trend in normal conditions, whereas VHI and TCI displayed inconsistency in statistical trends. By extensively exploring the agricultural drought conditions within specific climate zones, this study offers valuable insights for agronomists and stakeholders involved in climate resilience planning and agricultural development in Bangladesh.

Modelling vegetation dynamics for future climates in Australian catchments: Comparison of a conceptual eco-hydrological modelling approach with a deep learning alternative

Dynamically simulating leaf area index assists in modelling the feedbacks between eco-hydrologic and climatic processes. The particular challenge for Australia is the prevalence of arid and semi-arid ecosystems where water availability plays a crucial role in vegetation productivity. To understand whether existing LAI models can capture plant dynamics under changing climates, we tested two competing models across Australia's different climate zones: a conceptual eco-hydrologic model that applies water use efficiency term to relate LAI to water uptake, and a deep learning approach. An initial virtual catchment experiment with deep learning showed that it only uses information from potential evapotranspiration. For future climates, the conceptual model captured a negative trend and increasing variance in LAI, which is plausible given projected rainfall changes, while deep learning did not. Our study demonstrated an example of ‘right answer for the wrong reasons’, and the importance of incorporating knowledge of water-carbon coupling for appropriate scenarios.

Understanding the variability of large-scale statistical downscaling methods under different climate regimes

Large-scale downscaling plays an important role in assessing global impacts on hydrological sphere due to climate changes. In such downscaling efforts, it is essential to consider the various climate regimes. Although previous studies have indirectly suggested that the accuracy of downscaling might differ among climate regimes, research that systematically understands or quantifies the variability of this accuracy remains scarce. This study addresses this gap by systematically quantifying the performance of five different large-scale downscaling methods across various climate regimes in the context of downscaling hydroclimatic indicators. Our findings indicate that large-scale downscaling yields the highest accuracy on average when applied to temperature, precipitation, and runoff in tropical, arid, and temperate climate regimes, respectively, while showing poor accuracy in polar regimes for all variables. The maximum difference of normalized root mean squared errors for hydroclimate indicators is 69 % across climate zones, and the spatial distribution of downscaling accuracy aligns with spatial distribution of climate zones. The variation of downscaling accuracy is particularly significant in temperature, precipitation, and seasonal runoff indicators. Furthermore, linkages between accuracy of climate and hydrological indicators differ by climate zones. The underlying reasons for the different accuracy of downscaling are spatially different accuracy of global climate models (GCMs) and interaction of downscaling structure and climate regimes. This study articulated the source of spatially different accuracy/uncertainties for large-scale downscaling that have never been addressed before. The findings of this study provide valuable support in selecting appropriate downscaling methods, ultimately enhancing the spatial reliability and accuracy of large-scale downscaling methods.