Regional Energy Balance Research Articles

Surface Albedo Dominates Radiation Efficiency of Alpine Grasslands Along an Elevation Profile of Qilian Mountains

AbstractQuantifying the elevation dependency of radiation partitioning in high‐altitude mountains is crucial for projecting regional energy balance while remains highly uncertain. We compared the surface radiation partitioning parameters across a meadow (3,200 m), shrub (3,400 m), and forb (3,600 m) along a southern slope of the Qilian Mountains. At a daily scale, the downward shortwave radiation (Rs) fluctuated minimally among the grassland types probably induced by similar site orientations. The greatest downward and upward longwave radiation (Ld and Lu) happened at the lowest meadow while the largest net shortwave (Sn) and longwave (Ln) radiation occurred at the deciduous shrub. The net all‐wave radiation (Rn) of the meadow and shrub was similar and exceeded that of the forb by ∼20%. The differences in Rn between the sites were jointly explained by those of upward shortwave radiation (Ru) and Lu, more than by Rs and Ld, suggesting the importance of surface attributes. The monthly normalized effective terrestrial radiation (λ, the ratio of Ln to Rs) varied insignificantly among the sites and averaged 0.25 ± 0.05, which was comparable to the global mean value (0.26). The smallest surface albedo (α, the ratio of Ru to Rs) and largest radiation efficiency (η, the ratio of Rn to Rs) were 0.13 ± 0.02 and 0.64 ± 0.09, respectively, both at the shrub. Grassland type dominated the spatial variations of monthly α and η. These findings highlighted the importance of grassland types to explain the spatiotemporal variations of radiation partitioning parameters in high‐altitude alpine grasslands.

Hourly values of an advanced human-biometeorological index for diverse populations from 1991 to 2020 in Greece

Existing assessments of the thermal-related impact of the environment on humans are often limited by the use of data that are not representative of the population exposure and/or not consider a human centred approach. Here, we combine high resolution regional retrospective analysis (reanalysis), population data and human energy balance modelling, in order to produce a human thermal bioclimate dataset capable of addressing the above limitations. The dataset consists of hourly, population-weighted values of an advanced human-biometeorological index, namely the modified physiologically equivalent temperature (mPET), at fine-scale administrative level and for 10 different population groups. It also includes the main environmental drivers of mPET at the same spatiotemporal resolution, covering the period from 1991 to 2020. The study area is Greece, but the provided code allows for the ease replication of the dataset in countries included in the domains of the climate reanalysis and population data, which focus over Europe. Thus, the presented data and code can be exploited for human-biometeorological and environmental epidemiological studies in the European continent.

Integration of Power to X Economy in Existing Energy System – Case Finland

Hydrogen or Power-to-X economy is seen as the most promising way to carry out the energy transition from fossil to renewable carbon free energy system. Electrification of the society and green hydrogen-based energy system require huge capacity increase in renewable energy production and energy transmission. This paper introduces a simulation study about the integration of green hydrogen-based energy system to existing power system structure in Finland. Regional energy balances are studied with different options of locations of renewable energy and hydrogen production plants and needs for energy transportation either as electricity or hydrogen. The model includes sectoral couplings between electricity, hydrogen and heating grids and finds synergies of flexibility between different energy grids.

Evaluation of multiple gridded snowfall datasets using gauge observations over high mountain Asia

Snowfall is an important component of the High Mountain Asia (HMA) cryosphere; its changes affect the regional energy and water balance. Snowfall data from numerous gridded products provide the basis for conducting climate-change studies; however, the applicability of these datasets in HMA has not been assessed comprehensively in terms of precipitation. This study comprehensively evaluates the applicability of the following widely used gridded datasets in HMA for the period 1991–2014: fifth generation of European ReAnalysis (ERA5), Global Land Data Assimilation System (GLDAS), High Asia Refined analysis (HAR), Multi-Source Weather (MSWX), National Centers for Environmental Prediction (NCEP), and China Meteorological Forcing Dataset (CMFD). The accuracy features of each dataset were evaluated based on observations from 148 gauges at various temporal scales (i.e., annual, monthly, and daily), elevations, sub-regions, and snowfall intensities. The results show that CMFD performs better at multiple temporal scales (i.e., annual, cool season, monthly, and daily) than other snowfall data. In addition, the CMFD accuracy has good stability, even at different elevations. CMFD is advantageous regarding interpreting the frequency and amount of different levels of (even extreme) snowfall events. The slightly inferior MSWX also performs well at different timescales, spatial scales, and elevations, and constitutes an attractive alternative to CMFD. HAR captures better light and moderate snowfall, and is the most accurate of the three sets of gridded products that contain direct snowfall data. This study constitutes an important reference for selecting suitable snowfall datasets for future scientific research.

Global perspectives of transition to green energy generation in the Saratov region

The article discusses global prospects for the transition to green energy generation in the Saratov region. The literature data on the current situation in the alternative energy sector (wind energy, solar energy, and bioenergy) in the Saratov region have been studied and systematized. The data were obtained from the most relevant and cited publications in the world databases Scopus, Google Scholar, RSCI and among Internet media articles. The pros and cons are described, as well as the overall impact on the environment of each type of renewable energy that exists in the Saratov region. Much attention is paid to the impact of wind turbines and solar power plants on the environment. It has been established that the use of solar energy is most developed in this area. This is due to climatic conditions and the number of sunny days in the region. In view of the large formation of organic waste in the Saratov region, it is rational to use biomass to obtain bioenergy. The Saratov region government intends to continue to steadily increase the share of renewable energy sources in the energy balance of the region. By 2035, the share of renewable energy sources in the regional energy balance will reach 6%, and in 2050 it will be about 13%. We believe that the global prospects for green energy transitions in the Russian Federation, as well as in the transmission to the Saratov region, consist in a partial transition to alternative energy (solar and wind), using bioenergy and highly efficient technologies for cleaning CO2 emissions from traditional fuel.

A monthly 1° resolution dataset of daytime cloud fraction over the Arctic during 2000–2020 based on multiple satellite products

Abstract. The low accuracy of satellite cloud fraction (CF) data over the Arctic seriously restricts the accurate assessment of the regional and global radiative energy balance under a changing climate. Previous studies have reported that no individual satellite CF product could satisfy the needs of accuracy and spatiotemporal coverage simultaneously for long-term applications over the Arctic. Merging multiple CF products with complementary properties can provide an effective way to produce a spatiotemporally complete CF data record with higher accuracy. This study proposed a spatiotemporal statistical data fusion framework based on cumulative distribution function (CDF) matching and the Bayesian maximum entropy (BME) method to produce a synthetic 1∘ × 1∘ CF dataset in the Arctic during 2000–2020. The CDF matching was employed to remove the systematic biases among multiple passive sensor datasets through the constraint of using CF from an active sensor. The BME method was employed to combine adjusted satellite CF products to produce a spatiotemporally complete and accurate CF product. The advantages of the presented fusing framework are that it not only uses the spatiotemporal autocorrelations but also explicitly incorporates the uncertainties of passive sensor products benchmarked with reference data, i.e., active sensor product and ground-based observations. The inconsistencies of Arctic CF between passive sensor products and the reference data were reduced by about 10 %–20 % after fusing, with particularly noticeable improvements in the vicinity of Greenland. Compared with ground-based observations, R2 increased by about 0.20–0.48, and the root mean square error (RMSE) and bias reductions averaged about 6.09 % and 4.04 % for land regions, respectively; these metrics for ocean regions were about 0.05–0.31, 2.85 %, and 3.15 %, respectively. Compared with active sensor data, R2 increased by nearly 0.16, and RMSE and bias declined by about 3.77 % and 4.31 %, respectively, in land; meanwhile, improvements in ocean regions were about 0.3 for R2, 4.46 % for RMSE, and 3.92 % for bias. The results of the comparison with ERA5 and the Meteorological Research Institute – Atmospheric General Circulation model version 3.2S (MRI-AGCM3-2-S) climate model suggest an obvious improvement in the consistency between the satellite-observed CF and the reanalysis and model data after fusion. This serves as a promising indication that the fused CF results hold the potential to deliver reliable satellite observations for modeling and reanalysis data. Moreover, the fused product effectively supplements the temporal gaps of Advanced Very High Resolution Radiometer (AVHRR)-based products caused by satellite faults and the data missing from MODIS-based products prior to the launch of Aqua, and it extends the temporal range better than the active product; it addresses the spatial insufficiency of the active sensor data and the AVHRR-based products acquired at latitudes greater than 82.5∘ N. A continuous monthly 1∘ CF product covering the entire Arctic during 2000–2020 was generated and is freely available to the public at https://doi.org/10.5281/zenodo.7624605 (Liu and He, 2022). This is of great importance for reducing the uncertainty in the estimation of surface radiation parameters and thus helps researchers to better understand the Earth's energy imbalance.

A Quantification of Heat Storage Change-Based Evaporation Behavior in Middle–Large-Sized Lakes in the Inland of the Tibetan Plateau and Their Temporal and Spatial Variations

A large number of different-sized lakes exist in the inland area of the Tibetan Plateau (TP), which are examples of the important connection between the atmosphere and hydrosphere through the analysis of lake surface convergence and evaporation processes. The evaporation level changes that occur in middle–large-sized lakes (surface area > 50 km2) in the area directly influence the regional mass and energy balance values, atmospheric boundary layer heat and humidity structures, and weather processes occurring in the lower-reach areas. The studies conducted in the literature at present, concerning lake evaporation processes, generally overlook the differences in lake heat storage behavior due to the reduced amount of data in the literature concerning lake bathymetry. According to the in situ bathymetric data obtained for 68 middle–large-sized lakes in the inner basin of the TP, in this study, we calculated their heat storage (G) change values by using the different vertical-depth water-temperature-change integral method, and we established a regression equation for the heat storage and lake surface net radiation values for 68 lakes. The evaporation rates of 134 middle–large-sized lakes larger than 50 km2 in the inland are of the TP were calculated by obtaining the G regression result and adopting it into the Penman model, as well as estimating the evaporation losses of theses 134 lakes from 2002 to 2018. The result shows that the annual average evaporation rate for these lakes is 927.39 mm/year, with an insignificant upward trend (0.10 mm/year). This method achieved good accuracy compared with the Bowen ratio method, which estimates the evaporation rate during the ice-free season, with a high correlation coefficient (R) value of 0.95 and least root mean square error (RMSE) value of 61 mm. The annual mean evaporation rate can be divided into the southern and northern lake groups along a 34°N line with a difference of 314.41 mm/year. The annual average evaporation volume of these lakes was 25.02 km3 and showed an upward trend of 0.35 km3/year. Among them, the annual average evaporation volume contribution ratio of level-1 lakes (50 km2 ≤ lake’s area < 100 km2, 61 lakes) was 14.04%, showing an upward trend, and the contribution of level-3 lakes (lake’s area ≥ 500 km2, 10 lakes) was 41.50%, showing a downward trend. There were no obvious changes in the level-2 lakes (100 km2 ≤ lake’s area < 500 km2, 63 lakes), which maintained at the same level in approximately 44.46%. Air temperature is the most important factor affecting the evaporation rate of lakes, while the lake surface area is the main factor affecting lake evaporation volume. Our study, considering the actual lake heat storage value, provides a useful reference for further improving lake water budget balance values and watershed hydrologic features in the inland closed lakes located in the TP.

Attribution of Extreme Drought Events and Associated Physical Drivers across Southwest China Using the Budyko Framework

Drought is a meteorological phenomenon that negatively impacts agricultural production. In recent years, southwest China has frequently experienced agricultural droughts; these have significantly impacted the economy and the ecological environment. Although several studies have been conducted on agricultural droughts, few have examined the factors driving agricultural droughts from the perspective of water and energy balance. This study aimed to address this gap by utilizing the Standardized Soil Moisture Index (SSMI) and the Budyko model to investigate agricultural drought in southwest China. The study identified four areas in Southwest China with a high incidence of agricultural drought from 2000 to 2020. Yunnan and the Sichuan-Chongqing border regions experienced drought in 10% of the months during the study period, while Guangxi and Guizhou had around 8% of months with drought. The droughts in these regions exhibited distinct seasonal characteristics, with Yunnan experiencing significantly higher drought frequency than other periods from January to June, while Guizhou and other areas were prone to severe droughts in summer and autumn. The Budyko model is widely used as the mainstream international framework for studying regional water and energy balance. In this research, the Budyko model was applied to analyze the water and energy balance characteristics in several arid regions of southwest China using drought monitoring data. Results indicate that the water and energy balances in Yunnan and Sichuan-Chongqing are more moisture-constrained, whereas those in Guizhou and Guangxi are relatively stable, suggesting lower susceptibility to extreme droughts. Furthermore, during severe drought periods, evapotranspiration becomes a dominant component of the water cycle, while available water resources such as soil moisture decrease. After comparing the causes of drought and non-drought years, it was found that the average rainfall in southwest China is approximately 30% below normal during drought years, and the temperature is 1–2% higher than normal. These phenomena are most noticeable during the spring and winter months. Additionally, vegetation transpiration is about 10% greater than normal during dry years in Southwest China, and soil evaporation increases by about 5% during the summer and autumn months compared to normal conditions.

The start of frozen dates over northern permafrost regions with the changing climate.

The soil freeze-thaw cycle in the permafrost regions has a significant impact on regional surface energy and water balance. Although increasing efforts have been made to understand the responses of spring thawing to climate change, the mechanisms controlling the global interannual variability of the start date of permafrost frozen (SOF) remain unclear. Using long-term SOF from the combinations of multiple satellite microwave sensors between 1979 and 2020, and analytical techniques, including partial correlation, ridge regression, path analysis, and machine learning, we explored the responses of SOF to multiple climate change factors, including warming (surface and air temperature), start date of permafrost thawing (SOT), soil properties (soil temperature and volume of water), and the snow depth water equivalent (SDWE). Overall, climate warming exhibited the maximum control on SOF, but SOT in spring was also an important driver of SOF variability; among the 65.9% significant SOT and SOF correlations, 79.3% were positive, indicating an overall earlier thawing would contribute to an earlier frozen in winter. The machine learning analysis also suggested that apart from warming, SOT ranked as the second most important determinant of SOF. Therefore, we identified the mechanism responsible for the SOT-SOF relationship using the SEM analysis, which revealed that soil temperature change exhibited the maximum effect on this relationship, irrespective of the permafrost type. Finally, we analyzed the temporal changes in these responses using the moving window approach and found increased effect of soil warming on SOF. In conclusion, these results provide important insights into understanding and predicting SOF variations with future climate change.

Irrigation‐Induced Crop Growth Enhances Irrigation Cooling Effect Over the North China Plain by Increasing Transpiration

AbstractThe large‐scale irrigation is demonstrated to significantly affect regional hydroclimatic regime by changing underlying surface (e.g., crop growth and soil moisture), yet the contribution of irrigation‐induced crop “greening” to climatic effects is not well understood. In this study, we use a window‐searching algorithm to evaluate the irrigation effects on vegetation with leaf area index (LAI) as a vegetation index, and further conduct three regional climate simulations to investigate the comprehensive effects of irrigation on surface fluxes and local climate over the North China Plain. We find that irrigation leads to a more significant greening in March‐May (MAM) than in June‐September (JJAS). Especially in April and May, the irrigation‐induced change in LAI (ΔLAI) exceeds 0.4 m2 m−2 in intensively irrigated areas. Irrigation induces a cooling effect in air temperature at 2 m with decreasing magnitudes of 0.58°C in MAM and 0.43°C in JJAS, respectively, in which ΔLAI contributes about 34.5% (0.20°C) and 14.0% (0.06°C). Likewise, the irrigation‐induced changes in latent heat flux, sensible heat flux, and transpiration are all enlarged via the irrigation greening effect. Whereas the increase in soil evaporation is alleviated, because the greening‐triggered enhancement of crop root uptake reduces the increase in soil moisture. Moreover, irrigation effects on net radiation depend on the competing influences of irrigation‐triggered cooling and cloud formation. This study provides beneficial references to understand the impact of irrigation on regional energy balance and water cycle, and highlights that in modeling hydroclimatic feedback to irrigation, the greening effect induced by irrigation should be considered.

Vertical transport of water in isolated convective clouds in the interior western United States as observed using airborne in-situ measurements

Redistribution of water by convection is important to the regional and global energy balance, but the characteristics of vertical water transport in convective clouds at different heights are not well understood. In this study, the vertical transport of water in continental isolated convective clouds is investigated using airborne in-situ measurements made in High Plain Cumulus (HiCu) project. The profiles of the mass flux of water vapor and condensed water, and the cloud width and vertical velocity dependency of the relative contribution to the water budget are statistically analyzed. The results show: 1) The probability density functions (PDFs) of the water vapor mass flux were in lognormal distributions and narrowed with height, while the PDFs of the mass flux of condensed water broadened with height from 2 to 8 km. 2) The net flux of vapor decreased significantly with height. Though the flux of condensed water increased with height, the net flux of total water at cloud top was only about 40% of the influx at cloud base. Averagely, about 80% of the observed condensed water at cloud top was in liquid phase. 3) The clouds provided the main contribution to the water vapor flux transit from small-scale at low-levels to medium and large-scale at high-levels. Overall, the clouds in a scale of 1–5 km had the major contribution to the water mass flux. 4) As the clouds developed vertically, stronger drafts accounted for an increasing contribution to the water budget, and most of the upward water transport can be attributed to updraft cores.

Possibility of balanced development of the Volga Federal District of Russia while maintaining the carbon cycle and transition to renewable energy

The article provides an assessment of the possibility of a balanced development of the regions of the Volga Federal District of the Russian Federation due to an increase in the share of renewable energy in the regional energy balance and subject to the preservation of the carbon cycle. Particular attention was paid to agricultural areas, which, in a case of irrational nature management, can significantly disrupt the carbon cycle. Thanks to the use of a special indicator, it was found that the Volga Federal District is in an unbalanced state. It is possible to correct the situation by increasing the share of alternative energy to 52-96 per cent in regional energy balances (77 per cent on average in the district). In eleven regions of the District, there is carbon dioxide emission from anthropogenically disturbed territories, covered mainly by agricultural areas. In order to preserve the carbon cycle in the Volga Federal District, it is necessary to bring 136,200 sq. km of territory into a special nature management regime, mainly steppe ecosystems and pastures, which cover 10.9 per cent of the total area of the district.

Prediction of Future Spatial and Temporal Evolution Trends of Reference Evapotranspiration in the Yellow River Basin, China

Reference evapotranspiration (ET0) is an integral part of the regional hydrological cycle and energy balance and is extremely sensitive to climate change. Based on temperature data from 24 global climate models (GCMs) in the Coupled Model Intercomparison Project Phase 6 (CMIP6), this study developed a multi-model ensemble based on delta statistical downscaling with multiple interpolation methods and evaluation indicators to predict the spatial and temporal evolution trends of ET0 in the Yellow River Basin (YRB) under four emission scenarios (SSP126, SSP245, SSP370, and SSP585) for the near- (2022–2040), mid- (2041–2060), and long- (2081–2100) term future. Results demonstrate that regional data generated based on delta statistical downscaling had good simulation performance for the monthly mean, maximum, and minimum temperatures in the YRB, and the developed multi-model ensemble had better simulation capability than any single model. Compared to the historical period (1901–2014), the annual ET0 showed a highly significant increase for different future emission scenarios, and the increase is faster with increasing radiative forcing. The first main cycle of ET0 change was 52, 53, 60, and 48 years for the SSP126, SSP245, SSP370, and SSP585, respectively. ET0 in the YRB had positive values for EOF1 under all four emission scenarios, responding to a spatially consistent trend across the region. Compared to the historical period, the spatial distribution of ET0 under different future emission scenarios was characterized by being larger in the west and smaller in the east. As the radiative forcing scenario increased and time extended, ET0 significantly increased, with a maximum variation of 112.91% occurring in the western part of the YRB in the long-term future under the SSP585 scenario. This study can provide insight into the water cycle patterns of watersheds and scientific decision support for relevant departments to address the challenges of climate change.

Can evaporation from urban impervious surfaces be ignored?

Urban evapotranspiration is an important component linking the urban hydrological cycle and energy balance. Previous studies on urban evapotranspiration (ET) mainly focused on evaporation from the soil, vegetation, and water surfaces. However, the urban Ei process was overlooked mainly due to its low amount. In this study, the urban Ei was determined by eddy correlation (EC) observations combined with a flux footprint model and urban land use information for two urban EC sites. The evaluation of flux data quality indicated that more than 76 % of observed flux data can be used for Ei analysis at the two stations during rainy periods. The daily urban Ei exhibited high intermittent temporal patterns. The Ei value was 0 during the non-rainy period and peaked on the following 1–2 days after the rainy event, then gradually decreased to 0. The average daily Ei accounted for 11–14 % of the total ET for the two EC stations during the rainy period. Further analysis indicated that urban Ei significantly altered the urban energy balance and turbulent transport processes. The urban Ei reduced sensible heat flux (H) and Bowen ratio (BR) during the rainfall period, thereby playing a vital role in mitigating urban heat land effect. Moreover, urban Ei improved the turbulent transport efficiency of latent heat flux (rwq) and restored turbulent transport similarity in an urban area. The temporal characteristics of urban Ei exhibited a large discrepancy among different impervious surface materials, which mainly depend on the water retention and thermodynamic properties of the impervious surface. Gravel had the best water retention capability and resulted in the highest spatially averaged Ei, while the concrete surface had the best thermal storage capability and resulted in the longest duration time of Ei. The spatial average of urban Ei and its duration time were positively correlated with the water retention and heat storage capabilities of impervious surface materials. This study gained insight into urban Ei, including its determination method, temporal characteristics, controlling factors, and impacts of urban Ei on the regional energy balance and turbulent transport process.

A comparison study on the role of urbanization in altering the short-duration and long-duration intense rainfall

Urbanization has significantly changed the regional hydrological cycle and energy balance. However, the different roles of urbanization on intense rainfall with short and long persistence need a better understanding, particularly in coastal mega cities with complex terrains. In this study, we compared the spatial and diurnal characteristics of intense rainfall across two coastal cities with different degrees of urbanization with 70 meteorological stations. The effects of anthropogenic and geographical factors on short-duration intense rainfall (SDIR) and long-duration intense rainfall (LDIR) were investigated using the statistical method. SDIR and LDIR events show different spatial patterns that SDIR events center in the highly urbanized regions while LDIR events center in the coastal mountainous regions. In terms of diurnal features, it is found that the higher occurrence frequency of SDIR in the dense urban region than that in suburbs occurs over the period with strong urban heat island intensity (UHII). It indicated that thermal contrast between urban and suburbs breeds an atmospheric environment for inspiriting the convection and SDIR events. The urban-induced increase in SDIR events depended on urbanization stages, with only dense urban regions showing a significant influence. The results of geographical detector model (GDM) also demonstrated that the synergy of build-up area and population explained 48 %–51 % of spatial heterogeneity of SDIR. Nevertheless, urbanization has little effect in modifying the diurnal features of LDIR events, while it might influence spatial rainfall patterns by enhancing rainfall peaks. The GDM results indicated that terrain positively dominates the spatial distribution for LDIR events, and the interactions of multi-factors (terrain, urbanization, or distance to the coast et al.) have enhanced explanatory power (q values up to 0.70). The results provide a fundamental understanding for the effects of anthropogenic and geographical factors on different types of rainfall events in coastal mega cities.

An inter-city energy migration framework for regional energy balance through daily commuting fuel-cell vehicles

Spatiotemporal energy interaction and sharing are promising solutions to penetrate renewable energy, enhance grid power stability, and improve regional energy flexibility. However, the current literature is restrained in a small-scale neighborhood level, without considering inter-city energy migration through spatiotemporal complementarity between renewable-abundant regions (like suburb or countryside areas) and demand-shortage regions (like city centers). In this study, the energy interaction boundary is extended from a neighborhood scale to an inter-city scale, to maximize the renewable energy penetration, demand coverage, and reduce regional energy imbalance. This study firstly proposes a holistic framework on inter-city transportation-based energy migration, consisting of a residential community with rooftop photovoltaic systems and electrical batteries, an office building, hydrogen vehicles (HVs), a hydrogen (H2) station, and local power grids, for the energy transmission between building groups in spatially different regions through the daily commuting of HVs. Optimal grid-regulation strategies are thereafter proposed and adopted to stabilize the grid power and reduce energy costs. Parametric analysis on energy trading strategies and prices has been conducted, to improve the participation motivations of different stakeholders. Results indicate that, compared to the reference case with isolated buildings and vehicles, the transportation-based energy migration framework covers 23.2 % of the office energy demand and elevates the community’s renewable self-use ratio from 72.7 % to 98.6 %. Meanwhile, the maximum grid-export power in the renewable-abundant region (suburb residential community) and the annual grid-import power in the demand-shortage region (city-center office) are reduced by up to 86.9 % (from 155.7 to 20.4 kW) and 29.4 % (from 49.0 to 34.6 kW), respectively. Moreover, even considering the fuel cell degradation cost of HVs, the transportation-based energy migration framework reduces the operating costs of the office building and HVs (the H2 cost and the fuel cell degradation cost) by 16.4 % (from $52791.3 to $44154.7) and 1.7 % (from $27172.5 to $26707.4), respectively. Afterward, compared to the reference case, the peak-shaving and load-shaping grid-regulation strategies can decrease the peak grid-export power of the community by about 71.6 % (from 155.7 to 44.2 kW), and the maximum grid-import power of the office by 23.7 % (from 49.0 to 37.4 kW), respectively. Furthermore, the transportation-based energy migration framework is economically feasible, only when the renewable export price for H2 production is 0.07 $/kWh, the onsite-renewable-generated H2 lower than 6.5 $/kg for the HV owners, and the vehicle-to-building electricity lower than 0.3 $/kWh for the office building. This study provides a novel inter-city energy migration framework with hydrogen networks to enhance district energy sharing, improve regional energy balance and reduce carbon emission, together with frontier guidelines on energy trading prices to promote participation motivations from different stakeholders.

Methodological toolkit for assessing the impact of renewable energy on the balanced development and preservation of the regional carbon cycle

The article provides methodological tools that allow to carry out a socio-ecological and economic assessment of the balance of regions in the development of renewable (alternative) energy sources. When forecasting the development of renewable energy in the region, it was taken into account that, unlike fossil fuel-based energy, it does not have a significant impact on the environment, since it uses material and energy flows circulating in natural systems. During the study, it was found that only one transition to renewable energy sources will not allow achieving carbon balance in the regions. The landscape features of the region significantly affect the possibility of its conservation. Therefore, in each region, it will be necessary to identify the area of territories with a special nature conservation status that will allow maintaining the carbon cycle. Methodological tools were tested on the example of the Chelyabinsk region of the Russian Federation. As a result, it was found to maintain a balanced carbon cycle, it will be necessary to increase the share of natural steppes to 49.1% of the total area of the region, and achieve the value of renewable energy in the regional energy balance up to 93%.

Energy sharing and trading on a novel spatiotemporal energy network in Guangdong-Hong Kong-Macao Greater Bay Area

Spatiotemporal energy network with smart transportations for energy sharing can address regional imbalance distribution of renewable resources, dynamic intermittence and fluctuation of renewable power supply, enhance grid independence, or even provide voltage/power stabilization services to local power grids. Furthermore, difference in regional energy pricing policy can be fully utilised to economically incentivise participation willingness of stakeholders. However, the current literature provides few studies and little knowledge on the topic. In this study, a spatiotemporal energy network in Guangdong-Hong Kong-Macao Greater Bay Area was proposed for regional energy balance with different types of buildings in demand-shortage and renewable-abundant regions, and electric vehicles for daily commuting. Roles of electricity-powered vehicles include renewable charging for clean transportation through Building-to-Vehicle (B2V) interaction, spatiotemporal energy sharing through Vehicle-to-Building (V2B) interaction, voltage/power stabilization of local grids through Vehicle-to-Grid (V2G) interaction. Techno-economic-environmental potentials of the proposed spatiotemporal energy network are evaluated for carbon neutrality transition. Advanced energy pricing policy and energy interaction modes are identified, based on dynamic energy trading and associated cost, among building owners, vehicle owners, and utility companies of power grids. Economic analysis and decision-making on multi-stakeholders are analysed, including external cost with power grids, internal cost, and battery degradation cost. Results showed that the proposed spatiotemporal energy network with the V2G interaction mode can improve the renewable penetration ratio from 0.7 to 0.722, the demand coverage ratio from 0.021 to 0.039, with the decrease in equivalent CO2 emission from 5.04x106 to 4.78x106 kg by 5.2%, and annual import cost from 8.16x106 to 8.0x106 HK$ by 2.0%. However, compared to the isolated system, the spatiotemporal energy network will lead to the increase of battery degradation cost from 0.594x105 to 0.688x105 HK$, by 15.8%, due to the increase in battery charging/discharging cycles. In order to economically incentivise participation willingness of all stakeholders, advanced energy pricing policy and energy interaction modes are identified as Policy 5 and V2G interaction mode, under which vehicles can increase the internal trading cost from 0 to 1.78x105 HK$, overwhelming its battery depreciation cost at 0.688 x105 HK$. This study formulates a spatiotemporal energy network for regional energy balance, contributing to the carbon neutrality transition and development of Guangdong-Hong Kong-Macao Greater Bay Area with frontier energy planning, smart energy interaction and advanced energy price policies.

A Study on Evaporation Calculations of Agricultural Reservoirs in Hyper-Arid Areas

Free surface evaporation is an important process in regional water cycles and energy balance. The accurate calculation of free surface evaporation is of great significance for evaluating and managing water resources. In order to improve the accuracy of estimating reservoir evaporation in data-scarce arid regions, the applicability of the energy balance method was assessed to calculate water surface evaporation based on the evaporator and reservoir evaporation experiment. A correlation analysis was used to assess the major meteorological factors that affect water surface temperature to obtain the critical parameters of the machine learning models. The water surface temperature was simulated using five machine learning algorithms, and the accuracy of results was evaluated using the root mean square error (RMSE), correlation coefficient (r), mean absolute error (MAE), and Nash efficiency coefficient (NSE) between observed value and calculated value. The results showed that the correlation coefficient between the evaporation capacity of the evaporator, calculated using the energy balance method and the observed evaporation capacity, was 0.946, and the RMSE was 0.279. The r value between the calculated value of the reservoir evaporation capacity and the observed value was 0.889, and the RMSE was 0.241. The meteorological factors related to the change in water surface temperature were air temperature, air pressure, relative humidity, net radiation and wind speed. The correlation coefficients were 0.554, −0.548, −0.315, −0.227, and 0.141, respectively. The RMSE and MAE values of five models were: RF (0.464 and 0.336), LSSVM (0.468 and 0.340), LSTM (1.567 and 1.186), GA-BP (0.709 and 0.558), and CNN (1.113 and 0.962). In summary, the energy balance method could accurately calculate the evaporation of evaporators and reservoirs in hyper-arid areas. As an important calculation parameter, the water surface temperature is most affected by air temperature, and the RF algorithm was superior to the other algorithms in predicting water surface temperature, and it could be used to predict the missing data. The energy balance model and random forest algorithm can be used to accurately calculate and predict the evaporation from reservoirs in hyper-arid areas, so as to make the rational allocation of reservoir water resources.

Assessing the impact of urbanization on urban evapotranspiration and its components using a novel four-source energy balance model

Urbanization substantially changes many aspects including the regional hydrological cycle, energy balance, and microclimate. However, the degree to which urbanization alters urban evapotranspiration (ET) and its components (soil evaporation (E), vegetation transpiration (T), impervious surface evaporation (I) and water body evaporation (W)) remains unclear. A significant obstacle is the absence of a multi-source energy balance model for an urban area. To solve this issue, a customized four-source energy balance model for urban areas (FSU model) is proposed that differentiates between urban E, T, W, and I. The performance of the FSU model was verified using the eddy correlation (EC), stable hydrogen and oxygen isotope observations in a mega city: Tianjin, China. Long-term urban ET and its composition changes were reconstructed using the Landsat image during the period of 1986–2021 in Tianjin. Trend analyses demonstrate that urban ET, E, and T exhibit significant decreases of trend, while urban W, sensible heat flux (H), and Bowen ratio (BR) exhibit significant increases in trends with urbanization. Urban ET decreased at a rate of 1.41 mm/yr, corresponding to a ∼ 13% decrease below the long-term mean value of total urban ET during the period 1986–2021. Correlation analyses revealed a declining trend of urban ET, E, and T primarily caused by urban land use changes, while the increasing trends of urban W, H, and BR were mainly due to the urban microclimate changes. The proposed FSU model aids in assessment of the urban heat island (UHI) effect and facilitates scientific water resources management in urban areas. This research improves the in-depth understanding of the impact of urbanization on urban ET and its components.