Sunshine Hours Research Articles

Coupling artificial neural network and sperm swarm optimization for soil temperature prediction at multiple depths.

Soil temperature (ST) stands as a pivotal parameter in the realm of water resources and irrigation. It serves as a guide for farmers, enabling them to determine optimal planting and fertilization timings. In the backdrop of regions like Iran, where water resources are scarce, a proficient and economical prediction model for ST, particularly at lower depths, becomes imperative. While recent models have demonstrated adeptness in predicting ST, in general, their error decreases with increasing depth, so that they had the lowest error at a depth of 100 cm. Addressing this gap, our study pioneers a novel hybrid model that excels in accurate daily ST prediction as it delves deeper. The models deployed encompass the multilayer perceptron (MLP) and an enhanced version, MLP coupled with the Sperm Swarm Optimization Algorithm (MLP-SSO). These models prognosticate daily ST across varying depths (5-100cm), leveraging meteorological parameters such as air temperature, relative humidity, wind speed, sunshine hours, and precipitation. These parameters are anchored to the Ahvaz and Sabzevar synoptic stations in Iran, spanned over the period from 1997 to 2022. Evaluation of our research outcomes unveils that the root mean square error (RMSE) witnesses its most substantial reduction at a depth of 100cm. For instance, at the Ahvaz station, the MLP-SSO model diminishes the RMSE value from 1.25 to 1.12°C, in contrast to the MLP model. Similarly, at the Sabzevar station, the RMSE value drops from 1.78 to 1.49°C using the coupled MLP-SSO model. These results robustly highlight the considerable enhancement brought about by the utilization of the MLP-SSO model, clearly surpassing the performance of the standalone MLP model. This emphasizes the potential and promise of the MLP-SSO model for future investigations, offering insights that can significantly advance the domain of soil temperature prediction and its implications for agricultural decision-making.

Effect of biotic and abiotic factors on the population dynamics of Nilaparvata lugens in the middle Gangetic Plains of India

Aim: To investigate the effect of biotic and abiotic factors on the population dynamics of brown planthopper Nilaparvata lugens (Stal) (Hemiptera: Delphacidae) in the middle Gangetic plains of India. Methodology: Field trials were conducted in the middle Gangetic plains of India at Agricultural Research Farm, B.H.U., Varanasi, Uttar Pradesh, India during Kharif season of 2018 and 2019 on the variety Swarna sub-1 to study the population dynamics and certain biotic and abiotic interaction with brown planthopper. Results: During Kharif 2018 and 2019, the highest mean number of brown planthopper population (45.00 ± 2.31 and 39.33 ±1.82 nos., respectively) were recorded during September in both seasons. Abiotic factors such as rainfall (r = - 0.556), relative humidity in the morning (r = 0.476), maximum temperature (r = 0.511), and sunshine hour (r = - 0.546) influenced planthopper population dynamics, and predictions were made with reasonable accuracy (R2 = 0.89) using the principal component regression analysis technique. Crop phenology was the most influential biotic factor on pest population density. The crop's booting stage had the highest incidence of planthoppers. Three different predatory populations (wolf spider, damselfly and ladybird beetle) were observed to feed on planthoppers in Kharif 2018 and four different predatory populations (wolf spider, damselfly, ladybird beetle and green mirid bug) were observed to feed on planthoppers in Kharif 2019, resulting in a reduction in crop pest density. Interpretation: These findings can be used to develop the precise management strategies for brown planthopper in the rice ecosystem. Key words: Abiotic, Biotic factors, Brown planthopper, Crop phenology, Middle Gangetic Plains, Natural enemies, Rice

Prediction of Soil Temperature in Wheat Field Using Machine Learning Models

ABSTRACT Wheat is a very vital cereal crop in the development of a nation’s economy and agricultural sector. A crucial element that profoundly affects the growth and productivity of wheat is the soil temperature. As an insulating layer, straw mulch controls the soil temperature. Field measurement of the soil temperature is time-consuming and costly; therefore, the present study investigates the applicability of random forest (RF), support vector machine (SVM) and generalized regression neural network (GRNN) models for estimating soil temperature in wheat fields covered with straw mulch using meteorological, soil cover and agronomical parameters. A field study was conducted on the wheat cultivar with two soil cover treatments for measuring the soil temperature at three depths (5, 50 and 80 cm). Soil plant analysis development value; soil cover and depth; daily meteorological data such as average air temperature, average relative humidity, sunshine hours and vapor pressure were used as inputs for machine learning models in training and testing. Statistical performance indicators showed applicability of three models with a root mean square error (RMSE) ranging from 0.245–0.595°C, 0.867–0.886°C and 0.557–0.812°C, Nash-Sutcliffe efficiency (NSE) ranging from 0.917–0.987, 0.823–0.836 and 0.804–0.935 and Lin’s concordance correlation coefficient (CCC) ranging from 0.949–0.991, 0.887–0.897 and 0.881–0.957 for the RF, SVM and GRNN, respectively. Results indicated better performance of the RF model for predicting the soil temperature at different depths in the case of both crop and soil cover conditions. Also, the uncertainty analysis indicated the better performance of RF with R-factor and P-factor value of 0.40 and 0.84, respectively.

Spatiotemporal changes in tourism climate comfort in Sichuan Province from 1961 to 2022

AbstractBased on the daily temperature, wind speed, relative humidity, sunshine hours and precipitation in Sichuan Province from 1961 to 2022, the spatiotemporal variation characteristics of tourism climate comfort in Sichuan Province were analysed using the temperature–humidity index (THI), precipitation index (P), sunshine duration index (SSD), wind chill index (WCI) and tourism climate index (TCI). The results show that the average annual TCI in Sichuan Province over the past 62 years is “acceptable” or above and shows a significant increasing trend at a rate of 0.018 per decade. On average, the best travel comfort period in Sichuan Province is from March to May and from October to November. The TCI increased significantly in cold seasons such as February and November with rates of 0.064 and 0.053 per decade, respectively. The increase in TCI is mainly controlled by the increase in THI and WCI, both of which are related to the increase in temperature, with a greater effect in cold seasons and at higher elevations. Most of China's 5A‐level tourist attractions in Sichuan Province (14 of 17) have an annual TCI of “acceptable” or better. Similarly, the top 3 months for climate comfort at these attractions tend to be March to May and October, with ratings reaching “good” or even “very good.” For the other three destinations, all characterized by glaciers and mountains, the top 3 months for climate comfort are concentrated in the warm months. The overall tourism climate comfort of Sichuan Province is increasing, and the climate is changing in a positive way for the tourism industry.

Risk-return analysis of clean energy grid project investment based on integrated ISM and Monte Carlo model

To solve the problem of complex and difficult to quantify factors affecting investment returns and risks in clean energy power grids, this study comprehensively applies the interpretive structural model and Monte Carlo model to the analysis of investment risk-returns in clean energy power grid projects. The interpretive structural model is utilized to analyze project investment returns, while the Monte Carlo model is used to analyze project investment risks. The project investment risk is based on the factor analysis of project investment returns, and key risk factors are identified through 1000 simulations, and the impact of these risks on project returns is quantified. By combining the two, the investability of the project is analyzed. The results showed that grid electricity prices, kilowatt hour subsidies, technology learning rates, total annual sunshine hours, and system power generation efficiency were key factors driving investment returns. The average expected value of investment return was about 20%, and the probability of investment return below 6% was close to 0. The overall project is worth investing in. From this, it can be seen that the research designed investment risk-return analysis methods for clean energy grid projects can effectively distinguish the main factors affecting investment returns and risks, and pre simulate the risk situation of returns. This study can provide reference for investor decision-making.

Analysis of ultra-high concentration solar cells integrated with a confined microjet impingement cooling

The high solar concentration levels result in an intense influx of energy onto the concentrated triple-junction solar cell, leading to elevated temperatures that can degrade the cell’s performance and overall lifespan. This necessitates the development of innovative and efficient cooling strategies to mitigate temperature-induced losses and ensure optimal energy conversion. This study introduces an innovative micro jet impingement cooling system designed for ultrahigh concentrated solar cells, addressing the critical challenge of temperature management under high solar concentration levels. Furthermore, structural modifications were proposed, including reducing ceramic layer thickness to decrease the thermal resistance and using thermal interface materials to improve the cell temperature uniformity. A three-dimensional computational fluid dynamics model was developed to evaluate the proposed system’s performance and compare it with the traditional jet impingement heat sink. The model was verified and validated using empirical data in the literature. In addition, the Response Surface Method was employed to provide a comprehensive understanding of the system’s behavior under various weather conditions and cooling fluid effects. The results showed that concentration ratio and direct normal irradiance significantly affect the system’s performance. Moreover, the new cooling system maintains temperatures below the recommended operating temperature. Finally, the environmental assessment indicated substantial energy payback time reductions at 20,000 Suns, which is significantly lower than at 1000 Suns. The CO2 emissions analysis shows a marked potential for emissions mitigation, particularly at higher CRs and longer sunshine hours. These insights offer a comprehensive overview of ultrahigh concentrated solar cell thermal system performance, highlighting its efficiency and environmental sustainability in solar energy applications.

Investigating the Impact of Climate Change Impacts on Direct-seeded Rice Production in Middle Gujarat Using the InfoCrop-rice Model

Rice (Oryza sativa L.) is a global food staple, essential for human nutrition and calorie intake, particularly in low- and middle-income countries. This study assesses the impact of climate change on direct-seeded rice (DSR) production in Middle Gujarat (India) using the InfoCrop-Rice model. The study evaluates potential climate scenarios under Representative Concentration Pathways (RCP) 4.5 and RCP 8.5, focusing on projected changes in temperature, rainfall, and sunshine hours, and their effects on rice yield and phenology. Field data from Kharif 2023 were used to calibrate the model, which projected significant increases in maximum and minimum temperatures, variable changes in rainfall, and consistent reductions in sunshine hours. By 2100, maximum temperatures are expected to rise by up to 6°C under RCP 8.5, and minimum temperatures could increase by as much as 7.5°C. Rainfall projections show high variability, with potential reductions and increases depending on the scenario and time period. Sunshine hours are projected to decline by approximately 6.6% under both RCP scenarios. The model simulations indicate a notable decline in rice yields, with reductions ranging from 18% to 36% by 2100. These results underscore the severe impact of climate change on DSR productivity. The study highlights the need for adaptive strategies, including improved water management, resilient crop varieties, and sustainable agricultural practices, to mitigate the adverse effects of climate change and ensure the sustainability of direct seeded rice production in Middle Gujarat.

Spatial and Temporal Variations of Climate Resources during the Growing Season of Early-Season Rice in Hunan Province

The rising temperatures and changes in precipitation due to climate change have significantly impacted agricultural production. Evaluating the effects of climate change on rice production is crucial for improving rice cultivation techniques and ensuring food security. It is essential to comprehensively examine the climatic, spatial, and temporal variations during the duration of crop production. Previous research has mainly focused on different rice planting areas, rice types, and various growth stages of rice. However, more research is needed on the climatic changes during the crop-growing season in specific regions. Therefore, this study compiled complete daily meteorological data from 37 meteorological stations in Hunan Province from 1961 to 2020. The period from 1961 to 2020 was divided into three segments: 1961–1980 (a), 1981–2000 (b), and 2001–2020 (c), to analyze the characteristics of agricultural climate resource changes during different growth stages of early-season rice in Hunan Province. Results show that the heat resources were significantly increased (accumulated temperature growth rate of 43.36 °C/10a), the sunshine resources were decreased by −14.60 h/10a, and the precipitation resources were slightly increased by 6.85 mm/10a. The increase in heat resources mainly occurs during the vegetative growth stage of early-season rice. Additionally, the high-value regions of heat resources and precipitation in period c are 97.8% and 34.2% higher than the average values of periods a and b, respectively. In contrast, the regions with high sunshine hours significantly decreased in period c compared to periods a and b. In summary, the heat, sunshine, and water resources in the central and eastern regions of Hunan Province increase simultaneously, and appropriate cultivation measures should be adopted in the future to improve the yield and resource utilization efficiency of early-season rice in a double-cropping system.

Succession of Major Pests and Predatory Fauna in Okra

Investigations on pest succession in okra crops were carried out during kharif, 2023 at the Department of Entomology, B. A. College of Agriculture, Anand Agricultural University, Anand. Pest succession based on crop growth stages showed that the population of sucking insect-pests viz., jassid (Amrasca biguttula biguttula Ishida), aphid (Aphis gossypii Glover), whitefly (Bemisia tabaci Gennadius.) observed from vegetative stage and remained up to crop matured while, red spider mite, Tetranychus cinnabarinus incidence appeared from reproductive stage to maturity stage. Among the lepidopteran pests, the infestation of semi-looper, Anomis flava (Fabricius) started in the second week after germination and remained till maturity of the crop whereas, shoot and fruit borer (Earias insulana Boisd.) and fruit borer (Helicoverpa armigera (Hubner) Hardwick) damaged the crop from the vegetative stage and remained up to crop maturity. Coccinellids and spider activity initiated with sucking insect-pest incidence and remained up to crop maturity. The weather parameters, Bright sunshine hours and maximum minimum temperature had significant relationships with A. biguttula biguttula, T. cinnabarinus, E. vittella and H. armigera. Whereas, relative humidity significantly negatively influenced the activity of red spider mite, E. vittella, H. armigera and positively on predator coccinellids. Furthermore, windspeed had a significant positive association with A. biguttula biguttula and E. vittella (shoot damage) while, negative with H. armigera larva.

Influence of weather parameters on rice blast disease progression in Tamil Nadu, India

Rice cultivation in Madurai district, Tamil Nadu, spans distinct cropping seasons: Kar (May – Jun), Semi-dry (Jul – Aug), Samba/ Late Samba (Aug – Sep), and Navarai (Dec – Jan), each with unique weather conditions and rice varieties. This study explores the correlation between weather parameters temperature, rainfall, humidity, sunshine hours, and wind speed and rice blast disease severity from 2021 to 2023. Using multiple linear regression with ordinary least squares (OLS), the analysis achieves high predictive accuracy (R² = 0.98). Results show that the maximum temperatures correlated negatively with disease severity (r = -0.869 to -0.892), while rainfall (r = 0.768 to 0.804) and wind speed (r = 0.766 to 0.938) correlated positively during the semi-dry season. Relative humidity exhibits varying impacts across seasons. These findings underscore the importance of tailored disease management strategies, such as targeted fungicidal applications during warmer seasons and optimized water management in others. By elucidating these dynamics, the study enhances understanding of weather-disease interactions, providing actionable insights to optimize disease management and enhance crop resilience in Madurai district.

Growth, yield and quality variability of flue-cured tobacco in response to soil and climatic factors in Northern Vietnam

Flue-cured tobacco is an annual industrial crop cultivated widely in more than 120 countries, with yield and quality varying between cultivars and with ecological factors. This study investigated variability of four flue-cured tobacco cultivars in response to 11 environmental factors, including soil properties and climatic conditions. The cultivars were grown in four locations in the northern upland region of Vietnam over two years. Growth characteristics, yield, leaf quality, and stability statistics were assessed. Variability and significant effects of cultivar, location, year, and their interactions on plant performance were observed. Positive associations between plant growth characteristics and yield were identified. Fresh and dry yields were significantly positively correlated with soil available nitrogen phosphorus, and temperature. All leaf quality characteristics were significantly correlated with soil available nitrogen, phosphorus, and sunshine hours. Canonical correlation analysis showed that yield was strongly influenced by four ecological factors. Leaf nitrogen and chlorine contents were influenced by seven and nine ecological factors, respectively. Low stability statistics, low average sum of ranks, moderate to high yield and leaf quality indicated that D65 was the most stable cultivar across locations and years. GL2 and GL7 were desired for their stability in leaf quality and yield, respectively.

Integrated membrane distillation and absorption chiller driven by solar energy: Concept and analysis

Solar energy linked to absorption chiller system is used to supply the heating and cooling energies to membrane distillation (MD) process. The heating load is taken directly from the solar energy system. The cooling load is provided by the absorption chiller system, which converts the solar energy into refrigeration power. Using a solar collector area of 60 m2 and MD feed flow rate of 600 kg/h, the maximum distillate production for a single MD can reach 61.5 kg/h, which corresponds to a recovery ratio of 10.2% and a gain output ratio (GOR) of 3.2. Increasing the MD feed flow rate necessitates enlarging the solar collector area to meet the escalating energy demand. An additional MD unit was also integrated and powered by the internal energy of the absorption chiller system. The total distillate production approaches 83 kg/h and the GOR enhances to 4.5. The condenser stream of the two integrated MD units is quenched by the refrigeration power of the absorption chiller system under split and joint scenarios. The split scenario was found to outperform the joint option in terms of providing higher average distillate production over the period of daily sunshine hours. However, the joint scenario can activate both MD units only if a larger solar collector of 100 m2 is employed and the condenser of the absorption chiller system is operated at 40 °C. Similarly, the split scenario can activate the two MD units only if split ratio equal or higher than 60% is enforced.

Population Dynamics of Major Insect Pests and Natural Enemies in Tomato

An experiment on major insect pests and natural enemies of tomato was conducted during rabi, 2022-23 at the experimental farm for PG research, Biswanath College of Agriculture, Sonitpur. Results revealed eleven insect pests; whitefly, Bemisia tabaci (Gennadius); aphid, Aphis craccivora Koch; leafminer, Lyriomyza trifolii (Burgess); fruitborer, Helicoverpa armigera (Hubner); jassid, Amrasca biguttula biguttula (Ishida); semilooper, Trichoplusia ni (Hubner) were found to be the most dominant species. Two coccinellid predators viz., Micraspis discolor(F) and Cheilomenes sexmaculata (F) and two spiders Lycosa tista Tikader, Anyphaena accentuata (Walckenaer) were recorded as natural enemies. It was observed that the incidence of B. tabaci, A. craccivora and A. biguttula biguttula showed significant negative correlation with both maximum and minimum temperature. B. tabaci population registered a positive significant correlation with morning relative humidity. Both L. trifolii and H. armigera exhibited significant negative correlation with bright sunshine hour. Furthermore, T. ni showed significant negative correlation with maximum temperature and bright sunshine hour. As regards coccinellids, a significant negative correlation existed with maximum and minimum temperature, and rainfall and significant positive correlation with morning relative humidity. A significant positive correlation of aphids was also observed with coccinellids.

Monitoring of Fall Army Worm <i>Spodoptera frugiperda</i> (J E Smith)

Study on monitoring of male moth fall army worm, Spodoptera frugiperda (J E Smith) was carried out in Agro-climatic zone-III at agricultural landscape of different farms of Anand Agricultural University, Anand during June 2020 to May 2022. A fifteen funnel shaped traps with lure for FAW were installed on border area and army-maize lure of Albero green organicz was suspended. The traps were arranged with at least 50 m distance between two traps which were spread over approximately 83 ha, Data from 104 standard meteorological week (SMW). Observation reveal that the counts ranged from 0.04 to 0.87 moths/trap/week from 24th SMW of 2020 to 23rd SMW of 2022. Higher numbers of non-target populations were captured in trap than the S. frugiperda. FAW moth catches/ week reported low positive correlation with bright sunshine hours (r=0.203) and very low positive correlation with maximum temperature (r=0.098); and markedly low negative correlation with evening relative humidity (r=-0.220), wind speed (r=-0.171) and rain fall (r=-0.126). It exhibited very low negative correlation with morning relative humidity (r=-0.048) and minimum temperature (r=-0.093).

Occurrence and distribution of phytic acid and its degradation products in soybeans in China: Analytical challenges

Phytic acid (IP6) and its degradation products lower myo-inositol phosphates exert different impacts on nutrient bioavailability and product quality characteristics. However, information regarding the occurrence of IP6 and its degradation products is scarce. In this work, simultaneous determination of IP6 and its degradation products in soybeans was developed, with emphasis on analysis by UPLC-MS/MS and a BEH Amide column both with hybrid surface technology. The retention and analyte/metal surface interactions issues were effectively addressed without ion-pairing reagents addition or derivatization. This method was applied to analyze soybeans from China. Total contents were 0.44–13.2 mg/g, and IP6 and its degradation product myo-inositol pentakisphosphate (IP5) were the predominant analytes, accounting for over 99%. Accession type significantly affected IP5 content, and landraces had significantly higher IP5 than cultivars. Geographically, the lowest IP6 was concentrated in the Huanghuaihai region. Significant correlations existed between IP6 and longitude, altitude, and annual cumulative sunshine hours. This study provides comprehensive insights into the IP6 and its degradation product profile in soybeans, which will benefit breeding soybeans based on specific requirements.

Climatic Suitability for Outdoor Tourism in Romania’s Big Cities

This research aims to assess the climatic temporal suitability over the year and identify the appropriate season for open-air tourism in ten Romanian cities. It was evaluated using the Enhanced Tourism Climatic Index on a temporal scale of one day and then aggregated to 10 days over 61 years (1961–2021). Daily mean and maximum temperature, mean and minimum relative humidity, wind speed, precipitation, and sunshine hours were employed in the investigation. The Mann–Kendall test and Sen’s slope were used for trend detection in the frequency, season duration, and first/last suitable day during the year for outdoor tourism. Acceptable or better weather conditions usually begin in the last part of April and end in mid-October, with Good or better conditions lasting between 260 and 310 days/year. The trend shows a shift of Good conditions earlier in the year (0.3–9.0 days/decade), resulting in a longer season duration (0.8–13.0 days/decade) for open-air activities. The trend is statistically significant mainly for the extra-Carpathian regions. Big differences in open-air events number during the climatically suitable season have been identified among the cities considered (2–19 events/year). This study is useful for better planning open-air events and activities for tourism and recreation.

The influence of cloudiness, hours of sunshine and precipitation on the occurrence of oestrus in cows and heifers in a temperate climate

This study aimed to determine whether the hours of sunshine and/or the amount of precipitation affect the number of cows and heifers that will show oestrus in the following days and the extent to which these climatic elements affect the average number of artificially inseminated cows and heifers per day. During the course of three consecutive years, a total of 21,043 cows and heifers were artificially inseminated in Koprivnica-Križevci County, Croatia. Descriptive statistics, analysis of variance, and Tukey pairwise comparisons were performed between the number of artificially inseminated cows and heifers, cloud cover, rain intensity, and number of days after rain. Significant differences were recorded in relation to cloud cover and the average number of cows and heifers artificially inseminated per day. The average number of artificially inseminated cows and heifers per day did not differ significantly among seasons (p>0.05). Significant differences were observed in the number of artificially inseminated cows and heifers per day in relation to rain (1, 2, 3, 4 or more days after rain) and in relation to the amount of precipitation (dry, light rain, rain, heavy rain, extreme rain). We can conclude that in a temperate climate, significantly fewer cows and heifers were in natural oestrus and artificially inseminated on rainy or cloudy days, while significantly more cattle were artificially inseminated on dry days following rain and on days with more hours of sunshine, regardless of season.

Spatiotemporal change characteristics of NDVI and response to climate factors in the Jixi Wetland, Eastern China.

Exploring the spatiotemporal variation characteristics of vegetation in the confluent area of water systems in western Jinan and its response mechanism to climatic factors is of great significance for the scientific evaluation of the benefits of the water system connectivity project and eco-environmental protection and can provide a reference for ecotourism development in the Jixi wetland park. Based on the Landsat series of images and meteorological data, this study used ENVI to interpret the normalized difference vegetation index (NDVI) of the confluent area from 2010 to 2021, and the spatiotemporal change characteristics and trends of NDVI were quantitatively analyzed. The response of the growing-season NDVI (GSN) to climate factors and its time-lag effect were explored. The results showed that the overall change in the interannual NDVI in the confluent area from 2010 to 2021 was stable. The GSN in the confluent area was significantly positively correlated with precipitation, average temperature, and relative humidity in 37.64%, 25.52%, and 20.87% of the area respectively, and significantly negatively correlated with sunshine hours in 15.32% of the area. There was a time-lag effect on the response of the GSN to climate factors; the response to precipitation and sunshine hours lagged by 1month, and the response to average temperature and relative humidity was longer.

Performance Evaluation of Artificial Neural Networks for Estimating Refer-ence Evapotranspiration in Shahat, Libya using limited climatic data

This study was conducted with the aim of evaluating the performance of artificial neural networks (ANNs) to estimate the reference evapotranspiration using limited climate data in Shahat region in Libya, compared to using the FAO Penman-Monteith equation (FPM), which requires temperature, wind speed, relative humidity and number of sunshine hours, which are rarely available in most meteorological stations in developing countries. In this study, we used the average temperature (Tmean) and the average relative humidity (RHmean) obtained from Shahat meteorological station for the period from 1963 to 1999, and the extraterrestrial radiation (Ra), which can be calculated given the location and time of the day. These data are divided into two groups, from 1963 to 1988 and from 1989 to 1999 for the training and validation phases of the neural networks, respectively. This study concluded that using (Tmean), (RHmean) and (Ra) gave the best agreement with the results calculated with the FAO Penman-Monteith equation, where the values of R2 and RMSE are equal to 0.98 and 0.26, respectively.

Spatial–temporal variation and correlation analysis of ecosystem service values and ecological risks in winter city Shenyang, China

Ecosystems in winter cities are complex and fragile, experiencing significant changes due to climate variations and human construction activities. Previous studies on the assessment of overall ecosystem service value (ESV) and ecological risk index (ERI) in winter cities are scarce. In this study, we constructed ESV and ERI measurement models using land use data in 2000, 2010, and 2020 using the improved value per unit area factor method and the landscape pattern index method, respectively, to reveal their spatial and temporal change characteristics. Geographic detectors were used to explore the driving roles of natural and artificial factors on the changes of ESV and ERI. The combination in ESV and ERI can then provide a more quantitative and accurate basis for policy decisions, identify priority areas for urban ecological restoration, and reduce the risk to ecosystems. The results of the study show that the total ESV of Shenyang city decreased from 273.97 × 108 CNY to 270.38 × 108 CNY during 2000–2020. Although the decrease is not large, the ESV changes structurally with the advancement of urbanization. During the 20 years, the construction land with the lowest ecological service function continues to expand, increasing by 354 km2, the grassland decreased by 215.9 km2, and the arable land decreased by 196.6 km2. The ecological service function of the water area is the strongest, with an increase of 51.3 km2 in the water area, ensuring that there is no significant decline in ESV. The size of the ERI is Very high, High, and Medium value zones remained relatively stable, while the size of the Very Low-value zone decreased by 12.78% and the size of the Low-value zone increased by 13.21%. The interaction factors that contributed most to the changes in ESV and ERI were annual evapotranspiration (EVP)/ Normalized Difference Vegetation Index (NDVI) and Annual sunshine hours (SSD)/ Digital Elevation Model (DEM) , respectively. There was a spatial correlation between ESV and ERI. The areas with the highest ESV supply capacity and at the same time facing severe ecological risks to the landscape pattern are distributed in the northeastern hilly lands. This area should be prioritized to develop planning and control measures to prevent further erosion of forest lands and grasslands and reduce ecological risks. These results provide a theoretical basis for ensuring ecological security and sustainable development in winter cities.