Article
Geosciences, Multidisciplinary
Fei Feng, Kaicun Wang
Summary: By combining SunDu, satellite cloud fraction, and AOD data, this study generated high-spatial-resolution Rs over China from 2000 to 2017, with geographically weighted regression (GWR) showing better performance. The study found that incorporating satellite cloud fraction and AOD data into GWR can produce reliable long-term Rs variations based on SunDu-derived data.
EARTH SYSTEM SCIENCE DATA
(2021)
Article
Environmental Sciences
Irfan Uckan, Kameran Mohammed Khudhur
Summary: This study compares sunshine duration-based models with other meteorological parameter-based models and develops new forecasting models. The results show that models based on other meteorological parameters have better predictions, and the newly proposed models provide more accurate estimates of global solar radiation at different locations.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Environmental Sciences
Marcia Akemi Yamasoe, Nilton Manuel Evora Rosario, Samantha Novaes Santos Martins Almeida, Martin Wild
Summary: The study found a dimming effect on surface solar irradiation in Sao Paulo between 1961 and the early 1980s, with no brightening effect observed. There was an increasing trend in cloud cover fraction and surface downward irradiation, while sunshine duration and diurnal temperature range showed decreasing trends.
ATMOSPHERIC CHEMISTRY AND PHYSICS
(2021)
Article
Green & Sustainable Science & Technology
Zia ul Rehman Tahir, Saiqa Hafeez, Muhammad Asim, Muhammad Amjad, Muhammad Farooq, Muhammad Azhar, Ghulam Murtza Amjad
Summary: This study evaluated 30 models for estimating diffuse horizontal irradiance, with newly developed models outperforming those selected from literature. The performance of models varies across different zones and stations.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2021)
Article
Energy & Fuels
Shujing Qin, Zhihe Liu, Rangjian Qiu, Yufeng Luo, Jingwei Wu, Baozhong Zhang, Lifeng Wu, Evgenios Agathokleous
Summary: Accurate forecasting of daily global solar radiation (Rs) is important for photovoltaic power and other sectors. A novel sunshine duration converting method (n_new) based on forecasted temperature and weather types data was proposed and validated. The Rs_n new model showed better accuracy than the Rs_n com model and Rs_T model, with increased correlation coefficients (R) and index of agreement (dIA) and decreased root mean squared error (RMSE) for the 1-7 days lead time over 86 sites. The Rs_n new model is recommended for short-term daily Rs forecasting.
Article
Meteorology & Atmospheric Sciences
Greg Spellman, Danielle Bird
Summary: Solar power is becoming increasingly important as a clean energy source for mid-latitude nations like the UK. A study shows that there has been a continuous increase in sunshine duration since the mid-1980s worldwide. The study also explores the relationship between weather types and surface circulation features, finding a strong association between sunshine duration and cloudiness.
THEORETICAL AND APPLIED CLIMATOLOGY
(2023)
Review
Chemistry, Physical
Cicero Manoel dos Santos, Joao Francisco Escobedo, Amaury de Souza, Mauricio Bruno Prado da Silva, Flavio Aristone
Summary: This work describes the application of models to estimate the transmitted fraction of direct solar irradiation, with ANFIS models outperforming statistical models in terms of accuracy. Different regression methods and databases were used to validate the models, showing that ANFIS performed better in predicting solar irradiation transmission ratios.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2021)
Article
Astronomy & Astrophysics
Thomas Leirvik, Menghan Yuan
Summary: This study found that spatial and time series variables are more important than climatic variables in predicting surface solar radiation. The Random Forest method outperformed conventional interpolation methods in terms of accuracy.
EARTH AND SPACE SCIENCE
(2021)
Article
Chemistry, Analytical
Cesar G. Villegas-Mier, Juvenal Rodriguez-Resendiz, Jose Manuel Alvarez-Alvarado, Hugo Jimenez-Hernandez, Akos Odry
Summary: This study predicts solar radiation in the Queretaro area of Mexico using machine learning algorithms and compares the results with other models. The optimized models show significant improvements in accuracy compared to conventional methods, without increasing computational time and performance requirements.
Article
Thermodynamics
Khalil Benmouiza
Summary: In this study, the Kriging method is used to analyze sunshine duration data from 56 meteorological stations in Algeria between 1992 and 2002. The results provide valuable solar zoning maps, which categorize Algeria into regions with similar energy qualities and offer insights into the spatial and temporal distribution of solar energy resources. The utilization of the Kriging method enhances our ability to assess the solar potential of different regions and facilitates better planning and utilization of solar energy resources.
INTERNATIONAL JOURNAL OF HEAT AND TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Siwei Lin, Nan Chen
Summary: This study proposed a Martian SASR model by combining Earth's SASR theory, previous Martian SASR model, and planetary science theory. By introducing the spectrum method of geography, two new concepts of spectrums were defined to explore the spatial-temporal distribution of SASR and PSD in different Martian landforms. The results showed that SASR and PSD on Mars were influenced by terrain relief and latitude, with a gradual attenuation with terrain relief and a regular pattern of latitude anisotropy.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2021)
Article
Environmental Sciences
Fei Feng, Kaicun Wang
Summary: This study shows that SunDu-derived R-s is more accurate than direct observed R-s and satellite R-s products at monthly or longer time scales. The three AVHRR-based satellite R-s products have significant biases in quantifying the trend of R-s due to inhomogeneity in satellite cloud products and lack of information on atmospheric aerosol optical depth. Adjusting the inhomogeneity, a geographically weighted regression fusion method (HGWR) merges ISCCP-HXG R-s with SunDu-derived R-s to produce a high-resolution R-s product over China from 1983 to 2017 with similar trends to SunDu-derived R-s.
Article
Chemistry, Multidisciplinary
Stelios Pashiardis, Alexandros Pelengaris, Soteris A. Kalogirou
Summary: This study assessed hourly measurements of global solar irradiance obtained from different stations in Cyprus. The data was analyzed to provide useful information for engineers working on solar energy capture systems and energy efficiency. The study specifically focused on characterizing and analyzing hourly and daily solar global radiation.
APPLIED SCIENCES-BASEL
(2023)
Article
Energy & Fuels
Venant Sorel Chara-Dackou, Donatien Njomo, Mahamat Hassane Babikir, Ibrahim Ngapouth Mbouombouo, Sidica Aicha Pofoura Gboulie, Rene Tchinda
Summary: The objective of this study conducted in the Central African Republic is to propose new correlations between solar radiation and sunshine duration on the ground, and evaluate the solar potential in cities of Bambari, Birao, and Bangui. The results show that the solar potential in these regions is very favorable for photovoltaic applications.
JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME
(2022)
Article
Environmental Sciences
Kai Jin, Peng Qin, Chunxia Liu, Quanli Zong, Shaoxia Wang
Summary: The study found a trend of solar dimming in the Hangzhou region from 1987 to 2016, with significant differences in sunshine duration changes between urban, suburban, and rural stations. Urbanization effects, especially in the period of 2002-2016, were evident on sunshine duration trends, indicating the need to consider urbanization impacts when analyzing regional solar radiation trends.
Article
Economics
Jiaqi Ge, Lee-Ann Sutherland, J. Gary Polhill, Keith Matthews, Dave Miller, Douglas Wardell-Johnson
Article
Economics
S. Viglia, K. B. Matthews, D. G. Miller, D. Wardell-Johnson, M. Rivington, S. Ulgiati
Article
Agronomy
D. Cammarano, M. Rivington, K. B. Matthews, D. G. Miller, G. Bellocchi
EUROPEAN JOURNAL OF AGRONOMY
(2017)
Article
Economics
Jonathan Hopkins, Lee-Ann Sutherland, Melf-Hinrich Ehlers, Keith Matthews, Andrew Barnes, Luiza Toma
FOREST POLICY AND ECONOMICS
(2017)
Review
Agronomy
Gianni Bellocchi, Mike Rivington, Keith Matthews, Marco Acutis
AGRONOMY FOR SUSTAINABLE DEVELOPMENT
(2015)
Article
Computer Science, Information Systems
J. Gareth Polhill, Jiaqi Ge, Matthew P. Hare, Keith B. Matthews, Alessandro Gimona, Douglas Salt, Jagadeesh Yeluripati
Article
Multidisciplinary Sciences
Jiaqi Ge, J. Gareth Polhill, Keith B. Matthews, David G. Miller, Michael Spencer
Article
Green & Sustainable Science & Technology
Keith B. Matthews, Ansel Renner, Kirsty L. Blackstock, Kerry A. Waylen, Dave G. Miller, Doug H. Wardell-Johnson, Alba Juarez-Bourke, Juan Cadillo-Benalcazar, Joep F. Schyns, Mario Giampietro
Summary: The paper presents insights from conducting a pan-EU sustainability assessment using FADN data and SMA processes, focusing on the interactions between crop and livestock systems and the resulting impacts and challenges on the environment and other aspects.
Article
Environmental Studies
K. B. Matthews, Doug Wardell-Johnson, Dave Miller, Nuala Fitton, Ed Jones, Stephen Bathgate, Tim Randle, Robin Matthews, Pete Smith, Mike Perks
Article
Geography
Lee-Ann Sutherland, Jonathan Hopkins, Luiza Toma, Andrew Barnes, Keith Matthews
SCOTTISH GEOGRAPHICAL JOURNAL
(2017)
Article
Geography
Lee-Ann Sutherland, Luiza Toma, Andrew P. Barnes, Keith B. Matthews, Jon Hopkins
JOURNAL OF RURAL STUDIES
(2016)
Article
Environmental Studies
Bill Slee, Iain Brown, David Donnelly, Iain J. Gordon, Keith Matthews, Willie Towers
Article
Geography
Lee-Ann Sutherland, Keith Matthews, Kevin Buchan, Dave Miller
SCOTTISH GEOGRAPHICAL JOURNAL
(2014)
Article
Environmental Studies
K. B. Matthews, K. Buchan, D. G. Miller, W. Towers
Article
Environmental Studies
Andrew Barnes, Lee-Ann Sutherland, Luiza Toma, Keith Matthews, Steven Thomson
Article
Agronomy
Wenyi Xu, Bo Elberling, Per Lennart Ambus
Summary: The frequency and extent of wildfires in the Arctic have been increasing due to climate change. In this study, researchers conducted experiments in West Greenland to investigate the long-term impacts of climate warming on post-fire carbon dioxide exchange in arctic tundra ecosystems. They found that fire increased soil organic phosphorus concentrations and burned areas remained a net CO2 source five years after the fire. However, with four to five years of summer warming, the burned areas turned into a net CO2 sink.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Yuanhang Yang, Jiabo Yin, Shengyu Kang, Louise J. Slater, Xihui Gu, Aliaksandr Volchak
Summary: This study investigates the impacts of water and heat stress on carbon uptake in China and explores the driving mechanisms of droughts using a machine learning model. The results show that droughts are mostly driven by atmospheric dryness, with precipitation, relative humidity, and temperature playing dominant roles. Water and heat stress have negative impacts on carbon assimilation, and drought occurrence is projected to increase significantly in the future. Improving ecosystem resilience to climate warming is crucial in mitigating the negative effects of droughts on carbon uptake.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Ningbo Cui, Shunsheng Zheng, Shouzheng Jiang, Mingjun Wang, Lu Zhao, Ziling He, Yu Feng, Yaosheng Wang, Daozhi Gong, Chunwei Liu, Rangjian Qiu
Summary: This study proposes a method to partition evapotranspiration (ET) into its components in agroforestry systems. The method is based on water-carbon coupling theory and flux conservation hypothesis. The results show that the partitioned components agree well with measurements from other sensors. The study also finds that atmospheric evaporation demand and vegetation factors greatly influence the components of ET, and increased tree leaf area limits understory grass transpiration.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Xinhao Li, Tianshan Zha, Andrew Black, Xin Jia, Rachhpal S. Jassal, Peng Liu, Yun Tian, Chuan Jin, Ruizhi Yang, Feng Zhang, Haiqun Yu, Jing Xie
Summary: With the rapid increase of urbanization, evapotranspiration (ET) in urban forests has become increasingly important in urban hydrology and climate. However, there is still a large uncertainty regarding the factors that regulate ET in urban areas. This study investigates the temporal variations of ET in an urban forest park in Beijing using the eddy-covariance technique. The results show that daily ET is close to zero during winter but reaches 3-6 mm day-1 in summer. Daily ET increases with vapor pressure deficit (VPD) and soil water content (SWC). Monthly ET increases linearly with normalized difference vegetation index and shows a strong correlation with surface conductance (gs), while exhibiting saturated responses to increasing monthly precipitation (PPT). Annual ET ranges from 326 to 566 mm, and soil water replenishment through PPT from the previous year is responsible for the generally higher monthly ET in spring relative to PPT. Biotic factors and PPT seasonality play essential roles in regulating ET at different scales.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Zhaogang Liu, Zhi Chen, Meng Yang, Tianxiang Hao, Guirui Yu, Xianjin Zhu, Weikang Zhang, Lexin Ma, Xiaojun Dou, Yong Lin, Wenxing Luo, Lang Han, Mingyu Sun, Shiping Chen, Gang Dong, Yanhong Gao, Yanbin Hao, Shicheng Jiang, Yingnian Li, Yuzhe Li, Shaomin Liu, Peili Shi, Junlei Tan, Yakun Tang, Xiaoping Xin, Fawei Zhang, Yangjian Zhang, Liang Zhao, Li Zhou, Zhilin Zhu
Summary: This study investigates the responses of temperate grassland (TG) and alpine grassland (AG) to climate change by studying carbon (C) fluxes across different regions in China. The results reveal that water factors consistently increase C fluxes, while temperature factors have opposite effects on TG and AG. The study enhances our understanding of C sinks and grassland sensitivity to climate change.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Peng Li, Huijie Li, Bingcheng Si, Tao Zhou, Chunhua Zhang, Min Li
Summary: This study mapped the distribution of forest age on the Chinese Loess Plateau using the LandTrendr algorithm. The results show that the LT algorithm is a convenient, efficient, and reliable method for identifying forest age. The findings have important implications for assessing and quantifying biomass and carbon sequestration in afforestation efforts on the Chinese Loess Plateau.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Review
Agronomy
Yean-Uk Kim, Heidi Webber, Samuel G. K. Adiku, Rogerio de S. Noia Junior, Jean-Charles Deswarte, Senthold Asseng, Frank Ewert
Summary: As climate change is expected to increase the intensity and frequency of extreme weather events, it is crucial to assess their impact on cropping systems and explore adaptation options. Process-based crop models (PBCMs) have improved in simulating the impacts of major extreme weather events, but still struggle to reproduce low crop yields under wet conditions. This article provides an overview of the yield-loss mechanisms of excessive rainfall in cereals and the associated modelling approaches, aiming to guide improvements in PBCMs.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Xiaodong Liu, Yingjie Feng, Xinyu Zhao, Zijie Cui, Peiling Liu, Xiuzhi Chen, Qianmei Zhang, Juxiu Liu
Summary: Understanding the impact of climate on litterfall production is crucial for simulating nutrient cycling in forest ecosystems. This study analyzed a 14-year litterfall dataset from two subtropical forests in South China and found that litterfall was mainly influenced by wind speed during the wet season and by temperature during the dry season. These findings have potential significance in improving our understanding of carbon and nutrient cycling in subtropical forest ecosystems under climate change conditions.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Ruonan Chen, Liangyun Liu, Zhunqiao Liu, Xinjie Liu, Jongmin Kim, Hyun Seok Kim, Hojin Lee, Genghong Wu, Chenhui Guo, Lianhong Gu
Summary: Solar-induced chlorophyll fluorescence (SIF) has the potential to estimate gross primary production (GPP), but the quantitative relationship between them is not constant. In this study, a mechanistic model for SIF-based GPP estimation in evergreen needle forests (ENF) was developed, considering the seasonal variation in a key parameter of the model. The GPP estimates from this model were more accurate compared to other benchmark models, especially in extreme conditions.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Jingyi Zhu, Yanzheng Yang, Nan Meng, Ruonan Li, Jinfeng Ma, Hua Zheng
Summary: This study developed a random forest model using climate station and satellite data to generate high-precision precipitation datasets for the Qinghai-Tibet Plateau. By incorporating multisource satellite data, the model achieved a significant enhancement in precipitation accuracy and showed promising results in regions with limited meteorological stations and substantial spatial heterogeneity in precipitation patterns.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Yulin Yan, Youngryel Ryu, Bolun Li, Benjamin Dechant, Sheir Afgen Zaheer, Minseok Kang
Summary: Sustainable rice farming practices are urgently needed to meet increasing food demand, cope with water scarcity, and mitigate climate change. Traditional farming methods that prioritize a single objective have proven to be insufficient, while simultaneously optimizing multiple competing objectives remains less explored. This study optimized farm management to increase rice yield, reduce irrigation water consumption, and tackle the dilemma of reducing GHG emissions. The results suggest that the optimized management can maintain or even increase crop yield, while reducing water demand and GHG emissions by more than 50%.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Sasha D. Hafner, Jesper N. Kamp, Johanna Pedersen
Summary: This study compared micrometeorological and wind tunnel measurements using a semi-empirical model to understand wind tunnel measurement error. The results showed differences in emission estimates between the two methods, but the ALFAM2 model was able to reproduce emission dynamics for both methods when considering differences in mass transfer. The study provides a template for integrating and comparing measurements from different methods, suggesting the use of wind tunnel measurements for model evaluation and parameter estimation.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Wenfang Xu, Wenping Yuan, Donghai Wu, Yao Zhang, Ruoque Shen, Xiaosheng Xia, Philippe Ciais, Juxiu Liu
Summary: In the summer of 2022, China experienced record-breaking heatwaves and droughts, which had a significant impact on plant growth. The study also found that heatwaves were more critical than droughts in limiting vegetation growth.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Jiaqi Guo, Xiaohong Liu, Wensen Ge, Liangju Zhao, Wenjie Fan, Xinyu Zhang, Qiangqiang Lu, Xiaoyu Xing, Zihan Zhou
Summary: Vegetation photosynthetic phenology is an important indicator for understanding the impacts of climate change on terrestrial carbon cycle. This study evaluated and compared the abilities of different spectral indices to model photosynthetic phenology, and found that NIRv and PRI are effective proxies for monitoring photosynthetic phenology.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Arango Ruda Elizabeth, M. Altaf Arain
Summary: Temperate deciduous forests have significant impacts on regional and global water cycles. This study examined the effects of climate change and extreme weather events on the water use and evapotranspiration of a temperate deciduous forest in eastern North America. The results showed that photosynthetically active radiation and air temperature were the primary drivers of evapotranspiration, while vapor pressure deficit regulated water use efficiency. The study also found a changing trend in water use efficiency over the years, influenced by extreme weather conditions.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)