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Global climate change and human activities have resulted in immense changes in the Earth’s ecosystem, and the interaction between the land surface and the atmosphere is one of the most important processes. Wind is a reference for studying atmospheric dynamics and climate change, analyzing the wind speed change characteristics in historical periods, and studying the influence of wind on the Earth-atmosphere interaction; additionally, studying the wind, contributes to analyzing and alleviating a series of problems, such as the energy crisis, environmental pollution, and ecological deterioration facing human beings. In this study, data from 697 meteorological stations in China from 2000 to 2019 were used to study the distribution and trend of wind speed over the past two decades. The relationships between wind speed and climate factors were explored using statistical methods; furthermore, combined with terrain, climate change, and human activities, we quantified the contribution of environmental factors to wind speed. The results show that a downward trend was recorded before 2011, but overall, there was an increasing trend that was not significant; moreover, the wind speed changes showed obvious seasonality and were more complicated on the monthly scale. The wind speed trend mainly increased in the western region, decreased in the eastern region, was higher in the northeastern, northwestern, and coastal areas, and was lower in the central area. Temperature, bright sunshine duration, evaporation, and precipitation had a strong influence, in which wind speed showed a significant negative correlation with temperature and precipitation and vice versa for sunshine and evapotranspiration. The influence of environmental factors is diverse, and these results could help to develop environmental management strategies across ecologically fragile areas and improve the design of wind power plants to make better use of wind energy.
Yuming Lu; Bingfang Wu; Nana Yan; Weiwei Zhu; Hongwei Zeng; Zonghan Ma; Jiaming Xu; Xinghua Wu; Bo Pang. Quantifying the Contributions of Environmental Factors to Wind Characteristics over 2000–2019 in China. ISPRS International Journal of Geo-Information 2021, 10, 515 .
AMA StyleYuming Lu, Bingfang Wu, Nana Yan, Weiwei Zhu, Hongwei Zeng, Zonghan Ma, Jiaming Xu, Xinghua Wu, Bo Pang. Quantifying the Contributions of Environmental Factors to Wind Characteristics over 2000–2019 in China. ISPRS International Journal of Geo-Information. 2021; 10 (8):515.
Chicago/Turabian StyleYuming Lu; Bingfang Wu; Nana Yan; Weiwei Zhu; Hongwei Zeng; Zonghan Ma; Jiaming Xu; Xinghua Wu; Bo Pang. 2021. "Quantifying the Contributions of Environmental Factors to Wind Characteristics over 2000–2019 in China." ISPRS International Journal of Geo-Information 10, no. 8: 515.
Assessment of soil loss and understanding its major drivers are essential to implement targeted management interventions. We have proposed and developed a Revised Universal Soil Loss Equation framework fully implemented in the Google Earth Engine cloud platform (RUSLE-GEE) for high spatial resolution (90 m) soil erosion assessment. Using RUSLE-GEE, we analyzed the soil loss rate for different erosion levels, land cover types, and slopes in the Blue Nile Basin. The results showed that the mean soil loss rate is 39.73, 57.98, and 6.40 t ha−1 yr−1 for the entire Blue Nile, Upper Blue Nile, and Lower Blue Nile Basins, respectively. Our results also indicated that soil protection measures should be implemented in approximately 27% of the Blue Nile Basin, as these areas face a moderate to high risk of erosion (>10 t ha−1 yr−1). In addition, downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from 25 km to 1 km spatial resolution significantly impacts rainfall erosivity and soil loss rate. In terms of soil erosion assessment, the study showed the rapid characterization of soil loss rates that could be used to prioritize erosion mitigation plans to support sustainable land resources and tackle land degradation in the Blue Nile Basin.
Abdelrazek Elnashar; Hongwei Zeng; Bingfang Wu; Ayele Almaw Fenta; Mohsen Nabil; Robert Duerler. Soil erosion assessment in the Blue Nile Basin driven by a novel RUSLE-GEE framework. Science of The Total Environment 2021, 793, 148466 .
AMA StyleAbdelrazek Elnashar, Hongwei Zeng, Bingfang Wu, Ayele Almaw Fenta, Mohsen Nabil, Robert Duerler. Soil erosion assessment in the Blue Nile Basin driven by a novel RUSLE-GEE framework. Science of The Total Environment. 2021; 793 ():148466.
Chicago/Turabian StyleAbdelrazek Elnashar; Hongwei Zeng; Bingfang Wu; Ayele Almaw Fenta; Mohsen Nabil; Robert Duerler. 2021. "Soil erosion assessment in the Blue Nile Basin driven by a novel RUSLE-GEE framework." Science of The Total Environment 793, no. : 148466.
Cropland evapotranspiration (ET) is the major source of water consumption in agricultural systems. The precise management of agricultural ET helps optimize water resource usage in arid and semiarid regions and requires field-scale ET data support. Due to the combined limitations of satellite sensors and ET mechanisms, the current high-resolution ET models need further refinement to meet the demands of field-scale ET management. In this research, we proposed a new field-scale ET estimation method by developing an allocation factor to quantify field-level ET variations and allocate coarse ET to the field scale. By regarding the agricultural field as the object of the ET parcel, the allocation factor is calculated with combined high-resolution remote sensing indexes indicating the field-level ET variations under different crop growth and land-surface water conditions. The allocation ET results are validated at two ground observation stations and show improved accuracy compared with that of the original coarse data. This allocated ET model provides reasonable spatial results of field-level ET and is adequate for precise agricultural ET management. This allocation method provides new insight into calculating field-level ET from coarse ET datasets and meets the demands of wide application for controlling regional water consumption, supporting the ET management theory in addressing the impacts of water scarcity on social and economic developments.
Zonghan Ma; Bingfang Wu; Nana Yan; Weiwei Zhu; Hongwei Zeng; Jiaming Xu. Spatial Allocation Method from Coarse Evapotranspiration Data to Agricultural Fields by Quantifying Variations in Crop Cover and Soil Moisture. Remote Sensing 2021, 13, 343 .
AMA StyleZonghan Ma, Bingfang Wu, Nana Yan, Weiwei Zhu, Hongwei Zeng, Jiaming Xu. Spatial Allocation Method from Coarse Evapotranspiration Data to Agricultural Fields by Quantifying Variations in Crop Cover and Soil Moisture. Remote Sensing. 2021; 13 (3):343.
Chicago/Turabian StyleZonghan Ma; Bingfang Wu; Nana Yan; Weiwei Zhu; Hongwei Zeng; Jiaming Xu. 2021. "Spatial Allocation Method from Coarse Evapotranspiration Data to Agricultural Fields by Quantifying Variations in Crop Cover and Soil Moisture." Remote Sensing 13, no. 3: 343.
Accurate precipitation data at high spatiotemporal resolution are critical for land and water management at the basin scale. We proposed a downscaling framework for Tropical Rainfall Measuring Mission (TRMM) precipitation products through integrating Google Earth Engine (GEE) and Google Colaboratory (Colab). Three machine learning methods, including Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Artificial Neural Network (ANN) were compared in the framework. Three vegetation indices (Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI; Leaf Area Index, LAI), topography, and geolocation are selected as geospatial predictors to perform the downscaling. This framework can automatically optimize the models’ parameters, estimate features’ importance, and downscale the TRMM product to 1 km. The spatial downscaling of TRMM from 25 km to 1 km was achieved by using the relationships between annual precipitations and annually-averaged vegetation index. The monthly precipitation maps derived from the annual downscaled precipitation by disaggregation. According to validation in the Great Mekong upstream region, the ANN yielded the best performance when simulating the annual TRMM precipitation. The most sensitive vegetation index for downscaling TRMM was LAI, followed by EVI. Compared with existing downscaling methods, the proposed framework for downscaling TRMM can be performed online for any given region using a wide range of machine learning tools and environmental variables to generate a precipitation product with high spatiotemporal resolution.
Abdelrazek Elnashar; Hongwei Zeng; Bingfang Wu; Ning Zhang; Fuyou Tian; Miao Zhang; Weiwei Zhu; Nana Yan; Zeqiang Chen; Zhiyu Sun; Xinghua Wu; Yuan Li. Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing. Remote Sensing 2020, 12, 3860 .
AMA StyleAbdelrazek Elnashar, Hongwei Zeng, Bingfang Wu, Ning Zhang, Fuyou Tian, Miao Zhang, Weiwei Zhu, Nana Yan, Zeqiang Chen, Zhiyu Sun, Xinghua Wu, Yuan Li. Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing. Remote Sensing. 2020; 12 (23):3860.
Chicago/Turabian StyleAbdelrazek Elnashar; Hongwei Zeng; Bingfang Wu; Ning Zhang; Fuyou Tian; Miao Zhang; Weiwei Zhu; Nana Yan; Zeqiang Chen; Zhiyu Sun; Xinghua Wu; Yuan Li. 2020. "Downscaling TRMM Monthly Precipitation Using Google Earth Engine and Google Cloud Computing." Remote Sensing 12, no. 23: 3860.
This review describes the latest progress of dryland ecosystem dynamic change in the Mediterranean region. Recent findings indicate that extent of dryland in the Mediterranean region has been expanding in the past decades and will continue to expand in the coming decades due to the stronger warming effect than other regions. The warming trend with intensified human activities has generated a series of negative impacts on productivity, biodiversity, and stability of the dryland ecosystem in Mediterranean region. Increased population, overgrazing and, grazing abandonment intensified the land degradation and desertification. The coverage, richness, and abundance of biological soil crust have been reduced due to the decline of soil water availability and increased animals. Future studies are required to further our understanding of the process and mechanism of the dryland dynamics, including the identification of essential variables, discriminating human and climate-induced changes, and modeling future trajectories of dryland changes.
Hongwei Zeng; Bingfang Wu; Miao Zhang; Ning Zhang; Abdelrazek Elnashar; Liang Zhu; Weiwei Zhu; Fangming Wu; Nana Yan; Wenjun Liu. Dryland ecosystem dynamic change and its drivers in Mediterranean region. Current Opinion in Environmental Sustainability 2020, 48, 59 -67.
AMA StyleHongwei Zeng, Bingfang Wu, Miao Zhang, Ning Zhang, Abdelrazek Elnashar, Liang Zhu, Weiwei Zhu, Fangming Wu, Nana Yan, Wenjun Liu. Dryland ecosystem dynamic change and its drivers in Mediterranean region. Current Opinion in Environmental Sustainability. 2020; 48 ():59-67.
Chicago/Turabian StyleHongwei Zeng; Bingfang Wu; Miao Zhang; Ning Zhang; Abdelrazek Elnashar; Liang Zhu; Weiwei Zhu; Fangming Wu; Nana Yan; Wenjun Liu. 2020. "Dryland ecosystem dynamic change and its drivers in Mediterranean region." Current Opinion in Environmental Sustainability 48, no. : 59-67.
This study designed an approach to derive land-cover in the South Africa with insufficient ground samples, and made a case demonstration in Nzhelele and Levhuvu catchments, South Africa. The method was developed based on an integration of Landsat 8, Sentinel-1, and Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and the Google Earth Engine (GEE) platform. Random forest classifier with 300 trees is employed as land-cover classification model. In order to overcome the defect of insufficient ground data, the stratified sampling method was used to generate the training and validation samples from the existing land-cover product. Likewise, in order to recognize different land-cover categories, the percentile and monthly median composites were employed to expand input metrics of random forest classifier. Results showed that the overall accuracy of the land-cover of Nzhelele and Levhuvu catchments, South Africa in 2017–2018 reached to 76.43%. Three important results can be drawn from our research. 1) The participation of Sentinel-1 data can slightly improve overall accuracy of land-cover while its contribution on land-cover classification varied with land types. 2) Under-fitting problem was observed in the training of non-dominant land-cover categories using the random sampling, the stratified sampling method is recommended to make sure the classification accuracy of non-dominant classes. 3) When related reflectance bands participated in the training process, individual Normalized Difference Vegetation index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI) have little effect on final land-cover classification result.
Hongwei Zeng; Bingfang Wu; Shuai Wang; Walter Musakwa; Fuyou Tian; Zama Eric Mashimbye; Nitesh Poona; Mavengahama Syndey. A Synthesizing Land-cover Classification Method Based on Google Earth Engine: A Case Study in Nzhelele and Levhuvu Catchments, South Africa. Chinese Geographical Science 2020, 30, 397 -409.
AMA StyleHongwei Zeng, Bingfang Wu, Shuai Wang, Walter Musakwa, Fuyou Tian, Zama Eric Mashimbye, Nitesh Poona, Mavengahama Syndey. A Synthesizing Land-cover Classification Method Based on Google Earth Engine: A Case Study in Nzhelele and Levhuvu Catchments, South Africa. Chinese Geographical Science. 2020; 30 (3):397-409.
Chicago/Turabian StyleHongwei Zeng; Bingfang Wu; Shuai Wang; Walter Musakwa; Fuyou Tian; Zama Eric Mashimbye; Nitesh Poona; Mavengahama Syndey. 2020. "A Synthesizing Land-cover Classification Method Based on Google Earth Engine: A Case Study in Nzhelele and Levhuvu Catchments, South Africa." Chinese Geographical Science 30, no. 3: 397-409.
Hongwei Zeng; Lijuan Li; Jinming Hu; Liqiao Liang; Jiuyi Li; Bin Li; Kai Zhang. Accuracy validation of TRMM Multisatellite Precipitation Analysis daily precipitation products in the Lancang River Basin of China. Theoretical and Applied Climatology 2012, 112, 389 -401.
AMA StyleHongwei Zeng, Lijuan Li, Jinming Hu, Liqiao Liang, Jiuyi Li, Bin Li, Kai Zhang. Accuracy validation of TRMM Multisatellite Precipitation Analysis daily precipitation products in the Lancang River Basin of China. Theoretical and Applied Climatology. 2012; 112 ():389-401.
Chicago/Turabian StyleHongwei Zeng; Lijuan Li; Jinming Hu; Liqiao Liang; Jiuyi Li; Bin Li; Kai Zhang. 2012. "Accuracy validation of TRMM Multisatellite Precipitation Analysis daily precipitation products in the Lancang River Basin of China." Theoretical and Applied Climatology 112, no. : 389-401.
Drought is one of the most destructive disasters in the Lancang River Basin, which is an ungauged basin with strong heterogeneity on terrain and climate. Our validation suggested the version-6 monthly TRMM multi-satellite precipitation analysis (TMPA; 3B43 V.6) product during the period 1998 to 2009 is an alternative precipitation data source with good accuracy. By using the standard precipitation index (SPI), at the grid point (0.25°×0.25°) and sub-basin spatial scales, this work assessed the effectiveness of TMPA in drought monitoring during the period 1998 to 2009 at the 1-month scale and 3-months scale; validated the monitoring accuracy of TMPA for two severe droughts happened in 2006 and 2009, respectively. Some conclusions are drawn as follows. (1) At the grid point spatial scale, in comparison with the monitoring results between rain gauges (SPI1g) and TMPA grid (SPI1s), both agreed well at the 1-month scale for most of the grid points and those grid points with the lowest critical success index (CSI) are distributed in the middle stream of the Lancang River Basin. (2) The same as SPI1s, the consistency between SPI3s and SPI3g is good for most of the grid points at the 3-months scale, those grid points with the lowest were concentrated in the middle stream and downstream of the Lancang River Basin. (3) At the 1-month scale and 3-months scale, CSI ranged from 50% to 76% for most of the grid points, which demonstrated high accuracy of TMPA in drought monitoring. (4) At the 3-months scale, based on TMPA basin-wide precipitation estimates, though we tended to overestimate (underestimate) the peaks of dry or wet events, SPI3s detected successfully the occurrence of them over the five sub-basins at the most time and captured the occurrence and development of the two severe droughts happened in 2006 and 2009. This analysis shows that TMPA has the potential for drought monitoring in data-sparse regions.
Hongwei Zeng; Lijuan Li; Jiuyi Li. The evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) in drought monitoring in the Lancang River Basin. Journal of Geographical Sciences 2012, 22, 273 -282.
AMA StyleHongwei Zeng, Lijuan Li, Jiuyi Li. The evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) in drought monitoring in the Lancang River Basin. Journal of Geographical Sciences. 2012; 22 (2):273-282.
Chicago/Turabian StyleHongwei Zeng; Lijuan Li; Jiuyi Li. 2012. "The evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) in drought monitoring in the Lancang River Basin." Journal of Geographical Sciences 22, no. 2: 273-282.