Percent Cloud Cover Research Articles

The Inter‐Model Uncertainty of Projected Precipitation Change in Northern China: The Modulating Role of North Atlantic Sea Surface Temperature

AbstractPrecipitation changes in northern China are projected to increase in the Coupled Model Inter‐comparison Project Phase 6 (CMIP6) multi‐model ensemble. However, these projections are accompanied by notable inter‐model uncertainty, and the sources of this uncertainty remain largely unexplored. By analyzing 30 CMIP6 models, this research explores the source of inter‐model uncertainty in projected precipitation change and reveals the fundamental mechanism driving uncertainty spread. Following the empirical orthogonal function of inter‐model projected precipitation change, the leading mode displays a seesaw spatial pattern between northwest and north China. This phenomenon predominantly stems from the inter‐model divergence of projected sea surface temperature (SST) warming in the North Atlantic. Further scrutinizing the ocean mixed layer heat budget, we discover that the combined effect of surface sensible heat flux, net surface shortwave radiation flux, and ocean heat transport convergence influences heat flux and SST change of North Atlantic. The multi‐model projections indicate that localized increases in solar radiation and heat convergence warm sea surface, raising SST and initiating convective motion. This convective motion subsequently transforms the 200 hPa teleconnection wave train, leading to an anti‐phase pattern over northern China. This wave pattern modulates total cloud cover percentage, influences surface upward latent heat flux, and adjusts the top of atmosphere outgoing longwave radiation, collectively resulting in the seesaw pattern. Our study underscores the pivotal role of inter‐model disparities in North Atlantic SST warming projection, which is a primary driver of precipitation uncertainty in northern China. These insights offer an essential foundation for refining and diminishing inter‐model uncertainty.

Current Output Estimation of 12 Volts Solar Power Line Source Using Fuzzy Logic-Based Modelling Technique

This study used the fuzzy logic-based system to estimate the output current of a solar panel system at specified amount of power and percentage of cloud cover in the atmosphere. This established the fuzzy rule-based system which was applied from a research study to analyze the behavior of the current output wherein the available solar panels are three 100 watts and two 50 watts making it difficult to measure the current with power of 175 watts and 350 watts. The amount of the produced current was established through the fuzzy rule-based system stated as the following: if the power is low and the atmosphere is almost cloudy then the current produced is very small; if the power is high and the atmosphere is cloudy then the current produced is small; if the power is medium and the atmosphere is partially cloudy then the current produced is average; if the power is high and the atmosphere is clear then the current produced is large. Through defuzzification the fuzzy system was implemented by computing the output value using the centroid method.

Cloudy Weather Prediction Using CNN Models and Satellite Images

Abstract: Cloudy weather classification is a vital task in meteorology and remote sensing, facilitating various applications such as weather forecasting, climate monitoring, and environmental analysis. In this study, we explore the application of convolutional neural network (CNN) techniques for classifying cloudy weather conditions using the Cloudy Weather Dataset sourced from Kaggle. The primary CNN architectures investigated include AlexNet, LeNet, and ResNet. The dataset undergoes preprocessing steps, including resizing and normalization to floating-point representation. Additionally, for calculating cloud cover percentage, the images are processed through grayscaling followed by thresholding. The performance of each CNN model is evaluated based on metrics of accuracy, that is providing insights into their effectiveness for cloudy weather classification

Variations in the Odonata Assemblages: How Do the Dry Season and Water Bodies Influence Them?

Diverse abiotic and biotic factors drive the ecological variation of communities across spatial and temporal dimensions. Within the Amazonian landscape, various freshwater environments exhibit distinct physicochemical characteristics. Thus, our study delved into the fluctuations of Odonata assemblages amidst distinct water bodies within Amazonia, encompassing two distinct climatic seasons. Comparative analysis was conducted on Odonata species diversity and assemblage composition across a blackwater pond, a lake, and a stream, spanning the initiation and culmination of the dry season in the southwestern Amazon region in Peru. Our methodology involved capturing adult Odonata using entomological nets on three separate occasions between 11:00 and 14:00h for each water body in May (beginning of the dry season) and October (end of the dry season) of 2018. We also evaluated the influence of temperature, precipitation, and percent cloud cover on the abundance and richness of adult Odonata. Species richness and composition differed among the three water bodies in both periods of the dry season. No effect of the dry season periods on species richness and abundance was observed. However, except in the oxbow lake, the more abundant species were substituted to the end of the dry season. Our study highlights the influence of water body types on Odonata species diversity and composition. The effects of the sampling period during the dry season may not be immediately apparent in conventional diversity metrics, such as species richness and abundance. Instead, its effects manifest predominantly in the relative abundance of the species that compose these assemblages.

Observations and Estimates of Wet-Bulb Globe Temperature in Varied Microclimates

Abstract Wet-bulb globe temperature (WBGT) is used to assess environmental heat stress and accounts for the influences of air temperature, humidity, wind speed, and radiation on heat stress. Measurements of WBGT are highly sensitive to slight changes in environmental conditions and can vary several degrees Celsius across small distances (tens to hundreds of meters). Relative to observations with an International Organization for Standardization (ISO)-compliant WBGT meter, this work assesses the accuracy of WBGT measurements made with a popular handheld meter (the Kestrel 5400 Heat Stress Tracker) and WBGT estimates. Measurements were made during the summers of 2019–21 in a variety of suburban and urban environments in North Carolina, including three high school campuses. WBGT can be estimated from standard weather station variables, and many of these stations report cloud cover in lieu of solar radiation. Therefore, this work also evaluates the accuracy of clear-sky radiation estimates and adjustments to those estimates based on cloud cover. WBGT estimated with the method from Liljegren et al. from a weather station were on average 0.2°C warmer than Observed WBGT, while the Kestrel 5400 WBGT was 0.7°C warmer. Large variations in WBGT were observed across surfaces and shade conditions, with differences of 0.9°C (0.3°–1.4°C) between a tennis court and a neighboring grass field. The method for estimating clear-sky radiation in Ryan and Stolzenbach was most accurate and the clear-sky radiation modified by percentage cloud cover was found to be within 75 W m−2of observations on average. Significance Statement Wet-bulb globe temperature (WBGT) is a heat stress index that accounts for the effects of air temperature, humidity, wind, and radiation on humans. However, WBGT is not routinely measured at weather stations. This work demonstrated the accuracy of estimating WBGT with methods from Liljegren et al. (2008), finding it to be more accurate than measurements from a popular handheld meter, the Kestrel 5400 Heat Stress Tracker. Variations in WBGT that result in different danger levels were found between measurements over a tennis court and a neighboring grass field, and between sun and shade conditions. Understanding the magnitude of these differences and the biases with WBGT estimates and measurements can inform the planning of outdoor activity to robustly safeguard health.

Detection of Coastline Changing by Using Remote Sensing Imagery (Case Study in Talawi District, Tanjung Tiram District, Lima Puluh Pesisir District Batu Bara Regency)

The high pressure in utilizing coastal areas and oceans causes in coastal damage. The coastline is the confluence of land areas and sea areas, where the existence of the line and the position of the coastline can change and undergo changes over time, and the position of the existence of the line is not fixed. The purpose of this study is to find out the changes in the coastline of the Coal District, Talawi District, Tanjung Tiram District and Lima Puluh Pesisir District in 2017-2022. The data analysis technique was used in this study is quantitative descriptive analysis. The data processing includes: Data Acquisition using Satellite Imagery (Raster) data. The data is used by satellite imagery was selected based on 2017 until 2022 coverage representatives, the lowest percentage of cloud cover in the study area. Supporting Spatial Data (vectors). The Digital Shoreline Analysis System (DSAS) is a plug in for esri ArcGIS 10.4 - 10.6 desktops to calculate historical coastline change rate statistics. Based on the results of coastline delianiation, it can be seen that the length of coastal gasis in 2017 in The Lima Puluh Pesisir District is 10.48 Km, in 2022 there was a decrease to 10.42 Km while based on RBI data on a scale of 1:50,000 in the 2014 update year was 17.37 Km. The length of the coastline of Tanjung Tiram District in 2017 was 24.31 Km, there was a decrease in 2022 to 23.44 Km and based on RBI data of 16.90 Km.The Talawi District has the length of the coastline was 3.16 Km in 2017, there is an increase in 2022 to 3.22 Km while the coastline on the RBI map was 3.23 Kilometers in length.

Application of Sentinel-2 Satellite Data to Map Forest Cover in Southeast Sri Lanka through the Random Forest Classifier

Sentinel-2 satellite data has been used for forest cover monitoring for almost five years. Mapping with Sentinel data will be a cost-effective solution for Sri Lanka, where the lack of updated land cover maps with high spatial resolution is a significant challenge in the land resource management of the country. A study area of about 5,000 km2 located in southeast Sri Lanka was selected for this study. Agricultural lands, forests including Yala national park, and villages with perennial crops make up the region. A Level-2A Sentinel-2 image with less than 10 percent cloud cover was used in the European Space Agency's (ESA) SNAP software version 8.0.0 for image processing and the forest cover of the study area was mapped through the Random Forest classifier (RFC). Normalized Difference Vegetation Index (NDVI) is also calculated as a Sentinel product to support RFC output. For RFC, ground truth data were collected through the reference of Google Earth high-resolution data. The classification accuracy was assessed using the Google Earth image as the reference dataset. Furthermore, RFC results were compared with NVDI greenness values. The classification accuracy was calculated using a confusion matrix (error matrix) through randomly selected 100 sample points. The overall accuracy of the land cover map was 85 percent, with a 96 percent accuracy for forest cover identification. The study found RFC as an effective method to isolate forest cover in Sri Lanka.KEYWORDS: Sentinel-2, Random Forest Classifier, Land cover classification, Land cover mapping, Normalized Difference Vegetation Index.

Analysis of multitemporal compositing techniques from regular and non-regular intervals using MODIS daily images in the Amazon region with a high percentage of cloud cover

One challenge in the study of optical remotely sensed time series in the Amazon is the constant cloud cover. The present study evaluates different compositing techniques using regular and non-regular intervals to obtain cloud-free images over large areas. The study area was the municipality of Capixaba in the State of Acre, belonging to the Amazon region. The tests considered four compositing algorithms (maximum, minimum, mean, and median) for daily MODIS sensor data (b1 and b2, 250m). The compositing technique from regular intervals adopted the following periods: 8, 16, 24, 32, 40, and 48 days. The irregular interval composite images adopted different composition intervals for dry seasons (April to September) and rainy (October to March). The cloud mask and viewing angle constraint allowed to obtain information without atmospheric interference and closest to nadir view. The composite images using regular intervals did not allow to overcome the high frequency of cloud cover of the region. The composite images from non-regular intervals presented a higher percentage of cloud-free pixels. The mean and median methods provided the better visual appearance of the images, corroborating with the homogeneity test. Therefore, composite images from non-regular intervals may be an appropriate alternative in places with constant cloud coverage.

Quantifying the Trends and Drivers of Ice Thickness in Lakes and Rivers across North America

Monitoring the timing of ice-on and ice-off has been instrumental in estimating the long-term effects of climate change on freshwater lakes and rivers. However, ice thickness has been studied less intensively, both spatially and temporally. Here, we quantified the trends and drivers of ice thickness from 27 lakes and rivers across North America. We found that ice thickness declined on average by 1.2 cm per decade, although ice thickness declined significantly in only four waterbodies. Local winter air temperature, cloud cover, and winter precipitation were the most important determinants of ice thickness, explaining over 81% of the variation in ice thickness. Ice thickness was lower in years and regions with higher air temperatures, high percentage of cloud cover, and high winter precipitation. Our results suggest that warming is contributing to thinning ice, particularly at high latitudes, with potential ramifications to the safety of humans and wildlife populations using freshwater ice for travel and recreation.

Environmental factors predicting the orientation of sea turtle hatchlings on a naturally lighted beach: A baseline for light-management goals

On sea turtle nesting beaches, artificial lighting associated with human development interferes with hatchling orientation from nest to sea. Although hatchling disorientation has been documented for many beaches, data that managers can use in understanding, predicting, and managing the issue are of limited detail. The present study provides baseline hatchling orientation data that can be compared to those from beaches with artificial lighting to prioritize light-management efforts there. In 2014, the precision of hatchling orientation was quantified for 87 nests on a naturally lighted beach that had little to no artificial lighting. Precision of hatchling orientation was regressed against seven environmental variables: beach slope, distance from nest to dune, dune height, apparent dune silhouette height relative to nest site, moon illumination percentage, cloud cover percentage, and relative humidity. Results favored a regression model that included distance from nest to dune, with shorter distances from the dune predicting a narrower angular range (i.e., greater precision) of hatchling orientation. The study confirmed findings of an earlier laboratory experiment that highlighted the importance to accurate hatchling orientation of a dark silhouette (dune) on the side of the nest site opposite the ocean side. Reducing artificial light and promoting the planting of pioneer plants that assist dune formation can increase hatchling survival.

Impact of lake surface changes on climate fluctuation within a lake‐affected region

Wetlands are one of the most important aquatic ecosystems that are vulnerable to various factors, including human activity or natural events such as drought. Remote sensing technology is an effective technique for managing and monitoring changes in these ecosystems caused by natural events and human activities. The present study evaluated the effects of changes at Bakhtegan Lake on six climatic parameters in its surrounding environments. Landsat satellite imagery was used to monitor changes in the Bakhtegan Lake surface during times of maximum extent in the period from 2000 to 2015. Support vector machine (SVM) classification was applied to extract the characteristics of the lake surface. Then, the distance of 80 km from the lakeshore was identified as the environment of its influence on the surrounding land, which was divided into three buffers from 0 to 25, 26–55 and 56–80 km. The results showed that a higher accuracy than the index for extracting lake area was achieved using the SVM classification method. Additionally, the climate parameters of mean annual precipitation, relative humidity, and percentage of cloud cover significantly influenced (0.001) lake surface changes in buffer one, indicating the highest impact among all climatic parameters. In buffer two, the significance level of the precipitation parameter decreased to about 0.05, and the annual mean minimum temperature was significantly lower (0.05); however, the mean annual minimum and maximum temperatures were not significant

Patrones espaciotemporales de las observaciones de Sentinel-2 a nivel de imagen y píxel sobre el territorio mexicano entre 2015 y 2019

<p class="p1">Sentinel-2 imagery has the highest temporal, spectral and spatial resolution to monitor land surface among the freely available multispectral collections. However, the possibility to use these images in different applications is conditioned by the number of cloudless observations available for a certain spatiotemporal window. Thus, the objective of this article is to analyze the number of Sentinel-2 observations available for the Mexican territory at image and pixel level. In the first case, the total number of available images and its cloud cover percentage was calculated; while in the second case, the number of cloudless observations was estimated for each pixel. Additionally, in order to take into account the territory diversity, the monthly mean number of cloudless observations, as well as the proportion of its surface with at least one cloudless observation in monthly, bimonthly, trimonthly and annual intervals, was computed for each one of the seven ecoregions of the country. The results show that annually, the number of valid observations per pixel is between 0 and 121 observations, while in monthly evaluations, between 0 and 6.58 observations. Additionally, in the 2017-2019 period annual observations can be obtained for the entire Mexican land surface, while in 2018-2019, monthly or trimonthly evaluations can be achieved, depending on the ecoregion. We consider that these results will provide useful information for researchers that are interested in using Sentinel-2 imagery for different applications.</p>

Assessment of climate change pattern in the Pauri Garhwal of the Western Himalayan Region: based on climate parameters and perceptions of forest-dependent communities.

This study assessed the climate change in Pauri district, Uttarakhand, India, a region highly vulnerable to climate change with potentially high loss of livelihoods and lives. The scale of change in the district's climate was analyzed using meteorological station data (1901-2000) and grid data (1985-2015). Perceptions of climate change among forest-dependent communities in three altitude zones (< 1200 m asl (zone A); 1200-1800 m asl (zone B), and > 1800 m asl (zone C)) in the study region were surveyed with respect to 14 climate-specific indicators. Annual mean, maximum, and minimum temperature of seasonal data indicated increasing trends except monsoon. Percentage cloud cover showed an increase, of approximately 3%, while diurnal temperature displayed decreasing trends. Rainfall in the district showed a decreasing trend, with more than 50% of years 1985-2015 receiving less rainfall than the annual average. More than 90% of respondents in zones A and B, and around 65-70% respondents in zone C, reported changes in climate parameters. These findings confirm the long-term observable changes in climate in the region and demonstrate the utility of station data, grid data, and surveys of local communities' perceptions when analyzing climate change. The analysis provided important clues about the nature of climate changes in the district. The results can be used to reduce the gap between bottom-up understanding and top-down policies and to formulate precautionary and ongoing site-specific adaptation practices for communities in different altitude zones in the study region, leading to effective and efficient mitigation of climate change impacts.

EXPLORING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR CROP CLASSIFICATION USING SENTINEL 2 DATA

Abstract. Crop Classification and recognition is a very important application of Remote Sensing. In the last few years, Machine learning classification techniques have been emerging for crop classification. Google Earth Engine (GEE) is a platform to explore the multiple satellite data with different advanced classification techniques without even downloading the satellite data. The main objective of this study is to explore the ability of different machine learning classification techniques like, Random Forest (RF), Classification And Regression Trees (CART) and Support Vector Machine (SVM) for crop classification. High Resolution optical data, Sentinel-2, MSI (10 m) was used for crop classification in the Indian Agricultural Research Institute (IARI) farm for the Rabi season 2016 for major crops. Around 100 crop fields (~400 Hectare) in IARI were analysed. Smart phone-based ground truth data were collected. The best cloud free image of Sentinel 2 MSI data (5 Feb 2016) was used for classification using automatic filtering by percentage cloud cover property using the GEE. Polygons as feature space was used as training data sets based on the ground truth data for crop classification using machine learning techniques. Post classification, accuracy assessment analysis was done through the generation of the confusion matrix (producer and user accuracy), kappa coefficient and F value. In this study it was found that using GEE through cloud platform, satellite data accessing, filtering and pre-processing of satellite data could be done very efficiently. In terms of overall classification accuracy and kappa coefficient, Random Forest (93.3%, 0.9178) and CART (73.4%, 0.6755) classifiers performed better than SVM (74.3%, 0.6867) classifier. For validation, Field Operation Service Unit (FOSU) division of IARI, data was used and encouraging results were obtained.

An innovative intelligent system based on remote sensing and mathematical models for improving crop yield estimation

There are many crop yield estimation techniques which are used in countries around the world, but the most effective is the one based on remote sensing data and technologies. However, remote sensing data which are needed to estimate crop yield is incomplete most of the time due to many obstacles such as climate conditions (percentage of cloud cover), and low temporal resolution. These problems reduce the effectiveness of the known crop yield estimation techniques and render them obsolete. There was many attempts to solve these problems by using high temporal resolution and low spatial resolution images. However, this type of images are suitable for very large homogeneous crop fields. To compensate for the lack of high spatial resolution satellite images, a new mathematical model is created. Based on the new mathematical model an intelligent system is implemented that includes the use of energy balance equation to improve the crop yield estimation. To verify the results of the intelligent system, several farmers are interviewed and information about their crops yield is collected. The comparison between the estimated crop yield and the actual production in different fields proves the high accuracy of the intelligent system.

The Relationship between Stem Diameter Shrinkage and Tree Bole Moisture Loss Due to Transpiration

The responsiveness of four types of stem diameter shrinkage indicators to sap flow changes was compared under four air temperature and cloudiness conditions: above 0 °C below 80% cloud cover days; above 0 °C large percentage cloud cover days; low temperature below 80% cloud cover days; and low temperature large percentage cloud cover days. In this study, we investigated the effects of indicative functions of relatively easy-to-access stem diameter shrinkage on variation characteristics of sap flow. High-resolution-based stem diameter shrinkage is related to changes in tree moisture content. Stem diameter shrinkage indicators are adopted to confirm sap flow changes resulting from transpiration pull, which may enhance the power of stem diameter shrinkage as an index for tree bole moisture loss. After measuring stem diameter variations, the following stem diameter shrinkage indicators were calculated: maximum daily shrinkage, daily stem diameter increment, daily stem diameter variation, and tree water deficit-induced stem shrinkage (TWD). Sap flow was measured synchronously, and stem diameter shrinkage indicators were analyzed to confirm their responses to sap flow. TWD was positively correlated (r ≥ 0.317) with daily variations in sap flow and reached extremely significant levels (p ≤ 0.001) under all conditions. TWD and maximum daily shrinkage were able to better reflect the correlation between changes in stem diameter and sap flow on a daily scale, except large percentage cloud cover days with low temperatures. Changes in stem diameter had no correlation with sap flow during low temperature and large percentage cloud cover days. Among all stem diameter shrinkage indicators, TWD showed the highest correlation (r ≥ 0.601 and p ≤ 0.001) with sap flow under all conditions, except during large percentage cloud cover days with low temperatures. The stem diameter shrinkage indicators did not reflect sap flow changes during large percentage cloud cover days with low temperatures. The indicator that best reflected moisture loss of trees was TWD.

Trends of night-time hourly cloud-cover values over Manila Observatory: ground-based remote-sensing observations using a digital camera for 13 months

ABSTRACTNight-time cloud detection provides data sets of cloud-cover percentage. Although night-time cloud-cover data sets from satellite-based instruments are common, these data sets do not have relatively high temporal resolution. To quantify local temporal cloud-cover variability and to attain long-term cloud-cover measurements, ground-based instruments would be the appropriate apparatus. In this study, a digital camera is used to continuously gather images of the night sky at 5-min intervals over Manila Observatory (14.64° N, 121.07° E). For the first time in Manila, ground-based remote-sensing data gathered from October 2015 to October 2016 are analysed for hourly cloud cover. The results indicate that wet season has relatively higher cloud-cover values (median >40%) as compared to the dry season (median <40%). Moreover, cloud-cover values are observed to decrease during the night. For the wet season, August having the highest cloud-cover values has the highest value of change of hourly cloud-cover percentage (−0.82% h−1). For the dry season, February having the lowest cloud-cover values has the highest value of change of hourly cloud-cover percentage (−1.04% h−1).

Factors influencing peak summer surface water temperature in Canada’s large lakes

Seasonal water temperature data from 388 large Canadian lakes (area ≥ 100 km2) were used to develop improved empirical tools for forecasting the impacts of climate change on the magnitude (TP) and time of occurrence (JP) of annual peak surface water temperatures. Analyses of remotely sensed open-water temperatures with sinusoidal models produced estimates of TP and JP predominately better than other models. Those estimates were analyzed for lake and climate patterns. Linear mixed effects regression produced a significant model of TP with fixed positive effects for mean July and annual air temperatures and lake perimeter, but negative effects with mean July and annual percent cloud cover, mean annual precipitation, range of monthly mean global clear sky radiation, area, and elevation. Subsets of the estimates with mean, maximum, or Secchi depth values produced similarly significant models with negative depth coefficients. JP was relatively invariant but small, significant lake and climate effects were detected. The best models identified in our analyses will be useful tools for forecasting how climate change will alter aspects of the limnetic seasonal water temperature cycle that strongly influences the species composition and productivity of their fisheries.

西南近50年实际蒸散发反演及其时空演变

PDF HTML阅读 XML下载 导出引用 引用提醒 西南近50年实际蒸散发反演及其时空演变 DOI: 10.5846/stxb201805231129 作者: 作者单位: 中国科学院地球化学研究所,中国科学院地球化学研究所,中国科学院地球化学研究所,中国科学院地球化学研究所,中国科学院地球化学研究所,中国科学院地球化学研究所,中国科学院地球化学研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划（2016YFC0502102）；中国科学院科技服务网络计划（KFJ-STS-ZDTP-036）；"西部之光"人才培养计划（A类）（〔2018〕X）；贵州省科技计划（2017-2966） Inversion and spatiotemporal evolution of actual evapotranspiration in southwest China for the past 50 years Author: Affiliation: Institute of Geochemistry, Chinese Academy of Sciences,Institute of Geochemistry, Chinese Academy of Sciences,Institute of Geochemistry, Chinese Academy of Sciences,Institute of Geochemistry, Chinese Academy of Sciences,Institute of Geochemistry, Chinese Academy of Sciences,Institute of Geochemistry, Chinese Academy of Sciences,Institute of Geochemistry, Chinese Academy of Sciences Fund Project: This research work was supported jointly by national key research program of China (No. 2016YFC0502102 & 2016YFC0502300), 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:利用CRU 4.0及GLDAS Noah 2.1数据集，采用随机森林算法对1966年至2016年中国西南陆面月尺度实际蒸散发（ETa）进行逐像元反演，结合袋外误差均方值（MSEOOB）、解释方差百分数（PVE）和均方根误差（RMSE）评价模型以及与其他典型数据集对比的方法对模型和反演结果进行精度评价，在对中国西南ETa的空间格局及时空演变特征进行分析的基础上，利用因子置换重要性评价模型（PIM）对特征因子进行重要性评价。结果表明：（1） MSEOOB均值为4.14，标准偏差仅为3.73，PVE均值为99.36%，标准偏差仅为0.33，模型基于2000年至2016年月尺度拟合结果的RMSE均值仅为1.04 mm/月，标准偏差为0.52，反演结果与GLDAS 2.1、2.0及MOD16数据的R2分别为0.99、0.89、0.95，总体而言模型及拟合结果可信度和精度较高；（2）西南地区ETa整体上表现出随着纬度的降低而增加的特征，从西北高原地区向东南沿海区域逐步增加，不同季节上西南的ETa空间分布差异较为明显，从春季到夏季先呈现出由东南向西北逐步增加的态势，夏季到冬季则呈现出从西北向东南减弱的特征，在每年的7、8月份左右各区域的ETa达到最大值，在1、2月份左右为最低值，并呈现起伏的周期特征；（3）以横断山脉为分界，横断山脉以南的丰水区的ETa主要受云覆盖百分数、月均气温日较差与月均日最高温共同驱动，而横断山脉以北的少水区域主要受云覆盖百分数、月霜日频率与月均水汽压共同驱动，而无论是在丰水区还是少水区，云覆盖百分数都是所有因素中最主要的驱动因子。 Abstract:In this study, we combined the CRU 4.0 and GLDAS Noah 2.1 datasets and used the random forest algorithm (RF) to calculate the monthly actual evapotranspiration (ETa) in southwestern China between 1966 and 2016. The accuracy of our model and the inversion results were evaluated by the MSEOOB, PVE, and RMSE indices and a comparison with other typical datasets. Based on this, the spatiotemporal distribution and evolution pattern of ETa over multiple time scales were fully discussed. We extended our research by using the PIM model to evaluate the importance of the feature factors in each pixel. We obtained the following results:1) the mean value and the standard deviation (Std Dev) of MSEOOB were 4.14 and 3.73, and those of PVE were 99.36% and 0.33; in addition, the mean RMSE of the monthly inversion results for 2000 to 2016 was 1.04 mm per month and the corresponding Std Dev was 0.52; moreover, the R2 values for the inversion results of ETa from GLDAS 2.1, GLDAS 2, and MOD16 were 0.99, 0.89, and 0.95, respectively. All these evaluation indices illustrated the credibility and precision of the model and the inversion results were sufficiently high. 2) Our inversion results indicated that ETa increased with a decrease in latitude and gradually increased from the northwest plateau to the southeast coastal area; in addition, the spatial distribution patterns in southwestern China in different seasons were quite different:from spring to summer, high ETa expanded from the southeast to the northwest; in contrast, in winter, ETa diminished remarkably from northwest to southeast. The maximum value was reached around July every year and had the lowest value was reached around February, which showed periodic characteristics with fluctuations. 3) We found that the Hengduan Mountains were the boundary of the driving factors for ETa in arid and humid regions. The ETa in the humid regions south of the mountains was jointly driven by the cloud cover percentage (CCP), diurnal temperature range (DTR), and monthly average daily maximum temperature (TMX). In contrast, ETa in arid regions north of the mountains was mainly affected by CCP, frost day frequency (FDF), and vapor pressure (VAP). Notability, CCP is the most important driving factor weather in both humid and arid areas. 参考文献 相似文献 引证文献

Small Scale Habitat Preferences of Myotis daubentonii, Pipistrellus pipistrellus, and Potential Aerial Prey in an Upland River Valley

Distribution and abundance of two temperate-zone insectivorous bats, Daubenton's (Myotis daubentonii) and common pipistrelle (Pipistrellus pipistrellus), and their potential prey were studied along an altitudinal river gradient in relation to environmental variables including air temperature, wind speed, water surface state, and presence or absence of bank-side trees. Using a Latin square design at ten different habitat combination types, ultrasound recordings and insect sampling were carried out to quantify bat habitat preferences and potential prey abundance and classification. Myotis daubentonii and P. pipistrellus activity was significantly higher over smooth water river sections with trees on either or both banks while cluttered and rapid water sections were avoided. Conversely, insect abundance was not related to water surface condition or the presence or absence of bank-side trees. Nematoceran dipterans made up 98% of insect numbers, with small numbers of brachycerans and cyclorrhaphans. The most common insect families were Chironomidae and Ceratopogonidae. There was no correlation between bat activity and aerial insect activity, suggesting that aerial prey availability is not the sole driver of bat habitat choice. Bat and insect abundance were each correlated positively with night-time air temperature. No bat passes or flying insects were recorded at temperatures < 4°C. At 5°C, only M. daubentonii were observed foraging, and at 6°C there were more M. daubentonii present than any other bat species. No correlation was found between number of bat passes hr-1 and wind speed, moon visibility, moon phase, and percentage cloud cover. Rain did not affect M. daubentonii, but P. pipistrellus preferred to forage on dry nights. Bats were predicted to forage preferentially where aerial insect abundance was highest but this was found to not be case, and other aspects such as detection of prey against clutter may have an important role to play in habitat choice.