College of Sciences
Permanent URI for this communityhttps://hdl.handle.net/20.500.12588/256
Browse
Browsing College of Sciences by Department "Earth and Planetary Sciences"
Now showing 1 - 20 of 34
- Results Per Page
- Sort Options
Item A New Digital Lake Bathymetry Model Using the Step-Wise Water Recession Method to Generate 3D Lake Bathymetric Maps Based on DEMs(2019-05-31) Zhu, Siyu; Liu, Baojian; Wan, Wei; Xie, Hongjie; Fang, Yu; Chen, Xi; Li, Huan; Fang, Weizhen; Zhang, Guoqing; Tao, Mingwei; Hong, YangThe availability of lake bathymetry maps is imperative for estimating lake water volumes and their variability, which is a sensitive indicator of climate. It is difficult, if not impossible, to obtain bathymetric measurements from all of the thousands of lakes across the globe due to costly labor and/or harsh topographic regions. In this study, we develop a new digital lake bathymetry model (DLBM) using the step-wise water recession method (WRM) to generate 3-dimensional lake bathymetric maps based on the digital elevation model (DEM) alone, with two assumptions: (1) typically, the lake's bathymetry is formed and shaped by geological processes similar to those that shaped the surrounding landmasses, and (2) the agent rate of water (the thickness of the sedimentary deposit proportional to the lake water depth) is uniform. Lake Ontario and Lake Namco are used as examples to demonstrate the development, calibration, and refinement of the model. Compared to some other methods, the estimated 3D bathymetric maps using the proposed DLBM could overcome the discontinuity problem to adopt the complex topography of lake boundaries. This study provides a mathematically robust yet cost-effective approach for estimating lake volumes and their changes in regions lacking field measurements of bathymetry, for example, the remote Tibetan Plateau, which contains thousands of lakes.Item A Review on Applications of Remote Sensing and Geographic Information Systems (GIS) in Water Resources and Flood Risk Management(2018-05-07) Wang, Xianwei; Xie, HongjieWater is one of the most critical natural resources that maintain the ecosystem and support people's daily life. Pressures on water resources and disaster management are rising primarily due to the unequal spatial and temporal distribution of water resources and pollution, and also partially due to our poor knowledge about the distribution of water resources and poor management of their usage. Remote sensing provides critical data for mapping water resources, measuring hydrological fluxes, monitoring drought and flooding inundation, while geographic information systems (GIS) provide the best tools for water resources, drought and flood risk management. This special issue presents the best practices, cutting-edge technologies and applications of remote sensing, GIS and hydrological models for water resource mapping, satellite rainfall measurements, runoff simulation, water body and flood inundation mapping, and risk management. The latest technologies applied include 3D surface model analysis and visualization of glaciers, unmanned aerial vehicle (UAV) video image classification for turfgrass mapping and irrigation planning, ground penetration radar for soil moisture estimation, the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) satellite rainfall measurements, storm hyetography analysis, rainfall runoff and urban flooding simulation, and satellite radar and optical image classification for urban water bodies and flooding inundation. The application of those technologies is expected to greatly relieve the pressures on water resources and allow better mitigation of and adaptation to the disastrous impact of droughts and flooding.Item An Evaluation of Satellite Estimates of Solar Surface Irradiance Using Ground Observations in San Antonio, Texas, USA(2017-12-07) Xia, Shuang; Mestas-Nuñez, Alberto M.; Xie, Hongjie; Vega, RolandoEstimates of solar irradiance at the earth's surface from satellite observations are useful for planning both the deployment of distributed photovoltaic systems and their integration into electricity grids. In order to use surface solar irradiance from satellites for these purposes, validation of its accuracy against ground observations is needed. In this study, satellite estimates of surface solar irradiance from Geostationary Operational Environmental Satellite (GOES) are compared with ground observations at two sites, namely the main campus of the University of Texas at San Antonio (UTSA) and the Alamo Solar Farm of San Antonio (ASF). The comparisons are done mostly on an hourly timescale, under different cloud conditions classified by cloud types and cloud layers, and at different solar zenith angle intervals. It is found that satellite estimates and ground observations of surface solar irradiance are significantly correlated (p < 0.05) under all sky conditions (r: 0.80 and 0.87 on an hourly timescale and 0.94 and 0.91 on a daily timescale, respectively for the UTSA and ASF sites); on the hourly timescale, the correlations are 0.77 and 0.86 under clear-sky conditions, and 0.74 and 0.84 under cloudy conditions, respectively for the UTSA and ASF sites, and mostly >0.60 under different cloud types and layers for both sites. The correlations under cloudy-sky conditions are mostly stronger than those under clear-sky conditions at different solar zenith angles. The correlation coefficients are mostly the smallest with solar zenith angle in the range of 75–90◦ under all sky, clear-sky and cloudy-sky conditions. At the ASF site, the overall bias of GOES surface solar irradiance is small (+1.77 W/m2 ) under all sky while relatively larger under clear-sky (-22.29 W/m2 ) and cloudy-sky (+40.31 W/m2 ) conditions. The overall good agreement of the satellite estimates with the ground observations underscores the usefulness of the GOES surface solar irradiance estimates for solar energy studies in the San Antonio area.Item An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery(2020-04-17) Sha, Dexuan; Miao, Xin; Xu, Mengchao; Yang, Chaowei; Xie, Hongjie; Mestas-Nuñez, Alberto M.; Li, Yun; Liu, Qian; Yang, JingchaoSea ice acts as both an indicator and an amplifier of climate change. High spatial resolution (HSR) imagery is an important data source in Arctic sea ice research for extracting sea ice physical parameters, and calibrating/validating climate models. HSR images are difficult to process and manage due to their large data volume, heterogeneous data sources, and complex spatiotemporal distributions. In this paper, an Arctic Cyberinfrastructure (ArcCI) module is developed that allows a reliable and efficient on-demand image batch processing on the web. For this module, available associated datasets are collected and presented through an open data portal. The ArcCI module offers an architecture based on cloud computing and big data components for HSR sea ice images, including functionalities of (1) data acquisition through File Transfer Protocol (FTP) transfer, front-end uploading, and physical transfer; (2) data storage based on Hadoop distributed file system and matured operational relational database; (3) distributed image processing including object-based image classification and parameter extraction of sea ice features; (4) 3D visualization of dynamic spatiotemporal distribution of extracted parameters with flexible statistical charts. Arctic researchers can search and find arctic sea ice HSR image and relevant metadata in the open data portal, obtain extracted ice parameters, and conduct visual analytics interactively. Users with large number of images can leverage the service to process their image in high performance manner on cloud, and manage, analyze results in one place. The ArcCI module will assist domain scientists on investigating polar sea ice, and can be easily transferred to other HSR image processing research projects.Item Analysis of Water Resource Carrying Capacity and Obstacle Factors Based on GRA-TOPSIS Evaluation Method in Manas River Basin(2023-01-05) Gulishengmu, Anfuding; Yang, Guang; Tian, Lijun; Pan, Yue; Huang, Zhou; Xu, Xingang; Gao, Yongli; Li, YiThe investigation of water resource carrying capacity (WRCC) in oasis cities in Northwest China is useful for guiding the sustainable development of arid regions. To quantify the WRCC of Shihezi, an oasis city in the Manas River Basin (MRB), Northwest China, a total of 21 indicators from three subsystems were selected to construct an evaluation index system based on the theory of the water resource–socio-economic–ecological complex system. Our study utilized a combination of the CRITIC method and the entropy weight method to determine the synthesis weight, the GRA-TOPSIS approach to comprehensively evaluate the WRCC, and the obstacle degree model to identify its main obstacle factors. Our results showed that the WRCC of Shihezi showed an increasing trend from 2011 to 2020, with the compositive index increasing from 0.3454 to 0.5210. The carrying capacities of the ecological environment and socio-economic subsystems were generally on the rise, but the rate of change was relatively gentle from 2017 to 2020. The carrying capacity index of the water resource subsystem dropped significantly from year to year from 2016 to 2020. The irrigation coverage rate, the proportion of agricultural water, water consumption per 10,000 CNY of GDP, the modulus of water production, water resource development and its utilization ratio, the water supply modulus, and the proportion of ecological water were the seven most significant obstacles. Our findings could serve as scientific references for enhancing WRCC and promoting the sustainable development of oasis cities in arid regions.Item Assessing Scale Dependence on Local Sea Level Retrievals from Laser Altimetry Data over Sea Ice(2020-11-13) Tian, Liuxi; Xie, Hongjie; Ackley, Stephen F.; Mestas-Nuñez, Alberto M.The measurement of sea ice elevation above sea level or the "freeboard" depends upon an accurate retrieval of the local sea level. The local sea level has been previously retrieved from altimetry data alone by the lowest elevation method, where the percentage of the lowest elevations over a particular segment length scale was used. Here, we provide an evaluation of the scale dependence on these local sea level retrievals using data from NASA Operation IceBridge (OIB) which took place in the Ross Sea in 2013. This is a unique dataset of laser altimeter measurements over five tracks from the Airborne Topographic Mapper (ATM), with coincidently high-spatial resolution images from the Digital Mapping System (DMS), that allows for an independent sea level validation. The local sea level is first calculated by using the mean elevation of ATM L1B data over leads identified by using the corresponding DMS imagery. The resulting local sea level reference is then used as ground truth to validate the local sea levels retrieved from ATM L2 by using nine different percentages of the lowest elevation (0.1%, 0.5%, 1%, 1.5%, 2%, 2.5%, 3%, 3.5%, and 4%) at seven different segment length scales (1, 5, 10, 15, 20, 25, and 50 km) for each of the five ATM tracks. The closeness to the 1:1 line, R2 , and root mean square error (RMSE) is used to quantify the accuracy of the retrievals. It is found that all linear least square fits are statistically significant (p < 0.05) using an F test at every scale for all tested data. In general, the sea level retrievals are farther away from the 1:1 line when the segment length scale increases from 1 or 5 to 50 km. We find that the retrieval accuracy is affected more by the segment length scale than the percentage scale. Based on our results, most retrievals underestimate the local sea level; the longer the segment length (from 1 to 50 km) used, especially at small percentage scales, the larger the error tends to be. The best local sea level based on a higher R2 and smaller RMSE for all the tracks combined is retrieved by using 0.1–2% of the lowest elevations at the 1–5 km segment lengths.Item C3 Vegetation Mapping and CO2 Fertilization Effect in the Arid Lower Heihe River Basin, Northwestern China(2015-12-04) Bi, Yunbo; Xie, HongjieIn arid regions, C3 vegetation is assumed to be more sensitive to precipitation and CO2 fertilization than C4 vegetation. In this study, normalized difference vegetation index (NDVI) is used to examine vegetation growth in the arid Lower Heihe River Basin, northwestern China, for the past three decades. The results indicate that maximum NDVI (MNDVI) of the area increases over the years and is significantly correlated with precipitation (R = 0.47 and p < 0.01), not temperature (R = −0.04). The upper limit of C3 vegetation cover of the area shows a yearly rising trend of 0.6% or an overall increase of 9% over the period of 25 years, primarily due to the CO2 fertilization effect (CO2 rising 14%) over the same period. C3 dominant areas can be potentially distinguished by both MNDVI asynchronous seasonality and a significant relation between MNDVI and cumulative precipitation. This study provides a potential tool of identifying C3 vegetation from C4 vegetation and confirms the CO2 fertilization effect in this arid region.Item Characterizing Variability of Solar Irradiance in San Antonio, Texas Using Satellite Observations of Cloudiness(2018-12-12) Xia, Shuang; Mestas-Nuñez, Alberto M.; Xie, Hongjie; Tang, Jiakui; Vega, RolandoSince the main attenuation of solar irradiance reaching the earth's surface is due to clouds, it has been hypothesized that global horizontal irradiance attenuation and its temporal variability at a given location could be characterized simply by cloud properties at that location. This hypothesis is tested using global horizontal irradiance measurements at two stations in San Antonio, Texas, and satellite estimates of cloud types and cloud layers from the Geostationary Operational Environmental Satellite (GOES) Surface and Insolation Product. A modified version of an existing solar attenuation variability index, albeit having a better physical foundation, is used. The analysis is conducted for different cloud conditions and solar elevations. It is found that under cloudy-sky conditions, there is less attenuation under water clouds than those under opaque ice clouds (optically thick ice clouds) and multilayered clouds. For cloud layers, less attenuation was found for the low/mid layers than for the high layer. Cloud enhancement occurs more frequently for water clouds and less frequently for mixed phase and cirrus clouds and it occurs with similar frequency at all three levels. The temporal variability of solar attenuation is found to decrease with an increasing temporal sampling interval and to be largest for water clouds and smallest for multilayered and partly cloudy conditions. This work presents a first step towards estimating solar energy potential in the San Antonio area indirectly using available estimates of cloudiness from GOES satellites.Item Correction: Zhu, S., et al. A New Digital Lake Bathymetry Model Using the Step-Wise Water Recession Method to Generate 3D Lake Bathymetric Maps Based on DEMs. Water 2019, 11, 1151(2019-11-19) Zhu, Siyu; Liu, Baojian; Wan, Wei; Xie, Hongjie; Fang, Yu; Chen, Xi; Li, Huan; Fang, Weizhen; Zhang, Guoqing; Tao, Mingwei; Hong, YangIn the published article [1], the authors realized some errors in the affiliation and email address of Yang Hong, and thus wish to make the following revisions: [...]Item Defense Priming and Jasmonates: A Role for Free Fatty Acids in Insect Elicitor-Induced Long Distance Signaling(2016-01-08) Li, Ting; Cofer, Tristan; Engelberth, Marie; Engelberth, JurgenGreen leaf volatiles (GLV) prime plants against insect herbivore attack resulting in stronger and faster signaling by jasmonic acid (JA). In maize this response is specifically linked to insect elicitor (IE)-induced signaling processes, which cause JA accumulation not only around the damage site, but also in distant tissues, presumably through the activation of electrical signals. Here, we present additional data further characterizing these distal signaling events in maize. Also, we describe how exposure to GLV increases free fatty acid (fFA) levels in maize seedlings, but also in other plants, and how increased fFA levels affect IE-induced JA accumulation. Increased fFA, in particular α-linolenic acid (LnA), caused a significant increase in JA accumulation after IE treatment, while JA induced by mechanical wounding (MW) alone was not affected. We also identified treatments that significantly decreased certain fFA level including simulated wind and rain. In such treated plants, IE-induced JA accumulation was significantly reduced when compared to un-moved control plants, while MW-induced JA accumulation was not significantly affected. Since only IE-induced JA accumulation was altered by changes in the fFA composition, we conclude that changing levels of fFA affect primarily IE-induced signaling processes rather than serving as a substrate for JA.Item Developing the Remote Sensing-Gash Analytical Model for Estimating Vegetation Rainfall Interception at Very High Resolution: A Case Study in the Heihe River Basin(2017-06-27) Cui, Yaokui; Zhao, Peng; Yan, Binyan; Xie, Hongjie; Yu, Pengtao; Wan, Wei; Fan, Wenjie; Hong, YangAccurately quantifying the vegetation rainfall interception at a high resolution is critical for rainfall-runoff modeling and flood forecasting, and is also essential for understanding its further impact on local, regional, and even global water cycle dynamics. In this study, the Remote Sensing-based Gash model (RS-Gash model) is developed based on a modified Gash model for interception loss estimation using remote sensing observations at the regional scale, and has been applied and validated in the upper reach of the Heihe River Basin of China for different types of vegetation. To eliminate the scale error and the effect of mixed pixels, the RS-Gash model is applied at a fine scale of 30 m with the high resolution vegetation area index retrieved by using the unified model of bidirectional reflectance distribution function (BRDF-U) for the vegetation canopy. Field validation shows that the RMSE and R2 of the interception ratio are 3.7% and 0.9, respectively, indicating the model's strong stability and reliability at fine scale. The temporal variation of vegetation rainfall interception and its relationship with precipitation are further investigated. In summary, the RS-Gash model has demonstrated its effectiveness and reliability in estimating vegetation rainfall interception. When compared to the coarse resolution results, the application of this model at 30-m fine resolution is necessary to resolve the scaling issues as shown in this study.Item Editorial for Special Issue "Remote Sensing Water Cycle: Theory, Sensors, Data, and Applications"(2019-05-22) Wan, Wei; Xie, Hongjie; Hasan, Emad; Hong, YangGlobal water cycle dynamics involve the exchange of water and energy matter among the atmosphere, hydrosphere, geosphere, cryosphere, and biosphere. Remote sensing provides a unique advantage of observing and acquiring complex water cycle and hydrological state variables across a wide range of spatial and temporal scales. The recent advances in remote sensing technology and numerical hydrological models alleviate our ability to observe and predict the storage, fluxes, and movement of water in time and space. Remote sensing offers unprecedented opportunities to gain a better and comprehensive understanding and mapping of water distribution and variability, in response to climate change and human activities. Besides, remote sensing data enables global and regional hydrological applications, and water resources management, motivates new theories in mapping applications and offers new ways to predict and resolve global water resources conflicts. This Special Issue encompasses a number of contributions in satellite and airborne sensors applications in hydrology, including: mapping theories and applications, i.e., [1–3], new methods to better observe hydrological component, i.e., precipitation [4,5], precipitable water vapor (PWV) and vapor pressure deficit (VPD) [6–8], energy fluxes and evapotranspiration [9], and snowfall [10], and new methods to improve hydrological decision support system, i.e., [11]. The following section briefs the overall contributions in this Special Issue.Item Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model(2020-01-07) Koo, YoungHyun; Oh, Myeongchan; Kim, Sung-Min; Park, Hyeong-DongThe power capacity of solar photovoltaics (PVs) in Korea has grown dramatically in recent years, and an accurate estimation of solar resources is crucial for the efficient management of these solar PV systems. Since the number of solar irradiance measurement sites is insufficient for Korea, satellite images can be useful sources for estimating solar irradiance over a wide area of Korea. In this study, an artificial neural network (ANN) model was constructed to calculate hourly global horizontal solar irradiance (GHI) from Korea Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) images. Solar position variables and five COMS MI channels were used as inputs for the ANN model. The basic ANN model was determined to have a window size of five for the input satellite images and two hidden layers, with 30 nodes on each hidden layer. After these ANN parameters were determined, the temporal and spatial applicability of the ANN model for solar irradiance mapping was validated. The final ANN ensemble model, which calculated the hourly GHI from 10 independent ANN models, exhibited a correlation coefficient (R) of 0.975 and root mean square error (RMSE) of 54.44 W/m2 (12.93%), which were better results than for other remote-sensing based works for Korea. Finally, GHI maps for Korea were generated using the final ANN ensemble model. This COMS-based ANN model can contribute to the efficient estimation of solar resources and the improvement of the operational efficiency of solar PV systems for Korea.Item Estimation of Alpine Grassland Forage Nitrogen Coupled with Hyperspectral Characteristics during Different Growth Periods on the Tibetan Plateau(2019-09-06) Gao, Jinlong; Liang, Tiangang; Yin, Jianpeng; Ge, Jing; Feng, Qisheng; Wu, Caixia; Hou, Mengjing; Liu, Jie; Xie, HongjieThe applicability of hyperspectral remote sensing models for forage nitrogen (N) retrieval during different growth periods is limited. This study aims to develop a multivariate model feasible for estimating the forage N for the growth periods (June to November) in an alpine grassland ecosystem. The random forest (RF) algorithm is employed to determine the optimum combinations of 38 spectral variables capable of capturing dynamic variations in forage N. The results show that (1) throughout the growth period, the red-edge first shifts toward longer wavelengths and then shifts toward shorter wavelengths, the amplitude (AMP) and absorption depth (AD) gradually decrease, and the absorption position (AP) changes slightly; (2) the importance of spectral variables for forage N estimation differs during the different growth periods; (3) the multivariate model achieves better results for the first four periods (June to October) than for the last period (when the grass is completely senesced) (V-R2: 0.58–0.68 versus 0.23); and (4) for the whole growth period (June to November), the prediction accuracy of the general N estimation model validated by the unknown growth period is lower than that validated by the unknown location (V-R2 is 0.28 and 0.55 for the validation strategies of Leave-Time-Out and Leave-Location-Out, respectively). This study demonstrates that the changes in the spectral features of the red wavelength (red-edge position, AMP and AD) are well coupled with the forage N content. Moreover, the development of a multivariate RF model for estimating alpine grasslands N content during different growth periods is promising for the improvement of both the stability and accuracy of the model.Item Estimation of Snow Depth over the Qinghai-Tibetan Plateau Based on AMSR-E and MODIS Data(2018-12-08) Dai, Liyun; Che, Tao; Xie, Hongjie; Wu, XuejiaoSnow cover over the Qinghai-Tibetan Plateau (QTP) plays an important role in climate, hydrological, and ecological systems. Currently, passive microwave remote sensing is the most efficient way to monitor snow depth on global and regional scales; however, it presents a serious overestimation of snow cover over the QTP and has difficulty describing patchy snow cover over the QTP because of its coarse spatial resolution. In this study, a new spatial dynamic method is developed by introducing ground emissivity and assimilating the snow cover fraction (SCF) and land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive snow depth at an enhanced spatial resolution. In this method, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) brightness temperature and MODIS LST are used to calculate ground emissivity. Additionally, the microwave emission model of layered snowpacks (MEMLS) is applied to simulate brightness temperature with varying ground emissivities to determine the key coefficients in the snow depth retrieval algorithm. The results show that the frozen ground emissivity presents large spatial heterogeneity over the QTP, which leads to the variation of coefficients in the snow depth retrieval algorithm. The overestimation of snow depth is rectified by introducing the ground emissivity factor at 18 and 36 GHz. Compared with in situ observations, the snow cover accuracy of the new method is 93.9%, which is better than the 60.2% accuracy of the existing method (old method) which does not consider ground emissivity. The bias and root-mean-square error (RMSE) of snow depth are 1.03 cm and 7.05 cm, respectively, for the new method; these values are much lower than the values of 6.02 cm and 9.75 cm, respectively, for the old method. However, the snow cover accuracy with depths between 1 and 3 cm is below 60%, and snow depths greater than 25 cm are underestimated in Himalayan mountainous areas. In the future, the snow cover identification algorithm should be improved to identify shallow snow cover over the QTP, and topography should be considered in the snow depth retrieval algorithm to improve snow depth accuracy in mountainous areas.Item Evaluation of Remote Sensing Inversion Error for the Above-Ground Biomass of Alpine Meadow Grassland Based on Multi-Source Satellite Data(2017-04-16) Meng, Baoping; Ge, Jing; Liang, Tiangang; Yang, Shuxia; Gao, Jinglong; Feng, Qisheng; Cui, Xia; Huang, Xiaodong; Xie, HongjieIt is not yet clear whether there is any difference in using remote sensing data of different spatial resolutions and filtering methods to improve the above-ground biomass (AGB) estimation accuracy of alpine meadow grassland. In this study, field measurements of AGB and spectral data at Sangke Town, Gansu Province, China, in three years (2013–2015) are combined to construct AGB estimation models of alpine meadow grassland based on these different remotely-sensed NDVI data: MODIS, HJ-1B CCD of China and Landsat 8 OLI (denoted as NDVIMOD, NDVICCD and NDVIOLI, respectively). This study aims to investigate the estimation errors of AGB from the three satellite sensors, to examine the influence of different filtering methods on MODIS NDVI for the estimation accuracy of AGB and to evaluate the feasibility of large-scale models applied to a small area. The results showed that: (1) filtering the MODIS NDVI using the Savitzky–Golay (SG), logistic and Gaussian approaches can reduce the AGB estimation error; in particular, the SG method performs the best, with the smallest errors at both the sample plot scale (250 m × 250 m) and the entire study area (33.9% and 34.9%, respectively); (2) the optimum estimation model of grassland AGB in the study area is the exponential model based on NDVIOLI, with estimation errors of 29.1% and 30.7% at the sample plot and the study area scales, respectively; and (3) the estimation errors of grassland AGB models previously constructed at different spatial scales (the Tibetan Plateau, Gannan Prefecture and Xiahe County) are higher than those directly constructed based on the small area of this study by 11.9%–36.4% and 5.3%–29.6% at the sample plot and study area scales, respectively. This study presents an improved monitoring algorithm of alpine natural grassland AGB estimation and provides a clear direction for future improvement of the grassland AGB estimation and grassland productivity from remote sensing technology.Item Evaluation of the Water Resource Carrying Capacity on the North Slope of the Tianshan Mountains, Northwest China(2022-02-07) Zhi, Xiaojun; Anfuding, Gulishengmu; Yang, Guang; Gong, Ping; Wang, Chunxia; Li, Yi; Li, Xiaolong; Li, Pengfei; Liu, Chenxi; Qiao, Changlu; Gao, YongliWater resource carrying capacity (WRCC) is essential for characterizing the harmony between humans and water resources in an area. Investigation of the WRCC is useful for guiding the sustainable development of a region. The northern slope of the Tianshan Mountains is an important area for the economic development of Xinjiang, China. In recent years, the supply of water in the area barely satisfies the demand. To quantitatively evaluate the WRCC, data for four indicators including the water resources, social and economic development, and ecological environment of the area were utilized. The comprehensive weighting method, which combines the entropy and analytic hierarchy processes, was used to assess these indicators. A fuzzy comprehensive evaluation model was employed to evaluate the urban WRCC of the northern slope of the Tianshan Mountain for 2018. The results showed urban WRCC values varying between good and moderate for the northern slope of the Tianshan Mountains, and this indicates that the study area is in a loadable state. Although the water supply can meet the development of cities on the northern slope of the Tianshan Mountains to a certain extent at this stage, because it is located in the arid region of western China, the shortage and uneven distribution of water resources are one of the biggest limiting factors for the future development of this region. The findings of the present study provide a basis for the development, rational allocation, and sustainable utilization of urban water resources on the northern slope of the Tianshan Mountains.Item Flash Flood Risk Analysis Based on Machine Learning Techniques in the Yunnan Province, China(2019-01-17) Ma, Meihong; Liu, Changjun; Zhao, Gang; Xie, Hongjie; Jia, Pengfei; Wang, Dacheng; Wang, Huixiao; Hong, YangFlash flood, one of the most devastating weather-related hazards in the world, has become more and more frequent in past decades. For the purpose of flood mitigation, it is necessary to understand the distribution of flash flood risk. In this study, artificial intelligence (Least squares support vector machine: LSSVM) and classical canonical method (Logistic regression: LR) are used to assess the flash flood risk in the Yunnan Province based on historical flash flood records and 13 meteorological, topographical, hydrological and anthropological factors. Results indicate that: (1) the LSSVM with Radial basis function (RBF) Kernel works the best (Accuracy = 0.79) and the LR is the worst (Accuracy = 0.75) in testing; (2) flash flood risk distribution identified by the LSSVM in Yunnan province is near normal distribution; (3) the high-risk areas are mainly concentrated in the central and southeastern regions, where with a large curve number; and (4) the impact factors contributing the flash flood risk map from higher to low are: Curve number > Digital elevation > Slope > River density > Flash Flood preventions > Topographic Wetness Index > annual maximum 24 h precipitation > annual maximum 3 h precipitation.Item Ice Production in Ross Ice Shelf Polynyas during 2017–2018 from Sentinel–1 SAR Images(2020-05-07) Dai, Liyun; Xie, Hongjie; Ackley, Stephen F.; Mestas-Nuñez, Alberto M.High sea ice production (SIP) generates high-salinity water, thus, influencing the global thermohaline circulation. Estimation from passive microwave data and heat flux models have indicated that the Ross Ice Shelf polynya (RISP) may be the highest SIP region in the Southern Oceans. However, the coarse spatial resolution of passive microwave data limited the accuracy of these estimates. The Sentinel-1 Synthetic Aperture Radar dataset with high spatial and temporal resolution provides an unprecedented opportunity to more accurately distinguish both polynya area/extent and occurrence. In this study, the SIPs of RISP and McMurdo Sound polynya (MSP) from 1 March–30 November 2017 and 2018 are calculated based on Sentinel-1 SAR data (for area/extent) and AMSR2 data (for ice thickness). The results show that the wind-driven polynyas in these two years occurred from the middle of March to the middle of November, and the occurrence frequency in 2017 was 90, less than 114 in 2018. However, the annual mean cumulative SIP area and volume in 2017 were similar to (or slightly larger than) those in 2018. The average annual cumulative polynya area and ice volume of these two years were 1,040,213 km2 and 184 km3 for the RSIP, and 90,505 km2 and 16 km3 for the MSP, respectively. This annual cumulative SIP (volume) is only 1/3–2/3 of those obtained using the previous methods, implying that ice production in the Ross Sea might have been significantly overestimated in the past and deserves further investigations.Item Imaging Floods and Glacier Geohazards with Remote Sensing(2020-11-26) Cigna, Francesca; Xie, HongjieGeohazards associated with the dynamics of the liquid and solid water of the Earth's hydrosphere, such as floods and glacial processes, may pose significant risks to populations, activities and properties [...]