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Shiqiang Zhang      
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Shiqiang Zhang published an article in February 2019.
Top co-authors
Chang Huang

32 shared publications

Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity; Northwest University; Xi'an China

Yun Chen

15 shared publications

CSIRO Land and Water, Canberra 2601, Australia

Publication Record
Distribution of Articles published per year 
(2016 - 2019)
Total number of journals
published in
Article 0 Reads 0 Citations Global warming weakening the inherent stability of glaciers and permafrost Yongjian Ding, Shiqiang Zhang, Lin Zhao, Zhongqin Li, Shicha... Published: 01 February 2019
Science Bulletin, doi: 10.1016/j.scib.2018.12.028
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Article 0 Reads 3 Citations Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review Chang Huang, Yun Chen, Shiqiang Zhang, Jianping Wu Published: 06 June 2018
Reviews of Geophysics, doi: 10.1029/2018rg000598
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Observation of surface water is a functional requirement for studying ecological and hydrological processes. Recent advances in satellite‐based optical remote sensors have promoted the field of sensing surface water to a new era. This paper reviews the current status of detecting, extracting and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field, including spatio‐temporal scale issues, integration with in situ hydrological data and elevation data, obscuration caused by clouds and vegetation, and the growing need to map surface water at a global scale. Historically, sensors have exhibited a contradiction in resolutions. Techniques including pixel unmixing and reconstruction, and spatio‐temporal fusion have been developed to alleviate this contradiction. Spatio‐temporal dynamics of surface water have been modeled by combining remote sensing data with in situ river flow. Recent studies have also demonstrated that the river discharge can be estimated using only optical remote sensing imagery, providing valuable information for hydrological studies in ungauged areas. Another historical issue for optical sensors has been obscuration by clouds and vegetation. An effective approach of reducing this limitation is to combine with Synthetic Aperture Radar (SAR) data. Digital Elevation Model (DEM) data have also been employed to eliminate cloud/terrain shadows. The development of big data and cloud computation techniques make the increasing demand of monitoring global water dynamics at high resolutions easier to achieve. An integrated use of multi‐source data is the future direction for improved global and regional water monitoring.
CONFERENCE-ARTICLE 5 Reads 0 Citations Mapping Lake-water area at sub-pixel scale using Suomi NPP-VIIRS imagery Chang Huang, Yun Chen, Shiqiang Zhang Published: 22 November 2016
The 1st International Electronic Conference on Water Sciences, doi: 10.3390/ecws-1-f001
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Capturing the variation of lake-water area using remotely sensed imagery is an essential topic in many related fields. There are a variety of remote sensing data that can serve this purpose. Generally speaking, higher spatial resolution data are able to derive better results. However, most high spatial resolution data are sometimes defective because of their low temporal resolution and limited scene coverage. Visible Infrared Imaging Radiometer Suite onboard Suomi National Polar-orbiting Partnership (Suomi NPP-VIIRS) provides a newly-available and appropriate manner for monitoring large lakes because of its frequent revisit and wide breadth. But its spatial resolution is relatively low, from 375m to 750m. This study introduces a two-step method that integrates spectral unmixing and sub-pixel mapping to map lake-water area at sub-pixel scale from NPP-VIIRS imagery. Accuracy was assessed by employing corresponding Landsat images as the reference. Five plateau lakes in Yunnan province, China, were selected as the case study areas. Results suggest that the proposed method is able to derive finer resolution lake maps that show more details of the shoreline. The accuracy was significantly improved comparing to traditional classification method. Analysis also reveals that errors and uncertainties also exist in this method. Most of them come from the spectral unmixing procedure that retrieve water fraction from NPP-VIIRS data. Therefore, in order to achieve better lake mapping result, future work should concentrate more on improving this part to produce a better water fraction map first.

Conference 0 Reads 1 Citation Surface water change detection using change vector analysis Chang Huang, Xiaoyu Zan, Xuewen Yang, Shiqiang Zhang Published: 01 July 2016
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), doi: 10.1109/igarss.2016.7729732
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Article 0 Reads 0 Citations Hydrochemical Denudation and Transient Carbon Dioxide Drawdown in the Highly Glacierized, Shrinking Koxkar Basin, China Jian Wang, Haidong Han, Qiudong Zhao, Xiaowen Zhang Published: 01 January 2016
Advances in Meteorology, doi: 10.1155/2016/6791278
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This study considered solute fluxes and the transient CO2 drawdown process in the highly glacierized Koxkar basin in Central Eurasia, around 70.20% of which is covered by present-day ice. From 27 June to 30 September 2011, the total runoff depth was 671.70 mm, which yielded crustal solute fluxes of 213.65 ± 10.05 kg(km2d)−1 that accounted for 53.59% of the total solute flux of the river water. The solute fluxes derived directly from ice meltwater and precipitation were 70.02 ± 4.68 and 16.57 ± 1.13 kg(km2d)−1, respectively, which accounted for 17.57% and 4.16% of the total solute flux. The carbonation and hydrolysis of carbonate and feldspar minerals occurred because of the presence of H+, supplied by sulfide oxidation or CO2 drawdown. While the H+ yielded by sulfide oxidation was insufficient for hydrochemical reactions, atmospheric CO2 dissolved in the water generated H+ that drove follow-up reactions. The total transient drawdown of CO2 was 804.83 t C, which generated 39.61% of the total and 24.68% of the river water solute. Transient drawdown of CO2 in the glacier region indicated that change of glacial area and volume could influence atmospheric CO2 concentration and be important in the long-term global CO2 cycle.1. IntroductionFrom 1880 to 2012, the global mean surface air temperature has increased by 0.85°C and this increase has been especially pronounced since about 1950 [1]. For example, in the extensively glaciated Tarim basin in China, the mean surface air temperature has increased by 0.6°C since the 1980s (i.e., 0.2°C per decade). This rate of warming has had considerable influence on the alpine glaciers and hydrology of such regions. Overall, 82.2% of glaciers have retreated and the total glacial area has reduced by 4.5% [2]. Furthermore, because of climate warming resulting from increased greenhouse gas forcing, the volume of glacial meltwater has increased by about 1.24 × 108 m3·a−1, which accounts for about 15% of the increase in river discharge in the Tarim basin [3]. Increased river discharge increases crustal solute fluxes (or chemical denudation rates) and CO2 drawdown rates [4, 5] because of hydrochemical reactions.A few studies have reported on chemical denudation rates and CO2 drawdown rates in the glaciers of the Arctic, Alps, and Himalayan mountains [4, 6–13]. These reports suggested that denudation rates in glaciated areas were higher than in nonglaciated regions [7]. In Central Asia, there are many large glaciers (area > 50 km2) covered by supraglacial moraines. Because they are in regions far from the ocean, there is little precipitation and ice/snow meltwater has particular importance as a water resource. However, a review of chemical denudation rates is beyond the scope of the present paper.The focus of the present study was to examine the fluxes of major ions emanating from a subglacial outlet, to assess the rate of chemical denudation and sequestration of atmospheric CO2 in the glacierized Koxkar basin in Central Asia, based on major ion concentrations in the water. The results provide new data on ion concentrations in large alpine glacierized basins covered by supraglacial moraines, which could be used for modeling and the estimation of CO2 changes during the last glacial maximum.2. Study Area2.1. Site DescriptionThe Koxkar basin is located on the southern side of Mt. Toumuer in Northwest China (41°47′N, 80°04′E). The watershed covers an area of 118.12 km2, of which around 70.20% is covered by present-day ice (Figure 1). There are systems of deep meltwater shafts (moulins) above 3900 m a.s.l. The glacier has a subcontinental regime with subglacial outflow issuing from a conduit at the center of the glacier snout.Figure 1: Location of study area and positions of sampling sites in the Koxkar glacier region.The mean annual air temperature observed near the glacier terminus is 0.77°C, and the mean summer (May–September) temperature is 7.74°C [14]. The monthly mean air temperature is >0°C for about 6 months. The main source of precipitation is water vapor derived from the Atlantic and Arctic oceans [15]. The annual average precipitation is about 630.3 mm at the glacier terminus, 81.24% of which occurs in summer. Precipitation in the glacierized region is mainly solid state (snow or hail).A field investigation during 2003–2012 suggested that the discharge at the hydrological gauging station (HGS) at the glacial terminus was >1.0 × 108 m3·a−1 (Figure 1) and that the runoff flux from May to October accounted for ~94.5% of the annual total [14].2.2. Geological SettingTerranes from the Precambrian to Quaternary are exposed in the valleys of the Koxkar basin. Marine terrigenous clastic rocks and carbonates are very important to the regional geology, but their depths are unknown. There is little territorial volcanism [16]. Biotite monzogranite gneiss and augen granite gneiss are exposed above 3900 m a.s.l. in the Koxkar basin. From 3900 to 3400 m a.s.l., marble, shale, and rocks, which enrich the tremolite and biotite of the parametamorphic rock, are distributed on two hillsides, and marine sediment shale is exposed from 3300 to 3400 m a.s.l., supplying large quantities of substances that are important to the carbonation and oxidation processes in the subglacial environment. In other regions of the Koxkar basin, there are tertiary mudstones, siltstones, and glutenite distributed in supraglacial and terminal moraines. The area of the superglacial moraine accounts for ~83% of the total melting area [17].3. MethodsFour automatic weather stations (AWSs) were established in 2007 (Figure 1). Hourly air temperature, precipitation, wind direction and velocity, and radiation were measured and recorded by the AWS positioned near the camp, while the other AWSs mainly measured precipitation, air temperature, humidity, and wind speed.Since 28 June 2011, river water sampling has been conducted at the HGS 200 m downstream of the main subglacial outlet. This sampling site was chosen because of inaccessibility near the subglacial outlet and to avoid sampling before the different water masses were thoroughly mixed [9]. During the sampling period, bulk meltwater samples were taken manually at around 14:00 Beijing time (BT) daily (96 in total). This sampling time was chosen because the specific conductivity (SpC) at 14:00 BT represents 96.72% of the mean of the hourly samples taken during the first 10 days. Additionally, 18 ice samples and 42 precipitation (snow) samples from the ablating area of the Koxkar glacier were collected along the direction of glacial development between 2996 and 4026 m a.s.l. (Figure 1). Furthermore, 9 groundwater samples from a spring located south of the main river bed and 16 rainfall samples from the observation camp were collected.All samples were collected manually in prerinsed polypropylene bottles containing as little air as possible. Bottles and lids were rinsed in the sampling water before collection and disposable gloves were used to avoid contamination. The ice samples from the ablating region were collected after melting in a disposable polypropylene bag. At the camp, all samples were stored in a dark and cold location. Bottled samples, which were in a frozen state in insulated boxes, were transported to the State Key Laboratory of Cryosphere Science—of the Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences—and kept in a cold room at −20°C. Three blank samples were assessed to ensure that the cumulative contamination was below the baseline for each measured chemical species. After the samples were retrieved, they were immediately analyzed for pH and SpC using a pH meter (PHSJ-4A; measurement range of 0–14, uncertainty within ±0.005) and a conductivity meter (DDSJ-308A; measurement range of 0–999 μs·cm−1 and uncertainty of less than 5), respectively. Then, the precipitation, bulk meltwater, groundwater, and ice meltwater samples were gradually warmed to a temperature of 20°C.Major cations (Na+, K+, Mg2+, and Ca2+) were analyzed using a Dionex ISC 600 ion chromatograph with 20 mM MSA (methanesulfonic acid) eluent and CSRS suppresser (uncertainty <0.1%). Major anions (Cl−, , and ) were analyzed using a Dionex ISC 300 ion chromatograph with 25 mM KOH eluent and ASRS suppresser (measurement range of 0.5–400 μm, uncertainty <0.5%) [6]. The water samples were analyzed for values using the CO2 equilibration method with a gas bench, which was interfaced with a MAT-252 isotope ratio mass spectrometer. The 18O16O ratio was expressed as the difference in parts per thousand relative to the Vienna Standard Mean Ocean Water. The precision of the measurement was 0.2%.Notably, the summations of the contents of major cationic (Na+, K+, Mg2+, and Ca2+) and anionic (F−, Cl−, , and ) electronic charges appeared unbalanced. Ratios of (cations)/t(anions) for bulk river water, precipitation, groundwater, and glacial ice meltwater were 3.49, 3.07, 3.92, and 2.81, respectively, implying that there was at least one anion present that was not considered in the experiment. The mean pH of 8.12 and the maximum value of only 8.70 indicated that was not present in the different waters from the study area. Therefore, we determined the concentration from ionic charge balances [8, 12, 18, 19], that is, the sum of all cationic charges minus the sum of all anionic charges :To verify the reliability of the calculation in (1), the 13 river water samples were analyzed using the titrimetric method. The average error was 2.30% and the maximum margin of absolute error was 7.40 × 10−5 mol·L−1. Unfortunately, most of the sample volumes were not large enough to be measured using the titrimetric method and ultimately, the titrimetric method was not considered because of atmospheric CO2 contamination to the at the time of sample collection.4. Results4.1. Meteorology and HydrologyFrom 27 June to 30 September 2011, the mean daily air temperature was 9.8°C. The highest tem