Application of Google Earth in Modern River Sedimentology Research

Zhou, Xuewen; Wang, Hongliang

doi:10.4236/gep.2015.38001

Cited by 6 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first-order channel-pattern type was recognised based on detailed observation from Google Earth imagery, including the number of styles of channel threads. Multi-thread river segments had multiple channel bars separated by channels, whereas single-thread channel segments had a single channel with a sinuous planform typically characterised by high sinuosity [35]. Due to the area of the basin, the distribution of night lights across the pattern type of only the Indus River was studied for this analysis; we did not consider local variations in the planform over short distances (<500 m).…”

Section: Presence or Absence Of Night Lights Across The Basin And Buf...mentioning

confidence: 99%

Investigating the Influence of River Geomorphology on Human Presence Using Night Light Data: A Case Study in the Indus Basin

Aggarwal,

Whittaker,

Gupta

2024

Remote Sensing

View full text Add to dashboard Cite

Human settlements have historically thrived near rivers due to enhanced navigation and trade, and the availability of water supply and resources. The use of night light data, representing economic activities, provides a novel approach to studying the interactions between human activity and rivers over time. Here, we use the Defense Meteorological Satellite Program (DMSP) stable night light data from 2000 to 2013 as a proxy for human presence and activities to quantify the statistical relationships between night light presence and intensity in the Indus Basin, Asia. We test how these data are affected by proximity to trunk channels and by channel type (single/multi-thread) in the study area. We find that night light presence is enhanced by 26% within a 0 to 5 km proximity range of the Indus River and its tributaries, relative to the basin as a whole. We interpret this to represent increased human presence and activity within this zone. However, the mean intensity is lower near the river and higher away from the river, signifying denser settlements, such as towns and cities, which are preferentially located away from the Indus and its tributaries. Moreover, the enhancement of lit pixels signifying human presence and activities is increased by 18% near single-thread sections of the Indus River, compared to segments of the Indus displaying multi-thread morphologies. We suggest that this is due to the enhanced stability of single-threaded channels, relative to mobile multi-threaded channel reaches. This study demonstrates how night lights are an important tool in studying the relationship between human presence and river dynamics in large catchments such as the Indus, and we suggest that these data will have an important role in assessing differential flood spatial and social vulnerability at a regional scale.

show abstract

Section: Presence or Absence Of Night Lights Across The Basin And Buf...mentioning

confidence: 99%

Investigating the Influence of River Geomorphology on Human Presence Using Night Light Data: A Case Study in the Indus Basin

Aggarwal,

Whittaker,

Gupta

2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In the Luangwa basin, very limited detailed in situ information was available on the river geometry such as cross sec- (Pandya et al, 2017;Zhou and Wang, 2015). This was done for each virtual station and the basin outlet.…”

Section: River Geometry Informationmentioning

confidence: 99%

“…However, the development of robust models is challenged by the limited availability of ground measurements in the vast majority of river basins world-wide (Hrachowitz et al, 2013). Therefore, modellers increasingly resort to alternative data sources such as satellite data (Lakshmi, 2004;Winsemius et al, 2008;Sun et al, 2018;Pechlivanidis and Arheimer, 2015;Demirel et al, 2018;Zink et al, 2018;Rakovec et al, 2016;Nijzink et al, 2018;Dembélé et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in a data-scarce region

Hulsman

Winsemius

Michailovsky

et al. 2020

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Limited availability of ground measurements in the vast majority of river basins world-wide increases the value of alternative data sources such as satellite observations in hydrological modelling. This study investigates the potential of using remotely sensed river water levels, i.e. altimetry observations, from multiple satellite missions to identify parameter sets for a hydrological model in the semi-arid Luangwa River basin in Zambia. A distributed process-based rainfall–runoff model with sub-grid process heterogeneity was developed and run on a daily timescale for the time period 2002 to 2016. As a benchmark, feasible model parameter sets were identified using traditional model calibration with observed river discharge data. For the parameter identification using remote sensing, data from the Gravity Recovery and Climate Experiment (GRACE) were used in a first step to restrict the feasible parameter sets based on the seasonal fluctuations in total water storage. Next, three alternative ways of further restricting feasible model parameter sets using satellite altimetry time series from 18 different locations along the river were compared. In the calibrated benchmark case, daily river flows were reproduced relatively well with an optimum Nash–Sutcliffe efficiency of ENS,Q=0.78 (5/95th percentiles of all feasible solutions ENS,Q,5/95=0.61–0.75). When using only GRACE observations to restrict the parameter space, assuming no discharge observations are available, an optimum of ENS,Q=-1.4 (ENS,Q,5/95=-2.3–0.38) with respect to discharge was obtained. The direct use of altimetry-based river levels frequently led to overestimated flows and poorly identified feasible parameter sets (ENS,Q,5/95=-2.9–0.10). Similarly, converting modelled discharge into water levels using rating curves in the form of power relationships with two additional free calibration parameters per virtual station resulted in an overestimation of the discharge and poorly identified feasible parameter sets (ENS,Q,5/95=-2.6–0.25). However, accounting for river geometry proved to be highly effective. This included using river cross-section and gradient information extracted from global high-resolution terrain data available on Google Earth and applying the Strickler–Manning equation to convert modelled discharge into water levels. Many parameter sets identified with this method reproduced the hydrograph and multiple other signatures of discharge reasonably well, with an optimum of ENS,Q=0.60 (ENS,Q,5/95=-0.31–0.50). It was further shown that more accurate river cross-section data improved the water-level simulations, modelled rating curve, and discharge simulations during intermediate and low flows at the basin outlet where detailed on-site cross-section information was available. Also, increasing the number of virtual stations used for parameter selection in the calibration period considerably improved the model performance in a spatial split-sample validation. The results provide robust evidence that in the absence of directly observed discharge data for larger rivers in data-scarce regions, altimetry data from multiple virtual stations combined with GRACE observations have the potential to fill this gap when combined with readily available estimates of river geometry, thereby allowing a step towards more reliable hydrological modelling in poorly gauged or ungauged basins.

show abstract

“…Google Earth can be applied in various fields of river study e.g. measuring the river distribution and geomorphological characteristics (Goudie, 2013;Zhou and Wang, 2015). The Google Earth software has the "Historical Imagery" function that can track the satellite images back to 1984 for medium resolution images, and back to 2006 for high resolution images.…”

Section: Methodsmentioning

confidence: 99%

Influences Of Geologic Conditions And Anthropogenic Activities On Changes Of Geomorphology And Sediment Load Characteristics Of The Ping And Chao Phraya Rivers

Chaiwongsaen

View full text Add to dashboard Cite

The Chao Phraya River flows into the largest river basin of Thailand. The Ping River is one of its the major upstream branches flowing down slope southwardly, joining the Chao Phraya River in the central plain and ends at the Gulf of Thailand. The sand-choked is extensively observed and the flood overflow occurs frequently and rapidly at the Lower Ping River. In contrary, the Chao Phraya River, the erosion of river bank and shoreline around the river mouth has been spatially increasing in place. The Landsat imageries taken in 1987, 1997, 2007 and 2017 were used to analyze geomorphological changes of both rivers. Results show that the total emerged sand bar area in the Lower Ping River had increased up to 28.8 km2. The excessive trapped bed sediments deposition along the upper reaches is responsible for the shallower of river embankment leading to rapid overflow during flooding. In contrary, at the Chao Phraya River mouth, a total of 18.8 km2 of the coastal area had been eroded. Along the Lower Ping River, the bedload transport rates significantly decline toward the downstream. Most of bedload has been trapped above Bhumibol Dam. Then the bedload has been resupplied again by tributaries downstream and trapped within the succession of weirs. The combination of high supplied bedload from tributaries and low and suppressed discharge by dams and weirs accelerates growth rate of sandbars. However, within the succession of weirs severe bank collapses can occur locally as rapid growth of sandbars abruptly change the direction and increase flow velocity. Both anthropologic, and geologic factors play important roles in changing hydrosedimentary conditions of the Lower Ping River. The anthropologic factors include, river regulations, high deforestation rate, and intense in-channel sand mining. Whereas geologic factors are the underlying lithology and degree of weathering and erosion. The high rate of bedload budget links to highly weathered granite outcrops in the mountainous regions. Using Digital Shoreline Analysis System (DSAS) software and Satellite images from Google Earth can locates significant changes of river accretion and erosion along the riverbanks. The Integrated GPR-Electrical Resistivity Survey (IGRS) technique has revealed the internal structure and determined the thickness of a sandbar on the Lower Ping River. The sandbar thickness detected from this study is 10-12 m. There are several possibility implications from this study involving construction of weir, in-channel sand mining, reservoir sedimentation and coastal erosion management.

show abstract

Application of Google Earth in Modern River Sedimentology Research

Cited by 6 publications

References 2 publications

Investigating the Influence of River Geomorphology on Human Presence Using Night Light Data: A Case Study in the Indus Basin

Investigating the Influence of River Geomorphology on Human Presence Using Night Light Data: A Case Study in the Indus Basin

Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in a data-scarce region

Influences Of Geologic Conditions And Anthropogenic Activities On Changes Of Geomorphology And Sediment Load Characteristics Of The Ping And Chao Phraya Rivers

Contact Info

Product

Resources

About