Predicting Channel Conveyance and Characterizing Planform Using River Bathymetry via Satellite Image Compilation (RiBaSIC) Algorithm for DEM-Based Hydrodynamic Modeling
Abstract
:1. Introduction
- to develop an algorithm for predicting channel conveyance and characterizing planform via satellite images and observed (in situ) WSE as input;
- to estimate river discharge using the predicted conveyance via an HD model after applying the algorithm.
2. Materials and Method
2.1. Developing a River Conveyance Prediction and Planform Characterization Algorithm via Satellite Image and Observed WSE
- the WSE of the study area is not affected by the tide;
- the seasonal variation of WSE within the study area is uniform;
- the river reach maintains flow continuity meaning no major lateral inflow or outflow;
- the reach for which the channel profile is determined does not have any abrupt changes in elevation such as a cascade or rapid;
- the river reach is free from any natural and/or artificial storage areas such as lakes;
- no significant natural and/or artificial changes happened in the river planform or conveyance within the study period;
- the width of the actively flowing channel increases with increasing WSE;
- the satellite image has sufficient bands to cover the visible and Infra-Red (IR) spectrum;
- the river bankfull width is at least three times the spatial resolution of the satellite image for identifying the channel and side slopes;
- there are a sufficient number of satellite images available to capture a minimum of one hydrologic cycle.
2.1.1. Time Window Selection
2.1.2. Input Data Processing
2.1.3. Shoreline Delineation
2.1.4. Bathymetry Prediction
- Image Processing: typically, water reflects 10% of the incoming radiation that is limited to visible to NIR spectrum but soil reflects throughout the IR bands [44,45]. The highest reflectance is provided by turbid water [45] which is commonly found in river channels. Additionally, pixels in a river channel are mainly covered by soil that is periodically inundated. Therefore, short wavelength infra-red (SWIR) bands are useful for separating inundated and non-inundated river pixels in an image. So, the ratio of visible and IR (NIR and SWIR) bands are used in this step for predicting bathymetry. Similar to Section 2.1.3, cloud-covered pixels are removed first via the “Fmask” tool. Once the cloudy pixels are removed, Equation (4) is used to calculate a novel index called Average Difference Water Index (ADWI) for each cell. The ADWI values are stored as integers where a positive index value indicates a channel cell (inundated) and a negative indicates non-channel (non-inundated) cell. The selected channel cells in each image are assigned WSE of the image acquisition date.
- Generating Library of ADWI and WSE: a library of ADWI and corresponding WSE for each cell within the RiBaSIC extent (also referred to as river points) is generated. The library is used to determine the lowest and highest WSE at which a cell is found to be within a channel (LWSE and HWSE, respectively) along with the highest WSE at which a cell is found to be dry or non-channel (DWSE). If any cell is found to be in the channel at only one instance, then that WSE would be categorized as LWSE for that cell. The ADWI value corresponding to a cell’s LWSE is stored as that cell’s “Low Water Index” (LWI).
- ADWI versus Depth Correlation: the historic LWL of the nearby streamflow gauge is required to establish the ADWI versus depth correlation. If the riverbed is static (i.e., no significant bed level aggradation-degradation), then the thalweg elevation (lowest elevation of the riverbed) should be less than the LWL. Riverbed elevation for each cell or river point is approximated by two trials using the equations provided below.
1st Trial
2nd Trial
2.1.5. Post-Processing
- Slope Transferred Elevation: the shoreline elevations and ADWI bathymetry elevations for the cells obtained in Section 2.1.3 and Section 2.1.4 are transferred to their geographic locations using the channel profile [refer to Equation (1)] and distance.
- RiBaSIC Bathymetry: the shoreline delineation method using NDWI (please refer to Section 2.1.3) cannot predict the bathymetry in the main channel that is under water during the dry season. Therefore, the shorelines delineated from a dry season cloud-free image is assumed as the main channel. The cells inside the main channel are assigned ADWI bathymetry estimated in Section 2.1.4. The cells outside of the main channel but found wet in at least one image in Section 2.1.3 are considered as overbank areas. The shoreline elevations are assigned for cells in overbank areas. This merged dataset is, hereafter, called “RiBaSIC bathymetry”.
- RiBaSIC DEM: the cells that are neither in the main channel nor in overbank areas are considered as floodplain areas. The elevations of the floodplain cells are obtained from a DEM. Finally, RiBaSIC bathymetry and the DEM are merged to create a raster surface that is, hereafter, called “RiBaSIC DEM”.
2.2. Discharge Estimation from HD Model Using a Moderate Quality Global DEM
2.2.1. Manning’s Roughness Factor Approximation
- Step 1:
- hydraulic radius of the cross-sections at upstream of the model extent is calculated.
- Step 2:
- linear slope at the upstream location is estimated. For more details on estimating linear slopes at upstream and downstream locations, the readers are referred to Section 2.1.2 and Appendix A.
- Step 3:
- Manning’s n values for the upstream location is calculated using the equations listed in Table 1.
- Step 4:
- any Manning’s n value less than 0.010 is considered as “unreasonable” for natural rivers (for the flow within the main channel) and hence discarded. This filtering is important because Manning’s n values can be as small as 0.025 for minor streams (width at flood stage < 30 m) although for major streams, the value can be smaller than that of a minor stream of similar characteristics [51]. Moreover, alluvial sand bed rivers could have Manning’s n value ranging from 0.018 to 0.035 [52]. The smallest prescribed Manning’s n value is 0.016, which is applicable for straight and uniform earthen channels [51]. Therefore, Manning’s n value as small as 0.010 is likely to be an erroneous one and needs to be excluded.
- Step 5:
- the average of Manning’s n estimates (from step 4) is calculated.
2.2.2. Study Areas
2.2.3. Data
3. Results
3.1. Validation of the RiBaSIC Algorithm
3.2. Discharge Estimation by a Moderate Resolution Global DEM-Based HD Modeling
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Data Availability
Abbreviatons
2D | Two-dimensional |
ADWI | Average Difference Water Index |
ALOS | Advanced Land Observing Satellite |
ASTER | Advanced Spaceborne Thermal Emission and Reflection Radiometer |
AW3D30 | Advanced Land Observing Satellite Global Digital Surface Model |
AW3DDEM | Advanced Land Observing Satellite World 3D-DEM |
BWDB | Bangladesh Water Development Board |
DEM | Digital Elevation Model |
DOGAMI | Department of Geology and Mineral Industries |
DWSE | Highest WSE at which a cell is found to be dry |
EGM96 | Earth Gravitational Model 1996 |
ESRI | Environmental Systems Research Institute |
FREEBIRD | Flow Resistance Equation-Based Imaging of River Depths |
HAB | Hydraulically Assisted Bathymetry |
HD | Hydrodynamic |
HEC-RAS | Hydrologic Engineering Center’s River Analysis System |
HWSE | Highest WSE at which a cell is found to be within a channel |
HydroSHEDS | Hydrological data and maps based on Shuttle Elevation Derivatives at multiple Scales |
ICESat-2 | Ice, Cloud and land Elevation Satellite - 2 |
IDQT | Image-to-Depth Quantile Transformation |
IR | Infra-Red |
LWI | Low Water Index |
LWL | Lowest Water level |
LWSE | Lowest WSE at which a cell is found to be within a channel |
MERIT | Multi-Error-Removed Improved-Terrain |
MODIS | Moderate Resolution Imaging Spectroradiometer |
NASA | National Aeronautics and Space Administration |
NDWI | Normalized Difference Water Index |
NIR | Near Infra-Red |
NSE | Nash–Sutcliffe model efficiency |
RiBaSIC | River Bathymetry via Satellite Image Compilation |
RRMSE | Relative Root Mean Squared Error |
SARAL | Satellite with ARgos and ALtiKa |
SRTM | Shuttle Radar Topography Mission |
SWIR | Short Wavelength Infra-Red |
SWOT | Surface Water and Ocean Topography |
TanDEM-X | TerraSAR-X add-on for Digital Elevation Measurement |
USGS | United States Geologic Survey |
WSE | Water Surface Elevation |
Appendix A
Appendix A.1. Time Window Selection
Appendix A.2. Input Data Processing
Appendix A.3. Shoreline Delineation
Appendix A.4. Bathymetry Prediction
- Image Processing: the ADWI values for channel cells in three images are shown in Figure A6.
- b.
- Generating Library of ADWI and WSE: the HWSE, LWSE, DWSE, and LWI is calculated in this step are presented in Figure A7.For cell ID number 7, from image processing:
- −
- the highest WSE at which it is in the channel is 20 m, therefore, HWSE = 20 m;
- −
- the lowest WSE at which it is in the channel is 18 m, therefore, LWSE = 18 m;
- −
- the cell is not found dry in any image, therefore, DWSE = null;
- −
- ADWI at 18 m WSE is 60, therefore, LWI = 60.
- c.
- ADWI versus Depth Correlation:
- Zt = LWL = 16 (see Figure A1)
- Therefore, depth factor, f = = 2
- For cell ID 7,
- HWSE − LWSE = 2
- Factored depth, df = f × (HWSE − LWSE) = 2 × 2 = 4 m
- Approximate bed elevation = 20 − 4 = 16 m
- Hence, depth in Image 1 = 18 − 16 = 2 m
- Depth in Image 2 = 20 − 16 = 4 m
- Depth in Image 3 = 19 − 16 = 3 m
- 1 percentile of the average depth values ≈ 0 m
- Thalweg elevation, Zt = 16 − 0 ≈ 16 m
- Since Zt remains the same as the 1st trial, the same ADWI versus depth correlation is found after the 2nd trial.
- d.
- ADWI Bathymetry:
- For cell ID 7,
- LWSE = 18 m
- LWI = 60
- Average depth from ADWI versus depth correlation = 2.8 m
- ADWI bathymetry = 18 − 2.8 = 15.2 m
Appendix A.5. Post-Processing
- Slope Transferred Elevation: the shoreline elevations and ADWI bathymetry for the cells are transferred to their corresponding geographic location using the channel profile shown in Figure A4.
- For cell ID 7,
- Distance, x = distance of RiBaSIC upstream from streamflow gauge + distance of cell center from RiBaSIC upstream = 100 + 15 = 115
- Therefore, using the channel profile from Figure A4
- Slope transferred shoreline elevation = 0.002 × 1152 − 0.11 × 115 + 18 = 8 m
- Slope transferred ADWI bathymetry = 0.002 × 1152 − 0.11 × 115 + 15.2 = 5.2 m
- The slope transferred cell values are presented in Figure A12.
- b.
- RiBaSIC Bathymetry: the main channel of the Fusion River is obtained from a dry season satellite image as shown in Figure A13. The cells inside the main channel are assigned. Slope transferred ADWI bathymetry and shoreline elevations are assigned for the cell outside of the main channel to get the RiBaSIC bathymetry as shown in Figure A13.Cell ID 7 falls inside the main channel; therefore, ADWI bathymetry has the same elevation as slope transferred ADWI bathymetry (5 m).
- c.
- RiBaSIC DEM: the empty cells in RiBaSIC bathymetry are filled by elevations extracted from the DEM to produce RiBaSIC DEM as shown in Figure A14. It can be seen that for cell ID 7, the DEM elevation is 9m but that has been modified to 5 m in RiBaSIC DEM.
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Equation | Method | Formula | Source | Remarks |
---|---|---|---|---|
9 | Lacey (1946) | [46] | R = Hydraulic radius (ft) S = Channel slope Sw = Slope of water surface Sf = Energy gradient | |
10 | Bray (1979) | [47,48] | ||
11 | Bray (1982) | [49] | ||
12 | Jarrett (1984) | [50] | ||
13 | Sauer (1990) | [48,49] |
Study Area | SA1 | SA2 | SA3 | SA4 | |
---|---|---|---|---|---|
River | Willamette | Kushiyara | Jamuna | Solimoes | |
Basin | Columbia | Meghna | Brahmaputra | Amazon | |
Country | USA | Bangladesh | Bangladesh | Brazil | |
River characteristics | Planform | Meandering | Meandering | Braided | Wandering |
Top width (m) | 120 | 200 | 12,000 | 7000 | |
Tidal effect | No | No | No | No | |
Season | Dry | Jul–Nov | Jan–May | Jan–May | Aug–Dec |
Wet | Dec–Jun | Jun–Dec | Jun–Dec | Jan–Jul | |
Extent | RiBaSIC | Harrisburg to Albany (67 km) | Fenchuganj to Markuli (80 km) | Kazipur (25 km) | Iranduba (15 km) |
HD model | Harrisburg to Corvallis (47 km) | Fenchuganj to Monu confluence (32 km) | |||
Period | RiBaSIC | 2013–2015 | 2000–2002 | 2010 | 2010 |
HD model | 2009–2013 | 2001–2005 | 2009–2013 | 2009–2013 | |
HD model | Grid size (m) | 30 × 30 | 30 × 30 | 100 × 100 | 100 × 100 |
Time step (s) | 300 | 300 | 300 | 300 | |
Satellite Image | Landsat | 7, 8 | 5, 7 | 5, 7 | 5, 7 |
Count | 129 | 67 | 29 | 30 | |
DEM | Slope estimation | SRTM | SRTM | SRTM | 1 [52] |
Floodplain | MERIT | MERIT | MERIT | MERIT | |
WSE | Location | Harrisburg | Fenchuganj | Bahadurabad | 2 Manacapuru |
Source | USGS | BWDB | BWDB | 2 SO-HYBAM | |
Discharge | Location | Harrisburg | Sherpur | Bahadurabad | Manacapuru |
Source | USGS | BWDB | 3FAP24 | HYBAM | |
Bathymetric survey | Period | 2015 | 2004 | 2011 | 4 N/A |
Source | 5 USGS | BWDB | BWDB | 4 Brazil Navy | |
Slope (upstream–downstream) | RiBaSIC | 0.0009–0.0002 | 0.00006–0.000005 | 0.00008 | 0.000019 |
ID | SA1 | SA2 | SA3 | SA4 | |
---|---|---|---|---|---|
River | Willamette | Kushiyara | Jamuna | Solimoes | |
Location | Harrisburg | Monu confluence | Kazipur | Iranduba | |
Streamflow gauge | Harrisburg | Sherpur | Bahadurabad | Manacapuru | |
Slope | Upstream | 0.0009 | 0.000059 | 0.00008 | 0.000019 |
Downstream | 0.0004 | 0.000056 | 0.00008 | 0.000019 | |
Hydraulic Radius (m) | Upstream | 3.75 | 7.47 | 4.04 | 17.33 |
Downstream | 3.29 | 7.39 | 4.04 | 17.33 | |
Manning’s n (upstream) | Lacey (1946) | 0.029 | 0.018 | 0.019 | 0.015 |
Bray (1979) | 0.030 | 0.019 | 0.020 | 0.015 | |
Bray (1982) | 0.034 | 0.020 | 0.020 | 0.017 | |
Jarrett (1984) | 0.018 | 0.006 | 0.007 | 0.003 | |
Sauer (1990) | 0.038 | 0.025 | 0.025 | 0.021 | |
Average | 0.030 | 0.020 | 0.021 | 0.017 | |
Manning’s n (downstream) | Lacey (1946) | 0.025 | 0.018 | 0.019 | 0.015 |
Bray (1979) | 0.026 | 0.018 | 0.020 | 0.015 | |
Bray (1982) | 0.028 | 0.020 | 0.020 | 0.017 | |
Jarrett (1984) | 0.013 | 0.006 | 0.007 | 0.003 | |
Sauer (1990) | 0.032 | 0.024 | 0.025 | 0.021 | |
Average | 0.025 | 0.020 | 0.021 | 0.017 | |
RRMSE | Dry | 11% | 19% | 25% | 21% |
Wet | 17% | 17% | 16% | 10% | |
Annual | 17% | 19% | 19% | 14% | |
NSE | 0.97 | 0.95 | 0.98 | 0.93 | |
Correlation coefficient | 0.98 | 0.99 | 0.98 | 0.98 |
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Bhuyian, M.N.M.; Kalyanapu, A. Predicting Channel Conveyance and Characterizing Planform Using River Bathymetry via Satellite Image Compilation (RiBaSIC) Algorithm for DEM-Based Hydrodynamic Modeling. Remote Sens. 2020, 12, 2799. https://doi.org/10.3390/rs12172799
Bhuyian MNM, Kalyanapu A. Predicting Channel Conveyance and Characterizing Planform Using River Bathymetry via Satellite Image Compilation (RiBaSIC) Algorithm for DEM-Based Hydrodynamic Modeling. Remote Sensing. 2020; 12(17):2799. https://doi.org/10.3390/rs12172799
Chicago/Turabian StyleBhuyian, Md N M, and Alfred Kalyanapu. 2020. "Predicting Channel Conveyance and Characterizing Planform Using River Bathymetry via Satellite Image Compilation (RiBaSIC) Algorithm for DEM-Based Hydrodynamic Modeling" Remote Sensing 12, no. 17: 2799. https://doi.org/10.3390/rs12172799