Assessing Streambed Stability Using D50-Based Stream Power Across Contiguous U.S.
2. Materials and Methods
2.1. Stream Power
2.2. Sources of D50 and Hydrological Data
2.3. Scale of Analysis
2.4. Stream Power Screening Tool
2.5. Slope at D50 Data Points
2.6. Bankfull Discharge at D50 Data Points
3. Results and Discussion
3.1. Stream Power Screening Tool by Physiographic Provinces
3.2. Interpolation of Stream Power across Contiguous U.S.
3.3. Analysis of Stream Power Based on the Stream Order
3.4. Comparison with Long-Term Erosion/Deposition Temporal Data
- Stream power as a sole indicator for degradation/aggradation: This study relies on stream power to discretize the major geomorphological changes that could unfold in a stream channel. However, as noted by Bizzi and Lerner , stream power mainly accounts for the drivers of stream processes, slope, and discharge, with no characterization given to the resisting forces: sediments size distribution, bed lithology, and bedforms . Yochum et al.  showed that variable stream power thresholds had been attributed to certain geomorphological changes in different studies, demonstrating the inadequacy of stream power to serve as a sole indicator of dominant channel processes. It is vital to understand and integrate both the driving and resisting forces while establishing threshold stream power values for actual channel condition prediction .
- Threshold limits on the screening tool: Threshold values of stream power for stream conditions were developed and recommended based on limited datasets. These were intended to be used in an environment that resembles the original study sites . Application of these threshold values in a broad spatial extent with different streambed substrate types, channel geometry, and geological conditions can lead to an obscured result. Nevertheless, this study adopted a range of threshold values to discriminate between channel types, intending to encapsulate minimum stream power values required for erosion instigation in both coarse- and fine-bedded stream channels. The effect of streambed grain size was also accounted for here through the computation of critical stream power. This enhances the channel discrimination competency conducted here compared to previous studies that disregard the effect of particle size distribution while establishing stream power thresholds.
- Data limitation for large-scale application: The data used in this study was representative of the contiguous U.S. except for areas where spatial gaps were present. The data scarcity is majorly noticeable in the Great Plains, Basin and Range, and Colombia Plateaus provinces. Interpolation techniques used in the development of spatial maps highly depend on the density and distribution of data points. The accuracy of this map could have been improved if more data were available and added to the dataset. Nonetheless, the data presented in this study can serve as a foundation upon which a more comprehensive and representative analysis can be built.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Data Source||Hydrological Parameter||Number of Data Points|
|Bieger et al. ||Q, D50, W, D, S and DA||642|
|Bledsoe et al. ||Q, D50 and DA||103|
|Hawley and Bledsoe ||D50||66|
|Slater and Singer ||D50, W, D, S and DA||255|
|Range||Percentage of Data with Stream Power|
|Physiographic Provinces||Slope||Width (m)||Discharge (Q) (m3/s)||Stream Power (W/m2)||No. of Data||<15 W/m2||>35 W/m2||>300 W/m2|
|Basin And Range||0.00001–0.158||1.70–109.3||0.17–152||0.02–422||50||14.0%||76.0%||2.0%|
|Interior Low Plateaus||0.00001–0.061||3.12–57.93||2.03–589||0.44–799||65||6.2%||83.1%||3.1%|
|Middle Rocky Mountains||0.00001–0.209||3.01–37.87||0.12–20||0.03–358||27||18.5%||66.7%||3.7%|
|Northern Rocky Mountains||0.00001–0.207||2.74–79.37||0.07–1695||0.08–700||197||2.5%||92.4%||3.6%|
|Southern Rocky Mountains||0.00001–0.113||4.34–99.55||0.29–132||0.04–240||78||3.8%||83.3%||0.0%|
|St. Lawrence Valley||0.00420–0.017||5.75–14.22||3.22–24||70.04–197||3||0.0%||100.0%||0.0%|
|Valley and Ridge||0.00040–0.048||2.56–31.07||1.35–169||15.61–564||140||0.0%||96.4%||6.4%|
|Range||Percentage of Data Points with Stream Power|
|Stream Order||Slope||Discharge (Q) (m3/s)||Stream Power (W/m2)||No. of Data||<15 W/m2||>35 W/m2||>300 W/m2|
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Jha, M.K.; Asamen, D.M.; Allen, P.M.; Arnold, J.G.; White, M.J. Assessing Streambed Stability Using D50-Based Stream Power Across Contiguous U.S. Water 2022, 14, 3646. https://doi.org/10.3390/w14223646
Jha MK, Asamen DM, Allen PM, Arnold JG, White MJ. Assessing Streambed Stability Using D50-Based Stream Power Across Contiguous U.S. Water. 2022; 14(22):3646. https://doi.org/10.3390/w14223646Chicago/Turabian Style
Jha, Manoj K., Dawit M. Asamen, Peter M. Allen, Jeffrey G. Arnold, and Michael J. White. 2022. "Assessing Streambed Stability Using D50-Based Stream Power Across Contiguous U.S." Water 14, no. 22: 3646. https://doi.org/10.3390/w14223646