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Hydraulic geometry parameters describing river hydrogeomorphic relationships are critical for determining a channel's capacity to convey water and sediment which is important for flood forecasting. Although well‐established, power‐law hydraulic geometry curves have been widely used to understand riverine systems and mapping flooding inundation worldwide for the past 70 years, we have become increasingly aware of their limitations. In the present study, we have moved beyond these traditional power‐law relationships, testing the ability of machine‐learning models to provide improved predictions of river width and depth. For this work, we have used an unprecedentedly large river measurement data set (HYDRoSWOT) as well as a suite of watershed predictor data to develop novel data‐driven approaches to better estimate river geometries over the contiguous United States (CONUS). Our Random Forest, XGBoost, and neural network models out‐performed the traditional, regionalized power law‐based hydraulic geometry equations for both width and depth, providing R‐squared values of as high as 0.75 for width and as high as 0.67 for depth, compared with R‐squared values of 0.45 for width and 0.18 for depth from the regional hydraulic geometry equations. Our results also show diverse performance outcomes across stream orders and geographical regions for the different machine‐learning models, demonstrating the value of using multi‐model approaches to maximize the predictability of river geometry. The developed models have been used to create the newly publicly available STREAM‐geo data set, which provides river width, depth, width/depth ratio, and river and stream surface area (%RSSA) for nearly 2.7 million NHDPlus stream reaches across the contiguous US. Plain Language Summary Scientists and river managers use measurements of river geometry such as width and depth to forecast floods and understand river behavior. However, the methods used to estimate river geometry that have been used for decades are imprecise and thus lead to poor predictions of river discharge dynamics. Here, we've used new machine learning‐based modeling approaches to provide better predictions of river width and depth. We tested different machine‐learning models, which were developed based on the HYDRoSWOT set of measurements of rivers across the U.S. These new models all provide better estimates of river width and depth than the old methods. Our research can help us to provide better estimates of flood dynamics and improve our understanding of rivers across the U.S. Key Points Machine Learning models outperform regional (physiographic) hydraulic geometry equations for predicting stream width and depth Model performance varies by stream orders and geographical regions, demonstrating the utility of multi‐model machine‐learning approaches The STREAM‐geo data set provides predictions of river width, depth, width‐to‐depth ratio, and river area for the NHDPlus stream reaches

In many gravel-bedded rivers, floods that fill the channel banks create just enough shear stress to move the median-sized gravel particles on the bed surface (D 50). Because this observation is common and is supported by theory, the coincidence of bankfull flow and the incipient motion of D 50 has become a commonly used assumption. However, not all natural gravel channels actually conform to this simple relationship; some channels maintain bankfull stresses far in excess of the critical stress required to initiate sediment transport. We use a database of >300 gravel-bedded rivers and >600 10Be-derived erosion rates from across North America to explore the hypothesis that sediment supply drives the magnitude of bankfull shear stress relative to the critical stress required to mobilize the median bed surface grain size ( τ b f * / τ c * ). We find that τ b f * / τ c * is significantly higher in West Coast river reaches (2.35, n = 96) than in river reaches elsewhere on the continent (1.03, n = 245). This pattern parallels patterns in erosion rates (and hence sediment supplies). Supporting our hypothesis, we find a significant correlation between upstream erosion rate and local τ b f * / τ c * at sites where this comparison is possible. Our analysis reveals a decrease in bed surface armoring with increasing τ b f * / τ c * , suggesting channels accommodate changes in sediment supply through adjustments in bed surface grain size, as also shown through numerical modeling. Our findings demonstrate that sediment supply is encoded in the bankfull hydraulic geometry of gravel bedded channels through its control on bed surface grain size.

During the first week of July 2018, widespread flooding caused extensive damage across several river basins in western Japan. Among the affected basins were the Mabicho district of Kurashiki city in the lower part of the Oda river basin of the Okayama prefecture. An analysis of such a historical flood event can provide useful input for proper water resources management. Therefore, to improve our understanding of the flood inundation profile over the Oda river basin during the period of intense rainfall from 5–8 July 2018, the Rainfall-Runoff-Inundation (RRI) model was used, with radar rainfall data from the Japan Meteorological Agency (JMA) as the input. River geometries—width, depth, and embankments—of the Oda river were generated and applied in the simulation. Our results show that the Mabicho district flooding was due to a backwater effect and bursting embankments along the Oda River. The model setup was then redesigned, taking into account these factors. The simulated maximum flood-affected areas were then compared with data from the Japanese Geospatial Information Authority (GSI), which showed that the maximum flood inundation areas estimated by the RRI model and the GSI flood-affected area matched closely. River geometries were extracted from a high-resolution digital elevation model (DEM), combined with coarser resolution DEM data (global data), and then utilized to perform a hydrological simulation of the Oda river basin under the scenarios of backwater effect and embankment failure. While this approach produced a successful outcome in this study, this is a case study for a single river basin in Japan. However, the fact that these results yielded valid information on the extent of flood inundation over the flood-affected area suggests that such an approach could be applicable to any river basin.

This paper is an extension and continuation of an earlier work by the authors on the phenomenon of meandering in Al-Abbasia reach located in the middle of the Euphrates river, Al-Najaf governorate in Iraq. The authors have developed several power functions and models depending on dimensional analysis and Buckingham π-theorem for modeling and predicting the hydraulic geometry of the selected reach. The paper employs the non-linear regression technique for developing mathematical models for computing the width and mean depth of the reach depending on its hydraulic characteristics. This paper is part of an M. Sc. thesis carried out in 2014. The developed relationships are straightforward to be applied in design and analysis with results of high acceptability; the reach width (W) model has an R2 of 0.97, while the reach mean depth (Dm) model has an R2 of 0.93. Different statistical methods have been utilized to compare the different models. The results reveal that non-linear regression models are the best models to correlate the different characteristics of the reach.

A confluence is defined as a meeting point where two or more rivers merge to become the source for a new river. This river merge adjusts its geometric parameters depending of the characteristics of its confluent rivers, particularly wide and intersection angle. Based on this idea and supported by the availability of satellite images, in this paper we classificated 43 confluences located in Tabasco, Mexico. We considered the geometry (plan view) and the intersection angle as a key elements, and applied multivariate statistical analysys to did the clasification. Results shown three groups: I. Similarity between the width of the three rivers and intersection angle less than 80°; II. Similarity between the width of the three rivers and intersection angle between 80° and 160°; and group III. Similarity between the width of the main river (the largest confluence river) and the river merge, intersection angle less than 100°. Once this classification was done, next step is to do both hydraulic and sedimentological studies, to understand the integral behavior of the confluences and verify that the proposed classification, not only have geometrical similarities, but its hydraulic and sedimentological operation are also similar. Due to difficulty to study many confluences, the best way is to chosee the representative and analyze it. Here we proposed an alternative to do it, that can be useful for scientist, enginnerings and students interested in to study confluences.

Sound understanding of hydrological alterations and the underlying causes means too much for the water resource management in the Pearl River Delta. Incision of river channels plays the key role in the hydrological alterations. As for the causes behind the river channel incision, sand dredging within the river network of the Pearl River Delta is usually assumed to play the overwhelming role in changes of geometric shapes of the river channels. Based on thorough analysis of well-collected data of channel geometry, streamflow, sediment load and water level, this study exposes new findings, investigating possible underlying causes behind the changes of the geometric shapes of the river channels at the Sanshui and Makou station. The results of this study indicate: (1) different changing properties of the geometric shapes are identified at the Sanshui and Makou stations. Larger magnitude of changes can be found in the river channel geometry of the cross section at the Sanshui station when compared to that at the Makou station. Lower water level due to fast riverbed downcutting at the Sanshui station than that at the Makou station is the major reason why the reallocation of streamflow occurred and hence the hydrological alterations over the Pearl River Delta; (2) depletion of sediment load as a result of construction of water reservoirs in the middle and upper Pearl River basin, sand dredging mainly in the Pearl River Delta and heavy floods all contribute much to the incision or deposition of the riverbed. Regulations of erosion and siltation process of the river channel often alleviate the incision of the river channels after a relatively long time span, and which makes it even harder to differentiate the factors causing the river channel incision; (3) the intensifying urbanization in the lower Pearl River basin greatly alters the underlying surface properties, which has the potential to shorten the recession of the flood event and may cause serious scouring processes and this role of flash floods in the incision of the river channels can not be ignored. This study is of great scientific and practical merits in improving human understanding of regulations of river channels and associated consequences with respect to hydrological alterations and water resource management, particularly in the economically booming region of China.

Rivers provide critical water supply for many human societies and ecosystems, yet global knowledge of their flow rates is poor. We show that useful estimates of absolute river discharge (in cubic meters per second) may be derived solely from satellite images, with no ground-based or a priori information whatsoever. The approach works owing to discovery of a characteristic scaling law uniquely fundamental to natural rivers, here termed a river’s at-many-stations hydraulic geometry. A first demonstration using Landsat Thematic Mapper images over three rivers in the United States, Canada, and China yields absolute discharges agreeing to within 20–30% of traditional in situ gauging station measurements and good tracking of flow changes over time. Within such accuracies, the door appears open for quantifying river resources globally with repeat imaging, both retroactively and henceforth into the future, with strong implications for water resource management, food security, ecosystem studies, flood forecasting, and geopolitics.

This study investigated the capabilities and limitations of estimating spatially continuous river discharge using satellite‐observed river width and the AMHG‐based algorithm (BAM). We applied this method to 668 reaches along the Yellow River mainstem, examining whether width‐derived discharge estimates can capture both natural and anthropogenic streamflow variations. Results show that discharge increases at tributary confluences and decreases in irrigated sections were successfully reproduced, indicating the potential to represent realistic spatial patterns from satellite observations. We further evaluated the impact of prior discharge, which serves as an initial guess in BAM; incorporating irrigation‐corrected priors improved estimation accuracy, particularly in downstream reaches affected by human activities. However, large errors remained in levee‐confined reaches, where poor co‐variability between width and discharge limited performance. Our findings highlight that satellite‐based width measurements, when supported by appropriate priors, offer a promising means to monitor discharge in ungauged basins, while also revealing key challenges in regulated rivers.

River discharge and width, as essential hydraulic variables and hydrological data, play a vital role in influencing the water cycle, driving the resulting river topography and supporting ecological functioning. Insights into bankfull river discharge and bankfull width at fine spatial resolutions are essential. In this study, 10-m Sentinel-2 multispectral instrument (MSI) imagery and digital elevation model (DEM) data, as well as in situ discharge and sediment data, are fused to extract bankfull river widths on the upper Yellow River. Using in situ cross-section morphology data and flood frequency estimations to calculate the bankfull discharge of 22 hydrological stations, the one-to-one correspondence relationship between the bankfull discharge data and the image cover data was determined. The machine learning (ML) method is used to extract water bodies from the Sentinel-2 images in the Google Earth Engine (GEE). The mean overall accuracy was above 0.87, and the mean kappa value was above 0.75. The research results show that (1) for rivers with high suspended sediment concentrations, the water quality index (SRMIR-Red) constitutes a higher contribution; the infrared band performs better in areas with greater amounts of vegetation coverage; and for rivers in general, the water indices perform best. (2) The effective river width of the extracted connected rivers is 30 m, which is 3 times the image resolution. The R2, root mean square error (RMSE), and mean bias error (MBE) of the estimated river width values are 0.991, 7.455 m, and −0.232 m, respectively. (3) The average river widths of the single-thread sections show linear increases along the main stream, and the R2 value is 0.801. The river width has a power function relationship with bankfull discharge and the contributing area, i.e., the downstream hydraulic geometry, with R2 values of 0.782 and 0.630, respectively. More importantly, the extracted river widths provide basic data to analyze the spatial distribution of bankfull widths along river networks and other applications in hydrology, fluvial geomorphology, and stream ecology.

As rivers flow, they slowly transform the landscape. Their channels migrate, banks erode, and rivers can even flow backward if merged with another river. To understand how river basins balance erosional forces with regional tectonic uplift, Willett et al. ( 10.1126/science.1248765 ) analyzed maps of a proxy for river elevation and horizontal movement of river drainage divides across three large river systems in China, Taiwan, and the United States. Along with numerical modeling, the results demonstrate the degree to which these basins are at topographic equilibrium. The changing connectivity and topography of river networks influences how species migrate and how much material is delivered to larger bodies of water. A proxy for river elevation demonstrates the degree to which river networks reorganize and equilibrate. River networks evolve as migrating drainage divides reshape river basins and change network topology by capture of river channels. We demonstrate that a characteristic metric of river network geometry gauges the horizontal motion of drainage divides. Assessing this metric throughout a landscape maps the dynamic states of entire river networks, revealing diverse conditions: Drainage divides in the Loess Plateau of China appear stationary; the young topography of Taiwan has migrating divides driving adjustment of major basins; and rivers draining the ancient landscape of the southeastern United States are reorganizing in response to escarpment retreat and coastal advance. The ability to measure the dynamic reorganization of river basins presents opportunities to examine landscape-scale interactions among tectonics, erosion, and ecology.