Search

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 depthModel performance varies by stream orders and geographical regions, demonstrating the utility of multi‐model machine‐learning approachesThe STREAM‐geo data set provides predictions of river width, depth, width‐to‐depth ratio, and river area for the NHDPlus stream reaches [ABSTRACT FROM AUTHOR]

Precipitation is the most important driver of the hydrological cycle, but it is challenging to estimate it over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR, hereafter PERCCDR) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against 1825 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly, and daily timescales, MSWEP followed by ERA5 demonstrated a higher correlation (CC) and Kling–Gupta efficiency (KGE) than other datasets for more than 50 % of the stations, whilst ERA5 was the second-highest-performing dataset, and it showed the highest error and bias for about 20 % of the stations. PERCCDR is the least-well-performing dataset, with a bias of up to 99 % and a normalised root mean square error of up to 247 %. PERCCDR only show a higher KGE and CC than the other products for less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region, or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products. [ABSTRACT FROM AUTHOR]

Manning's roughness coefficient, n, is used to describe channel roughness, and is a widely sought‐after key parameter for estimating and predicting flood propagation. Due to its control of flow velocity and shear stress, n is critical for modeling timing of floods and pollutants, aquatic ecosystem health, infrastructural safety, and so on. While alternative formulations exist, open‐channel n is typically regarded as temporally constant, determined from lookup tables or calibration, and its spatiotemporal variability was never examined holistically at large scales. Here, we developed and analyzed a continental‐scale n dataset (along with alternative formulations) calculated from observed velocity, slope, and hydraulic radius in 200,000 surveys conducted over 5,000 U.S. sites. These large, diverse observations allowed training of a Random Forest (RF) model capable of predicting n (or alternative parameters) at high accuracy (Nash Sutcliffe model efficiency >0.7) in space and time. We show that predictable time variability explains a large fraction (∼35%) of n variance compared to spatial variability (50%). While exceptions abound, n is generally lower and more stable under higher streamflow conditions. Other factorial influences on n including land cover, sinuosity, and particle sizes largely agree with conventional intuition. Accounting for temporal variability in n could lead to substantially larger (45% at the median site) estimated flow velocities under high‐flow conditions or lower (44%) velocities under low‐flow conditions. Habitual exclusion of n temporal dynamics means flood peaks could arrive days before model‐predicted flood waves, and peak magnitude estimation might also be erroneous. We therefore offer a model of great practical utility. Plain Language Summary: Stream channel roughness is a critical variable for many river‐related applications including modeling of flood inundation extent, pollutant transport, stormwater management, aquatic ecosystem health, infrastructural safety, and so on, and is traditionally assumed as being constant over time. Here we estimate channel roughness using in‐stream measurements from thousands of sites across the United States and show that its temporal dependence can be substantial. Our machine learning model can serve as a valuable and state‐of‐the‐art prediction of roughness, providing great practical value and a holistic view of the spatiotemporal variability of roughness. Moreover, the longstanding exclusion of temporal dynamics means that flood peaks could arrive days before model‐predicted flood waves, and peak magnitude estimation might also be inaccurate. Raising awareness of this issue can advance our understanding of channel flows, improve the accuracy of modeling, and save lives. Key Points: We developed a continental‐scale dataset of Manning's channel roughness values and trained accurate random forest models to predict themPredictable time variability explains a large fraction (∼35%) of variance in Manning's roughness compared to spatial variability (50%)Ignoring temporal dynamics means flood peaks could arrive days before predicted, and peak magnitude estimation might also be erroneous [ABSTRACT FROM AUTHOR]

The Amazon experiences thousands of square kilometers of deforestation annually with recent rates increasing to levels unseen since the late 2000s. These increased rates of deforestation within the basin have led to changes in sediment concentration within its river systems, with potential impacts on ecological functioning, freshwater availability, and fluvial and coastal geomorphic processes. The relationship between deforestation and fluvial sediment dynamics in the Amazon has not been extensively studied using a basin-wide, comparative approach primarily due to lack of data. In this study, we utilize a novel remote-sensing-derived sediment concentration dataset to analyze the impact of deforestation from 2001 to 2020 on suspended sediment in large rivers (>50 m wide) across the Amazon River basin. These impacts are studied using a lag-based approach to quantify the spatiotemporal relationships between observed suspended sediment and changes in land cover over time. The results show that large-scale deforestation of the Amazon during the 2001–2020 period are associated with significant changes in sediment concentration in the eastern portion of the basin. In the heavily deforested eastern regions, the hydrogeomorphic response to deforestation occurs relatively rapidly (within a year), whereas the less disturbed western areas exhibit delays of 1 to 2 years before responses are observable. Moreover, we observe that deforestation must be substantial enough to overcome the collective influences of human activities and natural sediment variations to result in a discernible impact on sediment concentration in large rivers. In 69 % of Amazonian major tributary basins with an immediate response, more than 5 % of the basin was deforested during the 2001–2020 period, while in 85 % of basins with lagged responses, less than 5 % of the land was cleared. These findings suggest severe implications for future sediment dynamics across the Amazon if deforestation is to further expand into the basin. [ABSTRACT FROM AUTHOR]