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This study presents an effective framework that combines artificial neural network (ANN) and statistical methods to more efficiently, consistently, and reliably estimate the daily streamflow in ungauged basins. First, two statistical methods, including drainage area ratio (DAR) and standardization with mean (SM), are used to transfer hydrological data from gauged (donor) to ungauged (target) basins, which is known as the regionalization process. Second, to get better estimation performance, an ensemble approach is applied, which is mainly based on a weighted combination of DAR and SM. Finally, a successful strategy with an optimized ANN structure is built using daily areal precipitation for the target basin, the daily streamflow of the selected donor basin, and the estimated daily streamflow for the target basin from the best-fit method as model inputs. Its performance is tested in a case study from the Coruh River Basin, Turkey, that involved using datasets from seven streamflow gauging stations on the mainstream of Coruh River. The proposed approach has indicated the best performance on both training and testing sets. The proposed approach proves to be one of the best available practical solutions in the streamflow estimation for ungauged basins.

For various reasons, it is not always possible to obtain adequate and reliable long-term streamflow records in a river basin. It is known that streamflow records are even shorter when the stations located on tributary channels are of the interest. Hence, it is necessary to develop dependable streamflow estimation models for the tributary streams that play a key role in the micro-hydrology of the basin. In this study, rainfall-runoff models are developed to estimate the daily streamflow in ungauged tributary streams. Precipitation and streamflow in the most similar gauging station on the main channel and lagged values up to three days before on the same tributary station are used as the input variables of the allocated models. To select the most similar gauging station, a similarity index criterion is developed and used in the analysis. Then, two scenarios based on the streamflow or the corresponding set of direct runoff and base-flow in the same station are used. By applying multivariate adaptive regression spline (MARS) and random forest (RF) methods, several rainfall-runoff models are developed and evaluated based on determination coefficient, mean absolute percentage error, root mean square error, relative peak flow, scatter plot and time series plot. Alternatively, the MARS and RF models are combined with a drainage area ratio (DAR) model to produce the DAR-MARS and DAR-RF models. It is concluded that the direct runoff in the mainstream is more effective on the streamflow of the tributary station, while the integration of models with DAR enhanced the capabilities of the models in estimation of extreme values in the streamflow time series.

Daily streamflow time series are critical to a very broad range of hydrologic problems. Whereas daily streamflow time series are readily obtained from gaged catchments, streamflow information is commonly needed at catchments for which no measured streamflow information exists. At ungaged catchments, methods to estimate daily streamflow time series typically require the use of a reference streamgage, which transfers properties of the streamflow time series at a reference streamgage to the ungaged catchment. Therefore, the selection of a reference streamgage is one of the central challenges associated with estimation of daily streamflow at ungaged basins. The reference streamgage is typically selected by choosing the nearest streamgage; however, this paper shows that selection of the nearest streamgage does not provide a consistent selection criterion. We introduce a new method, termed the map‐correlation method, which selects the reference streamgage whose daily streamflows are most correlated with an ungaged catchment. When applied to the estimation of daily streamflow at 28 streamgages across southern New England, daily streamflows estimated by a reference streamgage selected using the map‐correlation method generally provides improved estimates of daily streamflow time series over streamflows estimated by the selection and use of the nearest streamgage. The map correlation method could have potential for many other applications including identifying redundancy and uniqueness in a streamgage network, calibration of rainfall runoff models at ungaged sites, as well as for use in catchment classification.

How and to which extent terrain factors affecting the drainage area threshold (DAT) are disputable. This paper uses principal component analysis (PCA) and correlation analysis to study the influence degree of terrain factors on DAT. Firstly, 22 watersheds, locating in the severe soil erosion region (SSER) of Loess Plateau of China, are picked out as the example areas. The purpose of the mean change point method (MCP) to detect the relationship between DAT and gully density (GD) is to get a reasonable DAT. Secondly, nine terrain factors are calculated, and their statistical values are compared and put in the matrix to clear the different effects on DAT. Finally, the effects of statistical eigenvalues of terrain factors on DAT are compared with PCA and the correlation analysis. According to the PCA, the nine terrain factors are summarized into three principal components, which are slope, height variation, and relief factor. By calculating the score weighted by each factor coefficient matrix and eigenvalue, the result states that slope ( S ), terrain curvatures ( K ), and surface roughness ( SR ) are the factors that have great influence on DAT. Meanwhile, the results of correlation analysis indicate that S , SR , and K have exerted a great influence on the DAT, and the significance level was above 0.05. Both the results of PCA and correlation analysis make clear that the slope is the most direct and influential factor affecting DAT, while other factors are more or less related to slope directly and indirectly. The result implies that the vertical variation of terrain has a strong correlation with the slope, and also has a great influence on DAT. This research not only would be of great significance to recognize the mechanism of gully development, but also able to provide a scientific reference for soil and water conservation in the Loess Plateau.

The upcoming Surface Water and Ocean Topography (SWOT) mission will measure rivers wider than 50–100 m using a 21-day orbit, providing river reach derived discharges that can inform applications like flood forecasting and large-scale hydrologic modelling. However, these discharges will not be uniform in time or coincident with those of neighboring reaches. It is often assumed discharge upstream and downstream of a river location are highly correlated in natural conditions and can be transferred using a scaling factor like the drainage area ratio between locations. Here, the applicability of the drainage area ratio method to integrate, in space and time, SWOT-derived discharges throughout the observable river network of the Mississippi River basin is assessed. In some cases, area ratios ranging from 0.01 to 100 can be used, but cumulative urban area and/or the number of dams/reservoirs between locations decrease the method’s applicability. Though the mean number of SWOT observations for a given reach increases by 83% and the number of peak events captured increases by 100%, expanded SWOT sampled time series distributions often underperform compared to the original SWOT sampled time series for significance tests and quantile results. Alternate expansion methods may be more viable for future work.

The drainage areas (and volumes) near hydraulically fractured wells, computed and visualized in our study at high resolution, may be critically affected by the presence of natural fractures. Using a recently developed algorithm based on complex analysis methods (CAMs), the drained rock volume (DRV) is visualized for a range of synthetic constellations of natural fractures near hydraulic fractures. First, flow interference effects near a single hydraulic fracture are systematically investigated for a variety of natural fracture sets. The permeability contrast between the matrix and the natural fractures is increased stepwise in order to better understand the effect on the DRV. Next, a larger-scale model investigates flow interference for a full hydraulically fractured well with a variety of natural fracture sets. The time of flight contours (TOFCs) outlining the DRV are for all cases with natural fractures compared to a base case without any natural fractures. Discrete natural fractures, with different orientations, hydraulic conductivity, and fracture density, may shift the TOFC patterns in the reservoir region drained by the hydraulically fractured well, essentially shifting the location of the well’s drainage area. The CAM-based models provide a computationally efficient method to quantify and visualize the drainage in both naturally and hydraulically fractured reservoirs.

The effects of watershed subdivisions on hydrological simulations have not been evaluated in Arctic conditions yet. This study applied the Soil and Water Assessment Tool and the threshold drainage area (TDA) technique to evaluate the impacts of watershed subdivision on hydrological simulations at a 5,913-km2 Arctic watershed, Målselv. The watershed was discretized according to four TDA scheme scales including 200, 2,000, 5,000, and 10,000 ha. The impacts of different TDA schemes on hydrological simulations in water balance components, snowmelt runoff, and streamflow were investigated. The study revealed that the complexity of terrain and topographic attributes altered significantly in the coarse discretizations: (1) total stream length (−47.2 to −74.6%); (2) average stream slope (−68 to −83%); and (3) drainage density (−24.2 to −51.5%). The spatial density of weather grid integration reduced from −5 to −33.33% in the coarse schemes. The annual mean potential evapotranspiration, evapotranspiration, and lateral flow slightly decreased, while areal rainfall, surface runoff, and water yield slightly increased with the increases of TDAs. It was concluded that the fine TDAs produced finer and higher ranges of snowmelt runoff volume across the watershed. All TDAs had similar capacities to replicate the observed tendency of monthly mean streamflow hydrograph, except overestimated/underestimated peak flows. Spatial variation of streamflow was well analyzed in the fine schemes with high density of stream networks, while the coarse schemes simplified this. Watershed subdivisions affected model performances, in the way of decreasing the accuracy of monthly streamflow simulation, at 60% of investigated hydro-gauging stations (3/5 stations) and in the upstream. Furthermore, watershed subdivisions strongly affected the calibration process regarding the changes in sensitivity ranking of 18 calibrated model parameters and time it took to calibrate.

Low-flow quantiles at ungauged locations are generally estimated based on hydrological methods, such as the drainage area ratio and frequency analysis methods. In practice, the drainage area ratio approach is a popular but simple linear model. When hydrologically nonlinear characteristics govern the runoff process, the linear approach leads to significant bias. This study was conducted to develop an improved nonlinear approach using a canonical correlation analysis and neural network (CCA-NN)-based regional frequency analysis (RFA) for low-flow estimation. The jackknife technique was utilized to validate the two methods. The approaches were applied to 33 river basins in South Korea. In this work, we focused on two-year and five-year return periods. For the two-year return period, the BIAS, RMSE, and R2 were 0.013, 0.511, and 0.408 with the RFA, respectively, and −0.042, 1.042, and 0.114 with the drainage area ratio method, respectively; whereas for the five-year return period, the respective indices were −0.018, 0.316, and 0.573 with RFA, respectively, and 0.166, 0.536, and 0.044 with the drainage area ratio method, respectively. RFA outperformed the drainage area ratio method based on its high prediction accuracy and ability to avoid the bias problem. This study indicates that machine learning-based nonlinear techniques have the potential for use in estimating reliable low-flows at ungauged sites.

Flooding is one of the most common natural hazards, causing disastrous impacts worldwide. Stress-testing the global human-Earth system to understand the sensitivity of floodplains and population exposure to a range of plausible conditions is one strategy to identify where future changes to flooding or exposure might be most critical. This study presents a global analysis of the sensitivity of inundated areas and population exposure to varying flood event magnitudes globally for 1.2 million river reaches. Here we show that topography and drainage areas correlate with flood sensitivities as well as with societal behaviour. We find clear settlement patterns in which floodplains most sensitive to frequent, low magnitude events, reveal evenly distributed exposure across hazard zones, suggesting that people have adapted to this risk. In contrast, floodplains most sensitive to extreme magnitude events have a tendency for populations to be most densely settled in these rarely flooded zones, being in significant danger from potentially increasing hazard magnitudes given climate change. This study presents a global analysis of the sensitivity of inundated areas and population exposure to varying flood event magnitudes globally for 1.2 million river reaches. The authors show that topography and drainage areas correlate with flood sensitivities as well as with societal behavior.

The increase in bedrock channel width and the decline in bedrock channel slope with increasing drainage area are fundamental characteristics of mountainous landscapes. Compared with slope, little is known about controls on steady‐state bedrock channel width. We model steady‐state bedrock channel width by iteratively solving models for bed and bank erosion by impacting bedload, using measurable physical parameters, including uplift rate, water discharge, sediment supply, grain size, rock strength, and bank angle. The results indicate that width is largely controlled by sediment flux, rather than water discharge. The commonly used width‐discharge scaling relation is an artifact of the covariance of sediment flux and water discharge. Scaling up from cross‐section to drainage basin scales, our model reproduces the width‐drainage area scaling relation and suggests that the scaling exponent is largely controlled by the downstream change in the fraction of total sediment supply transported as bedload. Plain Language Summary Bedrock rivers become wider and less steep as water flows from upstream to downstream in mountainous landscapes. The reasons why bedrock channels widen are poorly understood. Here, we develop a method to predict typical channel width based on the mechanism of bedrock erosion by impacting sediment particles. Our method can be tested in natural bedrock rivers from measurable parameters. Our results show that channel width is largely controlled by the amount of sediment supplied from upstream, rather than the amount of water flow. We show the widely observed increase in bedrock river width with drainage area is due to the increase in sediment supply not water discharge. Key Points A new model is developed for steady‐state bedrock channel width based on bed and bank erosion by bedload particle impacts Model results indicate that the width‐drainage area scaling relation exponent is largely set by the coarse sediment fraction The width‐discharge scaling relation is an artifact of the covariance of sediment flux and water discharge