Peak Flow Events Research Articles

Integration of Generative-Adversarial-Network-Based Data Compaction and Spatial Attention Transductive Long Short-Term Memory for Improved Rainfall–Runoff Modeling

This work presents a novel approach to rainfall–runoff modeling. We incorporate GAN-based data compaction into a spatial-attention-enhanced transductive long short-term memory (TLSTM) network. The GAN component reduces data dimensions while retaining essential features. This compaction enables the TLSTM to capture complex temporal dependencies in rainfall–runoff patterns more effectively. When tested on the CAMELS dataset, the model significantly outperforms benchmark LSTM-based models. For 8-day runoff forecasts, our model achieves an NSE of 0.536, compared to 0.326 from the closest competitor. The integration of GAN-based feature extraction with spatial attention mechanisms improves predictive accuracy, particularly for peak-flow events. This method offers a powerful solution for addressing current challenges in water resource management and disaster planning under extreme climate conditions.

Physically-based modelling of UK river flows under climate change

This study presents the model setup and results from the first calibrated, physically-based, spatially-distributed hydrological modelling of combined land cover and climate change impacts on a large sample of UK river catchments. The SHETRAN hydrological model was automatically calibrated for 698 UK catchments then driven by the 12 regional climate model projections from UKCP18, combined with urban development and natural flood management scenarios. The automatic calibration of SHETRAN produces a median Nash-Sutcliffe efficiency value of 0.82 with 581 catchments having a value greater than 0.7. 24 summary metrics were calculated to capture changes to important aspects of the flow regime. The UKCP18 realisations in SHETRAN indicate that a warming climate will cause river flows, on average, to decrease. These decreases are simulated to be greatest in the south and east of the UK, with droughts becoming longer and more severe. While high flows also decrease on average, an increased number of extremes are exhibited, implying a greater number of extreme flood events in the future, particularly in the north and west of the UK. In the urban development scenarios, for flood events there is an increase in flow with the increased urbanization, with the 1 in 3-year peak flow event showing the greatest increase. The natural flood management scenarios consider the effect of increasing woodland and adding surface water storage ponds. The inclusion of these features produces a complex response but overall, the modelling shows a reduction in low, median, and high flows, although the more extreme the flow event the smaller the percentage change in flow. Simulated timeseries and summary metric datasets are freely available on the CEDA archive.

Rainfall, peak river flow and flow variability drive spatio‐temporal change in the extent of riparian woodland in an African protected area savanna

AbstractVerbal accounts, supported by limited ground‐based and satellite images, reveal decreasing riparian woodland and a loss of large trees along the rivers of the Kruger National Park (KNP, South Africa) over the last century. A multi‐decadal analysis was conducted to identify trends in extent and possible drivers of riparian woodland change. Aerial and satellite imagery (1936–2018) were used to quantify changes in the extent of riparian woodland at 18 sites on 14 rivers in KNP. These changes were compared in a multivariate time‐series with river flow and local rainfall. Particular attention was paid to cumulative flow effects, as well as the frequency and magnitude of large infrequent disturbances such as droughts and floods. Riparian tree cover fluctuated over the time period, and the trajectory of change varied between sites. Most (11) sites experienced a decrease in overall riparian tree cover over ~80 years, with these declines being significant at six sites. Peak flow and maximum rainfall events were strongly associated with these decreases, indicating that flood events are potentially the biggest driver of tree loss from the system. Indeed, the mega‐flood event of 2000 and subsequent large floods have resulted in substantial declines in riparian woodland extent in recent decades. Alternatively, flow variability and cumulative rainfall significantly influenced woodland expansion in isolated cases. With global change models predicting more erratic rainfall and an increased likelihood of large infrequent disturbances, together with increasing demands to abstract more water, the long‐term future of these dynamic habitats and their associated biota here is uncertain.

Respiration and CO2 evasion dynamics in moving streambeds as a response to flow regimes

Streams and rivers are subject to discharge fluctuations that may be natural or man-made, which influence biogeochemical processes and the release of CO2 to the atmosphere. Fluctuations in discharge are also associated with changing streamwater velocity that directly affects the movement of bedforms in sandy streambeds. However, bedform movement is rarely considered when studying effects of flow dynamics on biogeochemical processes. We used planar optodes during moving-bed flume experiments to quantify the effects of different flow regimes on aerobic respiration dynamics and the subsequent release of CO2. The flow regimes included constant flow (C), sinusoidal flow (S), and single peak flow events (F). Flow regimes substantially influence aerobic respiration in the streambed and, hence, the distribution of O2 and CO2 in the streambed. Hyporheic exchange flux (HEF), bedform celerity, and respiration rates increased non-linearly with streamwater velocity. It was found that HEF and CO2 evasion were highest under dynamic flow regimes S and F. However, respiration rates, the total O2 consumed, and the total CO2 produced were highest under flow regime C. This is due to low reaction rates once the streambed became stationary in the low discharge periods of flow regimes S and F. Thus, the distribution of celerities controls total respiration. Our results highlight that substantial aerobic respiration occurs even in slow-moving streambeds and that it is important to consider bedform movement when studying biogeochemical reactions in the hyporheic zone or predicting greenhouse gas emissions from catchments.

Peak Flow Event Durations in the Mississippi River Basin and Implications for Temporal Sampling of Rivers

AbstractThe impact of an episodic river flood is intimately linked to its duration. Yet it is still unclear how often should a river be observed to accurately determine the occurrence and duration of extreme events. Here we assess flow statistics along with peak flow event detection and duration as a function of the discharge sampling period for large tributaries of the Mississippi basin using hourly gages over 2010–2022. Median event durations above high quantiles spatially vary from around 2 days upstream to 30 days downstream. Discharge mean, standard deviation, and quantiles can all be estimated within 2.5% error for sampling periods up to 8 days. A minimum temporal sampling 4× (2×) finer than peak flow event median duration is required to detect 95 ± 3% (85 ± 5%) of events and to estimate their duration within 90 ± 5% (75 ± 10%) median accuracy. Our findings have direct implications for future satellite missions concerned with capturing flood events.

Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers.

Streamflow time series data typically exhibit nonlinear and nonstationary characteristics that complicate precise estimation. Recently, multifactorial machine learning (ML) models have been developed to enhance the performance of streamflow predictions. However, the lack of interpretability within these ML models raises concerns about their inner workings and reliability. This paper introduces an innovative hybrid architecture, the TCN-LSTM-Multihead-Attention model, which combines two layers of temporal convolutional networks (TCN) followed by one layer of long short-term memory (LSTM) units, integrated with a Multihead-Attention mechanism for predicting streamflow with streamflow causation-driven prediction samples (RCDP), employing local and global interpretability studies through Shapley values and partial dependency analysis. The find_peaks method was used to identify peak flow events in the test dataset, validating the model's generality and uncovering the physical causative patterns of streamflow. The results show that (1) compared to the LSTM model with the same hyperparameter settings, the proposed TCN-LSTM-Multihead-Attention hybrid model increased the R2 by 52.9%, 2.5%, 43.1%, and 10.7% respectively at four stations in the test set predictions using RCDP samples. Moreover, comparing the prediction results of the hybrid model under different samples in Hengshan station, the R2 for RCDP increased by 5.06% and 1.22% compared to streamflow autoregressive prediction samples (RAP) and meteorological-soil volumetric water content coupled autoregressive prediction samples (MCSAP) respectively. (2) Historical streamflow data from the preceding 3days predominantly influences predictions due to strong autocorrelation, with flow quantity (Q) typically emerging as the most significant feature alongside precipitation (P), surface soil moisture (SSM), and adjacent station flow data. (3) During periods of low and normal flow, historical data remains the most crucial factor; however, during flood periods, the roles of upstream inflow and precipitation become significantly more pronounced. This model facilitates the identification and quantification of various hydrodynamic impacts on flow predictions, including upstream flood propagation, precipitation, and soil moisture conditions. It also elucidates the model's nonlinear relationships and threshold responses, thereby enhancing the interpretability and reliability of streamflow predictions.

Quantifying the climate change impacts on the magnitude and timing of hydrological extremes in the Baro River Basin, Ethiopia

Extreme hydrological events, like floods and droughts, exert considerable effects on both human and natural systems. The frequency, intensity, and duration of these events are expected to change due to climate change, posing challenges for water resource management and adaptation. In this study, the Soil and Water Assessment Tool plus (SWAT +) model was calibrated and validated to simulate flow under future shared socioeconomic pathway (SSP2-4.5 and SSP5-8.5) scenarios in the Baro River Basin with R2 values of 0.88 and 0.83, NSE of 0.83 and 0.74, and PBIAS of 0.39 and 8.87 during calibration and validation. Six bias-corrected CMIP6 Global Climate Models (GCM) were selected and utilized to investigate the effects of climate change on the magnitude and timing of hydrological extremes. All climate model simulation results suggest a general increase in streamflow magnitude for both emission scenarios (SSP2-4.5 and SSP5-8.5). The multi-model ensemble projections show yearly flow increases of 4.8% and 12.4% during the mid-term (MT) (2041–2070) and long-term (LT) (2071–2100) periods under SSP2-4.5, and 15.7% and 35.6% under SSP5-8.5, respectively. Additionally, the analysis revealed significant shifts in the projected annual 1 day, 3 day, 7 day, and 30 day maximum flows, whereas the annual 3 day and 7 day minimum flow fluctuations do not present a distinct trend in the future scenario compared to the baseline (1985–2014). The study also evaluated the timing of hydrological extremes, focusing on low and peak flow events, utilizing the annual 7 day maximum and minimum flow for this analysis. An earlier occurrence was noted for both peak and low flow in the SSP2-4.5 scenario, while a later occurrence was observed in the SSP5-8.5 scenario compared to the baseline. In conclusion, this study showed the significant effect of climate change on river hydrology and extreme flow events, highlighting their importance for informed water management and sustainable planning.

Effect of landscape irrigation regulations on runoff volume from low-density residential catchments during two California droughts

The severe drought in California (2012–2016) generated significant public and government concern. State and local watering regulations were enacted to reduce residential and commercial water-use during the droughts. This study presents a comparison of residential runoff volumes before and after local landscape irrigation regulations were enacted during the droughts of 2008 and 2012–2016. Each sampling site (Folsom 1 and Folsom 2) was a storm drain outfall that drained a low-density residential catchment in the City of Folsom. Dry season runoff measured at the sampling sites represents neighborhood outdoor water waste, mainly from landscape irrigation. During the drought of 2012–2016, median runoff flows were significantly reduced after local landscape irrigation regulations were enacted. The daily runoff pattern was also highly influenced by regulation, with reductions of daily peak runoff flows on 4–5 days in a week after watering regulations were enacted. The number of peak flow events in the daily runoff pattern were reduced during this period. In addition, a significant reduction in mean runoff volume occurred. Based on these results, the watering regulations enacted by the City of Folsom had a positive effect on reducing urban runoff from residential neighborhoods during the dry season. As the results are from monitoring sites in a relatively small geographical area, further work should evaluate reductions in irrigation runoff from other California locations to determine if this is a localized phenomenon.

Artificial hummingbird algorithm-optimized boosted tree for improved rainfall-runoff modelling

Abstract Rainfall-runoff modelling is a critical component of hydrological studies, and its accuracy is essential for water resource management. Recent advances in machine learning have led to the development of more sophisticated rainfall-runoff models, but there is still room for improvement. This study proposes a novel approach to streamflow modelling that uses the artificial hummingbird algorithm (AHA) to optimize the boosted tree algorithm. the AHA-boosted tree algorithm model was compared against two established methods, the support vector machine (SVM) and the Gaussian process regression (GPR), using a variety of statistical and graphical performance measures. The results showed that the AHA-boosted tree algorithm model significantly outperformed the SVM and GPR models, with an R2 of 0.932, RMSE of 5.358 m3/s, MAE of 2.365 m3/s, and MSE of 28.705 m3/s. The SVM model followed while the GPR model had the least accurate performance. However, all models underperformed in capturing the peak flow of the hydrograph. Evaluations using both statistical and graphical performance measures, including time series plots, scatter plots, and Taylor diagrams, were critical in this assessment. The results suggest that the AHA-boosted tree algorithm could potentially be a superior alternative for enhancing the precision of rainfall-runoff modelling, despite certain challenges in predicting peak flow events.

Real-time peak flow prediction based on signal matching

Real-time peak flow prediction under heavy precipitation is critically important for flood emergency evacuation planning and management. In the case of emergency evacuation, every second matters as a slightly longer lead time could save more lives and reduce the associated social, economic, and health impacts. Here, we present a model (named SIGMA) based on the principle of signal matching to facilitate real-time peak flow prediction at sub-hourly scales (e.g., minutes to seconds). The SIGMA model divides the target watershed into small zones and the heavy precipitation falling into each zone is collected into a small water tank. As the water tank moves downstream and arrives in the watershed outlet, it will discharge the collected precipitation and generate a small single-pulse streamflow signal. By combining all small signals coming from all zones within the watershed, we will be able to generate a synthesized peak flow signal. The proposed model is applied to simulate the peak flow events observed in a real-world watershed to verify its effectiveness in real-time flood prediction. The results suggest that the presented model can reasonably predict three key aspects of a peak flow event, including the peak flow rate, the arrival time of peak flow, and the duration of the peak flow event. The proposed model is demonstrated to be effective in real-time flood prediction and can be used to support flood emergency evacuation planning and management.

Toward Improved Regional Hydrological Model Performance Using State‐Of‐The‐Science Data‐Informed Soil Parameters

AbstractAccurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF‐Hydro) over a central California domain. Our results show WRF‐Hydro soil moisture exhibits increased correlation coefficients (r), reduced biases, and increased Kling‐Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well‐established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS‐adjusted WRF‐Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF‐Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO‐informed total soil thickness. The magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre‐calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs.

Automated streamflow measurements in high‐elevation Alpine catchments

AbstractSalt dilution is a well‐established streamflow measurement method in creeks, which works particularly well downstream of turbulent flow sections as the mixing of the salt tracer is enhanced. Usually, salt dilution measurements are performed manually, which considerably limits the observations of rare peak flow events. These events are particularly important for constructing robust rating curves and avoiding large uncertainties in the extrapolation of streamflow values. An additional challenge is the variability of the river cross section, especially after larger discharge events, leading to nonstationary rating curves. Therefore, discharge measurements well distributed over time are needed to construct a reliable streamflow–water level relationship and to detect changes caused by erosion and deposition processes. To overcome these two issues, we used an automated streamflow measuring systems at three different sites with contrasting hydrological and hydraulic characteristics in the Alps. This system allowed us to measure discharge at nearly maximum flow of the observation period (2020–2021) at all three sites and to detect abrupt changes in the rating curve by performing event‐based salt injections. The uncertainty in the measurements was quantified, and the streamflow was compared with official gauging stations in the same catchment. Based on a very large dataset of almost 300 measurements, we were able to evaluate the reliability of the system and identify the primary sources of uncertainty in the experimental setup. One key aspect was the site selection for the downstream electrical conductivity sensors, as measurement location strongly controls the signal‐to‐noise ratio in the recorded breakthrough curves.

Image-based machine learning for monitoring the dynamics of deltaic islands in the Atchafalaya River Delta Complex between 1991 and 2019

Deltaic islands are distinct hydro-environmental zones with global significance in food security, biodiversity conservation, and fishery industry. These islands are the fundamental building blocks of many river deltas. However, deltaic islands are facing severe challenges due to intensive anthropogenic activities, sea level rise, and climate change. In this study, dynamic changes of deltaic islands in Wax Lake Delta (WLD) and Atchafalaya Delta (AD), part of the Atchafalaya River Delta Complex (ARDC) in Louisiana, USA, were quantified based on remote sensing images from 1991 to 2019 through a machine learning method. Results indicate a significant increase in deltaic islands area for the whole ARDC at a rate of 1.29 km2/yr, with local expansion rates of 0.60 km2/yr for WLD and 0.69 km2/yr for AD. All three parts of the WLD naturally prograded seaward, with the western part (WP) and central part (CP) expanding southwestward to the sea, while the eastern part (EP) prograding southeastwards. Differently from WLD, the three parts of AD irregularly expanded seaward under the impacts of anthropogenic activities. The WP and CP of the AD expanded respectively northwestwards and southwestwards, while the EP remained stable. Different drivers dominate the growth of deltaic islands in the WLD and AD. Specifically, fluvial suspended sediment discharge and peak flow events were responsible for the shift in the spatial evolution of WLD, while dredging and sediment disposal contributed to the expansion of AD. Tropical storms with different intensity and landing locations caused short-term deltaic island erosion or expansion. Tropical storms mainly generated erosion on the deltaic islands of the WLD, while causing transient erosion or siltation on the deltaic islands of the AD. In addition, high-intensity hurricanes that made landfall east of the deltas caused more erosion in the AD. Finally, sea level rise, at the current rate of 8.17 mm/yr, will not pose a threat to the deltaic island of WLD, while the eastern part of AD may be at risk of drowning. This study recognizes the complexity of factors influencing the growth of deltaic islands, suggesting that quantitative studies on the deltaic island extent are of critical for the restoration and sustainable management of the Mississippi River Delta and other deltas around the world.

Development and evaluation of the channel routing model and parameters within the National Water Model

AbstractThe National Water Model (NWM) was deployed by the National Oceanic and Atmospheric Administration to simulate operational forecasts of hydrologic states across the continental United States. This paper describes the geospatial river network (“hydro‐fabric”), physics, and parameters of the NWM, elucidating the challenges of extrapolating parameters a large scale with limited observations. A set of regression‐based channel geometry parameters are evaluated for a subset of the 2.7 million NWM reaches, and the riverine compound channel scheme is described. Based on the results from regional streamflow experiments within the broader NWM context, the compound channel reduced the root mean squared error by 2% and improved median Nash–Sutcliffe efficiency by 16% compared with a non‐compound formulation. Peak event analysis from 910 peak flow events across 26 basins matched from the US Flash Flood Observation Database revealed that the mean timing error is 3 h lagged behind the observations. The routing time step was also tested, for 5‐min (default, operational setting) and 1‐h increments. The model was computationally stable and able to convey the flood peaks, although the hydrograph shape and peak timing were altered.

Semicentennial Response of a Bifurcation Region in an Engineered River to Peak Flows and Human Interventions

AbstractA bifurcation in an engineered river system (i.e., fixed planform and width) has fewer degrees of freedom in its response to interventions and natural changes than a natural bifurcation system. Our objective is to provide insight into how a bifurcation in an engineered river responds to peak flows and human interventions. To this end, we analyze the change in hydraulics, bed level, and bed surface grain size in the region of two bifurcations in the upper Rhine delta in the Netherlands over the last century. We show that, over the last two decades, the water discharge in one bifurcate (the Waal branch) has steadily increased at the expense of the other. This gradual increase in the water discharge of the first branch is associated with its erosion rate being larger than the other branch. The quick succession of two or three peak flow events (1993, 1995, and 1998) caused rapid sediment deposition over the upstream part of the bifurcate that has gradually lost discharge, which seems to have triggered the slow change in flow partitioning.

Incorporating IMERG satellite precipitation uncertainty into seasonal and peak streamflow predictions using the Hillslope Link hydrological model

In global applications and data sparse regions, which comprise most of the earth, hydrologic model-based flood monitoring relies on precipitation data from satellite multisensor precipitation products or numerical weather forecasts. However, these products often exhibit substantial errors during the meteorological conditions that lead to flooding, including extreme rainfall. The propagation of precipitation forcing errors to predicted runoff and streamflow is scale-dependent and requires an understanding of the autocorrelation structure of precipitation errors, since error autocorrelation impacts the accumulation of precipitation errors over space and time in hydrologic models. Previous efforts to account for satellite precipitation uncertainty in hydrologic models have demonstrated the potential for improving streamflow estimates; however, these efforts use satellite precipitation error models that rely heavily on ground reference data such as rain gages or weather radar and do not characterize the nonstationarity of precipitation error autocorrelation structures. This work evaluates a new method, the Space-Time Rainfall Error and Autocorrelation Model (STREAM), which stochastically generates possible true precipitation fields, as input to the Hillslope Link Model to generate ensemble streamflow estimates. Unlike previous error models, STREAM represents the nonstationary and anisotropic autocorrelation structure of satellite precipitation error and does not use any ground reference to do so. Ensemble streamflow predictions are compared with streamflow generated using satellite precipitation fields as well as a radar-gage precipitation dataset during peak flow events. Results demonstrate that this approach to accounting for precipitation uncertainty effectively characterizes the uncertainty in streamflow estimates and reduces the error of predicted streamflow. Streamflow ensembles forced by STREAM improve streamflow prediction nearly to the level obtained using ground-reference forcing data across basin sizes.

WITHDRAWN: Incorporating IMERG satellite precipitation uncertainty into seasonal and peak streamflow predictions using the Hillslope Link hydrological model

In global applications and data sparse regions, which comprise most of the earth, hydrologic model-based flood monitoring relies on precipitation data from satellite multisensor precipitation products or numerical weather forecasts. However, these products often exhibit substantial errors during the meteorological conditions that lead to flooding, including extreme rainfall. The propagation of precipitation forcing errors to predicted runoff and streamflow is scale-dependent and requires an understanding of the autocorrelation structure of precipitation errors, since error autocorrelation impacts the accumulation of precipitation errors over space and time in hydrologic models. Previous efforts to account for satellite precipitation uncertainty in hydrologic models have demonstrated the potential for improving streamflow estimates; however, these efforts use satellite precipitation error models that rely heavily on ground reference data such as rain gages or weather radar and do not characterize the nonstationarity of precipitation error autocorrelation structures. This work evaluates a new method, the Space-Time Rainfall Error and Autocorrelation Model (STREAM), which stochastically generates possible true precipitation fields, as input to the Hillslope Link Model to generate ensemble streamflow estimates. Unlike previous error models, STREAM represents the nonstationary and anisotropic autocorrelation structure of satellite precipitation error and does not use any ground reference to do so. Ensemble streamflow predictions are compared with streamflow generated using satellite precipitation fields as well as a radar-gage precipitation dataset during peak flow events. Results demonstrate that this approach to accounting for precipitation uncertainty effectively characterizes the uncertainty in streamflow estimates and reduces the error of predicted streamflow. Streamflow ensembles forced by STREAM improve streamflow prediction nearly to the level obtained using ground-reference forcing data across basin sizes.

Contributions of Sediment From the Slate Canyon Alluvial Fan to the Formation and Morphogenesis of the Keeler Dunes, CA

The Keeler Dunes Complex is an active dunefield located adjacent to Owens (dry) Lake, California. The source of sediment to the Keeler Dunes area is often assumed to be from the Owens Lake playa; however, the dunes lie at the toe of the Slate Canyon alluvial fan (the Fan). Here hydrologic and hydraulic modeling was conducted for the Fan to assess the contribution of fan sediment to the Keeler Dunes. Assessment of the potential for sediment deposition was conducted for two scenarios based on the relocation of State Highway 136 from the Owens Lake playa upgradient on the Fan and the subsequent construction of flow diversion berms. The berm construction (1954 and 1967) coincided with observations of the destabilization and migration of the Keeler Dunes. Runoff from Slate Canyon watershed was estimated using a Hydrological Simulation Program–Fortran (HSPF) model based on hourly precipitation records. The resulting hydrology output served as inputs to FLO-2D models of the Fan. With the model of hydraulic output, it was estimated that approximately one million tons of sediment were moved from the Fan hydrographic apex toward the Keeler Dunes area during the peak streamflow event of record. This represents a significant volume with respect to the total volume of the Keeler Dunes. Our modeling of the peak flow event indicates the construction of the highway diversion berms resulted in the partial redirection of fan flows and therefore sediment deposition in relation to the Keeler Dunes. This localized change in sediment availability and spatial distribution is a likely factor in the subsequent morphogenesis of the dunes.

Pipe Dreams: The Effects of Stream Bank Soil Pipes on Hyporheic Denitrification Caused by a Peak Flow Event

AbstractPeak flow events in gaining stream/river channels cause lung model hyporheic exchange with the banks (bank storage), which fosters beneficial reactions as polluted channel water cycles through riparian groundwater. Soil pipes are common along stream/riverbanks, and enhance exchange, yet their effect on reactions such as denitrification is unknown. We used MODFLOW with the Conduit Flow Package to simulate lung model exchange during a peak flow event in a section of stream bank/riparian soil with soil pipes, and MT3D‐USGS to estimate nitrate transport and denitrification. We varied soil matrix hydraulic conductivity (K) and first‐order reaction constant (k), as well as soil pipe density, length, and height above the initial channel water surface elevation (H). The addition of soil pipes enhanced stream bank (riparian) denitrification relative to banks without pipes, e.g., a 76% increase due to adding a single 1.5 m pipe. Denitrification increased linearly with pipe density but exhibited nonlinear trends with other parameters. Sensitivity analysis revealed length and density to be most influential. Soil pipe enhancement of denitrification was governed by hyporheic volume in most cases in our study. Exceptions included (a) coarse soil (K = 10−3 m/s) and (b) low k and H > 0. Scaling our results to the stream corridor scale estimated that five soil pipes per m cumulatively induced 3% nitrate removal along a 1 km reach. Overall, soil pipes enhanced advection of nitrate into the banks, and also increased residence times of that nitrate under certain conditions, which together enhanced denitrification. This enhancement has implications for excess nitrate management in watersheds.

Morphodynamics of a meandering channel over decadal timescales in response to hydrological variations

AbstractThis article addresses a major theme in fluvial geomorphology on which Professor Ken Gregory contributed much pioneering research, that of quantitative relations of morphological change to discharge variations. It examines the morphodynamics and processes of adjustment of river channels to flow characteristics on event to decadal timescales, using field and remote sensing data collected over a 40‐year period for a very active meandering reach of the River Bollin, northwest England. The free meandering and rapid rates of changes provide insight into response timescales and processes. Morphological variations and rates of change are analysed in relation to peak flow parameters. The results show that the sinuosity has continuously increased since a resetting of the planform by multiple cutoffs in 2001, this autogenic trend underlying the effects of flow events. Width of the channel has varied by 50% between map dates, corresponding to discharge characteristics of each 6‐year period and integrating the impacts of individual events. A trend of decrease in intensity of erosion and deposition and of narrowing for the period 2001–2019 is apparent and may be an indication of the recovery and adjustment time to the major morphological changes in 2001. Other possible influences include decline in sediment supply but no obvious external cause is identified. A feedback effect of lower process rates producing less sediment from banks which decreases rates of channel movement may be occurring. A major contributor to the process rate decline and narrowing could be an increase in riparian vegetation cover. Complex sequences of change between peak flow events emerge, unrelated to discharge magnitude or numbers of peak events, and a process of basal bank sedimentation followed by upper bank deposition is evident. Channel capacity varies by up to 30% year to year, which has major implications for variability of flood risk and floodplain inundation.