Peak Streamflow Research Articles

Multiple Indicators of Extreme Changes in Snow-Dominated Streamflow Regimes, Yakima River Basin Region, USA

Snow plays a major role in the hydrological cycle. Variations in snow duration and timing can have a negative impact on water resources. Excluding predicted changes in snowmelt rates and amounts could result in deleterious infrastructure, military mission, and asset impacts at military bases across the US. A change in snowpack can also lead to water shortages, which in turn can affect the availability of irrigation water. We performed trend analyses of air temperature, snow water equivalent (SWE) at 22 SNOTEL stations, and streamflow extremes for selected rivers in the snow-dependent and heavily irrigated Yakima River Basin (YRB) located in the Pacific Northwest US. There was a clear trend of increasing air temperature in this study area over a 30 year period (water years 1991–2020). All stations indicated an increase in average air temperatures for December (0.97 °C/decade) and January (1.12 °C/decade). There was also an upward trend at most stations in February (0.28 °C/decade). In December–February, the average air temperatures were 0.82 °C/decade. From these trends, we estimate that, by 2060, the average air temperatures for December–February at most (82%) stations will be above freezing. Furthermore, analysis of SWE from selected SNOTEL stations indicated a decreasing trend in historical SWE, and a shift to an earlier peak SWE was also assumed to be occurring due of the shorter snow duration. Decreasing trends in snow duration, rain-on-snow, and snowmelt runoff also resulted from snow modeling simulations of the YRB and the nearby area. We also observed a shift in the timing of snowmelt-driven peak streamflow, as well as a statistically significant increase in winter maximum streamflow and decrease in summer maximum and minimum streamflow trends by 2099. From the streamflow trends and complementary GEV analysis, we show that the YRB basin is a system in transition with earlier peak flows, lower snow-driven maximum streamflow, and higher rainfall-driven summer streamflow. This study highlights the importance of looking at changes in snow across multiple indicators to develop future infrastructure and planning tools to better adapt and mitigate changes in extreme events.

Impact of Stream-Groundwater Interactions on Peak Streamflow in the Floods

Floods are the one of the most significant natural disasters, with a damaging effect on human life and properties. Recent global warming and climate change exacerbate the flooding by increasing the frequency and intensity of severe floods. This study explores the role of groundwater during the floods at the Miho catchment in South Korea. The Hydrological-Ecological Integrated watershed-scale Flow model (HEIFLOW) model is used for the flood simulations to investigate the impact of groundwater and streamflow interactions during floods. The HEIFLOW model is assessed by the Nash–Sutcliffe model efficiency coefficient (NSE) and Root Mean Square Error (RMSE) for the surface water and groundwater domains, respectively. The model evaluation shows the acceptable model performance (0.64 NSE and 0.25 m–2.06 m RMSE) with the hourly time steps. The HEIFLOW shows potential as one of the methods for the flood risk management in South Korea. The major findings of this study indicate that the stream runoff at the Miho catchment is highly affected by the groundwater flows during the dry and flood seasons. Thus, the interactions between surface water and groundwater domains should be fully considered to mitigate the water hazards at the catchment scale.

Changes in the frequency of global high mountain rain-on-snow events due to climate warming

Rain-on-snow (ROS) events can trigger severe floods in mountain regions. There is high uncertainty about how the frequency of ROS events (ROS) and associated floods will change as climate warms. Previous research has found considerable spatial variability in ROS responses to climate change. Detailed global assessments have not been conducted. Here, atmospheric reanalysis data was used to drive a physically based snow hydrology model to simulate the snowpack and the streamflow response to climate warming of a 5.25 km2 virtual basin (VB) applied to different high mountain climates around the world. Results confirm that the sensitivity of ROS to climate warming is highly variable among sites, and also with different elevations, aspects and slopes in each basin. The hydrological model predicts a decrease in the frequency of ROS with warming in 30 out 40 of the VBs analyzed; the rest have increasing ROS. The dominant phase of precipitation, duration of snow cover and average temperature of each basin are the main factors that explain this variation in the sensitivity of ROS to climate warming. Within each basin, the largest decreases in ROS were predicted to be at lower elevations and on slopes with sunward aspects. Although the overall frequency of ROS drops, the hydrological importance of ROS is not expected to decline. Peak streamflows due to ROS are predicted to increase due to more rapid melting from enhanced energy inputs, and warmer snowpacks during future ROS.

Rainfall spatial-heterogeneity accelerates landscape evolution processes

Catchment hydro-morphological response is mainly conditioned on rainfall properties, such as rainfall intensity, storm duration and frequency, and the timing of these events. Rainfall spatial variability is likewise a major determinant affecting streamflow, erosion, and sediment transport, and is explored largely in the context of heavy rain triggering floods and fast morphological changes on hillslopes and in channels. In this study, we examine how the spatial structure of rainfall influences landscape evolution at the catchment scale over hundreds of years. To achieve this, multiple realizations of hourly rainfall fields, each differing only by their spatial distribution but identical in all other respects, were simulated using a weather generator. The impact of storm spatial-heterogeneity on the catchment morphology was then assessed with a landscape evolution model (CAESAR-Lisflood). A virtual “open-book” type catchment was used for this numerical experiment. The mean streamflow and low-flows remained the same while the magnitude of the annual peak streamflow increased by up to 12% in response to higher rainfall spatial heterogeneity. However, the erosion and deposition rates significantly increased (up to 50%) and the net erosion and deposition areas changed (increased by up to 9% and decreased by 13.5%, respectively) when the rain became less uniform in space. Furthermore, new gullies were found to be longer, deeper, and more branched in response to increased rainfall heterogeneity. The results suggest that heterogeneity in rainfall spatial patterns speeds up landscape development, even when rainfall volumes and temporal structures are the same. This implies that the spatial structure of rainfall may have more of an influence on catchment morphology at long time scales than previously thought.

The Drying Regimes of Non‐Perennial Rivers and Streams

AbstractThe flow regime paradigm is central to the aquatic sciences, where flow drives critical functions in lotic systems. Non‐perennial streams comprise the majority of global river length, thus we extended this paradigm to stream drying. Using 894 USGS gages, we isolated 25,207 drying events from 1979 to 2018, represented by a streamflow peak followed by no flow. We calculated hydrologic signatures for each drying event and using multivariate statistics, grouped events into drying regimes characterized by: (a) fast drying, (b) long no‐flow duration, (c) prolonged drying following low antecedent flows, (d) drying without a distinctive hydrologic signature. 77% of gages had more than one drying regime at different times within the study period. Random forests revealed land cover/use are more important to how a river dries than climate or physiographic characteristics. Clustering stream drying behavior may allow practitioners to more systematically adapt water resource management practices to specific drying regimes or rivers.

The Response of the HydroGeoSphere Model to Alternative Spatial Precipitation Simulation Methods

This paper presents the simulation results obtained from a physically based surface-subsurface hydrological model in a 5730 km2 watershed and the runoff response of the physically based hydrological models for three methods used to generate the spatial precipitation distribution: Thiessen polygons (TP), Co-Kriging (CK) interpolation and simulated annealing (SA). The HydroGeoSphere model is employed to simulate the rainfall-runoff process in two watersheds. For a large precipitation event, the simulated patterns using SA appear to be more realistic than those using the TP and CK method. In a large-scale watershed, the results demonstrate that when HydroGeoSphere is forced by TP precipitation data, it fails to reproduce the timing, intensity, or peak streamflow values. On the other hand, when HydroGeoSphere is forced by CK and SA data, the results are consistent with the measured streamflows. In a medium-scale watershed, the HydroGeoSphere results show a similar response compared to the measured streamflow values when driven by all three methods used to estimate the precipitation, although the SA case is slightly better than the other cases. The analytical results could provide a valuable counterpart to existing climate-based drought indices by comparing multiple interpolation methods in simulating land surface runoff.

Effects of Atmospheric Circulation on Stream Chemistry in Forested Watersheds Across the Northeastern United States: Part 1. Synoptic‐Scale Forcing

AbstractSynoptic typing is a method of classifying atmospheric conditions (i.e., pressure, temperature, wind direction) and can be used to relate those conditions to terrestrial processes. In this study, a daily synoptic calendar was developed for the northeastern United States to assess the atmospheric controls on variations in stream chemistry across three forested watersheds in Vermont, Rhode Island, and Maryland. Stream discharge along with dissolved organic carbon and nitrate concentrations were monitored in situ from March to November for 3 years (2014–2016) and then compared to the regional daily synoptic calendar to assess (a) which atmospheric patterns exported the greatest flux of water and solutes, (b) whether all three watersheds responded similarly to the regional atmospheric patterns, and (c) how these fluxes changed seasonally. Seven broad categories of atmospheric patterns were identified. In general, low pressure systems situated over the Great Lakes region produced the most rainfall which resulted in the highest peak streamflow by event along with daily fluxes of DOC and ‐N across the watersheds. In contrast, southwest flow regimes and Northwest Flow regimes occurred the most frequently (36% and 14% of study days, respectively) and resulted in the largest overall export (+50% of total) of water and solutes from the three watersheds. Regardless of watershed size or location, streamflow dynamics were similar when classified using synoptic typing. As such, valuable insight into the meteorological mechanisms behind temporal variation in carbon and nitrate stream export in forested watersheds can be gained by employing regional synoptic weather analyses.

Seasonality in Intermittent Streamflow Losses Beneath a Semiarid Mediterranean Wadi

AbstractStreamflow losses beneath ephemeral and intermittent streams are potentially major contributors to groundwater recharge. Compared to ephemeral streams, intermittent streams often have more than one season of streamflow and often experience shorter zero‐flow periods during the dry seasons and typically longer wet season of steady streamflow. Intermittent streams losses are lacking significant investigation on their variability over wet and dry season, and on the influence on potential groundwater recharge of flooding streamflow compared with more gradual wet seasonal baseflow. To address these issues, both streambed‐sediment water content and temperature were continuously logged over a year for an intermittent stream (wadi) under semi‐arid conditions in Central Morocco. Water content measurements uniquely distinguished between downward percolation and upward advance of the wetting front due to the rise of the water table, whereas temperature modeling was used to estimate vertical fluxes of percolated water. For the year of data, more than 90% of the estimated potential recharge occurred during winter and spring, when the alluvium typically has a higher water moisture, with most recharge generated during flooding conditions. Winter and spring baseflow generally generated low infiltration, though importantly contributes to wetting the sediment for rapid, deep percolation during flooding. Alternatively, summer flashfloods over dry sediment resulted in shallower infiltration despite higher peak streamflows. Thus, clear seasonality (seasonal variation) in relations between amounts of streamflow, streamflow loss, and depth of wetting into the streambed were distinguished over the study year. Finally, lateral recharge was detected, leading to additional seasonal water‐table fluctuation beneath the wadi.

A Catalogue of Tropical Cyclone Induced Instantaneous Peak Flows Recorded in Puerto Rico and a Comparison with the World’s Maxima

Peak streamflow rates from the Insular Caribbean have received limited attention in worldwide catalogues in spite of their potential for exceptionality given many of the islands’ steep topographic relief and proneness to high rainfall rates associated with tropical cyclones. This study compiled 1922 area-normalized peak streamflow rates recorded during tropical cyclones in Puerto Rico from 1899 to 2020. The results show that the highest peak flow values recorded on the island were within the range of the world’s maxima for watersheds with drainage areas from 10 to 619 km2. Although higher tropical cyclone rainfall and streamflow rates were observed on average for the central–eastern half of Puerto Rico, the highest of all cyclone-related peaks occurred throughout the entire island and were caused by tropical depressions, tropical storms, or hurricanes. Improving our understanding of instantaneous peak flow rates in Puerto Rico and other islands of the Caribbean is locally important due to their significance in terms of flooding extent and its associated impacts, but also because these could serve as indicators of the implications of a changing climate on tropical cyclone intensity and the associated hydrologic response.

Evaluation of short-term streamflow prediction methods in Urban river basins

Efficient and accurate streamflow predictions are important for urban water management. Data-driven models, especially neural network (NN) models can predict streamflow fast, while the results are uncertain in some complex river systems. Physically based models can reveal the underlying physics, but it is relatively slow and computationally costly. This work focuses on evaluating the reliability of three NN models (artificial neural networks (ANN), long short-term memory networks (LSTM), adaptive neuro-fuzzy inference system (ANFIS)) and one physically based model (SOBEK) in terms of efficiency and accuracy for average and peak streamflow simulation. All the models are applied for a tidal river and a mountainous river in Shenzhen. The results show that, the ANN model calculates fastest since the hidden layer's structure is simple. The LSTM model is reliable in average streamflow simulation in tidal river with the lowest bias while the ANFIS model has the best accuracy for peak streamflow simulation. Furthermore, the SOBEK model shows reliability in simulating average and peak streamflow in mountainous river due to its ability to capture uneven spatial rainfall in the area. Overall, the results indicate that the LSTM model can be a helpful supplementary to physically based models in streamflow simulation of complex urban river systems, by giving fast streamflow predictions with usually acceptable accuracy. Our results can provide helpful information for hydrological engineers in the application of flooding early warning and emergency preparedness in the context of flooding risk management.

Flood risk behaviors of United States riverine metropolitan areas are driven by local hydrology and shaped by race.

Flooding risk results from complex interactions between hydrological hazards (e.g., riverine inundation during periods of heavy rainfall), exposure, vulnerability (e.g., the potential for structural damage or loss of life), and resilience (how well we recover, learn from, and adapt to past floods). Building on recent coupled conceptualizations of these complex interactions, we characterize human-flood interactions (collective memory and risk-enduring attitude) at a more comprehensive scale than has been attempted to date across 50 US metropolitan statistical areas with a sociohydrologic (SH) model calibrated with accessible local data (historical records of annual peak streamflow, flood insurance loss claims, active insurance policy records, and population density). A cluster analysis on calibrated SH model parameter sets for metropolitan areas identified two dominant behaviors: 1) "risk-enduring" cities with lower flooding defenses and longer memory of past flood loss events and 2) "risk-averse" cities with higher flooding defenses and reduced memory of past flooding. These divergent behaviors correlated with differences in local stream flashiness indices (i.e., the frequency and rapidity of daily changes in streamflow), maximum dam heights, and the proportion of White to non-White residents in US metropolitan areas. Risk-averse cities tended to exist within regions characterized by flashier streamflow conditions, larger dams, and larger proportions of White residents. Our research supports the development of SH models in urban metropolitan areas and the design of risk management strategies that consider both demographically heterogeneous populations, changing flood defenses, and temporal changes in community risk perceptions and tolerance.

Influences of the timing of extreme precipitation on floods in Poyang Lake, China

Abstract Changes in the timing of extreme precipitation have important ramifications for public safety and storm water management, but it has not received much attention in relation to flooding. This study analyzed the changes in the timing of extreme precipitation in the Poyang Lake basin and projected its future changes for the period 2020–2099. The study also quantified the influences of changes in the timing of peak flows on lake floods based on a hydrodynamic model. The results showed that peak rainfall in the Poyang Lake basin had occurred on later dates during the period 1960–2012, and it is this change that caused a delay in peak streamflows from five rivers in the lake basin. Moreover, the effects of these changes are expected to be more prominent during 2020–2099; for example, the rate of delay will be about 2.0 days per 10 years both for peak rainfall and for streamflow in the Poyang Lake basin. The hydrodynamic simulation further showed that a delay of peak streamflows from five rivers would significantly increase the flood level and outflow of the lake and also prolong the duration of floods. These results indicate that the risk of floods in Poyang Lake is likely to increase in the future, therefore making flood control in this region more challenging.

Continuous modelling of the Bouregreg watershed (Morocco) using the HEC-HMS model

A deep understanding of the rainfall-runoff mechanism is essential to estimate the runoff generated in a given basin. In this regard, this paper aims to develop a continuous hydrological model of the Bouregreg watershed. The objective of this modelling is to evaluate the inflow to the Sidi Mohamed Ben Abdellah (SMBA) dam, located at the outlet of this basin. To this end, using the HEC-HMS model, the Soil Moisture Accounting (SMA) Loss Method was used to model infiltration losses. The SCS Unit hydrograph (SCS UH) and the Recession method were chosen as transform model and baseflow model, respectively. As a result, the comparison shows an acceptable agreement between observed and simulated flow in terms of streamflow distribution and peak values (NSE=0.57, R2=0.58). During validation, the model retained its ability to sufficiently reproduce the rainfall-runoff mechanism of the studied basin with a slight overestimation of peaks (NSE=0.61, R2=0.60). This study allows to assess and predict the inter-annual and intra-annual variation of the SMBA dam reservoir’ inflows, and therefore to forecast the climate change impact on this basin.

Assessing Water Availability and Unmet Water Demand Using the WEAP Model in the Semi-Arid Bweengwa, Kasaka and Magoye Sub-Catchments of Southern Zambia

Located in the semi-arid zone of Zambia, the Mutama-Bweengwa, Kasaka and Magoye sub-catchments have witnessed a high demand for water due to increase in population and socio-economic activities putting more pressure on water resources. This study assesses the hydrological components and ascertains the available water resources and unmet demand in the sub-catchments using the Water Evaluation And Planning (WEAP) Model and hydrometeorological data collected between 1951 and 2018. The model was calibrated and validated on 1971-1981 and 2008-2018 data respectively. The results reveal that the sub-catchments have transitioned from positive to negative water balance with -164.295 Mm3/year for Mutama-Bweengwa, -19.021 Mm3/year for Kasaka and -86.368 Mm3/year for Magoye. Evaporation was 1815.259 Mm3/year for Mutama-Bweengwa, 1162.655 Mm3/year for Kasaka and 1505.664 Mm3/year for Magoye. The demand for water has been increasing over time for various purposes such as irrigation, domestic, urban/rural water supply and livestock. The overall water storage in the sub-catchments showed a negative water balance for the year 2018. The observed and simulated peak streamflow were 8.16 m3/s and 7.7 m3/s occurring during the month of January and February respectively. The WEAP model performance achieved R2 of 0.98 during calibration and 0.95 for validation, and an NSE of 0.83 for calibration and 0.85 during validation. The values of objective functions show that the hydrology of the Mutama-Bweengwa, Kasaka and Magoye sub-catchments as predicted by the WEAP model provides satisfactory confidence for prediction of future streamflow and hence projection based on future scenarios.

Winter Inputs Buffer Streamflow Sensitivity to Snowpack Losses in the Salt River Watershed in the Lower Colorado River Basin

Recent streamflow declines in the Upper Colorado River Basin raise concerns about the sensitivity of water supply for 40 million people to rising temperatures. Yet, other studies in western US river basins present a paradox: streamflow has not consistently declined with warming and snow loss. A potential explanation for this lack of consistency is warming-induced production of winter runoff when potential evaporative losses are low. This mechanism is more likely in basins at lower elevations or latitudes with relatively warm winter temperatures and intermittent snowpacks. We test whether this accounts for streamflow patterns in nine gaged basins of the Salt River and its tributaries, which is a sub-basin in the Lower Colorado River Basin (LCRB). We develop a basin-scale model that separates snow and rainfall inputs and simulates snow accumulation and melt using temperature, precipitation, and relative humidity. Despite significant warming from 1968–2011 and snow loss in many of the basins, annual and seasonal streamflow did not decline. Between 25% and 50% of annual streamflow is generated in winter (NDJF) when runoff ratios are generally higher and potential evapotranspiration losses are one-third of potential losses in spring (MAMJ). Sub-annual streamflow responses to winter inputs were larger and more efficient than spring and summer responses and their frequencies and magnitudes increased in 1968–2011 compared to 1929–1967. In total, 75% of the largest winter events were associated with atmospheric rivers, which can produce large cool-season streamflow peaks. We conclude that temperature-induced snow loss in this LCRB sub-basin was moderated by enhanced winter hydrological inputs and streamflow production.

An analytical solution for rapidly predicting post‐fire peak streamflow for small watersheds in southern California

AbstractFollowing wildfires, the probability of flooding and debris flows increase, posing risks to human lives, downstream communities, infrastructure, and ecosystems. In southern California (USA), the Rowe, Countryman, and Storey (RCS) 1949 methodology is an empirical method that is used to rapidly estimate post‐fire peak streamflow. We re‐evaluated the accuracy of RCS for 33 watersheds under current conditions. Pre‐fire peak streamflow prediction performance was low, where the average R2was 0.29 and average RMSE was 1.10 cms/km2for the 2‐ and 10‐year recurrence interval events, respectively. Post‐fire, RCS performance was also low, with an average R2of 0.26 and RMSE of 15.77 cms/km2for the 2‐ and 10‐year events. We demonstrated that RCS overgeneralizes watershed processes and does not adequately represent the spatial and temporal variability in systems affected by wildfire and extreme weather events and often underpredicted peak streamflow without sediment bulking factors. A novel application of machine learning was used to identify critical watershed characteristics including local physiography, land cover, geology, slope, aspect, rainfall intensity, and soil burn severity, resulting in two random forest models with 45 and five parameters (RF‐45 and RF‐5, respectively) to predict post‐fire peak streamflow. RF‐45 and RF‐5 performed better than the RCS method; however, they demonstrated the importance and reliance on data availability. The important parameters identified by the machine learning techniques were used to create a three‐dimensional polynomial function to calculate post‐fire peak streamflow in small catchments in southern California during the first year after fire (R2= 0.82; RMSE = 6.59 cms/km2) which can be used as an interim tool by post‐fire risk assessment teams. We conclude that a significant increase in data collection of high temporal and spatial resolution rainfall intensity, streamflow, and sediment loading in channels will help to guide future model development to quantify post‐fire flood risk.

Streamflow Variations across Nepal during 1986-2015

This paper presents a picture of the streamflow variation in major hydrologic stations across Nepal during 1986-2015 in two periods; before and after 2000. Our study selected 27 high-quality hydrologic monitoring stations maintained by the Department of Hydrology and Meteorology (DHM), Government of Nepal distributed across the country, representing a wide range of basin size, from ~308 to ~54100 km2. We compare the flow duration curves (FDCs) for two periods, T1: 1986-1999 and T2: 2000-2015. The study quantifies the changes of different streamflow indices including minimum, maximum, average daily streamflow, different percentiles and top 10, 20 and 50 maximum daily streamflow. Many studies reported that the climate (such as temperature and precipitation) showed an abrupt variation during the late 90s in the central Himalayas. This study explores whether similar effects are observed in the streamflow of the Himalayan catchments. The results of the study show a mixed pattern of positive and negative changes for different streamflow indices. However, about 60% of analyzed station (i.e. 17 out of 27) showed the positive deviation of the maximum daily streamflow meaning more extremes were observed in the latter period compared to the former period. Seven stations revealed > +20% shift in the maximum daily streamflow from period T1 to T2. In particular, East Rapti at Rajaiya showed more than 80% increase with respect to T1 which deviated from an average of 304 (ranging from 123 to 682) m3/s to an average of 555 (ranging from 171 to 1260) m3/s. Overall, about 10% of increment could be found on time-sliced averaged maximum daily discharge between two periods. Most importantly, the inter- and intra- annual variation of extreme streamflow show a clear tendency of the elevated peak streamflow recurrently over time.

Identifying climate change impacts on surface water supply in the southern Central Valley, California

Mountain regions in arid and semi-arid climates, such as California, are considered particularly sensitive to climate change because global warming is expected to alter snowpack storage and related surface water supply. It is therefore important to accurately capture snowmelt processes in watershed models for climate change impact assessment. In this study we use the Soil and Water Assessment Tool (SWAT) to estimate projected changes in snowpack and streamflow in four alpine tributaries to the agriculturally important but less studied southern Central Valley, California. Watershed responses are evaluated for four CMIP5 climate models (HadGEM_ES, CNRM-CM5, CanESM2 and MIROC5) and two emission scenarios (RCP 4.5 and RCP 8.5) for 2020–2099. SWAT models are calibrated following a dual-objective, lumped calibration approach with an automatic calibration against observed streamflow (stage 1) and a manual calibration against reconstructed Parallel Energy Balance (ParBal) snow water equivalent (SWE) data (stage 2). Results indicate that under a warming climate, peak streamflow is expected to increase 0.5–4 times in magnitude in the coming decades and to arrive 2–4 months earlier in the year because of earlier snowmelt. In the foreseeable future, snow cover will reduce gradually in the lower elevations and diminish at higher rates at higher elevation towards the end of the 21st century. Surface water supply is predicted to increase in the southern Central Valley under the evaluated scenarios but increased temporal variability (wetter wet seasons and drier dry seasons) will create new challenges for managing supply. The study further highlights that the use of remote sensing based, reconstructed SWE data could fill the current gap of limited in-situ SWE observations to improve the snow calibration of SWAT to better predict climate change impacts in semi-arid, snow-dominated watersheds.

The Influence of Tropical Cyclones on the Evolution of River Conveyance Capacity in Puerto Rico

AbstractTropical cyclones (TCs) in Puerto Rico and other tropical environments cause some of the world's most intense rainfall rates and pose major challenges to current and future flood resilience. One potential consequence of TC‐induced floods is the reconfiguration of river channels due to sediment scour or deposition. This reconfiguration has the potential to alter subsequent flood hazard by changing channel conveyance capacity, violating statistical independence assumptions that underpin conventional recurrence interval concepts. In this study, we examine changes in channel conveyance capacity in Puerto Rico and compare these changes to streamflow trends. Results show that relatively modest long‐term changes in river channel capacity are composed of numerous short‐term transients which are of much larger magnitude. These transients are most often caused by abrupt scour or deposition during TCs and are comparable in magnitude to long‐term trends in peak streamflows. An abrupt change is typically followed by a multiyear “recovery period” as the channel reestablishes quasi‐equilibrium. Flood events with recurrence intervals of approximately 10 years and above appear to be sufficient to cause these changes; channel reconfiguration thus may be more common in Puerto Rico than in less extreme hydroclimates. Short‐term conveyance capacity changes are not considered in typical flood hazard assessments, which could substantially overstate or understate flood threat at any particular time, depending on the recent history of TC rainfall and flooding. Improving flood resiliency in Puerto Rico and elsewhere will require better understanding of rapid conveyance capacity changes, their causes, and how they influence flood hazard and risk.

The Future of Sediment Transport and Streamflow Under a Changing Climate and the Implications for Long‐Term Resilience of the San Francisco Bay‐Delta

AbstractSedimentation and turbidity have effects on habitat suitability in the San Francisco Bay‐Delta (Bay‐Delta), concerning key species in the bay as well as the ability of the delta marshes to keep pace with sea level rise. A daily rainfall runoff and transport model of the Sacramento River Basin of northern California was developed to simulate streamflow and suspended sediment transport to the Bay‐Delta for the next century (water years, WY2010–2099). The model was calibrated to historical streamflow and sediment data and applied using 10 Global Climate Models with two representative concentration pathways (RCP) each for WY1980–2099 from the IPCC 5th Assessment Report. Results indicate average increases in peak streamflow of +58% and +66% for the RCP 4.5 and 8.5 ensembles, respectively, by mid‐century and +62 and +96% by end‐of‐century. Sediment loads increased by +39% and +69% by end‐of‐century. Suspended sediment concentrations (SSC) increased on average by +4.6% and +6.7% for RCP 4.5 and 8.5, respectively, by end‐of‐century. Individual scenario results varied, and statistically significant increasing trends of sediment loads to the Bay‐Delta were found for the RCP 4.5 and 8.5 ensembles and five individual scenarios. Increased suspended sediment loads may have negative effects such as contaminant transport but also have positive effects that help protect against sea level rise, increase turbidity and fish habitat, and sustain wetland habitats in the Bay‐Delta.