Streamflow Events Research Articles

Streamflow in the United States: Characteristics, trends, regime shifts, and extremes

Long-term streamflow observations contain essential information for understanding hydrological changes and managing water resources. A continental-scale dataset or analysis of temporal streamflow change is still lacking across hydrologic gauges in the Conterminous United States (CONUS). Here, we compiled 70 years of streamflow records from 1951 to 2021 at ~ 8000 hydrologic stations across the CONUS and characterized temporal trends, regime shifts, and extreme events using a Bayesian time series analysis algorithm. We found that the occurrences of sudden streamflow changes (e.g., regime shifts and extreme events) have been increasing with time across the CONUS. In addition, we derived 181 streamflow indicators that are valuable for hydrological and biological applications, such as the duration and frequency of high or low streamflow events. The Mississippi River Basin, especially the middle and lower parts, was a hot spot of high-frequency high-flow events. Overall, we anticipate the dataset generated here offers valuable information for understanding and quantifying changes in water resources across the CONUS.

Response of Streamflow to Future Land Use and Cover Change and Climate Change in the Source Region of the Yellow River

This study utilizes meteorological and leaf area index (LAI) data for three shared socioeconomic pathways (SSP1–2.6, SSP2–4.5, and SSP5–8.5) from four general circulation models (GCMs) of the sixth climate model intercomparison project (CMIP6) spanning from 2015 to 2099. Employing calibrated data and incorporating future land use data under three SSPs, the distributed hydrology soil vegetation model (DHSVM) is employed to simulate streamflow in the source region of the Yellow River (SRYR). The research aims to elucidate variations in streamflow across different future scenarios and to estimate extreme streamflow events and temporal distribution changes under future land use and cover change (LUCC) and climate change scenarios. The main conclusions are as follows: The grassland status in the SRYR will significantly improve from 2020 to 2099, with noticeable increases in temperature, precipitation, and longwave radiation, alongside a pronounced decrease in wind speed. The probability of flooding events increases in the future, although the magnitude of the increase diminishes over time. Both LUCC and climate change contribute to an increase in the multi-year average streamflow in the region, with respective increments of 48.8%, 24.5%, and 18.9% under SSP1–2.6, SSP2–4.5, and SSP5–8.5. Notably, the fluctuation in streamflow is most pronounced under SSP5–8.5. In SSP1–2.6, the increase in streamflow during the near future (2020–2059) exceeds that of the distant future (2059–2099). Seasonal variations in streamflow intensify across most scenarios, leading to a more uneven distribution of streamflow throughout the year and an extension of the flood season.

Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods

Considering the increased risk of urban flooding and drought due to global climate change and rapid urbanization, the imperative for more accurate methods for streamflow forecasting has intensified. This study introduces a pioneering approach leveraging the available network of real-time monitoring stations and advanced machine learning algorithms that can accurately simulate spatial–temporal problems. The Spatio-Temporal Attention Gated Recurrent Unit (STA-GRU) model is renowned for its computational efficacy in forecasting streamflow events with a forecast horizon of 7 days. The novel integration of the groundwater level, precipitation, and river discharge as predictive variables offers a holistic view of the hydrological cycle, enhancing the model’s accuracy. Our findings reveal that for a 7-day forecasting period, the STA-GRU model demonstrates superior performance, with a notable improvement in mean absolute percentage error (MAPE) values and R-square (R2) alongside reductions in the root mean squared error (RMSE) and mean absolute error (MAE) metrics, underscoring the model’s generalizability and reliability. Comparative analysis with seven conventional deep learning models, including the Long Short-Term Memory (LSTM), the Convolutional Neural Network LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the Spatio-Temporal Attention LSTM (STA-LSTM), the Gated Recurrent Unit (GRU), the Convolutional Neural Network GRU (CNNGRU), and the STA-GRU, confirms the superior predictive power of the STA-LSTM and STA-GRU models when faced with long-term prediction. This research marks a significant shift towards an integrated network of real-time monitoring stations with advanced deep-learning algorithms for streamflow forecasting, emphasizing the importance of spatially and temporally encompassing streamflow variability within an urban watershed’s stream network.

Influence of atmospheric rivers on extreme rainfall and high streamflow events in northwestern Europe: Rur (Roer) River basin

Study regionLimburg, the Netherlands; west Germany; Rur / Roer River basin. Study focusAtmospheric rivers (ARs) are long (≥ 2000 kilometers) tropospheric bands of water vapor responsible for 90% of poleward atmospheric water transport linked to extreme rainfall events (EREs). The meteorological impacts of ARs have been well-studied in high-relief regions with the implication being that rainfall generated by such events is exacerbated by the orographic influence of mountainous topography. While prior research has examined linkages between ARs and precipitation with ad-hoc flood events, few studies have systematically examined the influence of ARs to streamflow variability in regions low-to-moderate relief. We assess the effects of ARs on high precipitation and high streamflow events in northwestern Europe, and specifically for the Rur (Roer) River basin, a region characterized by low to moderate relief that has received little hydrologic research attention related to atmospheric rivers. New hydrological insights for the regionThe study is novel in that it reveals that daily precipitation and streamflow are significantly increased, by 2.84 mm and 1.91 m3/s, respectively, during AR conditions. Moreover, 47.7% of EREs and 23.2% of high streamflow events were found to be associated with ARs. An AR occurrence also increases the likelihood of a rain day occurring by 36.6%. Lastly, AR presence was significantly correlated (p < 0.001) with precipitation (ρ = 0.33). Study results illustrate that ARs are important to understanding EREs and high streamflow events, which is relevant considering a predicted increase in AR occurrence due to climate change.

Storm-driven sedimentation and dynamics of a sediment slug in an ephemeral stream: Influence on sediment-routing systems within source areas

Abstract Stream-terrace morphostratigraphy and optically stimulated luminescence (OSL) geochronology indicate that storm-driven sedimentation has caused down-system decoupling of the uppermost reaches of McMillan Creek (southern California, USA) from the lower reaches of McMillan Creek since 1960 ± 190 yr B.P. This is significant because source-to-sink studies report high degrees of sediment transport connectivity over millennial time scales during periods of high fluvial discharge in sediment routing systems. The most recent relatively large-magnitude episode of sedimentation emplaced a sediment slug in the ephemeral channel of McMillan Creek. The sediment slug is correlated to the “California Storm of January 1862” via OSL dating. In this paper, a conceptual model of sediment slug dynamics in an ephemeral stream over 16 decades is developed based on fluvial sedimentation events that in most instances included reworking slug-derived sediment. Due to the episodic nature of streamflow in ephemeral streams and the dearth of sediment transport between streamflow events, sediment slug coherency is sustained over longer periods of time in ephemeral streams than in perennial streams having steady or variable flow regimes. The longevity of sediment-slug coherency in ephemeral streams leads to more prolonged down-system decoupling in sediment routing systems than down-system decoupling caused by ordinary fluvial sedimentation. In McMillan Creek, it is possible that up-system decoupling driven by sedimentation has been contemporaneous with down-system decoupling, but factors other than sedimentation may have a more significant role in up-system decoupling. Source-to-sink studies completed in areas having a Mediterranean climate cannot assume that sediment flux out of upland source areas includes the total amount of sediment available for transport.

Climate change potentially induces ecological change in the Ethiopian Rift Valley Lakes Basin

Streamflow regime management is critical for preserving species in riverine and riparian habitats. Quantifying flow-ecology relationships can help to characterize the effect of flow regime change on species; however, dealing with this in ungauged catchments is challenging. By regionalizing simulation of streamflow to ungauged catchments, the Indicators of Hydrological Alterations (IHA) are calculated and used as a proxy for quantifying ecological impacts. High-resolution Regional Climate Models are used to derive the future streamflow in both gauged and ungauged catchments of the Ethiopian Rift Valley Lakes Basin (RVLB). Consequently, 32 IHAs representing the magnitude, duration, frequency, rate, and timing of streamflow events during their historical and future periods are calculated. The number of cases for future IHAs deviating from their historical range is calculated, which is called the non-attainment rate of future IHAs. A threshold value for the non-attainment rate is set to classify the impact as no-impact, low, medium, or high. The non-attainment rate for the future period indicates that, high ecological impact resulted in October, which is the local high flow month. Spatially, a shift towards a later date of the Julian date indicating the maximum flow resulted in a high ecological impact. This approach can be adapted to other regions with reliable regionalization results and suitable future climate models, which would be useful for the quantification and management of ecological impacts in ungauged regions.

Where the water goes: Partitioning surface flow and streambed infiltration in an ephemeral river laboratory experiment

Ephemeral and intermittent flow in dryland streams plays a vital role in supporting delicate ecosystems and recharging groundwater in underlying aquifers. Despite extensive research, accurately predicting surface runoff, streambed water infiltration, and groundwater recharge during ephemeral streamflow events remains challenging due to the complex physical processes involved. Such processes are logistically difficult to observe in the field and should not be assessed through numerical studies alone. To tackle this complexity, we utilized an 8 m long, 0.25 m wide, and 0.6 m deep laboratory flume filled with fine sand as a physical model for event flows along a dry stream. This flume allowed us to measure both streamflow and streambed infiltration volumetrically as the stream wetting front advanced along its length during 8 steady inflow rates. The flume was divided into separate compartments to measure water across the sediment surface (streamflow) and water exiting through the flume’s bottom (groundwater recharge). To validate our physical tests, we compared them with simulated flows in an integrated surface–subsurface hydrological numerical model. The model results exhibited good agreement with the observed stream and groundwater dynamics. Through a parametric numerical investigation, we identified that hydraulic conductivity significantly controlled the rate of surface infiltration and the advancement of the stream wetting front. Interestingly, we found a linear relationship between surface discharge and stream wetting front location, as well as groundwater recharge, under steady-state conditions, in line with the governing equations. However, our simulations of variable streambed heterogeneity and elevation unveiled the crucial influence of these factors in determining the timing of downstream flow progression. This insight is essential for establishing ecological connectivity along non-perennial rivers and has significant implications for delivering environmental flows in arid regions where ephemeral rivers are prevalent. Our study underscores the value of flume experiments in elucidating the transient physical processes during surface water and groundwater interactions in intermittent streams. By employing a combination of physical modelling and numerical simulations, we advance our understanding of these vital hydrological interactions, contributing to sustainable water management practices in semi-arid and arid environments.

Identifying the impacts of land use landscape pattern and climate changes on streamflow from past to future

Identifying the individual and combined hydrological response of land use landscape pattern and climate changes is key to effectively managing the ecohydrological balance of regions. However, their nonlinearity, effect size, and multiple causalities limit causal investigations. Therefore, this study aimed to establish a comprehensive methodological framework to quantify changes in the landscape pattern and climate, evaluate trends in streamflow response, and analyze the attribution of streamflow events in five basins in Beijing from the past to the future. Future climate projections were based on three general circulation models (GCMs) under two shared socioeconomic pathways (SSPs). Additionally, the landscape pattern in 2035 under a natural development scenario was simulated by the patch-generating land use simulation (PLUS). The Soil and Water Assessment Tool (SWAT) was applied to evaluate the streamflow spatial and temporal dynamics over the period 2005–2035 with multiple scenarios. A bootstrapping nonlinear regression analysis and boosted regression tree (BRT) model were used to analyze the individual and combined attribution of landscape pattern and climate changes on streamflow, respectively. The results indicated that in the future, the overall streamflow in the Beijing basin would decrease, with a slightly reduced peak streamflow in most basins in the summer and a significant increase in the autumn and winter. The nonlinear quadratic regression more effectively explained the impact of landscape pattern and climate changes on streamflow. The trends in the streamflow change depended on where the relationship curve was in relation to the threshold. In addition, the impacts of landscape pattern and climate changes on streamflow were not isolated but were joint. They presented a nonlinear, non-uniform, and coupled relationship. Except for the YongDing River Basin, the annual streamflow change was influenced more by the landscape pattern. The dominant factors and the critical pair interactions varied from basin to basin. Our findings have implications for city planners and managers for optimizing ecohydrological functions and promoting sustainable development.

Trend analysis and identification of possible periods of change in the occurrence of extreme streamflow events in a tropical basin

Knowledge of the hydrological patterns of a river basin is important for environmental management in times of scarcity and floods. Among the variables that can influence river streamflow are precipitation and land use, which can intensify the occurrence of extreme events. The objective of this work is to identify trends in the time series of minimum and maximum streamflows, on a monthly and annual scale, in a tropical basin, based on non-parametric techniques. We also aim to analyze the main climatic and anthropic factors that can explain the changes in the rates of streamflow. The Mann-Kendall, modified Mann-Kendall and Pettitt tests were used at different levels of significance, as well as the Theil-Sen slope, to identify the magnitude of the trends found. The results showed that the minimum and maximum streamflows had a decreasing tendency in the dry period of the basin, specifically in October. Changes in the monthly time series occurred mainly in 2012. Precipitation behaved similarly to that observed in streamflows, with significant changes in the dry and rainy seasons, mainly in the north of the basin. Land use and land cover showed little variation in the analyzed period and was not considered a determining factor in streamflow changes. The application of different statistical methods allowed for finding satisfactory results for the behavior of water in the basin, while the analysis of climatic and anthropic factors made it possible to know their possible influences on changes in streamflow trends.

Appraisal of data-driven techniques for predicting short-term streamflow in tropical catchment.

Short-term streamflow prediction is essential for managing flood early warning and water resources systems. Although numerical models are widely used for this purpose, they require various types of data and experience to operate the model and often tedious calibration processes. Under the digital revolution, the application of data-driven approaches to predict streamflow has increased in recent decades. In this work, multiple linear regression (MLR) and random forest (RF) models with three different input combinations are developed and assessed for multi-step ahead short-term streamflow predictions, using 14 years of hydrological datasets from the Kulim River catchment, Malaysia. Introducing more precedent streamflow events as predictor improves the performance of these data-driven models, especially in predicting peak streamflow during the high-flow event. The RF model (Nash-Sutcliffe efficiency (NSE): 0.599-0.962) outperforms the MLR model (NSE: 0.584-0.963) in terms of overall prediction accuracy. However, with the increasing lead-time length, the models' overall prediction accuracy on the arrival time and magnitude of peak streamflow decrease. These findings demonstrate the potential of decision tree-based models, such as RF, for short-term streamflow prediction and offer insights into enhancing the accuracy of these data-driven models.

Quantifying uncertainty in coastal salinity regime for biological application using quantile regression

AbstractSalinity regimes in coastal ecosystems are highly dynamic and driven by complex geomorphic and hydrological processes. Estuarine biota are generally adapted to salinity fluctuation, but are vulnerable to salinity extremes. Characterizing coastal salinity regime for ecological studies therefore requires representing extremes of salinity ranges at time scales relevant to ecology (e.g., daily, monthly, and seasonally). Here, we propose a framework for modeling coastal salinity with these overall goals: (1) quantify uncertainty in salinity associated with important terrestrial and oceanographic drivers, (2) examine time scales of salinity response to river streamflow events, and (3) predict salinity continuously over space at key time scales. Salinity is modeled as quantile surfaces related to river discharge, tidal dynamics, wind, and spatial location, applied to Suwannee Sound estuary, FL, USA, where salinity has been monitored spatially since 1981. Each quantile level is regressed independently, and together they comprise a distribution of salinity uncertainty across space, with upper and lower quantiles describing salinity extremes. Effects of physical drivers on salinity are compared through four base models with various combinations of tide and wind variables, each including spatial coordinates and a single streamflow metric (in cubic meters per second). Multiple time scales of streamflow are considered by taking means across various periods, from 1 to 12 days, and at various lagged intervals prior to salinity sample, totaling 144 streamflow metrics. We found that the Suwannee coastal salinity regime is dynamic at multiple time scales and varies nonlinearly across space from the river effluence outward. Salinity increases nonlinearly with decreasing river flow rates below 200 m3/s, most prominently in the lower quantiles of salinity (τ = 0.05–0.25). Wind appears to have a stronger influence on salinity than astronomic tides for this estuary. The regression approach developed here can be applied to any coastal system that has sufficient spatial and temporal monitoring coverage to capture multiple flood and drought events. It is implemented with a simpleRroutine, and is less computationally‐intensive than finite difference hydrodynamic modeling. The characterizations of salinity uncertainty developed in these analyses can be directly applied to future studies of fish and wildlife responses to changes in watershed management.

Investigating Drought and Flood Evolution Based on Remote Sensing Data Products over the Punjab Region in Pakistan

Over the last five decades, Pakistan experienced its worst drought from 1998 to 2002 and its worst flood in 2010. This study determined the record-breaking impacts of the droughts (1998–2002) and the flood (2010) and analyzed the given 12-year period, especially the follow-on period when the winter wheat crop was grown. We identified the drought, flood, and warm and cold edges over the plain of Punjab Pakistan based on a 12-year time series (2003–2014), using the vegetation temperature condition index (VTCI) approach based on Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data products. During the year 2010, the Global Flood Monitoring System (GFMS) model applied to the real-time Tropical Rainfall Measuring Mission (TRMM) rainfall incorporated data products into the TRMM Multi-Satellite Precipitation Analysis (TMPA) for the flood detection/intensity, stream flow, and daily accumulative precipitation, and presented the plain provisions to wetlands. This study exhibits drought severity, warm and cold edges, and flood levels using the VTCI drought-monitoring approach, which utilizes a combination of the normalized difference vegetation index (NDVI) with land surface temperature (LST) data products. It was found that during the years 2003–2014, the VTCI had a positive correlation coefficient (r) with the cumulative precipitation (r = 0.60) on the day of the year (D-073) in the winter. In the year 2010, at D-201, there was no proportionality (nonlinear), and at D-217, a negative correlation was established. This revealed the time, duration, and intensity of the flood at D-201 and D-217, and described the heavy rainfall, stream flow, and flood events. At D-233 and D-281 during 2010, a significant positive correlation was noticed in normal conditions (r = 0.95 in D-233 and r = 0.97 in D-281 during the fall of 2010), which showed the flood events and normality. Notably, our results suggest that VTCI can be used for drought and wet conditions in both rain-fed and irrigated regions. The results are consistent with anomalies in the GFMS model using the spatial and temporal observations of the MODIS, TRMM, and TMPA satellites, which describe the dry and wet conditions, as well as flood runoff stream flow and flood detection/intensity, in the region of Punjab during 2010. It should be noted that the flood (2010) affected the area, and the production of the winter wheat crop has consistently declined from 19.041 to 17.7389 million tons.

Habitat compression of an estuarine mytilid following half a century of streamflow decline

The distribution of species throughout estuaries is often regulated by salinity gradients, among other key environmental drivers. In many temperate estuaries, however, declining streamflow has led to increased marinization and thus potential habitat compression of some species. Since the mid-1970s, estuaries in south-western Australia have experienced among the greatest decline in streamflow in Australia due to a drying and warming climate. The resultant habitat modification would be particularly unfavourable to sessile fauna, such as mytilid mussels, which are unable to relocate due to their attachment to substrate via byssal threads. This study compared the settlement (and adult survival) of Black Pygmy Mussel Xenostrobus securis in the Swan-Canning Estuary with that described in this system sixty years earlier, over which time freshwater discharge declined by >50%. During the 1960s, settlement of X. securis occurred throughout much of the estuary, beginning in the estuary basin in November and in the upper Swan River in February/March as winter freshwater flow subsided and salinities increased. In contrast, in the current study (2019/2020), X. securis settlement was largely restricted to the upper zones of the estuary. Settlement of X. securis larvae also began much earlier, occurring in the upper Swan River in October, compared with February/March during the 1960s. This is consistent with the salinity tolerances of larval X. securis and declining streamflow, leading to increased marinization of the estuary basin and earlier encroachment of the salt-wedge upstream. In addition, the increased frequency of summer streamflow events in recent decades makes X. securis populations even more vulnerable to substantial mortalities caused by rapid dilution of salinity at warmer temperatures. Thus, the combination of habitat compression, from declining streamflow and increased occurrence of extreme summer rainfall events are likely to pose substantial problems for the survival of X. securis populations in this region into the future. The results of this study further our knowledge on the potential impacts of environmental change on sessile fauna and is of interest to environmental managers.

Estimating the scale parameter of the norming constants method in analysing nonstationary annual maximum floods

AbstractIn frequency analysis of annual maximum flood series (AMFS), it is mechanism‐based and significant to incorporate the information of the underlying “ordinary” daily streamflow events for improving accuracy of flood risk estimation and appropriate management. In previous studies related to flood nonstationarity, the classical norming constants method (C‐NCM) has been used to derive statistical parameters of annual maximum flood distribution from daily streamflow distribution. However, C‐NCM does not flexibly consider the feasible range of the scale parameter of annual maximum flood distributions, which can result in the inability to provide sufficient and reliable models to fit AMFS. This paper aims to investigate the potential of two alternative norming constants methods in fitting annual maximum floods, namely the Hall's norming constants method (H‐NCM) and the Fisher and Tippetts' norming constants method (FT‐NCM) respectively. A comparative study of these three methods was carried out using hydrological streamflow series of 77 stations in the Yangtze and Yellow River basins, China. The results found that H‐NCM outperforms both C‐NCM and FT‐NCM for the stations with relatively low‐skewness coefficient of AMFS. Therefore, H‐NCM is recommended to prioritize for practical applications when considering daily streamflow rather than small samples of flood events. Furthermore, if the skewness coefficient exceeds Cs,Gumbel≈1.14, the overall modelling performance of these three methods is significantly deteriorated. This can provide some reference for the applicability of the norming constants method in flood frequency analysis.

Non‐Stationary Influences of Large‐Scale Climate Drivers on Low Flow Extremes in Southeast Australia

AbstractThe intensity, duration, and frequency (IDF) of low streamflow events are often considered stationary in time. Given droughts are likely to become more frequent and extreme, here those IDFs are analyzed for their non‐stationarity in relation to three climate drivers, namely, the El Niño‐Southern Oscillation, the Indian Ocean Dipole, and the Southern Annular Mode. For this, low flow events were identified for 161 unregulated catchments in Victoria, Australia. The relationship between the climate drivers and the IDFs were developed using covariate‐based non‐stationary statistical models. We found that several of these catchments exhibit strong non‐stationary influences from the climate modes. Such catchments have a higher probability of experiencing more intense, prolonged, and/or frequent low flows when a dry phase of these climate drivers occurs. We found that catchments having strong evidence of non‐stationary frequency (n = 77) were more compared to those having non‐stationary intensity (n = 19) or duration (n = 34). Further, we derived projected indices of these climate drivers from CMIP5 models for the RCP8.5 scenario. These indicate an intensification in the dry phase of the climate drivers. Using information of the projected indices in the IDF models, we found that future dry phases of these climate drivers are likely to further increase the probability of experiencing higher magnitudes of low flow IDFs in catchments non‐stationary in these characteristics in relation to one or more of these climate drivers. Frequency shows greater response to future dry phases of these drivers than intensity or duration of low flows.

An integrated assessment of land use impact, riparian vegetation and lithologic variation on streambank stability in a peri-urban watershed (Nigeria)

Bank erosion is an important source of sediment and phosphorus to inland fluvial systems and is generally responsible for more than half of the total watershed sediment export. Numerous studies have quantified bank erosion and the spatio-temporal variation of sediment flux in different watersheds. However, there is sparse research to date on the linkages between bank erosion/accretion and sediment export under different land uses, especially in rapidly evolving peri-urban watersheds. This research, therefore, integrated remote sensing techniques and repeated field survey of erosion pin plots to quantify bank erosion and sediment flux in the 80 km2 Nkisi River watershed (NRW), southeast Nigeria, over a three-year period. The impact of land use change on streambank erosion was evaluated by utilising remotely sensed Landsat datasets of 2003, 2010, 2016 and 2019. Geotechnical tests were used to characterise the lithologic properties of the banks, while the Bank Stability and Toe Erosion Model (BSTEM) was used to determine the stability of the banks under various hydrological conditions and mechanical properties of the riparian vegetation. Mean bank recession rates increased from 10.7 cm during the 2017–2018 monitoring period to 17.5 cm for the 2019–2020 monitoring period. The percentage of total watershed export ascribed to bank erosion in the three stream reaches varied from 6.6 to 44.9%. The high rates of bank erosion and accretion within the NRW were attributed to rapid changes in land use, which evolved from grassland and woodland to cropland, built-up and bare land. The BSTEM accurately predicted the high erosion rates of the streambanks and showed that riparian vegetation has a mechanical effect on bank stability. However, the mechanical effect diminishes as the depth to water table rises during high streamflow events.

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.

Hydrological evaluation of radar and satellite gauge-merged precipitation datasets using the SWAT model: Case of the Terauchi catchment in Japan

Study regionThe Terauchi catchment, southwestern Japan Study focusThis paper evaluates two gauge-merged precipitation datasets derived from weather radars (R/A) and satellites (GsMAP_G) based on their capability to improve streamflow simulation using the Soil and Water Assessment Tool (SWAT) model. A third dataset includes measurements from local rain gauges used for producing the R/A database but not the GsMAP_G product was prepared for comparison reasons. The R/A and GsMAP_G data were first compared to gauge observations. The performance and prediction uncertainty of the SWAT model forced by the evaluated datasets were subsequently quantified and compared. New hydrological insights for the regionThe R/A dataset overestimated the precipitation, while the GsMAP_G underestimated it. After calibration, the R/A performed best (NSE = 88–91%), followed by the Gauge (NSE = 84%) and GsMAP_G (NSE = 54%) scenarios. The R/A product improved the overall simulation performance by 6.50% and 62.40% in terms of NSE and absolute percent bias compared to the Gauge model. The performance of the evaluated datasets varied depending on streamflow occurrence exceedance probability (OEP). The R/A dataset improved the simulation of extremely high (OEP < 1%) and low (OEP > 60%) streamflow events as it resulted in the lowest simulation biases and errors. The current investigation suggests the use of the R/A product for improving the simulation of daily streamflow, including hydro-climatic extremes.

Human influence on the 2021 British Columbia floods

A strong atmospheric river made landfall in southwestern British Columbia, Canada on November 14th, 2021, bringing two days of intense precipitation to the region. The resulting floods and landslides led to the loss of at least five lives, cut Vancouver off entirely from the rest of Canada by road and rail, and made this the costliest natural disaster in the province's history. Here we show that when characterised in terms of storm-averaged water vapour transport, the variable typically used to characterise the intensity of atmospheric rivers, westerly atmospheric river events of this magnitude are approximately one in ten year events in the current climate of this region, and that such events have been made at least 60% more likely by the effects of human-induced climate change. Characterised in terms of the associated two-day precipitation, the event is substantially more extreme, approximately a one in fifty to one in a hundred year event, and the probability of events at least this large has been increased by a best estimate of 45% by human-induced climate change. The effects of this precipitation on streamflow were exacerbated by already wet conditions preceding the event, and by rising temperatures during the event that led to significant snowmelt, which led to streamflow maxima exceeding estimated one in a hundred year events in several basins in the region. Based on a large ensemble of simulations with a hydrological model which integrates the effects of multiple climatic drivers, we find that the probability of such extreme streamflow events in October to December has been increased by human-induced climate change by a best estimate of 120–330%. Together these results demonstrate the substantial human influence on this compound extreme event, and help motivate efforts to increase resiliency in the face of more frequent events of this kind in the future.

Upland Rootzone Soil Water Deficit Regulates Streamflow in a Catchment Dominated by North American Tallgrass Prairie

Intermittent tallgrass prairie streams depend on surface runoff and are highly susceptible to hydrological disturbances such as droughts. The objective of this study was to investigate the timing of intermittent streamflow pulses and upstream rootzone soil water deficit in a watershed dominated by tallgrass prairie. The study was conducted from July to December 2021 in the Kings Creek watershed located within the Konza Prairie Biological station, Kansas, USA. Hourly precipitation and soil moisture observations in the 0–10, 10–30, and 30–50 cm depth were obtained from a hydrological network consisting of 16 monitoring stations across the Kings Creek watershed. Rootzone soil water storage (S) was computed at hourly time steps as the sum of the soil water storage of each soil layer. A drained upper limit (DUL) was estimated as the soil moisture remaining 24 h after the soil had been thoroughly wetted during large (~100 mm) rainfall events. A lower limit (LL) was estimated as the lowest rootzone soil water storage during the study period. Hourly soil water deficit (D) was computed as D = (DUL − S)/(DUL − LL). The study period had 19 precipitation events totaling 436 mm, and only 14 out of the 19 precipitation events exceeded a common canopy and litter interception threshold of 4 mm for tallgrass prairies in this region. Only two precipitation events resulted in measurable streamflow, and the inception of these two streamflow events was associated with a negative weighted soil water deficit (i.e., S &gt; DUL). This pilot study revealed that upland rootzone soil water deficit plays a major role controlling the timing of streamflow in the Kings Creek watershed and possibly in other catchment areas with intermittent prairie streams.