Streamflow Generation Research Articles

Parameter estimation for models of major rainfall induced floods in ungaged mountain basins of Colorado

Study regionRocky Mountains in Colorado including Front and San Juan ranges Study focusHydrologic modeling of ungaged mountain basins plays an important role in ensuring the safety of Colorado’s dams. Recent research has shown that infiltration-excess runoff, saturation-excess runoff, and subsurface stormflow can all contribute to streamflow generated by extreme rainfall in Colorado’s mountains, and the soil moisture accounting (SMA) method in HEC-HMS has been suggested as an appropriate approach to model these mechanisms. However, SMA requires estimation of parameters that have not been previously considered for large floods. The objective of this work is to develop methods to estimate SMA parameters for modeling the response to extreme rainfall in ungaged mountain basins of Colorado where calibration to observed discharges is not possible. New hydrological insights for the regionThe proposed parameter estimation methods are tested for five basins in the Front Range and three basins in the San Juan Range that have streamflow data available for major flood events. For these historical events, the proposed uncalibrated models produce the same streamflow generation processes as calibrated models, and the predicted peak discharges from the uncalibrated models usually have 25% errors or smaller. For design storms, the peak discharges from the uncalibrated models can differ substantially from the peak discharges from calibrated models but are conservative relative to the envelope of observed peak discharges.

Factors controlling the temporal variability of streamflow transit times in tropical alpine catchments

The mean transit time (MTT) of water is an essential descriptor of streamflow generation and catchment water storage. Research on how MTTs fluctuate over time and the variables influencing such variation is limited. In this study, bi-weekly stable isotopic data in precipitation and streamflow were used, together with daily records of hydrometeorological information, to investigate the temporal variability of streamflow MTTs. The data were collected over 8 years in a nested system of 8 tropical alpine catchments in the Zhurucay Ecohydrological Observatory in southern Ecuador (3,450 to 3,900 m a.s.l.). The temporal variability of streamflow MTTs was estimated using yearly periods and a 1-month moving window (i.e., 81 yearly calculated MTTs per catchment). The factors controlling the temporal variability of MTTs were identified using simple and multiple linear regression models with hydrometeorological parameters as explanatory variables. Results reveal that streamflow MTTs in all catchments were short (<1 year) and varied little among catchments (191.30 ± 47.10 days). A combination of hydrometeorological variables (i.e., precipitation, streamflow, and runoff coefficient) over antecedent periods up to 1 year was found to control MTT temporal variability. Overall, these findings point to the prevalence of low temporal variability of hydrological conditions in the investigated catchments. Our study is key to provide insights into the factors controlling the temporal variability of streamflow MTT in tropical catchments, overcoming data limitations of past investigations and with significant implications for improved water supply management.

Hydrochemistry of surface waters in a permafrost headwater catchment in the Northeastern Tibetan Plateau

The permafrost headwater catchments in the Tibetan Plateau (TP) have experienced extensive permafrost degradation, which may cause major changes in riverine solutes. However, surface water hydrochemistry and its influencing factors in such catchments are poorly understood. Hydrochemistry data for different surface waters were obtained for the Yakou catchment in the Northeastern TP. The results indicate that the ionic and organic concentrations of frozen soil seeps (FSS) were higher on the north-facing slope compared to the south-facing slope, and that FSS may be involved in streamflow generation processes and in determining the spatial pattern of riverine solutes. The north-facing slope of the catchment has a thin active layer and wet moisture conditions compared to the south-facing slope; hence supra-permafrost water, with high ionic concentrations, can drain to the ground surface as FSS in the riparian zone and then recharge the surface ponding water and the main stream water. The high ionic concentrations of the supra-permafrost water and FSS can be attributed to intense rock weathering and evaporative effect, together with the high mobility of elements and the transport of organic matter. The tributaries, with low ionic concentrations, comprise a mixture of infiltrating precipitation and diluted supra-permafrost water. Carbonate weathering is the dominant weathering type within the catchment, but the weathering of evaporite and silicate is more important on the north-facing and south-facing slopes, respectively, and chemical weathering on the north-facing slope may be enhanced by strong physical erosion during repeated freeze–thaw cycles due to the wet conditions. The results indicate that the surface water hydrochemistry is heterogeneous on the different hillslope units, and that a thicker active layer under climate change may lead to a shift of hydrological and hydrochemical pathways, and thus a decrease in water and solute flux from the hillslopes, with underlying permafrost, to the river channel.

Evaluation of seasonal catchment dynamic storage components using an analytical streamflow duration curve model

AbstractDynamic storage refers to groundwater storage that is sensitive to rainfall infiltration, streamflow generation, evapotranspiration, and other variables involving groundwater gain or loss. It plays a crucial role in habitat maintenance and the mitigation of environmental impacts on regional hydrological behaviors. Dynamic storage can be separated into direct storage, which contributes to the river channel, and indirect storage, which is insensitive to streamflow. The combination of diverse approaches would provide an estimation of the two storage types. This study estimated optimal baseflow coefficients and direct storage in the wet and dry seasons using an analytical streamflow duration curve model in eight catchments of the Choushui River Basin from 2013 to 2017. The water balance approach was then combined to assess indirect storage for evaluating seasonal dynamic storage components. The model applicability for each catchment of the Choushui River Basin in the wet and dry seasons was assessed using the similarity between observed and simulated flow duration curves, namely Kolmogorov–Smirnov distance. We also applied it to assess the performance difference between model and streamflow recession analysis, which is typically used to estimate baseflow coefficients. The results demonstrated that seasonal differences in baseflow coefficients were related to catchment characteristics as well as the aquifer extent through which groundwater flows. The model utilizing maximum likelihood estimation exhibited superior performance than streamflow recession analysis and was highly applicable in our study area in wet and dry seasons. Dynamic storage components demonstrated a considerable difference in the additional groundwater storage between dry and wet seasons and a loss of direct storage was observed in most catchments during the dry season.

Soil frost controls streamflow generation processes in headwater catchments

The relationship between snowmelt and spring streamflow is changing under warming temperatures and diminishing snowpack. At the same time, the hydrologic connectivity across catchment landscape elements, such as snowpack and surface wetlands, can play a critical role in controlling the routing of snowmelt to streams. The role of hydrologic connectivity is important in headwater regions of the continental northern latitudes, where catchments have low topographic relief and seasonally frozen ground. Nevertheless, the effects of soil frost on the sequence, timing, and magnitudes of hydrologic events that drive the movement of water from a snowpack to a stream are not fully understood. Therefore, we examine two questions: First, what is the flowpath that snow melt and precipitation from spring rain events takes to generate spring streamflow, and second, what hydrologic, climatic, or landscape variables exert the most control on the magnitude of streamflow? Here, we use long-term hydrological records from the two reference basins at the Marcell Experimental Forest in northern Minnesota to analyze the cascading effects across precipitation, snow, water table elevation, soil frost, and streamflow in peatland-dominated headwater catchments. We identify a sequence of fill-and-spill effects across the landscape that control the timing of spring streamflow generation. Then, we use stepwise regression to show that soil frost is a key supporting predictor for both the magnitude of streamflow in the spring as it adds significantly to the predictive power of precipitation and water table elevation. Our results highlight the importance of recognizing the role of soil frost, when present, on the partitioning of snowmelt between overland runoff and water table recharge during the critical snowmelt period, as well as the later partitioning between evapotranspiration and subsurface flows.

A Statistical Approach to Using Remote Sensing Data to Discern Streamflow Variable Influence in the Snow Melt Dominated Upper Rio Grande Basin

Since the middle of the 20th century, the peak snowpack in the Upper Rio Grande (URG) basin of United States has been decreasing. Warming influences snowpack characteristics such as snow cover, snow depth, and Snow Water Equivalent (SWE), which can affect runoff quantity and timing in snowmelt runoff-dominated river systems of the URG basin. The purpose of this research is to investigate which variables are most important in predicting naturalized streamflow and to explore variables’ relative importance for streamflow dynamics. We use long term remote sensing data for hydrologic analysis and deploy R algorithm for data processing and synthesizing. The data is analyzed on a monthly and baseflow/runoff basis for nineteen sub-watersheds in the URG. Variable importance and influence on naturalized streamflow is identified using linear standard regression with multi-model inference based on the second-order Akaike information criterion (AICc) coupled with the intercept only model. Five predictor variables: temperature, precipitation, soil moisture, sublimation, and SWE are identified in order of relative importance for streamflow prediction. The most influential variables for streamflow prediction vary temporally between baseflow and runoff conditions and spatially by watershed and mountain range. Despite the importance of temperature on streamflow, it is not consistently the most important factor in streamflow prediction across time and space. The dominance of precipitation over streamflow is more obvious during baseflow. The impact of precipitation, SWE, sublimation, and minimum temperature on streamflow is evident during the runoff season, but the results vary for different sub-watersheds. The association between sublimation and streamflow is positive in the runoff season, which may relate to temperature and requires further research. This research sheds light on the primary drivers and their spatial and temporal variability on streamflow generation. This work is critical for predicting how warming temperatures will impact water supplies serving society and ecosystems in a changing climate.

Rapid mobilization of old water during urban stormflow

AbstractUrban landscapes do not easily fit into common conceptual models of streamflow generation because extensive impervious surfaces, artificial drainage via sewers, stormwater control measures, and the removal of vegetation substantially modify the pathways rainfall and meltwater take to streams. Hydrologic responses are well characterized for urban streams, however, the relative sources and flow pathways of water within urban landscapes are relatively understudied compared with undisturbed areas. Different water sources (e.g. groundwater, or ‘old’ water, rainfall, or ‘new’ water) can have distinct chemical characteristics whose mixing determines the quality of streamwater. In this study, we investigated the relative contribution of different sources of water (pre‐event water/groundwater, rainfall, wastewater, and tap water) to urban stormflow. Water samples were collected from three highly urban streams (65%–89% impervious cover) during 11 storm events and analysed for stable isotopes of oxygen and hydrogen in water (δ18O and δ2H). Precipitation samples were collected from a nearby precipitation collector to characterize the isotopic signature of new water inputs. Isotopic hydrograph separation (IHS) was used to estimate the relative proportion of new and old water in each event. The IHS results indicated that 25%–63% (δ18O) of the storm hydrograph was old water. For 9 of the 11 storms, the peak in old water contribution coincided with peak flow. These results are similar to findings from IHS studies in undisturbed catchments which suggests that under certain conditions (i.e. low intensity, long duration rainfall) the contribution of old water to stormflow in urban catchments is of a similar magnitude to undisturbed catchments. The use of tracer data is important for further exploring the conceptual model of streamflow generation that suggests that new water dominates stormflow in flashy and heavily urbanized catchments, and can be useful for characterizing the role of greenspaces and storm characteristics on water partitioning.

A Risk-Based Framework to Evaluate Infrastructure Investment Options for a Water Supply System

Increasing water demand due to socio-economic development often requires structure-level interventions, e.g., infrastructure expansion of water supply systems to bridge the gap between demand and water supply. Evaluating different expansion options is critical to making informed decisions. This study focuses on evaluating infrastructure investment options from the reliability perspectives of a water supply system. The evaluation framework is comprised of probabilistic demand projections, stochastic streamflow generation, a mixed-integer programming, and system performance evaluation. Different “industry understood” performance metrics, e.g., annual reliability, maximum delivery capacity, the probability of unsuccessful status, and the magnitude of potential water shortage, can be employed to evaluate the performance of water supply systems. This framework has been applied to Tampa Bay Water, a regional supply agency on the west coast of Florida, United States. The probabilistic demand projections for the future (2021–2040) consider socio-economic projections and climate conditions based on the statistics of historical observations. The Latin Hypercube Sampling algorithm is used to capture the bivariate distribution of supply and demand. This mixed-integer programming is a daily system optimization that minimizes operational cost and considers operational constraints and preferences, e.g., water withdrawal permits, facility production capacity, and water production preferences. Current infrastructure capacity and operational conditions are defined as the baseline scenario. Two additional planning scenarios of expanding the treatment capacity of a surface water treatment plant by 75,708 m3/day [20 million gallons per day (mgd)] and 113,562 m3/day (30 mgd) in the year 2028 are investigated. Results reveal that annual reliability would increase with the expansion of surface water treatment plant capacity. The spatio-temporal supply and demand variability determines that the firm yield from the surface water treatment is less than its expanded production capacity. Except for low demand realizations in the future, the duration and magnitude of potential water shortage could be reduced in both scenarios with engineering intervention. Although this framework is demonstrated for a regional water utility in Florida, it can be applied to other water supply systems around the world.

The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records

AbstractIn this paper, we introduce a relatively simple data‐driven method for the representation of the uncertainty in daily discharge records. The proposed method relies only on hourly discharge data and takes advantage of a nonparametric difference‐based estimator in the characterization of random errors in discharge time series. We illustrate with corrupted streamflow data that the nonparametric estimator provides an accurate characterization of the nature (homoscedastic or heteroscedastic) and magnitude of these errors. In addition, we demonstrate the practical usefulness of the estimator using discharge time series of 500+ watersheds of the Catchment Attributes and MEteorology for Large‐sample Studies data set. This analysis reveals that the magnitude of errors of aleatory nature in the investigated discharge records is rather small (less than 3% for 80% of the records). We then combine the effect of random errors and measurement frequency into a daily variance estimate, which serves as input to a streamflow generation approach. This procedure produces replicates of the discharge record which portray accurately the assigned streamflow uncertainty, preserve key statistical properties of the discharge record and are hydrologically realistic. The proposed method facilitates Bayesian analysis and supports tasks such as model diagnostics, data assimilation, uncertainty quantification and regionalization.

Similarity of catchment dynamics based on the interaction between streamflow and forcing time series: Use of a transfer entropy signature

The transfer of the hydrological information between catchments is founded on the definition of hydrological similarity, which is in turn strictly connected to the features to be regionalised. In order to characterise the catchment behaviour in the streamflow generation processes, the similarity should reflect also the interaction between meteorological forcings and river streamflow time series. While previous hydrological research has identified basins with similar meteorological forcings (i.e. similarity of climate) or with similar streamflow time-series (i.e. similarity of runoff response), the present work proposes, for the first time, to quantify the interaction between the entire time-series of different forcing data and streamflow observations, to be considered as a novel hydrological signature and used as a catchment similarity metric.In particular, the present study proposes the use of a multi-variate entropy-based measure, the so-called transfer entropy, a time-asymmetric quantity which analyses the interaction between different signals. The concept of transfer entropy is applied for identifying the dominant hydrological processes occurring in a catchment, measuring the transfer of information from different meteorological forcings over the catchment to the corresponding observed time series of daily streamflow at the basin outlet. The resulting transfer entropy values are then used as signatures to characterise the main catchment dynamics, and a classification of the basins region is obtained assuming that similar values of transfer entropy correspond to hydrologically similar basins.The methodology is tested on a densely-gauged set of more than 200 catchments across Austria and the outcomes of the approach are evaluated against a set of morpho-climatic catchment attributes and typical streamflow signatures.Despite the limitations, the method is able to distinguish the predominant or partial role of snow melt and evapotranspiration across the dataset, helping to assess differences in catchment response time and to highlight the role of very high orographic precipitation in catchments with a dominant snow regime. The study demonstrates the potential of transfer entropy as complementary to consolidated streamflow signatures for assessing hydrological similarity and for quantifying the connection between different catchment processes.

South Africa needs a hydrological soil map: a case study from the upper uMngeni catchment

Accurate hydrological modelling to evaluate the impacts of climate and land use change on water resources is pivotal to sustainable management. Soil information is an important input in hydrological models but is often not available at adequate scale with appropriate attributes for direct parameterisation of the models. In this study, conducted in three quaternary catchments in the midlands of KwaZulu-Natal, three different soil information sets were used to configure SWAT+, a revised version of the Soil and Water Assessment Tool (SWAT). The datasets were: (i) the Land Type database (currently the only soil dataset covering the whole of South Africa), (ii) disaggregation of the Land Type database using digital soil mapping techniques (called DSMART), and (iii) a dataset where DSMART were complemented by field observations and interpretations of the hydropedological behaviour of the soils (DSMART+). Simulated streamflow was compared with measured streamflow at three weirs with long-term measurements, and the impact of the soil datasets on water balance simulations was evaluated. In general, the simulations were acceptable when compared to other studies, but could be improved through calibration and including small reservoirs in the model. The DSMART+ dataset yielded more accurate simulations of streamflow in all three catchments with Nash-Sutcliffe efficiencies increasing by between 9% and 67% when compared to the Land Type dataset. The value of the improved soil maps is, however, highlighted through the enhanced spatial detail of streamflow generation mechanisms and water balance components. The internal catchment processes are represented more accurately, and we argue that South Africa needs a detailed hydrological soil map for effective water resource management.

Hydrological process controls on streamflow variability in a glacierized headwater basin

AbstractMountain glacierized headwaters are currently witnessing a transient shift in their hydrological and glaciological systems in response to rapid climate change. To characterize these changes, a robust understanding of the hydrological processes operating in the basin and their interactions is needed. Such an investigation was undertaken in the Peyto Glacier Research Basin, Canadian Rockies over 32 years (1988–2020). A distributed, physically based, uncalibrated glacier hydrology model was developed using the modular, object‐oriented Cold Region Hydrological Modelling Platform to simulate both on and off‐glacier high mountain processes and streamflow generation. The hydrological processes that generate streamflow from this alpine basin are characterized by substantial inter‐annual variability over the 32 years. Snowmelt runoff always provided the largest fraction of annual streamflow (44% to 89%), with smaller fractional contributions occurring in higher streamflow years. Ice melt runoff provided 10% to 45% of annual streamflow volume, with higher fractions associated with higher flow years. Both rainfall and firn melt runoff contributed less than 13% of annual streamflow. Years with high streamflow were on average 1.43°C warmer than low streamflow years, and higher streamflow years had lower seasonal snow accumulation, earlier snowmelt and higher summer rainfall than years with lower streamflow. Greater ice exposure in warmer, low snowfall (high rainfall) years led to greater streamflow generation. The understanding gained here provides insight into how future climate and increased meteorological variability may impact glacier meltwater contributions to streamflow and downstream water availability as alpine glaciers continue to retreat.

Delineation of water flow paths in a tropical Andean headwater catchment with deep soils and permeable bedrock

AbstractTraditional hydrometric data combined with environmental tracers such as water stable isotopes contributes to improve the understanding of catchment hydrology. Nevertheless, the application of isotopic tracers in headwater catchments of the tropical Andes with deep soils and permeable parent material influenced by recent volcanism remains limited. In this study, the stable isotopic composition of precipitation, soil water, wetlands, and streamflow was studied to provide insights into the hydrology of a small tropical Andean catchment with deep and permeable volcanic soils, ash layers, and fractured bedrock resulting from Holocene volcanic activity. Although local precipitation forms under isotopic equilibrium conditions, the stable isotopic composition of precipitation is influenced by atmospheric moisture recycling processes. The spatial and temporal variability of isotopic signals and the analysis of inverse transit time proxies (ITTPs) of surface (streamflow) and subsurface (soil and wetlands) waters indicate that vertical flow paths through the deep volcanic ash soils are dominant across the catchment. The strongly damped isotopic composition of these waters points to high soil and wetlands water storage, increasing the transit time or age of stream water in the hydrological system. These findings indicate that water mobilizing through subsurface flow paths–that is, volcanic soils, ash layers, and cracks in the fractured bedrock resulting from Holocene volcanism–is the main contributor to streamflow generation. Comparison with previously published work from Andean catchments and other volcanic areas shows the diversity of hydrological conditions that can be found as a result of pedological and lithological differences shaped by volcanic activity. Therefore, site‐specific strategies may be needed to improve water resources management.

Rainfall-runoff modeling using HEC-HMS model for Meki river watershed, rift valley basin, Ethiopia

Detail understanding of rainfall-runoff processes is tremendously important for a watershed with variable streamflow generation. The streamflow of the Meki River fluctuates seasonally causing flooding on surrounding agricultural land. This study adopted Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) to model the streamflow and predict flood in the Meki watershed, Rift valley basin. The model was calibrated with observed streamflow data from (1987–2004) whereas the consecutive year's data (2005–2010) was used for model validation. The evaluation criteria namely: Nash Sutcliff Efficiency and coefficient of determination were preferred to evaluate the model performance. The finding relieved that, the model can perform very well (NSE = 0.83, R2 = 0.91) during Calibration, and (NSE = 0.804, R2 = 0.89) at validation period. Moreover, the predicted floods at 2, 10, 25, 50, and 100 years were 133.2, 178.1, 239.7, 313.2, and 346.19 m3/s, respectively in the watershed. The novelty of this study lies in evaluating the model results using both statistical parameters (NSE and R2) and the Generalized Extreme Value method). The finding of this study is vital to developing flood mapping and designing flood mitigation measures in the study area. Further, the developed models can be applied to other hydrology with similar hydrological conditions.

Development of operation policy for dry season reservoirs in tropical partially gauged river basins

ABSTRACT The present investigations focus on developing an appropriate model for streamflow generation of a partially gauged basin and the operation of small storage in a tropical, seasonal river basin. The small storages created through the vented dams effectively conserve and sustain the water resources for the lean season. Hence, it is pertinent to develop streamflow models to derive streamflow series at the vented dam locations. In the present investigation, streamflow modelling was attempted using response surface and neural network models in a first of its kind. Out of them, the Response Surface Box Behnken model was found to be most efficient in generating streamflow, with Nash Sutcliff's efficiency above 0.617. Further, it is also essential to operate these small storages to maintain a sustainable, ecological flow in the river course. The operation policy for seasonal storage like vented dams is yet to be reported in the literature. The present work uses reservoir simulation and multi-objective optimisation to derive such storages operation policy through hedging operations, with modified shortage index and mean event deficit as objectives. The performance indicators evaluated the operation policy for eight vented dams of the basin. The results illustrate that vented dams indicate shortages while satisfying the respective demands. The results demonstrate that hedging improves the reservoir's performance by reducing the mean event deficit of 0.268–0.044 Mm3 before and after hedging. The frequency and intensity of shortages were also reduced through hedging for the tropical, seasonal river basins.

Increasing wildfire impacts on snowpack in the western U.S.

Wildfire area has been increasing in most ecoregions across the western United States, including snow-dominated regions. These fires modify snow accumulation, ablation, and duration, but the sign and magnitude of these impacts can vary substantially between regions. This study compares spatiotemporal patterns of western United States wildfires between ecoregions and snow zones. Results demonstrate significant increases in wildfire area from 1984 to 2020 throughout the West, including the Sierra Nevada, Cascades, Basin and Range, and Northern to Southern Rockies. In the late snow zone, where mean annual snow-free date is in May or later, 70% of ecoregions experienced significant increases in wildfire area since 1984. The distribution of burned area shifted from earlier melt zones to later-melt snow zones in several ecoregions, including the Southern Rockies, where the area burned in the late snow zone during 2020 exceeded the total burned area over the previous 36 y combined. Snow measurements at a large Southern Rockies fire revealed that burning caused lower magnitude and earlier peak snow-water equivalent as well as an 18-24 d estimated advance in snow-free dates. Latitude, a proxy for solar radiation, is a dominant driver of snow-free date, and fire advances snow-free timing through a more-positive net shortwave radiation balance. This loss of snow can reduce both ecosystem water availability and streamflow generation in a region that relies heavily on mountain snowpack for water supply.

AN INTEGRATED STUDY USING GIS AND SWAT METHOD FOR RAINFALL RUNOFF MODELLING IN KULSI WATERSHED, ASSAM

The present study is based on the estimation of runoff using GIS and SWAT in the Kulsi Watershed, Assam. Soil and Water assessment tool (SWAT) is a physically based distributed parameter model which was developed to predict runoff, sediment, erosion from specific watersheds under different input conditions. For understanding the rainfall-runoff behavior of the basin, SWAT hydrological model was used and divided in 19 subwatersheds and 188 Hydrogical Response Units. The watershed comprises of four Land use classifications (River/water bodies, Open forest, dense forests and barren land) with three soil classes. The delineated area of the study is found to be 1754.39 km². The study indicates that the annual rainfall runoff relationship correlates with magnitude of total runoff. From the calibration output, the simulated stream flow hydrograph deviates slightly with the observed stream flow hydrograph. The model was auto calibrated and validated using SWAT-CUP SUFI2 for 10 year i.e. from 1988-1997 .The average Nash Sutcliffe efficiency (NSE) for monthly calibration and validation was found to be 0.18 and 0.70 respectively. The coefficient of determination (R²) value for monthly calibration and validation was found to be 0.31 and 0.73 and RSR value for calibration and validation was found to be 0.91 and 0.55 respectively. The study was also carried out to perform the sensitivity analysis of different parameters responsible for stream flow generation. Based on the analysis, SCS runoff curve number (CN2.mgt), Groundwater "revap" coefficient (GW_REVAP.gw), Groundwater delay days (GW_DELAY.gw), Saturated hydraulic conductivity (SOL_AWC.sol), Base flow alpha factor (ALPHA_BF.gw), Threshold depth of water in the shallow aquifer required for return flow to occur (mm) (GWQMN.gw) were found to be the most sensitive parameters. Overall the SWAT model performance was found to be satisfactory for stream flow simulation and the calibrated model can be used for runoff generation and sustainable water resource projects and development.

Hybrid modified continuous time Markov chain model for daily streamflow generation

A novel hybrid Modified Continuous Time Markov Chain (MCTMC)-based single site stochastic model is proposed for the synthetic generation of daily streamflow sequences. The inherent characteristic of the Continuous Time Markov Chain (CTMC) process to represent the dynamic and continuous behaviour of stochastic systems through discrete states and their persistence in time, has been perceived to be suitable for modelling the daily streamflows. The structure of the original CTMC is modified to model the asymmetry of the daily flow hydrograph and the seasonality characteristics involved. The de-seasonalized streamflow space is discretized into an appropriate number of Markov states and a normal probability distribution (parametric component) is fitted to each state. The state occurrences are modelled using the embedded transition probability matrix within the month and the state holding times. The state holding times are conditioned on the preceding state and the month of occurrence to reflect the inherent historical dependence and the non-homogeneity of the daily streamflows, and are resampled from the observed data. The normal distribution generated values within the state are reshuffled according to the ranks of the resampled historical sequence. The competence of the proposed model is illustrated through its application to the observed daily flows at three rivers in the Colorado basin. The model is shown to reproduce the summary statistics and the distributional characteristics at the daily as well as the aggregated time scales. The short-term and the long-term dependence, the asymmetric shape of the hydrograph as well as the water use statistics are well preserved in the simulations. The efficacy of the proposed model is also brought out through a comparison with the frequency-based model of Brunner et al. (2019).

Physically based cold regions river flood prediction in data‐sparse regions: The Yukon River Basin flow forecasting system

AbstractThe Yukon River Basin (YRB) is one of the most important river networks shared between Canada and The United States, and is one of the largest river basins in the subarctic region of North America. The Canadian part of the YRB is characterized by steeply sloped, partly glaciated mountain headwaters that generate considerable runoff during melt of glaciers and seasonal snowcover. Snow redistribution, snowmelt, glacier melt and freezing–thawing soil processes in winter and spring along with summertime rainfall‐runoff and evapotranspiration processes are thus key components of streamflow generation in the basin, making conceptual rainfall‐runoff models unsuitable for this cold region. Due to the remote high latitudes and high altitudes of the basin, there is a paucity of observational data, making heavily calibrated conceptual modeling approaches infeasible. At the request of the Yukon Government, this project developed and operationalized a streamflow forecasting system for the Yukon River and several of its tributary rivers using a distributed land surface modeling approach developed for large‐scale implementation in cold regions. This represents a substantial advance in bringing operational hydrological forecasting to the Canadian subarctic for the first time. This experience will inform future research to operation improvements as Canada develops a nationally coordinated flood forecast system.

CORRIGENDUM

Hydrological ProcessesVolume 36, Issue 7 e14610 CORRIGENDUMFree Access CORRIGENDUM This article corrects the following: Broad scale assessment of key drivers of streamflow generation in urban and urbanizing rivers Sarah S. Ariano, Claire J. Oswald, Volume 36Issue 4Hydrological Processes First Published online: April 26, 2022 First published: 04 July 2022 https://doi.org/10.1002/hyp.14610AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat The referenced article, Ariano and Oswald (2022), contains an incorrect citation. For ease of location, we refer to the PDF version of the article. The left-hand column on page 9 contains the following text:-. “Even though impervious cover is often discussed as the primary driver of the urban stream response to rainfall (Askarizadeh et al., 2015; Bell et al., 2016; Boyd et al., 1993; Brun & Band, 2000; Guan et al., 2016; Ma et al., 2018),…”. The first author's name in Ma et al., (2018) is incorrect. This citation should read Paule-Mercado et al., 2018). The reference list has also been amended to reflect this change. It should read:-. Paule-Mercado, M. C. A., Salim, I., Lee, B. Y., Memon, S., Sajjad, R. U., Sukhbaatar, C., & Lee, C. H. (2018). Monitoring and quantification of stormwater runoff from mixed land use and land cover catchment in response to land development. Ecological Indicators, 93, 1112–1125. https://doi.org/10.1016/j.ecolind.2018.06.006 These changes have also been made to the full-text version. REFERENCE Ariano, S. S., & Oswald, C. J. (2022). Broad scale assessment of key drivers of streamflow generation in urban and urbanizing rivers. Hydrological Processes, 36(4), e14579. https://doi.org/10.1002/hyp.14579 Volume36, Issue7July 2022e14610 ReferencesRelatedInformation