Ungauged Streams Research Articles

Predicting baseflow recession characteristics at ungauged stream locations using a physical and machine learning approach

Prediction of short- (i.e., aquifer is near or at saturated conditions) and long-time (i.e., aquifer is not near or at saturated conditions) baseflow recession characteristics at ungauged stream locations is a current challenge that has been primarily addressed by empirical approaches that relate these characteristics to basin attributes. However, the performance of these models is often only fair with coefficient of determination values ranging from 0.5 to 0.7. In this study, we propose a hybrid physical and machine learning approach to predict the long- and short-time baseflow recession characteristics at ungauged stream locations. This approach is compared to a machine learning method, random forest regression, that relates baseflow recession characteristics to basin attributes in 582 basins across the western and eastern United States. The new approach resulted in lower median and inner quartile ranges (IQR) of absolute normalized errors in predicting long-time baseflow recession characteristics (western: 23%, IQR=32%; eastern: 30%, IQR=39%) compared to estimates of those properties based on random forest regressions (western: 27%, IQR=34%; eastern: 38%, IQR=50%). For the short-time baseflow recession characteristics, the hybrid approach resulted in substantially lower median errors and IQR values (western: 79%, IQR=143%; eastern: 83%, IQR=140%) compared to estimates from random forest regressions (western: 1,577%, IQR=8,887%; eastern: 341%, IQR=2,154%). In addition, this approach identified four major regions in the western United States and three in the eastern United States where the baseflow recession characteristics are mostly constant, and these characteristics only vary based on the geometric properties of aquifers. Lastly, the inter-basin variability of the baseflow recession characteristics was not found to be strongly related to metrics measuring interstorm arrival periods, average number of storms, and average length of storms.

Assessing nutrient budget of ungauged catchment using intermittent water quality markers

This study is a preliminary spatial-temporal assessment method of the ungauged catchment to determine the variation in water quality (WQ) and the land use influence on river basins’ health. The intermittent WQ data, the principal component analysis, and the redundancy analysis were used to evaluate the (dis)similarity among the 10 ungauged streams and their significance in the entire catchment. These revealed some similarities/differences in nutrient pollution and latent land-use influence on the streams’ health. There were similarities between R6-R7, R9-R10, among R1 to R4 basins, while R5 and R8 had distinct variances in their WQ dynamics. The intensive vegetable and rice production in R5, R7, R8, R9, and R10 basins were the major sources of high nutrient concentrations. The unique variations, especially in R5 and R8 basins could be attributed to other different pollution sources. Hence, it’s of great significance to carry out comprehensive research in the above 5 river basins. That is the efficiency of management practices, identification of pollution sources, and the extent to which the elevated nutrients in the streams interact with biota within the river regime. This research offers a method to evaluate WQ dynamics in relation to human interferences in river basins of a catchment with limited data under similar climatic conditions.

Linking sediment flux to river migration in arid landscapes through mass balance

ABSTRACT Lateral migration is a key process shaping sinuous rivers and controlling sediment exchange with floodplains. The rate at which channels migrate is affected by bend curvature, bank erodibility, and sediment supply. The relationship between migration rate and sediment supply is poorly understood in dryland regions, where direct measurements are scarce. Here we propose a simple mass-balance model to estimate the sediment flux of ephemeral streams in North America's Great Basin and establish a comparison with timelapse photogrammetric data of lateral migration. The model takes into consideration variables such as long-term hillslope erosion, transient sediment storage in intra-catchment lowlands, and sediment bypass to depocenters. Our results point to first-order similarities in how sediment supply drives channel migration across diverse hydro-climatic regimes. However, we find that, for a given sediment supply and channel width, and despite their ephemeral discharge, dryland streams with minimal bank vegetation migrate about three times faster than humid-climate, vegetated ones. This difference in migration pace likely results from the compound effect of bank erodibility and bend geometry. Our model sheds new light on the driving mechanisms of channel mobility in dryland streams and may find application in assessing the sediment budgets of ungauged streams, reservoir trapping, and morphodynamic adjustments in stressed watersheds.

Water-level recession characteristics in isolated pools within non-perennial streams

Persistent surface water pools within non-perennial streams provide critical refuges for freshwater species by facilitating their survival during extended dry periods. However, our understanding of how pool water levels recede is limited, hindering our ability to quantify and predict aquatic refuge persistence. We characterise variations in water-level recession rates based on one year of water-level measurements in five non-perennial streams. Most of the observations show that the water level recedes at a constant rate after streamflow ceases in our study streams, a rate often significantly higher than that during low-flow periods. However, water-level recession rates varied considerably amongst cease-to-flow periods within pools in the same non-perennial stream and between nearby streams. The constancy of water-level recession during a cease-to-flow period is applicable to other non-perennial streams, but a detailed understanding of the factors influencing recession rates is needed for prediction in ungauged streams and identifying persistent aquatic refuges across river networks.

Using Sporadic Streamflow Measurements to Improve and Evaluate a Streamflow Model in Ungauged Basins in Wisconsin

Streamflows derived from hydrological models are widely used in decision-making processes in a broad array of natural resources applications. There remain substantial challenges in quantifying error and uncertainty in hydrological models, but understanding the sources and magnitudes of error and uncertainty are essential to support robust decision making. In this study, the accuracy of a mixed-effects model for streamflow (flow-duration curves) across the state of Wisconsin, the Natural Community Model (NCM), was evaluated. The NCM is used as the basis for scientific studies and management decisions in Wisconsin, but uncertainty in the NCM has not yet been quantified, and performance has not been assessed formally except at continuously monitored streamflow stations. Although there are a couple hundred long-term monitoring stations, there are thousands of short-term and sporadic monitoring stations in Wisconsin. To take advantage of the vast number of sparsely monitored and short-term stations, an index gauge approach was used to estimate long-term streamflow percentiles and flow-duration curves (with uncertainty). These flow-duration targets formed the basis for an assessment of NCM accuracy in ungauged streams. A random forest model for NCM error was developed that provides a qualitative understanding of sources of error in the NCM as well as a quantitative way to correct the NCM using information from the sporadic/short-term streamflow stations that could not be included in the original NCM training set. By combining the original NCM and the random forest model, an updated NCM was produced with reduced error (75th percentile of errors dropped from 0.23 to 0.07 m3/s), and uncertainty estimates were defined for use with the updated NCM in decision making and research applications.

Remote Sensing Methodology for Roughness Estimation in Ungauged Streams for Different Hydraulic/Hydrodynamic Modeling Approaches

This study investigates the generation of spatially distributed roughness coefficient maps based on image analysis and the extent to which those roughness coefficient values affect the flood inundation modeling using different hydraulic/hydrodynamic modeling approaches ungauged streams. Unmanned Aerial Vehicle (UAV) images were used for the generation of high-resolution Orthophoto mosaic (1.34 cm/px) and Digital Elevation Model (DEM). Among various pixel-based and object-based image analyses (OBIA), a Grey-Level Co-occurrence Matrix (GLCM) was eventually selected to examine several texture parameters. The combination of local entropy values (OBIA method) with Maximum Likelihood Classifier (MLC; pixel-based analysis) was highlighted as a satisfactory approach (65% accuracy) to determine dominant grain classes along a stream with inhomogeneous bed composition. Spatially distributed roughness coefficient maps were generated based on the riverbed image analysis (grain size classification), the size-frequency distributions of river bed materials derived from field works (grid sampling), detailed land use data, and the usage of several empirical formulas that used for the estimation of Manning’s n values. One-dimensional (1D), two-dimensional (2D), and coupled (1D/2D) hydraulic modeling approaches were used for flood inundation modeling using specific Manning’s n roughness coefficient map scenarios. The validation of the simulated flooded area was accomplished using historical flood extent data, the Critical Success Index (CSI), and CSI penalization. The methodology was applied and demonstrated at the ungauged Xerias stream reach, Greece, and indicated that it might be applied to other Mediterranean streams with similar characteristics and flow conditions.

Mapping Floods of Fenton River, an Ungauged Stream in Connecticut

The study describes the modeling of flood events of an ungauged river by using recorded data from a neighboring watershed. The Fenton and Mount Hope Rivers are two tributaries of the Thames River in Northwest Connecticut, New England. The Fenton River was an ungauged stream until 2006, the Mount Hope River has stream discharges measurements since 1940. Due to the lack of discharge measurements in the Fenton River during the XX century, there is a huge uncertainty about the characteristics of past flood events. An analysis was conducted to map the most important floods in the Fenton River around the pump wells of the University of Connecticut (UConn) with simulated peak discharges applying the drainage-area ratio methodology using recorded peak discharges from the Mount Hope River in 1955, 2005 and, 2008. Flood maps of the Fenton River were generated applying a one-dimensional (1D) HEC-RAS model for the 1955, 2005, 2008 events and a simulated 2022 flood. The methodology shows that it is possible to simulate flood maps of past events using a neighboring watershed discharges.

Hydraulic Properties of the Riffle Crest and Applications for Stream Ecosystem Management

AbstractRiffle crests are important hydraulic controls in riffle‐pool‐dominated streams, influencing pool hydraulics and water quality that collectively control lotic habitat for many organisms. We define a simple stream depth measurement, the riffle crest thalweg (RCT), describe measurement methods and utility, and suggest that RCT depth is a better independent variable than streamflow (Q) for many instream flow and habitat assessment applications. Using RCT depth as an independent variable, rather than streamflow, reduces the need for gauging or streamflow measurements in many management applications. Unlike streamflow, RCT depth varies directly with fundamental elements of riverine habitat such as channel morphology and bed roughness. We also suggest that relationships between RCT depth and streamflow (RCT‐Q curves) can be used to evaluate the risk of streamflow alteration at ungauged sites. We describe three case studies to demonstrate the utility of RCT depth and RCT‐Q rating curves in stream ecosystem management: (1) evaluating the effect of a top‐down flow prescription on modeled salmonid habitat, (2) estimating the risk from the incremental reduction of RCT depth on habitat and ecological responses, and (3) identifying relationships between RCT depth and dissolved oxygen in ungauged streams. An easy‐to‐measure, inexpensive, and ecologically sensitive metric, RCT depth holds promise as a useful tool in stream ecosystem management.

Uncertainty in remote sensing of streams using noncontact radars

Accounting for freshwater resources and monitoring floods are vital functions for societies throughout the world. Remote-sensing methods offer great prospects to expand stream monitoring in developing countries and to smaller, headwater streams that are largely ungauged worldwide. This study evaluates the potential to estimate discharge using eight radar units that have been installed over streams in diverse hydrologic and hydraulic settings across the United States. The research highlights error characteristics associated with the measurements of stage using pulsed wave radars, mean channel velocity from continuous wave Doppler radars, and their combined use to estimate discharge at sites that were collocated with conventional streamgauges. Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. A dry concrete, flume showed the temperature-dependent stage variations were no more than 2 cm. Surface velocity retrievals needed to be adjusted to represent the mean channel velocity when estimating discharge. Different approaches were evaluated and application of two different, depth-dependent adjustment factors was found to yield the most accurate estimates. This study found that it is possible to get accurate discharge estimates from noncontact radar measurements, providing cost-effective solutions for remote sensing of ungauged streams. Lastly, radar measurements of the raw variables (i.e., stage and surface velocity) can be used in an early alerting context to detect flash floods in ungauged streams.

Resistance Analysis of Morphologies in Headwater Mountain Streams

River flow velocity is determined by the energy available for flow motion and the energy fraction lost by flow resistance. We compared the performance of different equations for the Darcy-Weisbach resistance coefficient (f) and empirical equations to predict flow velocity. The set of equations was tested using data from the Quinuas headwater mountain river in the Andean region. The data was collected in three Cascades, two Step-pools, and one Plane-bed covering a wide range of velocity magnitudes. The results reveal that nondimensional hydraulic geometry equations (NDHG) with a Nash-Sutcliffe efficiency index (EF) varying from 0.6–0.85 provide the most accurate velocity prediction. Furthermore, the study proposes a methodology applicable to all morphologies for defining the NDHG parameters using easily measured field data. The results show an improvement in predictability with EF values in the range of 0.81–0.86. Moreover, the methodology was tested against data from the literature, which was not divided into morphologies providing EF values of around 0.9. The authors encourage the application of the presented methodology to other reaches to obtain additional data about the NDHG parameters. Our findings suggest that those parameters could be related to reach characteristics (e.g., certain characteristic grain size), and in that case, the methodology could be useful in ungauged streams.

Lake Inflow Simulation Using the Coupled Water Balance Method and Xin’anjiang Model in an Ungauged Stream of Chaohu Lake Basin, China

Water resources are crucial for maintaining daily life and a healthy ecological environment. In order to gain a harmonious development among water resources and economic development in Lake Watershed, it is urgent to quantify the lake inflow. However, the calculation of inflow simulations is severely limited by the lack of information regarding river runoff. This paper attempts calculated inflow in an ungauged stream through use of the coupling water balance method and the Xin’anjiang model, applying it to calculate the inflow in the Chaohu Lake Basin, China. Results show that the coupled model has been proved to be robust in determining inflow in an ungauged stream. The error of daily inflow calculated by the water balance method is between 1.4 and −19.5%, which is within the standard error range (±20%). The calibration and verification results of the coupled model suggest that the simulation results are best in the high inflow year (2016), followed by the normal inflow year (2007) and the low inflow year (1978). The Nash-Sutcliffe efficiencies for high inflow year, normal inflow year, and low inflow year are 0.82, 0.72, and 0.63, respectively, all of which have reached a satisfactory level. Further, the annual lake inflow simulation in the normal inflow year is 19.4 × 108 m3, while the annual average land surface runoff of the study area is 18.9 × 108 m3, and the relative error is −2.6% by the two ways. These results of the coupled model offer a new way to calculate the inflow in lake/reservoir basins.

Stream Flow Generation for Simulating Yearly Bed Change at an Ungauged Stream in Monsoon Region

The stream flow generation method is necessary for predicting yearly bed change at an ungauged stream in Monsoon region where there is no hydrologic and hydraulic information. This study developed the stream flow generation method of daily mean flow for each month over a year for bed change simulation at an ungauged stream. The hydraulic geometries of cross-sections and the corresponding bankfull indicators of the Byeongseong river of 4 km reach were analyzed to estimate the bankfull discharge. The estimated bankfull discharge of the target reach was 77.50 m3/s, and the total annual discharge estimated 3720 m3/s through the correlation equation with the bankfull discharge. The measured total annual discharge of the Byeongseong river was 3887.30 m3/s, which is greater by 167.30 m3/s of 4.3% relative error. The volume and bed changes over a year by the Center for Computational Hydroscience and Engineering Two-Dimension (CCHE2D) model simulated using the measured discharge during 2013 and 2014 coincided with the surveyed in the same period. Estimated total annual discharge was used for the scenarios of stream flow generation. The generated stream flow using the flow apportioned to each month on the basis of the flow percentage in an adjacent stream simulated the river bed most appropriately. The generated stream flow using the flow based on the monthly rainfall percentage of the rainfall station in the target stream basin also simulated river bed well, which is confirmed as an alternative. Quantitatively, the root mean square error (RMSE), mean bias error (MBE), and mean absolute percentage error (MAPE) in-depth change of thalweg between the measured and the simulated were found to be 0.25 m, 0.04 m, and 0.44%, respectively. The result of the simulated cross-sectional river bed change for target reach coincided well with the surveyed. The proposed method is highly applicable to generate the stream flow for analyzing the yearly bed change at an ungauged stream in Monsoon region.

Predicting mean annual and mean monthly streamflow in Colorado ungauged basins

AbstractRiver managers often need estimates of streamflow for ungauged streams. These estimates can be used in water rights acquisitions, in‐stream flow management, habitat assessment, water quality planning, and stream hazard identification. This publication describes new regression models for predicting mean annual and mean monthly streamflow in Colorado. Unlike previous regional regression studies, the new models incorporate snow persistence (SP), the fraction of time a watershed remains snow covered. Models were developed using streamflow data from 131 watersheds with drainage areas <1,500 km2, no transbasin diversions, and <10% urban area. In addition to SP, topographic, climate, geologic, and hydrologic region variables were used in model predictions. All new models had very good performance, with <6% absolute bias and stronger performance compared to current regional regression models in StreamStats. The mean annual model had the strongest performance, with Nash‐Sutcliffe efficiency coefficient (NSE) of 0.93 and <2% absolute bias. Mean monthly models had best performance during snowmelt runoff months (May‐Jul; NSE ≥0.79; absolute % bias ≤ 4) and weaker performance during low flow months (Aug‐Apr; NSE ≥ 0.59; % bias ≤ 5). Tests of the mean annual model using decadal average streamflow from 1910s to 2000s show very good performance (NSE > 0.75), but predictions were biased low by 14–28% in wetter decades. All equations and coefficients needed to run the models are presented in the publication appendix, and the associated data release includes the spatial data and model code, which can be applied using R or within an R‐based Shiny web app.

ESTIMATION OF ANNUAL RUNOFF AND PEAK FLOW AT NAZANIN CATCHMENT IN ERBIL, KURDISTAN REGION USING DIFFERENT APPROACHES

Runoff is excess rainfall or a portion of rainfall that flows over the watershed. In un-gauged streams, due to lack of data, many methods are used for runoff estimation. In this paper annual runoff and peak flow of Nazanin watershed were estimated at Nazanin Dam location using different methods. Soil Conservation Service (SCS) method, basin relation, (Hydrologic Engineering Center – Hydrologic Modeling System) HEC-HMS and Hydrologic engineering center (HEC-1) models were provided. The Watershed Modeling System (WMS) package was used for watershed delineation and computation of curve number. The area of Nazanin catchment is 72.14 km2, and the composite curve number is 81.3. The results of average annual rainfall estimated using the basin relationship method are more than that obtained from SCS method. The results of peak flood discharge for different return periods using different approaches such as SCS method, HEC-HMS and HEC-1 models are very close. But the results of basin relation are lower than SCS method. From the results of comparison, it was observed that HEC-HMS and HEC-1 models can be used for generation flood hydrograph at ungagged watershed.

Exploring the Use of Decision Tree Methodology in Hydrology Using Crowdsourced Data

AbstractTo fill the observations gap on ungauged streams, crowdsourced distributed hydrologic measurements were considered as a potential supplement for observational data networks. However, citizen science data come with uncertainty as they are provided by the general public. In order to investigate this uncertainty, a decision tree methodology was applied to evaluate existing citizen science data of stream stage based on the CrowdHydrology (CH) network. Quality control (QC) flags were developed and applied to CH sites, dividing Level 1 dataset (raw dataset) into Level 2 (flagged dataset) and Level 3 (processed dataset). Error estimates were calculated to determine uncertainty in the citizen science data. The results indicate that the decision tree could provide reliable QC for citizen science data and demonstrate how uncertainty can be quantified in the QC datasets.

Value of Crowd‐Based Water Level Class Observations for Hydrological Model Calibration

AbstractWhile hydrological models generally rely on continuous streamflow data for calibration, previous studies have shown that a few measurements can be sufficient to constrain model parameters. Other studies have shown that continuous water level or water level class (WL‐class) data can be informative for model calibration. In this study, we combined these approaches and explored the potential value of a limited number of WL‐class observations for calibration of a bucket‐type runoff model (HBV) for four catchments in Switzerland. We generated synthetic data to represent citizen science data and examined the effects of the temporal resolution of the observations, the numbers of WL‐classes, and the magnitude of the errors in the WL‐class observations on the model validation performance. Our results indicate that on average one observation per week for a 1‐year period can significantly improve model performance compared to the situation without any streamflow data. Furthermore, the validation performance for model parameters calibrated with WL‐class observations was similar to the performance of the calibration with precise water level measurements. The number of WL‐classes did not influence the validation performance noticeably when at least four WL‐classes were used. The impact of typical errors for citizen science‐based estimates of WL‐classes on the model performance was small. These results are encouraging for citizen science projects where citizens observe water levels for otherwise ungauged streams using virtual or physical staff gauges.

Estimation of the Darcy–Weisbach friction factor for ungauged streams using Gene Expression Programming and Extreme Learning Machines

The most uncertain parameter in determining the stage-discharge relationship of an ungauged stream is the friction factor, a parameter for which a gauged stream can be back-calculated using hydrometric survey data that has been compiled over a wide range of flow conditions. The existing friction factor models for ungauged streams that do not require hydrometric survey data as input can have very large prediction errors. In this study, 415 streamflow events collected from 52 rivers across three continents were assembled to allow for the use of a suitable global dataset size/range to develop a robust model for the prediction of the friction factor in ungauged streams using two machine learning algorithms (Gene Expression Programing (GEP) and Extreme Learning Machine (ELM)). The new GEP and ELM models outperformed existing models with significantly improved R-squared values of 0.76 and 0.78, respectively. The combination of the two input parameters (relative smoothness and the friction slope) allowed the new models to more accurately predict the friction factor for a wider range of streams and flow conditions. The forecasting uncertainty of the developed GEP and ELM models were compared with past models and found to be the least uncertain, with Width Uncertainty Band (WUB) values of ±0.0536 and ±0.0504, respectively. For the first time in this field of research, partial derivative sensitivity analysis was applied to the trends of the friction factor estimation by GEP and ELM. It was found that GEP and ELM models are extremely sensitive to friction slope in comparison with relative smoothness. The GEP and ELM model results for predicting the Darcy–Weisbach friction factor were compared with different existing relationships, and the superior performance of the proposed models was illustrated.

Theoretical link between rainfall and flood magnitude

AbstractThe rainfall–run‐off convolution integral is analytically solved for several models for the elementary hydrograph. These solutions can be combined with available rainfall frequency analyses to predict flood flows along streams for different recurrence intervals, using no free parameters for gauged streams and one estimable parameter for ungauged streams. Extreme discharge magnitudes at gauged sites can be typically estimated within a factor of two of actual records, using no historical data on extreme flows. The flow predictions reproduce several important characteristics of the flood phenomenon, such as the slope of the regression line between observed extreme flows and basin area on the conventional logQ versus logA plot. Importantly, for the models and data sets investigated, the storm duration of greatest significance to flooding was found to approximate the intrinsic transport timescale of the particular watershed, which increases with basin size. Thus, storms that deliver extraordinary amounts of rainfall over a particular time interval will most greatly activate basins whose time constants approximately equal that interval. This theoretical finding is supported by examination of the regional hydrological response to the massive storms of September 14, 2008, and April 28–30, 2017, which caused extraordinary record flooding of basins of about 5–100 km2 and 500–4,000 km2, respectively, but produced few records in basins that were larger or smaller than those ranges.

Estimation of peak runoff and frequency in an ungauged stream of a forested watershed for flood hazard mapping

Kaynasli District in the western Black Sea region of Turkey has long been vulnerable to frequent flood damage due to the establishment of settlements within and around stream channels without regard to fluctuating peak-streamflow frequencies. The aim of this research was to determine the measures needed to protect the towns and villages from this type of damage. Daily total precipitation data for 1975–2010 were analysed, and rainfall-runoff models developed to estimate the potential yearly maximum discharge from each stream of sub-watersheds dominated by forests and/or agriculture. This was then calculated for different frequencies of the yearly maximum discharge. Flood analysis and mapping was modified via the one-dimensional Hydrologic Engineering Centers-River Analysis System software to produce potential maximum discharge and geometric data for Kaynasli Creek. As the main creek of the sub-watershed, its cross-section was shown to be insufficient and incapable of containing the maximum discharge at the 100-year frequency presumed for the watershed, and subsequently was seen as having a high level of casualty risk. It was concluded that the one dimensional model could be useful, but 2D models were more suitable for these types of watersheds.

Tools for managing hydrologic alteration on a regional scale: Estimating changes in flow characteristics at ungauged sites

Abstract Hydrologic alteration is a predominant stressor for biological resources in streams. This stress is further aggravated by competing human and ecological demands for limited water resources. Understanding flow–ecology relationships and establishing relevant and implementable flow targets are essential to protect biological communities. Estimating degree of ecologically relevant hydrologic alteration depends on the availability of long‐term flow data at sites with biological information. However, measured flow data are seldom available at sufficient density to support large‐scale analyses of the biological effects of hydrologic alterations. The ability to accurately simulate flows and estimate flow metrics at many ungauged locations across a broad geographical area remains a fundamental challenge. We address this challenge by applying a novel technique to simulate flow regimes at any stream reach of interest by first developing an ensemble of regionally calibrated and validated hydrological models, and then using a selection tool to match the “best‐fit” model to ungauged stream reaches. An ensemble of 26 HEC‐HMS rainfall–runoff models were calibrated to represent the range of catchment conditions in the southern California region. We developed current and historical flow regimes and a suite of flow metrics at 572 ungauged sites in southern California with bioassessment monitoring data. The flow metrics represent hydrograph characteristics of magnitude, timing, frequency, duration and variability. The flow metrics were estimated under three precipitation conditions—dry, wet and average. In addition, we estimated aggregated flow metrics for (dry + wet + average) condition. Hydrologic alteration was estimated as the deviation between the modelled current and historical flow metrics. Approximately 79% of the region shows some degree of hydrologic alteration, and approximately 40% of the sites are estimated to be severely altered. Magnitude metrics tend to increase in response to urban and agricultural land uses, whereas the timing and duration metrics are mostly unchanged. This mechanistic modelling approach demonstrates the feasibility of estimating flow alterations for ungauged catchments with relative ease of transferability over a broad geographical region. The continuous granular flow data allow for computation and consideration of metrics that may be applicable to a variety of ecological endpoints and consideration of a range of management trade‐offs.