Streamflow Regime Research Articles

A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022)

Abstract. With the booming big data techniques, large-sample hydrological analysis on streamflow regime is becoming feasible, which could derive robust conclusions on hydrological processes from a big-picture perspective. However, there is a lack of a comprehensive global large-sample dataset for components of the streamflow regime yet. This paper presents a new time series dataset on global streamflow indices calculated from daily streamflow records after data quality control. The dataset contains 79 indices over seven major components of streamflow regime (i.e., magnitude, frequency, duration, changing rate, timing, variability, and recession) of 41 263 river reaches globally on yearly and multiyear scales. Streamflow indices values until 2022 are covered in the dataset. Time span of the time series dataset is from 1806 to 2022 with an average length of 36 years. Compared to existing global datasets, this global dataset covers more stations and more indices, especially those characterizing the frequency, duration, changing rate, and recession of streamflow regime. With the dataset, research on streamflow regime will become easier without spending time handling raw streamflow records. This comprehensive dataset will be a valuable resource to the hydrology community to facilitate a wide range of studies, such as studies of hydrological behaviour of a catchment, streamflow regime prediction in data-scarce regions, as well as variations in streamflow regime from a global perspective. The dataset can be accessed at https://doi.org/10.57760/sciencedb.07227 (Chen et al., 2023a).

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.

Evaluation of the Streamflow Response to Agricultural Land Expansion in the Thiba River Watershed in Kenya

The increasing global population necessitates increased agricultural production, driving the expansion of agricultural lands and advancement of irrigation farming to supplement the inconsistent and insufficient rainfall patterns prevalent in many regions. This study aimed to evaluate the potential impacts of the expansion of agricultural lands on the streamflow regime of the Thiba River and its impact on the downstream users. The study involved comparing the 2004 and 2014 land uses and using the Hydrologic Engineering Centre’s Hydrologic Modelling Systems (HEC-GeoHMS and HEC-HMS) for long-term impact simulations. The results showed a considerable decline in the streamflow in the dry months compared to the wet months, with increasing water abstraction trends from 2007 to 2014. The long-term impact assessment showed an average decline in streamflow in the near (2030) and far (2060) future due to land use and population changes with minimal impact from the increasing precipitation. Based on these findings, there is a need for proper water management and adaptation mechanisms to be put in place to maintain the future water supply from the Thiba River. This study’s findings could assist policy and decision-makers in making informed water resource management decisions.

Climate vulnerability for a desert fish: integrating hydrologic exposures, adaptive capacity, and growth potential

Climate vulnerability can be evaluated by multiple organismal responses. We developed a climate vulnerability framework focused on growth potential of redband trout ( Oncorhynchus mykiss newberri). We employed a bioenergetics model to evaluate spatial variability in growth potential in relation to constraints on body size imposed by stream flow, physiological responses linked to variable thermal regimes, and variation in physiological adaptive capacity inferred from field respirometry. Results indicate that maximum size (g) of redband trout increases with stream discharge. Growth potential is strongly linked to body size, with smaller individuals performing better relative to larger fish in cooler thermal regimes. Annual patterns of growth varied among sites and were related to body size and physiological adaptive capacity. Putatively cold-adapted fish were more likely to exhibit bimodal growth with peaks in spring and autumn, whereas warm-adapted fish exhibit a summer peak in growth potential. These findings offer insights into how climate vulnerability of stream-living fish can be conditioned on both stream flow and thermal regimes, and the potential for physiological adaptive capacity to influence vulnerability.

A hybrid machine learning-based multi-DEM ensemble model of river cross-section extraction: Implications on streamflow routing

Accurate representation of river channel and floodplain geometries is of prime concern for streamflow routing and flood inundation modelling under inadequate surveyed river cross-section data-condition, in many rivers across the globe. Due to global coverage and easy accessibility, the public domain medium resolution (1-arc second) DEMs such as SRTM, ASTER, and ALOS-AW3D30, with a linear vertical bias-correction approach, are widely used for hydrodynamic modelling of upland river networks. In this context, this study, for the first time, developed a hybrid machine learning-based Multi-DEM Ensemble (MDE) approach to simulate bias-corrected cross-sections using open source DEMs-extracted river cross-sections. Additionally, the hydraulic suitability of the simulated cross-sections and associated uncertainties were analysed. For this, the routing of streamflow and water level was performed using MIKE11-HD for a low-gradient flood-prone deltaic river basin of Eastern India (∼9000 km2) consisting of 19 river distributary systems. A comprehensive evaluation of the developed hybrid machine learning-based MDE models revealed an excellent performance of the Random Forest-Particle Swarm Optimisation-MDE (RF-PSO-MDE) model (NSE: 0.89–0.95; RMSE: 1.75–2.23 m) and Deep Neural Network-Particle Swarm Optimisation-MDE (DNN-PSO-MDE) model (NSE: 0.89–0.93; RMSE: 2.09–2.27 m) followed by the Support Vector Machine-Particle Swarm Optimisation-MDE (SVM-PSO-MDE) model (NSE: 0.87–0.92 and RMSE: 2.22–2.50 m). The MIKE11-HD simulations using RF-PSO-MDE and DNN-PSO-MDE model-simulated cross-sections were in close agreement with the observed streamflow and water level hydrographs, together with excellent matching of the flood peak, time to flood peak, and streamflow regimes. Predictive uncertainties analysis by the quantile regression technique confirmed an acceptable range of the Mean Prediction Interval and Percentage Interval Coverage Probability for the MIKE11-HD-RFMDE and MIKE11-HD-DNNMDE model variants including the MIKE11-HD-S model. Conclusively, the developed MDE models have potential for generating river cross-sections under sparse bathymetric conditions for the accurate simulation of streamflow and water level estimates using public domain digital elevation models.

The Music of Rivers: The Mathematics of Waves Reveals Global Structure and Drivers of Streamflow Regime

AbstractRiver flows change on timescales ranging from minutes to millennia. These vibrations in flow are tuned by diverse factors globally, for example, by dams suppressing multi‐day variability or vegetation attenuating flood peaks in some ecosystems. The relative importance of the physical, biological, and human factors influencing flow is an active area of research, as is the related question of finding a common language for describing overall flow regime. Here, we addressed both topics using a daily river discharge data set for over 3,000 stations across the globe from 1988 to 2016. We first studied similarities between common flow regime quantification methods, including traditional flow metrics, wavelets, and Fourier analysis. Across all these methods, the flow data showed low‐dimensional structure (i.e., simple and consistent patterns), suggesting that fundamental mechanisms are constraining flow regime. One such pattern was that day‐to‐day variability was negatively correlated with year‐to‐year variability. Additionally, the low‐dimensional structure in river flow data correlated closely with only a small number of catchment characteristics, including catchment area, precipitation, and temperature—but notably not biome, dam surface area, or number of dams. We discuss these findings in a framework intended to be accessible to the many communities engaged in river research and management, while stressing the importance of letting structure in data guide both mechanistic inference and interdisciplinary discussion.

Quantifying the impact of the urban karst on infiltrated stormwater

Abstract Urbanization alters the flow regime of streams, including increasing the frequency and magnitude of storm flows, along with reducing baseflows. An increasingly common management strategy is stormwater infiltration, which is thought to reduce surface runoff and recharge groundwater and thus restore lost baseflows to streams. Recent research has pointed to considerable uncertainty on the fate of infiltrated stormwater, particularly due to the presence of human-made underground infrastructure – e.g. sewer and water supply pipes and telecommunication cables. Such infrastructure is commonly housed in trenches partially filled with highly permeable material which can cause urban karst like flow conditions. We used a dynamic subsurface flow model (HYDRUS-3D) to predict the impact of the urban karst on the fate of infiltrated stormwater. The model was constructed with the presence of a sewer pipe situated between an infiltration basin and a stream. The model predicted that the impact of the urban karst on infiltrated stormwater increases with higher groundwater levels, and greater contrast between hydraulic conductivity of regional soil and gravel which surrounds the sewer pipe. Results suggest that it is important to consider the impact of the urban karst in cases where the goal of stormwater infiltration is baseflow restoration.

The impact of stormwater management strategies on the flow regime of a peri-urban catchment facing urbanisation and climate change: a distributed modelling study in Lyon, France

ABSTRACT The relative impact of urbanisation (urban sprawl), climate change (rainfall and evapotranspiration) and stormwater disconnection strategies (implementation of green infrastructure (GI)) on a peri-urban stream in the western suburbs of Lyon, France, was investigated using a distributed, physically based model (J2000P). Results showed that ambitious catchment-scale implementation of GI has the potential to mitigate some hydrological effects of urbanisation, especially a drastic reduction of combined sewer overflows. However, GI did not have a large impact on the stream flow regime, especially in the context of climate change, as the stream is dominated by non-urban contributions. Spatially distributed models have the capacity to inform river managers about the extent of implementation of GI needed to achieve given management goals and highlight some limitations of disconnection strategies. Indeed, while GI improved the resilience of sewer networks to global change, it was not sufficient to compensate for the effects of climate change on streamflow.

A copula model of extracting DEM-based cross-sections for estimating ecological flow regimes in data-limited deltaic-branched river systems

For operational flood control and estimating ecological flow regimes in deltaic branched-river systems with limited surveyed cross-sections, accurate river stage and discharge estimation using public domain Digital Elevation Model (DEM)-extracted cross-sections are challenging. To estimate the spatiotemporal variability of streamflow and river stage in a deltaic river system using a hydrodynamic model, this study demonstrates a novel copula-based framework to obtain reliable river cross-sections from SRTM (Shuttle Radar Topographic Mission) and ASTER (Advanced Spaceborne Thermal Emission and Reflection) DEMs. Firstly, the accuracy of the CSRTM and CASTER models was assessed against the surveyed river cross-sections. Thereafter, the sensitivity of the copula-based river cross-sections was evaluated by simulating river stage and discharge using MIKE11-HD in a complex deltaic branched-river system (7000 km2) of Eastern India having a network of 19 distributaries. For this, three MIKE11-HD models were developed based on surveyed cross-sections and synthetic cross-sections (CSRTM and CASTER models). The results indicated that the developed Copula-SRTM (CSRTM) and Copula-ASTER (CASTER) models significantly reduce biases (NSE>0.8; IOA>0.9) in the DEM-derived cross-sections and hence, are capable of satisfactorily reproducing observed streamflow regimes and water levels using MIKE11-HD. The performance evaluation metrics and uncertainty analysis indicated that the MIKE11-HD model based on the surveyed cross-sections simulates with higher accuracies (streamflow regimes: NSE>0.81; water levels: NSE>0.70). The MIKE11-HD model based on the CSRTM and CASTER cross-sections, reasonably simulates streamflow regimes (CSRTM: NSE>0.74; CASTER: NSE>0.61) and water levels (CSRTM: NSE>0.54; CASTER: NSE>0.51). Conclusively, the proposed framework is a useful tool for the hydrologic community to derive synthetic river cross-sections from public domain DEMs, and simulate streamflow regimes and water levels under data-scarce conditions. This modelling framework can be easily replicated in other river systems of the world under varying topographic and hydro-climatic conditions.

High-temporal-resolution monitoring of reservoir water storage of the Lancang-Mekong River

The Lancang-Mekong River (LMR) is an important transboundary river in Southeast Asia shared by China, Myanmar, Laos, Thailand, Cambodia, and Vietnam from upstream to downstream. Construction and operation of dams in the LMR basin has profoundly affected its natural streamflow regime. It is therefore important to monitor changes in reservoir water storage and to quantify the impact of reservoir operation on the redistribution of surface water resources over this basin. Given the difficulty in obtaining in-situ measurements of reservoirs on the LMR, we integrated multisource remote sensing, including satellite altimetry and optical and synthetic aperture radar (SAR) images, to generate weekly water levels and water storages of nine largest reservoirs on the main stem of the LMR from 2017 to 2021. Specifically, partial surface water extent (SWE) of reservoirs was extracted from Sentinel-1 SAR images and digital elevation models (DEMs), using Random Forest algorithms trained by partial SWE derived from Sentinel-2 optical images, showing an overall accuracy higher than 95%. Based on the partial SWE and water level estimates from ICESat-2 and Global Ecosystem Dynamics Investigation (GEDI, International Space Station-based) data, the relationships between water levels and partial SWE were derived to convert partial SWE into water level time series. Furthermore, water storage time series of the nine reservoirs were obtained from water level time series and hypsometric functions derived from SRTM DEMs that were corrected by ICESat-2 data to remove systematic errors. For the Xiaowan Reservoir on the Lancang River, there is close agreement between remote sensing-derived water levels and in-situ water levels in terms of a normalized RMSE lower than 5%. Results indicate that multisource remote sensing has large potential for high-temporal-resolution monitoring of reservoir water levels and water storage. This could more precisely evaluate impacts of cascade reservoirs on the streamflow of the LMR and facilitate drought and flood mitigation for riverine countries.

The Role of Inflow and Infiltration (I/I) in Urban Water Balances and Streamflow Regimes: A Hydrograph Analysis Along the Sewershed‐Watershed Continuum

AbstractUrbanization alters subsurface flow pathways through expansion of sanitary collection and conveyance infrastructure. Inflow and infiltration (I/I) into sewers redistributes slow subsurface flows to fast within‐sewer flows. Acting in concert with connected surface water, redistribution through I/I complicates the net impact of urbanization on streamflow. Elucidation of these processes is key to the characterization and prediction of urban hydrologic cycles. In this study, we collected sanitary sewer flow and streamflow data from 17 sewersheds and 18 watersheds in and around Milwaukee, WI, USA. We compared flow duration curves and baseflow recession characteristics of I/I and streamflow in urban and reference watersheds. Median depth‐normalized I/I (296 mm) was nearly 35% of mean annual precipitation (867 mm), and thereby a major component of the urban drainage budget. I/I flowrates were similar to urban streamflow during high flow events (10th percentile) and larger during intermediate (50th percentile) and low (90th percentile) flow events. Further, I/I recession was slower and more nonlinear in shape than urban streamflow. Increased imperviousness and sewer density were associated with increased high flows, decreased intermediate flows, and quicker streamflow recessions. Sewer density explained more variability in intermediate flows (35%) and baseflow recession rate (49%) than imperviousness (24% and 19% respectively). Based on our findings, I/I takes up valuable volume capacity in sewers, which leads to more frequent overflows and alters streamflow regimes with ecological implications. Thus, I/I should be better considered in the prediction of urban hydrologic fluxes, and the characteristics and hydro‐ecologic impact of I/I should be more thoroughly explored.

Extreme flooding controlled by stream network organization and flow regime

River floods are among the most common natural disasters worldwide, with substantial economic and humanitarian costs. Despite enormous efforts, gauging the risk of extreme floods with unprecedented magnitude is an outstanding challenge. Limited observational data from very high-magnitude flood events hinders prediction efforts and the identification of discharge thresholds marking the rise of progressively larger floods, termed flood divides. Combining long hydroclimatic records and a process-based model for flood hazard assessment, here we demonstrate that the spatial organization of stream networks and the river flow regime control the appearance of flood divides and extreme floods. In contrast with their ubiquitous attribution to extreme rainfall and anomalous antecedent conditions, we show that the propensity to generate extreme floods is well predicted by intrinsic properties of river basins. Most importantly, it can be assessed prior to the occurrence of catastrophes through measurable metrics of these properties derived from commonly available discharge data, namely the hydrograph recession exponent and the coefficient of variation of daily flows. These results highlight the propensity of certain rivers for generating extreme floods and demonstrate the importance of using hazard mapping tools that, rather than solely relying on past flood records, identify regions susceptible to the occurrence of extreme floods from ordinary discharge dynamics.

Comprehensive Methodology and Analysis to Determine the Environmental Flow Regime in the Temporary Stream “La Yerbabuena” in Aguascalientes, Mexico

In this study, a comprehensive methodology was adapted to determine the environmental flow regime of “La Yerbabuena”, a temporary stream located in the Aguascalientes Valley, Mexico. The analysis was divided into four stages: the geomorphological watershed analysis, a hydrologic analysis, hydraulic modeling, and environmental analysis. The main geomorphological features of the study area were defined from maps in the spatial block, and with them, a synthetic series of daily and monthly discharge was determined and further used in the next stages. In the hydrological stage, the IHA (Indicators of Hydrologic Alteration) methodology and the procedures from the Mexican regulation, named NMX-159, were applied to the stream, and their results were comparatively analyzed. A similar interannual flow variation from both methodologies was found for wet and dry seasons, ranging from 0.010 to 0.108 m3/s. In the hydraulic modeling stage, a micro-basin part of the stream was modeled in the software HEC RAS, observing that the IHA methodology results had water levels that matched the baseflow of the stream, which allows understanding the hydraulic behavior of the water flow through the generation of different profiles in function of the rainy season. Finally, for the environmental stage, the hydrological health of the stream was evaluated using the software Flow Health, additionally observing that the IHA methodology was closer to the desired water level of the reference. This study demonstrates that the proposed methodology achieves the objectives defined by the NMX-159, which establishes a streamflow regime considering a natural interval of hydrologic variability in both ordinary and after-disturbance conditions. This application of the methodology for temporary streams provides an understanding of the hydrological behavior of the environmental flow throughout the year, and regarding the existing regulations, it presents a correlation with the obtained results, as well as greater precision in the dry season.

Increasing Alaskan river discharge during the cold season is driven by recent warming

Arctic hydrology is experiencing rapid changes including earlier snow melt, permafrost degradation, increasing active layer depth, and reduced river ice, all of which are expected to lead to changes in stream flow regimes. Recently, long-term (>60 years) climate reanalysis and river discharge observation data have become available. We utilized these data to assess long-term changes in discharge and their hydroclimatic drivers. River discharge during the cold season (October–April) increased by 10% per decade. The most widespread discharge increase occurred in April (15% per decade), the month of ice break-up for the majority of basins. In October, when river ice formation generally begins, average monthly discharge increased by 7% per decade. Long-term air temperature increases in October and April increased the number of days above freezing (+1.1 d per decade) resulting in increased snow ablation (20% per decade) and decreased snow water equivalent (−12% per decade). Compared to the historical period (1960–1989), mean April and October air temperature in the recent period (1990–2019) have greater correlation with monthly discharge from 0.33 to 0.68 and 0.0–0.48, respectively. This indicates that the recent increases in air temperature are directly related to these discharge changes. Ubiquitous increases in cold and shoulder-season discharge demonstrate the scale at which hydrologic and biogeochemical fluxes are being altered in the Arctic.

Spectral Signatures of Flow Regime Alteration by Dams Across the United States

AbstractRiver scientists strive to understand how streamflow regimes vary across space and time because it is fundamental to predicting the impacts of climate change and human activities on riverine ecosystems. Here we tested whether flow periodicity differs between rivers that are regulated or unregulated by large dams, and whether dominant periodicities change over time in response to dam regulation. These questions were addressed by calculating wavelet power at different timescales, ranging from 6 hr to 10 years, across 175 pairs of dam‐regulated and unregulated USGS gages with long‐term discharge data, spanning the conterminous United States. We then focused on eight focal reservoirs with high‐quality and high‐frequency data to examine the spectral signatures of dam‐induced flow alteration and their time‐varying nature. We found that regulation by dams induces changes not only in flow magnitude and variability, but also in the dominant periodicities of a river's flow regime. Our analysis also revealed that dams generally alter multi‐annual and annual periodicity to a higher extent than seasonal or daily periodicity. Based on the focal reservoirs, we illustrate that alteration of flow periodicity is time varying, with dam operations (e.g., daily peaking vs. baseload operation), changes in dam capacity, and environmental policies shifting the relative importance of periodicities over time. Our analysis demonstrates the pervasiveness of human signatures now characterizing the U.S. rivers' flow regimes, and may inform the restoration of environmental periodicity downstream of reservoirs via controlled flow releases—a critical need in light of new damming and dam retrofitting for hydropower globally.

Enhancing urban runoff modelling using water stable isotopes and ages in complex catchments

AbstractIncreased urbanization, coupled with the projected impacts of climatic change, mandates further evaluation of the impact of urban development on water flow paths to guide sustainable land‐use planning. Though the general urbanization impacts of increased storm runoff peaks and reduced baseflows are well known; how the complex, non‐stationary interaction of the dominant water fluxes within dynamic urban water stores sustain streamflow regimes over longer periods of time is less well quantified. In particular, there is a challenge in how hydrological modelling should integrate the juxtaposition of rapid and slower flow pathways of the urban ‘karst’ landscape and different approaches need evaluation. In this context, we utilized hydrological and water stable isotope datasets within a modelling framework that combined the commonly used Hydrologic Engineering Center Hydrological Modelling System (HEC‐HMS) urban runoff model along with a simple hydrological tracer module and transit time modelling to evaluate the spatial and temporal variation of water flow paths and ages within a heavily urbanized 217 km2 catchment in Berlin, Germany. Deeper groundwater was the primary flow component in the upper reaches of the catchment within fewer urbanized regions, while the addition of wastewater effluent in the mid‐reaches of the catchment was the dominant water supply to sustain baseflow in the lower main stem stream, with additional direct storm runoff and shallow subsurface contributions in the more urbanized lower reaches. Water ages from each modelling approach mirrored flow contributions and water age mixing potential in subsurface storage; with older average water and lower young water contributions in less urbanized sub‐catchments and younger average water and higher young water contributions in more urbanized regions. The results from the first step towards more integrated tracer‐aided hydrologic modelling tools for similar peri‐urban catchments, given the potential limitations of simpler model frameworks. The results have broader implications for assessing the uncertainty in evaluating urban impacts on hydrological function under environmental change.

Regionalizing Streamflow Regime Function through Integrations of Geographical Controls in Mountainous Basins

Flow duration curves (FDCs) that represent streamflow regime function through an empirical relationship between the FDC parameters and basin descriptors are widely adopted for hydrologic applications. However, the applications of this method are highly dependent on the availability of observation data. Hence, it is still of great significance to explore the process controls of underpinning regional patterns on streamflow regimes. In this study, we developed a new regionalization method of FDCs to solve the problem of runoff prediction for ungauged mountainous basins. Five empirical equations (power, exponential, logarithmic, quadratic, and cubic) were used to fit the observed FDCs in the 64 mountainous basins in eastern China, and the power model outperforms other models. Stepwise regression was used to explore the differentiated control of 23 basin descriptors on the 13 percentile flows of FDCs, and seven descriptors remained as independent variables for further developing the regional FDCs. Application results with different combinations of these selected descriptors showed that five indices, i.e., average annual rainfall (P), average elevation (H), average gradient (β), average topographic index (TI), and maximum 7d of annual rainfall (Max7d), were the main control factors of FDCs in these areas. Through the regional method, we found that 95.31% of all the basins have NSE values greater than 0.60 and ε (namely the relative mean square error) values less than 20%. In conclusion, our study can guide runoff predictions to help manage booming demands for water resources and hydropower developments in mountainous areas.

A new wavelet combined innovative polygon trend analysis (W-IPTA) approach for investigating the trends in the streamflow regime in the Konya Closed Basin, Turkey

A new wavelet combined innovative polygon trend analysis (W-IPTA) approach for investigating the trends in the streamflow regime in the Konya Closed Basin, Turkey

Performance of automated geoprocessing methods for culvert detection in remote Forest environments

Greater availability of digital elevation models (DEMs) derived from airborne light detection and ranging (LiDAR) has made it possible to map precisely hydrographic features such as streams over large watersheds. Road embankments are precisely detected, given that DEMs are especially accurate over open areas, while culverts are not. Consequently, mapped stream positions are often erroneous along and through these anthropogenic structures. The position of actual culverts is often imprecise, incomplete or unavailable for large territories; thus, there is a need to develop and evaluate automated methods to locate culvert positions by remote sensing. Six geoprocessing methods were tested and compared to field-based culvert positioning data gathered in forested areas. These methods rely on preprocessing of depressions, manipulation of road embankment elevation, or both. When exact locations of culverts were unknown, the ‘Breach Depressions’ algorithm (WhiteBox GAT) was most accurate in reducing omission and commission errors. Depending upon the expected stream flow regime, it was possible to reduce cumulative error from 10% to 30% by using this method compared to less effective methods. When exact locations of culverts were known, it was possible to reduce cumulative error from 20% to 45% by burning them into the DEM. Comparisons of two different methods revealed that no automated geoprocessing allowed accurate detection of poorly located culverts, i.e. where small streams deviated into road-side ditches. Despite automated geoprocessing methods that are available, a database geolocating all culverts within a territory is the best way to create exact hydrographic networks without road embankment influence.

Can annual streamflow volumes be characterised by flood events alone?

Can total annual streamflow in any given year be largely characterised by a relatively small number of high flow events? A comprehensive assessment of this is of high value as there is evidence to suggest that as flood events increase in rarity a more consistent response between streamflow extremes and temperatures increases can be established — providing greater reliability in projections of rare events. We propose here a novel methodology to characterise streamflow regimes in the context of total annual streamflow for water supply. Using the Australian Bureau of Meteorology’s Hydrologic Reference Station database, we developed annual event flow distributions that standardise the relationship between total annual streamflow and event flows. It was found that the annual event flow distributions are primarily a function of local climate and catchment size and were largely insensitive to interannual variability represented by the El Niño Southern Oscillation Index, mean annual temperature, or total annual rainfall volume. Statistically significant trends were found in the timeseries of annual event flow distribution values, signalling a move to a less even distribution in the southern latitudes and a more even distribution in the northern latitudes. Our results show that total annual streamflows can be characterised by a small number of high flow events. This suggests that for Australia’s most critical surface drinking water supply catchments the streamflow yields can be represented by changes in a few, high flow events, independent of interannual variability. As these relationships are non-stationary, they may provide a basis for understanding changes in water supply into the future.