New Method Cuts Uncertainty in How Rainfall Becomes Runoff Across Australia

Every flood forecast, dam design, and climate impact study begins with a deceptively simple question: when does rainfall become runoff? For decades, hydrologists have answered it by slicing streamflow records into discrete events and pairing each one with the storm that produced it. But the way those events are carved out of the data has long been a matter of subjective judgment, with results swinging wildly depending on the parameters a researcher happens to choose. Now, a team at the Australian National University and the University of Sydney has unveiled a method that promises to strip much of that arbitrariness out of the process, and in doing so has produced the first comprehensive, continent-wide portrait of how Australian catchments convert rain into rivers.

The new technique, called the Robust Variance-based Event Identification Method, or RVEIM, was developed by Mohammad Masoud Mohammadpour Khoie, Danlu Guo, and Conrad Wasko and published in the journal Hydrology and Earth System Sciences. Its central insight is that a runoff event is best detected not by hunting for peaks in the streamflow record, but by watching for sudden surges in the variance of that record. When streamflow variance briefly and clearly exceeds what is normal for a catchment, the stream is doing something unusual, and that something is almost always a response to rain. By anchoring event detection to this statistical signature of physical process, RVEIM needs only two user-defined parameters, a dramatic reduction from the half-dozen or more that conventional methods demand.

The problem RVEIM addresses is one that has quietly undermined event-based hydrology for years. Because no instrument directly measures where a runoff event begins or ends, researchers must rely on rules: a threshold to filter out trivial flows, a minimum spacing to keep consecutive events independent, a search window to match each flood peak with its causative storm. None of these rules has a ground truth to validate against, so their values are chosen by expert judgment or local calibration. Sensitivity analyses have shown that even within accepted ranges, changing these values can alter the number, length, and volume of identified events by amounts comparable to the climatic differences between regions. In one striking example cited by the team, simply reversing the direction of the pairing search, from runoff to rainfall rather than rainfall to runoff, introduced as much variability as differences in climate itself.

Worse, the conventional two-step workflow, detecting runoff events first and pairing them to rainfall afterward, can produce physically impossible results. When a mismatched pair is created, the calculated runoff coefficient, the ratio of runoff volume to rainfall volume, can exceed one, implying that more water left the catchment than fell on it as rain. Such events are clear flags of identification error, yet some methods generate them routinely. RVEIM sidesteps this by detecting and pairing simultaneously: it identifies independent rainfall events first, then searches for the corresponding streamflow response within a window whose length is not fixed by the user but computed from the catchment’s characteristic rainfall-to-runoff lag plus the duration of each individual storm. This time-variant window mirrors the causal chain of runoff generation and allows the method to capture both brief, sharp responses and long, multi-peak events without extra tuning.

The variance threshold at the heart of the method is also derived rather than imposed. The team first applies the widely used Lyne and Hollick digital filter to estimate baseflow, the slow groundwater-fed component of streamflow. They then use a median absolute deviation test to exclude baseflow values inflated by event conditions, and take the variance of the remaining, baseflow-dominated records as the threshold below which streamflow behavior counts as normal. Because this threshold emerges from each catchment’s own data, it transfers across climates without recalibration. In tests across eight representative catchments spanning desert, grassland, temperate, subtropical, tropical, and equatorial zones, the variability of event characteristics under RVEIM stayed within roughly fifteen percent of the mean, while two benchmarking methods, a local maxima approach and the more elaborate Detrending Moving-average Cross-correlation Analysis, exhibited uncertainties of forty-three to ninety-three percent.

Physical plausibility improved as well. Across the representative catchments, the percentage of events with runoff coefficients exceeding one remained below about four percent under RVEIM, and was essentially zero for the DMCA benchmark, whereas the local maxima method produced consistently higher and more variable rates. The differences mattered most in wet catchments such as those in western Tasmania, where frequent storms and high soil moisture make event separation genuinely difficult. There, the three methods diverged sharply in their pictures of catchment behavior: RVEIM attributed far fewer events to near-zero runoff coefficients than its competitors, a result consistent with the hydrological reality of a saturated, fast-responding landscape.

With its robustness established, the team unleashed RVEIM on all 467 unregulated Hydrologic Reference Stations monitored by the Australian Bureau of Meteorology, catchments with at least thirty years of quality streamflow record and minimal human interference. The resulting atlas reveals a continent organized by water. Runoff coefficients climb toward the coasts and reach their highest values in Tasmania, where abundant rainfall, low evapotranspiration, and saturated soils conspire to send most event rainfall straight into streams. In the arid interior, by contrast, most storms vanish into infiltration and evaporation, leaving runoff coefficients clustered near zero. Northern Western Australia presents an intriguing anomaly: despite modest rainfall event volumes, its catchments yield substantial runoff, a consequence of low evapotranspiration and high antecedent soil moisture that leave little room for losses.

The most consequential finding is a systematic climate gradient in the shape of the runoff coefficient distributions. In desert and grassland catchments, the empirical distribution rises steeply at near-zero values, indicating that the vast majority of events produce negligible runoff, a signature pointing to infiltration-excess processes in which rain falls faster than parched soils can absorb it only during intense bursts. In temperate, subtropical, and equatorial regions, the distributions rise gradually toward higher coefficients, the hallmark of saturation-excess runoff from already-wet catchments. Tropical catchments occupy an intermediate, mixed regime. Moving from dry to wet climates, the distributions also become less skewed and more uniform, echoing patterns previously documented in Austria and the European Alps and suggesting the gradient is a global hydrological regularity rather than an Australian curiosity.

The implications reach well beyond academic taxonomy. Flood estimation, infrastructure design, and projections of how catchments will respond to a changing climate all depend on event-scale runoff coefficients, and biased event identification can distort all of them. By capturing the full spectrum of event magnitudes rather than privileging large floods or short responses, RVEIM offers a consistent yardstick for comparing catchments across regions and for tracking how runoff generation shifts as droughts, land-use change, and warming reshape the water cycle. The method has been released as a function in the open-source hydroEvents R package, and its developers argue that because its parameters are unlikely to require adjustment between climates, it should transfer readily to continents beyond Australia. If it does, hydrologists worldwide may finally agree on where one storm ends and the next river begins.

Subject of Research: A parsimonious rainfall-runoff event identification method applied across 467 Australian catchments to characterize event-scale runoff processes

Article Title: Characterising runoff processes for Australia: insights from a parsimonious rainfall-runoff event identification method

Article References: Mohammadpour Khoie, M. M., Guo, D., & Wasko, C. (2026). Characterising runoff processes for Australia: insights from a parsimonious rainfall-runoff event identification method. Hydrology and Earth System Sciences, 30(18), 5947-5970. https://doi.org/10.5194/hess-30-5947-2026

Image Credits: AI Generated

DOI: 10.5194/hess-30-5947-2026

Keywords: hydrology, rainfall-runoff, runoff coefficient, event identification, catchment response, Australia, baseflow separation, flood estimation, climate gradient, streamflow, RVEIM, large-sample hydrology