There is now a “Jurisdictional Specifics” page for Victorian on the ARR data hub which is based on a project undertaken by HARC which I was involved in. Our work looked at whether adopting losses from the ARR Data Hub would result in “good” flood estimates from hydrologic modelling. We defined good estimates as when the design flood peaks from modelling are close to the equivalent peaks from flood frequency analysis of gauged data.
In short, the Data Hub losses tend to be too large, which means modelled flood estimates are biassed low. We recommend this is addressed by increasing the preburst rainfall – using the 75th percentile value rather than the median. A caveat is that his recommendation only applies for continuing loss region 3 (see the figure below). There wasn’t enough information to make recommendations for the other loss regions.
There is more information available from:
Of course, the Data Hub isn’t the only source of loss estimates. A key outcome of the project was to develop an ordered list of approaches for estimating losses. Using losses from the Data Hub should only be contemplated when there is no better alternative.
Preferred approaches to loss estimates are, in order:
- Reconciliation with at-site flood frequency quantiles: initial and continuing losses are varied within their expected range to achieve a reasonable level of agreement between estimates derived from rainfall-based modelling and flood frequency analysis.
- Reconciliation using within-catchment transposed flood quantiles: streamflow observations are commonly available at gauging stations upstream or downstream of the site of interest, and flood quantiles derived from these sites can be transposed to the site of interest and used for reconciliation as described in approach 1.
- Event-based calibration: continuing losses obtained from calibration of historical events provide some indication of typical design values, noting that past historical events are biased towards wet catchment conditions; initial losses from historical events are highly variable and information from a small sample of events are of low utility (and therefore some form of reconciliation with other sources of information is recommended).
- Reconciliation using nearby catchment transposed flood quantiles: regional flood quantiles derived using RFFE and other procedures (Section 3, Book 3, ARR2019) can be used for reconciliation as described in approach 1.
- Transposition of losses: initial and continuing loss estimates validated on nearby catchments which are considered to be hydrologically similar.
- Regional losses (ARR Data Hub): unmodified initial and continuing loss estimates obtained from the Data Hub losses can be adopted in data poor areas, noting that in loss region 3 these should be combined with 75th percentilepre-burst values.
A similar list has also been developed for NSW which, unfortunately, differs from the this one developed for Victoria. The top recommendation from the NSW list is to use calibration losses from the study catchment. Calibration losses are the initial and continue loss that is required to make the output of a hydrologic model match a historical flood peak when historical rainfall is used as the input. This is number 3 on the Victorian list.
Usually, we calibrate to large floods. On average, large floods are likely to have small losses – that is one of the factors that caused the large flood. Therefore, calibration losses may not provide a representative sample of the loss from a catchment; they may be biassed low. Using losses that are biassed low means modelled flood estimates may be too high. Where possible, it would be checking losses through reconciliation with flood frequency analysis of gauged data, using tools such as the RFFE and comparing losses on nearby catchments (Items 2, 4 and 5 on the Victorian list).