Hydrometric network design using streamflow signatures and indicators of hydrologic alteration

- CR-DEMO method captures majority of the information in the two study basins.
- Streamflow signatures increase network distribution in headwater areas.
- Indicators of hydrologic alteration increase distribution in downstream areas.
- Network designer can choose objective to use based on areas of interest.

SummaryThis study highlights the impacts that the selected hydrologic characteristics of a basin have on the spatial variability of hydrometric networks. The study was conducted using streamflow monitoring networks in two Canadian basins, specifically in the Hamilton, Halton, Credit Valley basins of Ontario and the Columbia River basin of British Columbia. The Dual Entropy-Multiobjective Optimization (DEMO) model which has been demonstrated to be sufficiently robust for designing optimum networks in a large basin was used in these analyses. In addition to the entropy functions, the spatial variability of flow networks was examined by either excluding or including hydrologic signatures and indicators of hydrologic alteration (IHA) of a basin. The entropy functions are used to identify optimal trade-offs between the maximum possible information content and the minimum shared information among stations. The hydrologic signatures are used to quantify hydrological response characteristics among sub-basins, and the IHAs, which are normally used to determine how a hydrologic regime has been altered by an impact, are instead used to quantify differences between sub-basins. It was found that the optimal locations for the new stations were well captured by the objective functions. Also, new stations covered a wider area when hydrological signatures and IHAs were considered, enhancing the objective functions. The inclusion of streamflow signatures increased emphasis on the headwaters whereas the inclusion of IHAs increased emphasis on the downstream and disturbed regions. Accounting for such spatial variability in designing hydrometric networks is crucial in obtaining an optimal network.