Integrating Qualitative Flow Observations in a Lumped Hydrologic Routing Model

Abstract

This study aims at proposing novel approaches for integrating qualitative flow observations in a lumped hydrologic routing model and assessing their usefulness for improving flood estimation. Routing is based on a three‐parameter Muskingum model used to propagate streamflow in five different rivers in the United States. Qualitative flow observations, synthetically generated from observed flow, are converted into fuzzy observations using flow characteristic for defining fuzzy classes. A model states updating method and a model output correction technique are implemented. An innovative application of Interacting Multiple Models, which use was previously demonstrated on tracking in ballistic missile applications, is proposed as state updating method, together with the traditional Kalman filter. The output corrector approach is based on the fuzzy error corrector, which was previously used for robots navigation. This study demonstrates the usefulness of integrating qualitative flow observations for improving flood estimation. In particular, state updating methods outperform the output correction approach in terms of average improvement of model performances, while the latter is found to be less sensitive to biased observations and to the definition of fuzzy sets used to represent qualitative observations.

A mixed methods approach to reconstructing hydrographs of an extreme flood in an ungauged catchment in Ostional, Nicaragua

“…Yet, there is little guidance for interpreting qualitative information to improve estimates of flood magnitude and duration (Poser et al, 2008;Mazzoleni, Amaranto, & Solomatine, 2019). Observations cannot be calibrated, respondents may interpret observations differently, and dates and times are more difficult to recall as time passes after a catastrophic event.…”

Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges

“…Thus, the human sensors concept implies that in hydrology, citizen science data are not always generated for a scientific purpose, in opposition to traditional monitoring practices. The human sensors component, representing a humanmachine-environment interface, provides qualitative and quantitative observations that can be used within quantitative hydrological studies, with careful attention to the accuracy and scale of these observations (Buytaert et al, 2014, Kosmala et al, 2016, Mazzoleni et al, 2019, Seibert and Vis, 2016). See Table 1 for the definition of 'human sensors'.…”