From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution

Abstract

Abstract. Rainfall exhibits a large spatio-temporal variability, especially in complex alpine terrain. Additionally, the density of the monitoring network in mountainous regions is low and measurements are subjected to major errors, which lead to significant uncertainties in areal rainfall estimates. In contrast, the most reliable hydrological information available refers to runoff, which in the presented work is used as input for an inverted HBV-type rainfall-runoff model that is embedded in a root finding algorithm. For every time step a rainfall value is determined, which results in a simulated runoff value closely matching the observed runoff. The inverse model is applied and tested to the Schliefau and Krems catchments, situated in the northern Austrian Alpine foothills. The correlations between inferred rainfall and station observations in the proximity of the catchments are of similar magnitude compared to the correlations between station observations and independent INCA (Integrated Nowcasting through Comprehensive Analysis) rainfall analyses provided by the Austrian Central Institute for Meteorology and Geodynamics (ZAMG). The cumulative precipitation sums also show similar dynamics. The application of the inverse model is a promising approach to obtain additional information on mean areal rainfall. This additional information is not solely limited to the simulated hourly data but also includes the aggregated daily rainfall rates, which show a significantly higher correlation to the observed values. Potential applications of the inverse model include gaining additional information on catchment rainfall for interpolation purposes, flood forecasting or the estimation of snowmelt contribution. The application is limited to (smaller) catchments, which can be represented with a lumped model setup, and to the estimation of liquid rainfall.

Validation practices for satellite‐based Earth observation data across communities

“…In recent years, for estimating and correcting rainfall, similar studies were published in which the secondary variable included land surface temperature [Wanders et al, 2015], Ku-band backscattering coefficient [Turk et al, 2015], microwave surface emissivities [Birman et al, 2015], and discharge [Kirchner, 2009;Herrnegger et al, 2015]. Similarly, Tian et al [2014] considered snow water equivalent observations for estimating snowfall at high latitudes.…”

Behandlung künstlicher Speicher und Überleitungen in der alpinen Niederschlags-Abfluss-Vorhersage

“…Die hydrologischen Modellierungen werden mit dem konzeptionellen, kontinuierlichen Niederschlags-Abfluss-Modell COSERO (COntinuous SEmidistributed RunOff Model) -Abb. 5; Nachtnebel et al 1993;Eder et al 2005;Herrnegger et al 2018Herrnegger et al , 2015aHerrnegger et al , 2015bHerrnegger et al , 2012Kling und Nachtnebel 2009;Kling et al 2015;Stanzel et al 2008 durchgeführt, welches auf dem HBV-Modell nach Bergström (1995)…”

Optimisation of Dynamic Heterogeneous Rainfall Sensor Networks in the Context of Citizen Observatories

“…The analysis of the error propagation in rainfall-runoff models, revealed that the precipitation errors tend to influence the results for long periods of time, and this influence largely depends on the conditions of the catchment when these errors occurred. Consequently, we advocate that methods where precipitation is estimated using inverse rainfall-runoff type model (Herrnegger et al 2015) under the assumption of a Markovian Process, have fundamental problems. The reason is in the explicit use of exact representation of the model states, which in practice is not possible for conceptual models, as processes are lumped at scales that do not match, and therefore, not measurable.…”

“…Additionally, it is required to establish a way to map from the model error into the inputs (inverse model), and therefore to the position of the DSN. Applications of inverse models to establish precipitation estimates from discharge have been proposed (Herrnegger et al 2015, Volkmann 2011, Kunstmann et al 2006).…”

“…The current approaches for estimating precipitation from discharge (inverse models), assume that the processes are Markovian (Herrnegger et al 2015), and therefore, only a previous condition and a perturbation of the inputs is required to yield the next outputs and states. This assumption is valid as long as the model states converge close to the models of the forward model, implying that the effects of the precipitation variation are only immediately seen.…”