The role of observation uncertainty in the calibration of hydrologic rainfall-runoff models

International audienceHydrologic rainfall-runoff models are usually calibrated with reference to a limited number of recorded flood events, for which rainfall and runoff measurements are available. In this framework, model's parameters consistency depends on the number of both events and hydrograph points used for calibration, and on measurements reliability. Recently, to make users aware of application limits, major attention has been devoted to the estimation of uncertainty in hydrologic modelling. Here a simple numerical experiment is proposed, that allows the analysis of uncertainty in hydrologic rainfall-runoff modelling associated to both quantity and quality of available data. A distributed rainfall-runoff model based on geomorphologic concepts has been used. The experiment involves the analysis of an ensemble of model runs, and its overall set up holds if the model is to be applied in different catchments and climates, or even if a different hydrologic model is used. With reference to a set of 100 synthetic rainfall events characterized by a given rainfall volume, the effect of uncertainty in parameters calibration is studied. An artificial truth ? perfect observation ? is created by using the model in a known configuration. An external source of uncertainty is introduced by assuming realistic, i.e. uncertain, discharge observations to calibrate the model. The range of parameters' values able to "reproduce" the observation is studied. Finally, the model uncertainty is evaluated and discussed. The experiment gives useful indications about the number of both events and data points needed for a careful and stable calibration of a specific model, applied in a given climate and catchment. Moreover, an insight on the expected and maximum error in flood peak discharge simulations is given: errors ranging up to 40% are to be expected if parameters are calibrated on insufficient data sets

General calibration methodology for a combined Horton-SCS infiltration scheme in flash flood modeling

Abstract. Flood forecasting undergoes a constant evolution, becoming more and more demanding about the models used for hydrologic simulations. The advantages of developing distributed or semi-distributed models have currently been made clear. Now the importance of using continuous distributed modeling emerges. A proper schematization of the infiltration process is vital to these types of models. Many popular infiltration schemes, reliable and easy to implement, are too simplistic for the development of continuous hydrologic models. On the other hand, the unavailability of detailed and descriptive information on soil properties often limits the implementation of complete infiltration schemes. In this work, a combination between the Soil Conservation Service Curve Number method (SCS-CN) and a method derived from Horton equation is proposed in order to overcome the inherent limits of the two schemes. The SCS-CN method is easily applicable on large areas, but has structural limitations. The Horton-like methods present parameters that, though measurable to a point, are difficult to achieve a reliable estimate at catchment scale. The objective of this work is to overcome these limits by proposing a calibration procedure which maintains the large applicability of the SCS-CN method as well as the continuous description of the infiltration process given by the Horton's equation suitably modified. The estimation of the parameters of the modified Horton method is carried out using a formal analogy with the SCS-CN method under specific conditions. Some applications, at catchment scale within a distributed model, are presented

Regional scale analysis of the altimetric stream network evolution

International audienceFloods result from the limited carrying capacity of stream channels when compared to the discharge peak value. The transit of flood waves - with the associated erosion and sedimentation processes - often modifies local stream geometry. In some cases this results in a reduction of the stream carrying capacity, and consequently in an enhancement of the flooding risk. A mathematical model for the prediction of potential altimetric stream network evolution due to erosion and sedimentation processes is here formalized. It works at the regional scale, identifying the tendency of river segments to sedimentation, stability, or erosion. The model builds on geomorphologic concepts, and derives its parameters from extensive surveys. As a case study, tendencies of rivers pertaining to the Valle d'Aosta region are analyzed. Some validation is provided both at regional and local scales of analysis. Local validation is performed both through a mathematical model able to simulate the temporal evolution of the stream profile, and through comparison of the prediction with ante and post-event river surveys, where available. Overall results are strongly encouraging. Possible use of the information derived from the model in the context of flood and landslide hazard mitigation is briefly discussed

Downscaling stream flow time series from monthly to daily scales using an auto-regressive stochastic algorithm: StreamFARM

Downscaling methods are used to derive stream flow at a high temporal resolution from a data series that has a coarser time resolution. These algorithms are useful for many applications, such as water management and statistical analysis, because in many cases stream flow time series are available with coarse temporal steps (monthly), especially when considering historical data; however, in many cases, data that have a finer temporal resolution are needed (daily). In this study, we considered a simple but efficient stochastic auto-regressive model that is able to downscale the available stream flow data from monthly to daily time resolution and applied it to a large dataset that covered the entire North and Central American continent. Basins with different drainage areas and different hydro-climatic characteristics were considered, and the results show the general good ability of the analysed model to downscale monthly stream flows to daily stream flows, especially regarding the reproduction of the annual maxima. If the performance in terms of the reproduction of hydrographs and duration curves is considered, better results are obtained for those cases in which the hydrologic regime is such that the annual maxima stream flow show low or medium variability, which means that they have a low or medium coefficient of variation; however, when the variability increases, the performance of the model decreases

Applicability of a forecasting chain in a different morphological environment in Italy

International audienceThe operational meteo-hydrological forecasting chain of the Liguria Region (NW Italy) is applied to a different morphoclimatic environment, such as the Emilia Romagna Region (N Italy). Modification to the chain, both in models and in procedures, are introduced to overcome problems related to medium dimension catchments (A?1000km2), characterized by complex altimetry profiles and antropical interventions along the river. The main feature of the original operational procedure, that is the probabilistic approach, is maintained. Hydraulic hazard reduction through artificial reservoirs management is exploited with reference to a specific event occurred on the Reno basin (Emilia Romagna Region)

A hydrological analysis of the 4 November 2011 event in Genoa

On the 4 November 2011 a flash flood event hit the area of Genoa with dramatic consequences. Such an event represents, from the meteorological and hydrological perspective, a paradigm of flash floods in the Mediterranean environment. <br><br> The hydro-meteorological probabilistic forecasting system for small and medium size catchments in use at the Civil Protection Centre of Liguria region exhibited excellent performances for the event, by predicting, 24–48 h in advance, the potential level of risk associated with the forecast. It greatly helped the decision makers in issuing a timely and correct alert. <br><br> In this work we present the operational outputs of the system provided during the Liguria events and the post event hydrological modelling analysis that has been carried out accounting also for the crowd sourcing information and data. We discuss the benefit of the implemented probabilistic systems for decision-making under uncertainty, highlighting how, in this case, the multi-catchment approach used for predicting floods in small basins has been crucial