Thibault Mathevet

Impact of limited streamflow data on the efficiency and the parameters of rainfall—runoff models

Abstract Streamflow data are essential for the calibration of continuous rainfall—runoff (RR) models. The quantity and quality of streamflow data can significantly influence parameter calibration and thus model robustness. Most existing sensitivity analysis studies on the role of streamflow data have used continuous periods to calibrate model parameters, with a minimum of one year, though ideally much longer periods are generally advised. However, in practical model applications, streamflow data series available for model calibration may be rather short or non-continuous. This study aims at assessing the sensitivity of continuous RR models to the quantity of information used during model calibration when it is randomly sampled in the observed hydrograph, i.e. using non-continuous calibration periods. This sampling provides less auto-correlated streamflow information for model calibration than continuous records. Two daily RR models with four and six free parameters were tested on a sample of 12 basins in the USA to obtain more general conclusions. The results showed that, in general, 350 calibration days sampled out of a longer data set including dry and wet conditions are sufficient to obtain robust estimates of model parameters. The more parsimonious model requires fewer calibration data to obtain stable and robust parameter values. Stable parameter values prove more difficult to reach in the driest catchments.

Challenges of operational river forecasting

Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human–natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1)making themost of available data, 2)making accurate predictions usingmodels, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using humangenerated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors. Open Access Content

Panta Rhei 2013–2015: global perspectives on hydrology, society and change

ABSTRACT In 2013, the International Association of Hydrological Sciences (IAHS) launched the hydrological decade 2013–2022 with the theme “Panta Rhei: Change in Hydrology and Society”. The decade recognizes the urgency of hydrological research to understand and predict the interactions of society and water, to support sustainable water resource use under changing climatic and environmental conditions. This paper reports on the first Panta Rhei biennium 2013–2015, providing a comprehensive resource that describes the scope and direction of Panta Rhei. We bring together the knowledge of all the Panta Rhei working groups, to summarize the most pressing research questions and how the hydrological community is progressing towards those goals. We draw out interconnections between different strands of research, and reflect on the need to take a global view on hydrology in the current era of human impacts and environmental change. Finally, we look back to the six driving science questions identified at the outset of Panta Rhei, to quantify progress towards those aims. Editor D. Koutsoyiannis; Associate editor not assigned

Seeking genericity in the selection of parameter sets: Impact on hydrological model efficiency

This paper evaluates the use of a small number of generalist parameter sets as an alternative to classical calibration. Here parameter sets are considered generalist when they yield acceptable performance on a large number of catchments. We tested the genericity of an initial collection of 10(6) parameter sets sampled in the parameter space for the four-parameter GR4J rainfall-runoff model. A short list of 27 generalist parameter sets was obtained as a good compromise between model efficiency and length of the short list. A different data set was used for an independent evaluation of a calibration procedure, in which the search for an optimum parameter set is only allowed within this short list. In validation mode, the performance obtained is inferior to that of a classical calibration, but when the amount of data available for calibration is reduced, the generalist parameter sets become progressively more competitive, with better results for calibration series shorter than 1 year. Key Points We produce a generalist list of parameter sets Short-list calibration is evaluated on an independent catchment data set With short calibration series, the generalist parameter sets give better results 10.1002/(ISSN)1944-7973

Tall tales from the hydrological crypt: are models monsters?

Abstract “Bizarre,” “monstrous”: in society as well as in science, this is the way we are used to describing objects that deviate from an expected standard. Hydrology is no exception. The bizarre or the monstrous describes every object that has a low probability of occurring, or that our models fail to represent. Actually, the bizarre or the monstrous is often a demonstration of the limits of our models rather than an intrinsic characteristic of the objects we study. This article provides a reflection on the definition of bizarre and monstrous in the context of hydrology. We base our reflection on 60 years of experience in hydrometeorological operational management and applied research at the French national electricity company (EDF-DTG). First, we describe several classical a priori models or conceptions trusted by hydrologists, sometimes erroneously. These include classical rainfall or streamflow measurement issues, certain limits of the watershed concept or problems in the spatialization of local measurements. Then we attempt to show how the misuse of statistical models can generate bizarre or monstrous results. We give examples related to outliers and to the homogeneity and stationarity hypotheses. We show how difficult it may be for operational forecasters to anticipate and believe that extreme (monstrous) events will occur in the near future. Finally, we wish to show that the bizarre or the monstrous should not be rejected in hydrology, but instead is something to study in greater depth. We believe that this type of analysis offers new opportunities to improve the explanatory and predictive capacity of our models. Citation Mathevet, T. & Garçon, R. (2010) Tall tales from the hydrological crypt: are models monsters? Hydrol. Sci. J. 55(6), 857–871.

Impact of model structure on flow simulation and hydrologicalrealism: from lumped to semi-distributed approach

Model intercomparison experiments are widely used to investigate and improve hydrological model performance. However, a study based only on runoff simulation is not sufficient to discriminate between different model structures. Hence, there is a need to improve hydrological models for specific streamflow signatures (e.g., low and high flow) and multi-variable predictions (e.g., soil moisture, snow and groundwater). This study assesses the impact of model structure on flow simulation and hydrological realism using three versions of a hydrological model called MORDOR: the historical lumped structure and a revisited formulation available in both lumped and semi-distributed structures. In particular, the main goal of this paper is to investigate the relative impact of model equations and spatial discretization on flow simulation, snowpack representation and evapotranspiration estimation. Comparison of the models is based on an extensive dataset composed of 50 catchments located in French mountainous regions. The evaluation framework is founded on a multi-criterion split-sample strategy. All models were calibrated using an automatic optimization method based on an efficient genetic algorithm. The evaluation framework is enriched by the assessment of snow and evapotranspiration modeling against in situ and satellite data. The results showed that the new model formulations perform significantly better than the initial one in terms of the various streamflow signatures, snow and evapotranspiration predictions. The semi-distributed approach provides better calibration–validation performance for the snow cover area, snow water equivalent and runoff simulation, especially for nival catchments.

Reconstruction of missing daily streamflow data using dynamic regression models

River discharge is one of the most important quantities in hydrology. It provides fundamental records for water resources management and climate change monitoring. Even very short data-gaps in this information can cause extremely different analysis outputs. Therefore, reconstructing missing data of incomplete data sets is an important step regarding the performance of the environmental models, engineering, and research applications, thus it presents a great challenge. The objective of this paper is to introduce an effective technique for reconstructing missing daily discharge data when one has access to only daily streamflow data. The proposed procedure uses a combination of regression and autoregressive integrated moving average models (ARIMA) called dynamic regression model. This model uses the linear relationship between neighbor and correlated stations and then adjusts the residual term by fitting an ARIMA structure. Application of the model to eight daily streamflow data for the Durance river watershed showed that the model yields reliable estimates for the missing data in the time series. Simulation studies were also conducted to evaluate the performance of the procedure.