Improved interpolation of meteorological forcings and hydrologic implications for a Swiss alpine region

This paper presents a comparative study on the mapping of temperature and precipitation fields in complex Alpine terrain. Its relevance hinges on the major impact that inadequate interpolations of meteorological forcings bear on the accuracy of hydrologic predictions regardless of the specifics of the models, particularly during flood events. Three flood events measured in the Swiss Alps are analyzed in detail to determine the interpolation methods which best capture the distribution of intense, orographically-induced precipitation. The interpolation techniques comparatively examined include: Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Kriging with External Drift (KED). Geostatistical methods rely on a robust anisotropic variogram for the definition of the spatial rainfall structure. Results indicate that IDW tends to significantly underestimate rainfall volumes whereas OK and KED methods capture spatial patterns and rainfall volumes induced by storm advection. Using numerical weather forecasts and elevation data as covariates for precipitation, we provide evidence for KED to outperform the other methods. Most significantly, the use of elevation as auxiliary information in KED of temperatures demonstrates minimal errors in estimated instantaneous rainfall volumes and provides instantaneous lapse rates which better capture snow/rainfall partitioning. Incorporation of the temperature and precipitation input fields into a hydrological model used for operational management was found to provide vastly improved outputs with respect to measured discharge volumes and flood peaks, with notable implications for flood modeling.

Improving Alpine Flood Prediction through Hydrological Process Characterization and Uncertainty Analysis

Cara Christine Tobin

Among the many challenges of Alpine flood prediction is describing complex, meteo-hydrological processes in a simplified, robust manner that can be easily integrated into operational forecasting. In this dissertation, improved methods to characterize these processes are developed and integrated into the hydrological modeling component of an operational flood forecasting system used in the Swiss Alps. Detailed studies are conducted to improve hydrological model inputs, processes and outputs. Improvements, detailed in four chapters of this thesis, address the overarching goal of this work – the reduction of flood forecasting uncertainty. The accuracy of flood predictions in Alpine areas is contingent upon adequate interpolation of meteorological forcings, which has significant impacts on discharge volumes and flood peaks. This thesis demonstrates an improvement in the interpolation of temperature and precipitation inputs using a robust variogram which considers anisotropy and using a geostatistical interpolation method to distribute inputs in space and time. Results show that using elevation as the external drift factor better describes orographically-induced precipitation and temperature gradients. Also, the consideration of anisotropy is integral in detailing spatial patterns of precipitation induced by storm advection. Hydrological flood forecasting in mountainous areas also requires accurate partitioning between rain and snowfall to properly estimate the extent of runoff contributing areas. Partitioning is improved in this work by using a new method to integrate Limited Area Model output. Unlike standard hydrological procedures in inferring snowfall limit estimates based on dry, ground temperature measurements, Limited Area Model output considers the vertical, humid, atmospheric structure in its snowfall limit calculations. In effect, this method provides good estimates of runoff contributing areas in the spring as evidenced by validation on discharge measurements and satellite images of snow coverage. Accurately describing snowmelt processes on a sub-daily scale is also of critical importance in Alpine flood forecasting. However, the complex topography of the study region has limited observations available for validation. This thesis presents the development of a new physically-based snowmelt method applicable to regions with limited data. This method uses only daily minimum and maximum temperatures to mimic the effects of radiation. A comparative analysis of snowmelt methods is validated with snow lysimeter data and with a unique, distributed meteorological dataset collected by a wireless weather station network. Results demonstrate that the new method is competitive with more complex snowmelt methods as shown by accurately reproducing diurnal snowmelt cycles. Conveying limits of certainty on flood prediction outputs to users is critical because of epistemic and aleatory errors inherent to environmental modeling. Due to the presence of these errors, the GLUE methodology and multi-criteria performance ideas have been adapted with a fit-for-purpose uncertainty estimation technique in the final part of this thesis. With this method, hydrological model parameters are constrained based on hydrograph behavior, with a particular focus on flood peak response. A key component of the technique is a visualization tool which shows acceptable ensembles of discharge with respect to individual and combined criteria. By integrating the aforementioned input and process improvements into the hydrological model, calibration achieves model outputs that capture observed river discharge. Also, the uncertainty associated with hydrological modeling output error is reduced. Findings of this thesis are applicable to operational flood forecasting in general and have proven utility in improving hydrological model predictions in mountainous regions. Due to the novelty of the developments in terms of new methods or the use of tools and data sources previously unexploited in flood forecasting, further testing of the improvements is recommended. Future research in quantifying the chain of uncertainty produced by combining probabilistic forecast inputs with the hydrological output ensembles is also critical when the improved flood forecasting model becomes fully operational.

EPFL2012

Génération multi-sites de variables météorologiques horaires en zone alpine

Abdelkader Mezghani

Water resources management and hydrological risks are based, to a large extent, on our competence to generate relevant hydrological scenarios under present and future climatic situations. In alpine areas, characterized by highly complex topography, the availability of discharge data is generally very limited and the hydrological regime is frequently affected by hydraulic infrastructures (e.g. dams for hydro-power production). Flood scenario estimation is thus a complicated task; classical methods are badly adapted to such a context. The present work proposes a global concept based on the simulation of meteorological weather variables transformed subsequently in flood scenarios using a rainfall-runoff model. The meteorological scenarios are obtained from simulation using a stochastic weather generator conditioned by both atmospheric circulation indices and meteorological variables. Atmospheric circulation indices are derived from reanalysis data set provided by the National Centre for Environmental Prediction (NCEP) for the period 1982-2001. Appropriate circulation indices and meteorological series were selected by means of sensitivity analysis. The stochastic generator developed within the framework of this research combines two approaches widely used in recent prediction purposes. The first uses a set of generalized linear models – that extend the classical linear models – to explain regional and daily meteorological variables using the appropriate set of predictors. The second uses nearest neighbor method, based on the hypothesis that similar synoptic situations will produce similar meteorological situations, to disaggregate daily regional meteorological variables into hourly and locally distributed variables. The generation process was applied to the Upper Rhone River basin, an alpine catchment located in the Valais canton – Switzerland (upstream to Lake Leman) to generate a series of hourly meteorological scenarios (precipitation, temperature and lapse rate temperature) at multiple sites (48 raingauges and 11 thermal stations). The ability of the stochastic generator to reproduce statistics of key hydrological variables and extreme weather events was evaluated at multiple spatial (ranging from 100 to 5500 km2) and temporal (ranging from 3 h to 3 days) scales. These statistics are classical mean, standard deviation and lag-1 correlation coefficient for total and liquid precipitation, as well as temperature. Other associated variables, such as the snow/rain line altitude have been studied. In general, statistics related to meteorological scenarios show moderate to very good agreement between simulations and observations. For extreme events, distributions of annual maxima precipitations and of seasonal maximum and minimum temperatures are also well reproduced, even if results obtained at both hourly and locally scales show less good – but still satisfactory – performance than those obtained at both daily and regional scales. This is definitely not surprising since the calibration of the different model parameters has been performed at those latter scales. The meteorological scenarios are next used to simulate 20-year discharge time series at a number of discharge stations throughout the Rhône watershed using an appropriate rainfall-runoff model. The catchment was divided into elevation bands in order to be able to take into account the variation of temperature with altitude; this is of high importance, especially in areas showing highly complex topographical features, which is usually the case of the alpine watersheds. Here again, statistical properties of the hydrological scenarios show good accordance with observations, especially for flood events. Finally, a number of flood scenarios, corresponding either to floods localized in some specific sub-basins or to floods generalized over the whole basin, were extracted from the simulated discharge time series; they can be used to study the hydrological behavior of the basins submitted to various meteorological solicitations.

EPFL2009

Hydrologic response of an alpine watershed: Application of a meteorological wireless sensor network to understand streamflow generation

Guillermo Barrenetxea Kobas, Marc Diebold, François Ingelrest, Daniel Nadeau, Simone Padoan, Marc Parlange, Silvia Simoni, Martin Vetterli

A field measurement campaign was conducted from June to October 2009 in a 20 km2 catchment of the Swiss Alps with a wireless network of 12 weather stations and river discharge monitoring. The objective was to investigate the spatial variability of meteorological forcing and to assess its impact on streamflow generation. The analysis of the runoff dynamics highlighted the important contribution of snowmelt from spring to early summer. During the entire experimental period, the streamflow discharge was dominated by base flow contributions with temporal variations due to occasional rainfall-runoff events and a regular contribution from glacier melt. Given the importance of snow and ice melt runoff in this catchment, patterns of near-surface air temperatures were studied in detail. Statistical data analyses revealed that meteorological variables inside the watershed exhibit spatial variability. Air temperatures were influenced by topographic effects such as slope, aspect, and elevation. Rainfall was found to be spatially variable inside the catchment. The impact of this variability on streamflow generation was assessed using a lumped degree-day model. Despite the variability within the watershed, the streamflow discharge could be described using the lumped model. The novelty of this work mainly consists in quantifying spatial variability for a small watershed and showing to which extent this is important. When the focus is on aggregated outputs, such as streamflow discharge, average values of meteorological forcing can be adequately used. On the contrary, when the focus is on distributed fields such as evaporation or soil moisture, their estimate can benefit from distributed measurements.

American Geophysical Union2011