Baseflow index regionalization analysis in a mediterranean area and data scarcity context: Role of the catchment permeability index

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Summary

Low flow characteristics are affected by different physiographic factors such as climate, topography, geology and soils, and regional regression prediction models, to estimate low flow indexes at ungauged sites, mainly rely on these factors. The paper focuses on the baseflow index, one of the most important low flow characteristics for a catchment, and presents: (i) the analysis of baseflow separation algorithms for BFI evaluation and (ii) a regional approach to predict the BFI at ungauged sites in a Mediterranean region, for which only very poor data are available. The prediction of baseflow contribution to total streamflow is based on the introduction of a permeability index, at the catchment scale, and regional linear regression equations simply relate the latter to the BFI. For the studied area geological features have been found to be the major factor affecting baseflow and the permeability index estimation for a particular catchment, in an apparently over-simplified schematization, essentially reflects catchment lithology. As a matter of fact, an accurate catchment geology spatial variability description reduces the average long-term BFI index prediction error from 23% to 14% and above all increases the explained variance from 23% to 68%.

Introduction

Low flow hydrological features are crucial for efficient development and integrated water resources management and a lot of effort has been made by the scientific community to deal with low flow parameters estimation in ungauged sites. The statistical multiple linear regression model is one of the most popular approach in these cases. Within a European context, the first significant statistical low flow estimation procedure was proposed by the Institute of Hydrology (1980) and aimed at finding statistical relationships between low flow indexes and catchment characteristics for prediction in ungauged basins, followed a few years later by one of the FRIEND project (Gustard et al., 1989) aimed to improve understanding of hydrological variability across different regions. Both studies showed the importance of geology, hydrogeology and soil properties in estimating low flow characteristics.

Among others, the BFI index, calculated as the long-term ratio of baseflow volume to total streamflow volume, is one of the most important low flow indexes. Many studies (Vogel and Kroll, 1990, Vogel and Kroll, 1992; Ponce and Shetty, 1995a, Ponce and Shetty, 1995b; Nathan et al., 1996, Lacey and Grayson, 1998, Haberlandt et al., 2001, Mwakalila et al., 2002) have demonstrated that it is related to a number of climatic and topographic parameters, to vegetation and soil types, besides catchment geology, but that the latter plays the role of the dominating variable. Variables describing geology features are hard to establish. For this reason, and also depending on the available data quantity and quality, catchment geology, to be used for BFI prediction in ungauged basins, has been accounted for in different fashions. Frequently, soil classes systems, geology–vegetation groups, or combined hydrogeology and soil indexes have been used to this purpose (Gustard et al., 1989, Boorman et al., 1995, Lacey and Grayson, 1998). Very recently, Schneider et al. (2007) have proposed a reclassification of the Soil Geographical Database of Europe (SGDBE) adopting the well-known HOST system developed in the UK (Boorman et al., 1995), to predict the BFI index, in a European context. They have shown that the SGDBE is sufficient for hydrological classification but that the variability of BFI explained by soil classes tends to decrease from Northern to Southern Europe, probably because factors such as climate, vegetation and geomorphology, which are not used to differentiate HOST classes, have a greater influence especially in Mediterranean catchments. Indeed such an approach would require a good knowledge of soil and geology properties and would likely perform poorly in the case of dearth of appropriate data.

In this study, we introduce a permeability index P1, as an alternative variable accounting for geology features, that can be easily derived also in a scarcity data context and particularly suited for typical Mediterranean environment. Initially defined on the base of a hydro-geomorphological classification, successfully used for flood prediction in ungauged sites, it is later computed on the base of an apparently over-simplified scheme which only account for lithological and hydrogeological characteristics of the studied region. It will be shown that the corresponding procedure to compute the permeability index does not require extensive soil surveys, being particularly suited for very poorly gauged sites. The introduced permeability index is further proposed as an independent variable in a regional regression model to predict BFI at ungauged sites. In order to define the most reliable regional relationship between those variables, BFI has been derived by different techniques of baseflow separation, from data analysis to digital filtering algorithms and moreover to a conceptual model approach, and the results have been compared.

In summary, the present paper focuses on the following points:

  • application of four baseflow separation algorithms and results comparison to provide the most reliable set of regional model parameters;

  • catchment geology – soil and land cover maps analysis and alternative – objective definitions of a catchment permeability index to be used as independent variable in regional prediction equations;

  • assessment of linear regression regional relationships between BFI index and catchment permeability index to estimate average baseflow contribution to total streamflow in ungauged catchments and comparison with more regional relationships.

Section snippets

Comparison of BFI indexes derived from baseflow separation procedures

Hydrograph separation has been defined in the past as “one of the most desperate analysis techniques in use in hydrology” (Hewelett and Hibbert, 1967). Indeed the procedures available to this purpose are still, to a large extent, arbitrary (Nathan and McMahon, 1990, Chapman and Maxwell, 1996, Chapman, 1999, Eckhardt, 2005) but provide a repeatable methodology to derive objective measures or indexes related to a particular streamflow source. BFI values used in this paper have been estimated

The studied region

The study region is a complex relief area, with inland highlands running north-west to south-east and wide and flat plains facing the Tirrenian Sea, where all the river channels included in this analysis flow into. The geology is rather variable: it includes marly clayey impermeable complex in the north-east area, fissured calcareous and dolomitic complex in the central area, representing the most important regional acquifer with the highest potential infiltration coefficient, and alluvial

Regional regression and multiple regional regression approaches

Regional BFI prediction in ungauged catchments is based on the introduced permeability index P1 used as independent variable in a simple linear regression model:BFI=BFIperm×P1+BFImin=A×P1+BEven though the Smoothed Minima Technique has been found to be the most reliable baseflow separation algorithm for the studied area, since one of the goal of the paper was to find the optimal regional regression model for BFI prediction, all of the combinations between the permeability index and separation

Conclusions

This paper has presented a regional regression approach to predict the BFI index at ungauged sites, based on the introduction of a permeability index P1, within a Mediterranean region and data scarcity context. Two criterion of catchment area partition have been compared to calculate the permeability index: on one hand, a comprehensive soil–vegetation–morphology classification, proposed in the past and successfully used for flood prediction in ungauged sites, and on the other hand, a simple

Acknowledgement

The authors gratefully acknowledge funding support provided through the Instruction, University and Research Italian Ministry (MIUR) under the Grant PRIN 2005 2005080490_003. The authors would also thank the anonymous reviewers for their helpful comments, which resulted in a significant improvement of the manuscript.

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