TECHNICAL REPORTS 544 Modeling is a common practice to evaluate factors affecting water quality in environmental systems impaired by point and nonpoint losses of N and P. Nevertheless, in situations with inadequate information, such as ungauged basins, a balance between model complexity and data availability is necessary. In this paper, we applied a simplified analytical model to an artificially drained floodplain in central-western Italy to evaluate the importance of different nutrient sources and in-stream retention processes and to identify critical source areas. We first considered only a set of chemical concentrations in water measured from February through May 2008 and from November 2008 through February 2009. We then broadened available data to include water discharge and hydraulic-head measurements to construct a hydrogeological model using MODFLOW-2000 and to evaluate the reliability of the simplified method. he simplified model provided acceptable estimates of discharge (ranging from 0.03–0.75 m 3 s −1 ) and diffuse nutrient inputs from water table discharge and in-stream retention phenomena. Estimates of PO 4 –P and total P retention (ranging from 1.0 to 0.6 μg m −2 s −1 and from 1.18 to 0.95 μg m −2 s −1 for PO 4 –P and total P, respectively) were consistent with the range of variability in literature data. In contrast, the higher temporal variability of nitrate concentrations decreased model accuracy, suggesting the need for more intensive monitoring. he model also separated the dynamics of different reaches of the drainage network and identified zones considered critical source areas and buffer zones where pollutant transport is reduced. A Simple Model to Assess Nitrogen and Phosphorus Contamination in Ungauged Surface Drainage Networks: Application to the Massaciuccoli Lake Catchment, Italy C. Pistocchi,* N. Silvestri, R. Rossetto, T. Sabbatini, M. Guidi, I. Baneschi, E. Bonari, D. Trevisan I n the context of environmental systems impaired by losses of N and P, planners require assistance evaluat- ing the contribution of point- and nonpoint-source losses (Bowes et al., 2008) and identifying critical source areas (i.e., areas where a significant source of nutrient input is directly connected to receiving waters) (Heathwaite et al., 2005). Such evaluations of land-use effects on water quality are not straight- forward tasks considering the many factors that can affect the mobilization and transport of pollutants, such as land use, agricultural management practices, and soil type. Spatial and temporal changes in driving variables, system properties, and their interactions generate a complexity that makes description of all system dimensions impossible. In comparison, the infor- mation that one can acquire generally is limited to simple and inexpensive data, such as pollutant chemical concentrations in surface waters. To circumvent these difficulties, modeling is a common solution because it simplifies system description and reduces complexity (De Wit and Pebesma, 2001). Several modeling approaches for representing the function- ing of surface drainage networks exist, depending on the extent of available information. Physically based models integrate multiple scales of system complexity, such as the 1D models DRAINMOD (Campbell et al., 1995; Fernandez et al., 2006) and OTIS (Bencala and Walters, 1983) or the 2D/3D models HYDRUS (van Beek et al., 2007) and MODFLOW (Harbaugh et al., 2000). his also can be the case with empirical/ana- lytical approaches, which evaluate pollutant fluxes among several compartments of a studied system (e.g., Seneque/ Rivestrahler model [Billen et al., 2007; hieu et al., 2009] and INCA [Whitehead et al., 1998]). In contrast, simpler aggre- gated models require much less data to consider elementary stores and the fluxes between them (e.g., PolFlow [de Wit and Bendoricchio, 2000; de Wit and Pebesma, 2001]; two-store model [Ruiz et al., 2002]). Other approaches are based solely on statistical analysis of available data that describe the entire system or its subcompartments (e.g., Rupp et al., 2004; van Beek et al., 2004; Chardon and Schoumans, 2007). Statistical Abbreviations: RE, relative error; WWTP, wastewater treatment plant. C. Pistocchi, R. Rossetto, T. Sabbatini, and E. Bonari, Land Lab– Scuola Superiore S. Anna, Via S. Cecilia 3, 56124 Pisa, Italy; N. Silvetri, Dipartimento di Agronomia e Gestione dell’Agroecosistema, Via San Michele degli Scalzi 2, 56124 Pisa, Italy; M. Guidi and I. Baneschi, IGG–CNR, Via Moruzzi 1, 56124 Pisa, Italy; D. Trevisan, INRA, UMR 42 CARRTEL, 75 avenue de Corzent, F-74203 Thonon-les-Bains, France. Assigned to Associate Editor Joe Kozak. Copyright © 2012 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. J. Environ. Qual. 41:544–553 (2012) doi:10.2134/jeq2011.0302 Posted online 26 Jan. 2012. Received 24 Aug. 2011. *Corresponding author (c.pistocchi@sssup.it). © ASA, CSSA, SSSA 5585 Guilford Rd., Madison, WI 53711 USA Journal of Environmental Quality SURFACE WATER QUALITY TECHNICAL REPORTS