Reporting of Stream-Aquifer Flow Distribution at the Regional Scale with a Distributed Process-Based Model

Abstract

International audienceGroundwater withdrawals can reduce aquifer-to-stream flow and induce stream-to-aquifer flow. These effects involve potential threats over surface water and groundwater quantity and quality. As a result, the description of stream-aquifer flow in space and time is of high interest for water managers. In this study, the EauDyssée platform, an integrated groundwater/surface water model is extended to provide the distribution of stream-aquifer flow at the regional scale. The methodology is implemented over long periods (17 years) in the Seine river basin (76 375 km 2 , France) with a 6 481 km long simulated river network. The study scale is compatible with the scale of interest of water authorities, which is often larger than study scales of research projects. Net and gross stream-aquifer exchange flow are computed at the daily time step over the whole river network at a resolution of 1 km. Simula-tion results highlight that a major proportion of the main stream network (82 %) is supplied by groundwater. Groundwater withdrawals induce a reduction of net aquifer-to-stream flow (−19 %) at the basin scale and flow reversals in the vicinity of pumping locations. 140 Pryet et al. Such an integrated model provided at the appropriate regional scale is an essential tool provided to water managers for the implementation of the EU Water Framework Directive

Groundwater and Surface Water Interaction at the Regional-scale – A Review with Focus on Regional Integrated Models

“…Delfs et al 2012;IHMI Workshop 2011;Maxwell et al 2014). The problems used for comparison encompass rather small areas and are built on synthetic test cases (Pryet et al 2014). Maxwell et al (2014) report comparisons of larger and more complex cases.…”

“…They are often developed by national -Zhangye basin, China, 9,097 km 2 (Tian et al, 2015) (Wilcox et al, 2007) MODCOU / EauDyssée Saleh et al (2011) Pryet et al (2014) ? SAM model (Ledoux et al, 1989;Ledoux et al, 1984) RAPID, 1D Muskingum scheme (David et al, 2011) 1D Stream network hydraulic modelling HEC-RAS (Saleh et al, 2011) Conceptual model soil water-budget (Pryet et al, 2014) -Seine basin, France, 95,600 km 2 (Ledoux et al, 2007) -Rhône basin, 86,500 km 2 (Etchevers et al, 2001) -Upper Rhine basin, 13,900 km 2 (Thierion et al, 2012) -Somme basin, 6,433 km 2 (Habets et al, 2010;Korkmaz et al, 2009) (2000) Kim et al (2008) ? MODFLOW (river package) SWAT (Arnold et al, 1993;Arnold and Fohrer, 2005) Not represented (Sophocleous et al, 1999) Musimcheon Basin, South Korea, 198 km 2 (Kim et al, 2008) WaSiM, WaSiM-ETH Krause and…”

Continental hydrosystem modelling: the concept of nested stream–aquifer interfaces

“…In addition to these publications, many regional-scale models were developed with MODFLOW in the United States and China for integrated water management purposes (Rossman and Zlotnik, 2013;Zhou and Li, 2011). Except for Monteil (2011) and Pryet et al (2014), none of these explicitly focus on distributed stream-aquifer exchanged water flux. Moreover, among DPBMs dedicated to stream-aquifer exchanges, only Monteil (2011) and Pryet et al (2014) performed distributed estimations of streamaquifer exchanges at the regional scale.…”

“…Moreover, among DPBMs dedicated to stream-aquifer exchanges, only Monteil (2011) and Pryet et al (2014) performed distributed estimations of streamaquifer exchanges at the regional scale. These applications exclusively use the conductance model, for which the longitudinal distribution of the conductance along the stream network has to be calibrated (Pryet et al, 2014).…”

A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

“…In terms of applications, the Hudson River basin regional scale model may be used to continuously forecast the overall variability of the water resources Pryet et al, 2015;Saraiva Okello et al, 2015), predicting fate and transport of water quality such as nitrate (Schoonover and Lockaby, 2006;Wang et al, 2012;Bastola and Misra, 2015;Schuetz et al, 2016) and climate change scenarios (Ducharne et al, 2007(Ducharne et al, , 2010Graham et al, 2007;Quintana Seguí et al, 2010;Habets et al, 2013). Moreover, socio-economic analysis may be used to weigh how such improved forecasts can prevent loss of life and minimize the damage to property, with the aid of effective communication and social media.…”