Publication
Spatio-temporal Groundwater Recharge Assessment:A Data-integration and Modelling Approach
| dc.contributor.author | Francés, Alain Pascal | |
| dc.date.accessioned | 2010-02-09T11:18:47Z | |
| dc.date.available | 2010-02-09T11:18:47Z | |
| dc.date.issued | 2008 | |
| dc.description.abstract | Groundwater fluxes can be highly variable in both space and time, which is a factor of uncertainty in groundwater flow modelling. This project aims to implement appropriate techniques to assess spatio-temporally groundwater recharge and thus improve the reliability and forecasting capabilities of groundwater flow models. The methodology is composed of the following steps:(i) design of field specific data acquisition schema to capture the spatial variability; (ii) intensive hydrological data monitoring to obtain the temporal variability of fluxes; (iii) development of a spatio-temporal recharge assessment protocol for its dynamic integration with numerical groundwater flow model. The recharge model is developed as a lumped parameter approach and requires a set of soil and aquifer parameters that can be obtained by standard field work and laboratory measurements. The developed procedures are tested on the Pisoes catchment of the semi-arid zone of Alentejo province (Portugal). As the lateral heterogeneity of the clayey topsoil was considered crucial for the reliability of the recharge model, field data acquisition focused on:(i) apparent electrical conductivity measurements, using the GeonicsTM ground conductivity meter EM-31, to derive topsoil thickness and extrapolate it through kriging with external drift using high resolution multispectral images as auxiliary maps; (ii) drilling and augering, which allowed soil profiling observations and depth-sampling to determine vadose zone hydraulic properties in laboratory. Monitoring at strategic locations of the catchment provided the driving forces (rainfall and potential evapotranspiration) and the state variables (soil moisture and hydraulic heads) of the system. Data integration determined the depth-wise discretization of the vadose zone and its parameterization. The developed recharge model (pyEARTH-2D) solves the water balance in the topsoil layer through linear relations between fluxes and soil moisture. pyEARTH-2D is coupled with flow model MODFLOW and its calibration is done against transient groundwater hydraulic heads. First results showed improvements of the groundwater flow model solution in the Pisoes catchment. Further work in this research direction will focus on developing the dynamic link involving simultaneous calibration of pyEARTH-2D and groundwater flow model MODFLOW through the PEST parameter estimation algorithm. | pt |
| dc.identifier.citation | Francés, Alain Pascal. Spatio-temporal Groundwater Recharge Assessment: A Data-integration and Modelling Approach. Tese apresentada ao International Institute for Geo-information Science and Earth Observation para cumprimento dos requisitos necessários à obtenção do grau de Mestre em "Water Resources and Environmental Management, Specialisation Groundwater Assessment and Management", 2008. | pt |
| dc.identifier.uri | http://hdl.handle.net/10400.9/541 | |
| dc.language.iso | eng | pt |
| dc.subject | Hidrogeologia | pt |
| dc.subject | Águas subterrâneas | pt |
| dc.subject | Alentejo (Portugal) | pt |
| dc.subject | Pisões (Portugal) | pt |
| dc.title | Spatio-temporal Groundwater Recharge Assessment:A Data-integration and Modelling Approach | pt |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| oaire.citation.title | Spatio-temporal Groundwater Recharge Assessment:A Data-integration and Modelling Approach | pt |
| person.familyName | Francés | |
| person.givenName | Alain Pascal | |
| person.identifier | 1205453 | |
| person.identifier.ciencia-id | 951B-44AA-0717 | |
| person.identifier.orcid | 0000-0002-1823-3094 | |
| person.identifier.rid | K-8254-2012 | |
| person.identifier.scopus-author-id | 36514607700 | |
| rcaap.rights | openAccess | pt |
| rcaap.type | masterThesis | pt |
| relation.isAuthorOfPublication | e9e689af-1372-47f5-ba0a-48637bf2e94b | |
| relation.isAuthorOfPublication.latestForDiscovery | e9e689af-1372-47f5-ba0a-48637bf2e94b |