Computational uncertainty analysis of groundwater recharge in catchment

Nazzareno Diodato, Michele Ceccarelli

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

In this paper, a computational environinformatics (environmental informatics) operation for mapping the groundwater climatological recharge in regional sub-basin is presented. It is based on a soil-water balance (SWB) and spatial statistics integrated in a GIS environment. Mediterranean is a region with large demands for groundwater supplies. However, water catchment data are affected by large uncertainty, arising from sampling and modelling, which makes predicting groundwater recharge difficult. Geostatistic tools for GIS are able to incorporate imput data (coverages, shape files, raster, grids) in water data processing, allowing for modeling spatial patterns, prediction at unsampled locations, and assessment of the prediction uncertainty in a meaningful way that can provide a more suitable interpretation. An issue model of linear kriging, termed as lognormal kriging in form of a probability map (LKpm), is emphasized in this study because a soft description of the recharge in terms of probability is consistent to mitigate the uncertainty of the SWB estimates. The approach was applied to a test site in the Tammaro agricultural basin (South Italy) for the incorporation of change of support in water recharge downscaling modeling. So, the estimate of uncertainty at unsampled locations, via LKpm, was used to explain the probability of exceeding a value range of the water recharge samples' distribution. In this way, the probability of exceeding the median recharge (215 mm year- 1) is low in the southeastern portion (48%) of the basin area and high in the northwestern remaining portion (52%).

Original languageEnglish
Pages (from-to)377-389
Number of pages13
JournalEcological Informatics
Volume1
Issue number4
DOIs
Publication statusPublished - 1 Dec 2006
Externally publishedYes

Fingerprint

uncertainty analysis
Uncertainty Analysis
Uncertainty analysis
groundwater recharge
Computational Analysis
Ground Water
Catchments
Groundwater
recharge
uncertainty
catchment
Water
soil water balance
groundwater
kriging
basins
Uncertainty
water
Kriging
Geographic information systems

Keywords

  • GIS-geostatistcs
  • Groundwater recharge
  • Probability maps
  • Soil-water balance
  • Southern Italy

ASJC Scopus subject areas

  • Ecological Modelling
  • Modelling and Simulation
  • Applied Mathematics
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Ecology
  • Ecology, Evolution, Behavior and Systematics

Cite this

Computational uncertainty analysis of groundwater recharge in catchment. / Diodato, Nazzareno; Ceccarelli, Michele.

In: Ecological Informatics, Vol. 1, No. 4, 01.12.2006, p. 377-389.

Research output: Contribution to journalArticle

@article{b689e45c91874a1cabc5ac31d8d38f49,
title = "Computational uncertainty analysis of groundwater recharge in catchment",
abstract = "In this paper, a computational environinformatics (environmental informatics) operation for mapping the groundwater climatological recharge in regional sub-basin is presented. It is based on a soil-water balance (SWB) and spatial statistics integrated in a GIS environment. Mediterranean is a region with large demands for groundwater supplies. However, water catchment data are affected by large uncertainty, arising from sampling and modelling, which makes predicting groundwater recharge difficult. Geostatistic tools for GIS are able to incorporate imput data (coverages, shape files, raster, grids) in water data processing, allowing for modeling spatial patterns, prediction at unsampled locations, and assessment of the prediction uncertainty in a meaningful way that can provide a more suitable interpretation. An issue model of linear kriging, termed as lognormal kriging in form of a probability map (LKpm), is emphasized in this study because a soft description of the recharge in terms of probability is consistent to mitigate the uncertainty of the SWB estimates. The approach was applied to a test site in the Tammaro agricultural basin (South Italy) for the incorporation of change of support in water recharge downscaling modeling. So, the estimate of uncertainty at unsampled locations, via LKpm, was used to explain the probability of exceeding a value range of the water recharge samples' distribution. In this way, the probability of exceeding the median recharge (215 mm year- 1) is low in the southeastern portion (48{\%}) of the basin area and high in the northwestern remaining portion (52{\%}).",
keywords = "GIS-geostatistcs, Groundwater recharge, Probability maps, Soil-water balance, Southern Italy",
author = "Nazzareno Diodato and Michele Ceccarelli",
year = "2006",
month = "12",
day = "1",
doi = "10.1016/j.ecoinf.2006.02.003",
language = "English",
volume = "1",
pages = "377--389",
journal = "Ecological Informatics",
issn = "1574-9541",
publisher = "Elsevier",
number = "4",

}

TY - JOUR

T1 - Computational uncertainty analysis of groundwater recharge in catchment

AU - Diodato, Nazzareno

AU - Ceccarelli, Michele

PY - 2006/12/1

Y1 - 2006/12/1

N2 - In this paper, a computational environinformatics (environmental informatics) operation for mapping the groundwater climatological recharge in regional sub-basin is presented. It is based on a soil-water balance (SWB) and spatial statistics integrated in a GIS environment. Mediterranean is a region with large demands for groundwater supplies. However, water catchment data are affected by large uncertainty, arising from sampling and modelling, which makes predicting groundwater recharge difficult. Geostatistic tools for GIS are able to incorporate imput data (coverages, shape files, raster, grids) in water data processing, allowing for modeling spatial patterns, prediction at unsampled locations, and assessment of the prediction uncertainty in a meaningful way that can provide a more suitable interpretation. An issue model of linear kriging, termed as lognormal kriging in form of a probability map (LKpm), is emphasized in this study because a soft description of the recharge in terms of probability is consistent to mitigate the uncertainty of the SWB estimates. The approach was applied to a test site in the Tammaro agricultural basin (South Italy) for the incorporation of change of support in water recharge downscaling modeling. So, the estimate of uncertainty at unsampled locations, via LKpm, was used to explain the probability of exceeding a value range of the water recharge samples' distribution. In this way, the probability of exceeding the median recharge (215 mm year- 1) is low in the southeastern portion (48%) of the basin area and high in the northwestern remaining portion (52%).

AB - In this paper, a computational environinformatics (environmental informatics) operation for mapping the groundwater climatological recharge in regional sub-basin is presented. It is based on a soil-water balance (SWB) and spatial statistics integrated in a GIS environment. Mediterranean is a region with large demands for groundwater supplies. However, water catchment data are affected by large uncertainty, arising from sampling and modelling, which makes predicting groundwater recharge difficult. Geostatistic tools for GIS are able to incorporate imput data (coverages, shape files, raster, grids) in water data processing, allowing for modeling spatial patterns, prediction at unsampled locations, and assessment of the prediction uncertainty in a meaningful way that can provide a more suitable interpretation. An issue model of linear kriging, termed as lognormal kriging in form of a probability map (LKpm), is emphasized in this study because a soft description of the recharge in terms of probability is consistent to mitigate the uncertainty of the SWB estimates. The approach was applied to a test site in the Tammaro agricultural basin (South Italy) for the incorporation of change of support in water recharge downscaling modeling. So, the estimate of uncertainty at unsampled locations, via LKpm, was used to explain the probability of exceeding a value range of the water recharge samples' distribution. In this way, the probability of exceeding the median recharge (215 mm year- 1) is low in the southeastern portion (48%) of the basin area and high in the northwestern remaining portion (52%).

KW - GIS-geostatistcs

KW - Groundwater recharge

KW - Probability maps

KW - Soil-water balance

KW - Southern Italy

UR - http://www.scopus.com/inward/record.url?scp=37849186889&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=37849186889&partnerID=8YFLogxK

U2 - 10.1016/j.ecoinf.2006.02.003

DO - 10.1016/j.ecoinf.2006.02.003

M3 - Article

AN - SCOPUS:37849186889

VL - 1

SP - 377

EP - 389

JO - Ecological Informatics

JF - Ecological Informatics

SN - 1574-9541

IS - 4

ER -