| Abstract: | A decision framework is presented for assessing the value of ground-water sampling within the context of ground-water management under uncertainty. The framework couples two optimization models-a chance-constrained ground-water management model and an integer-programing sampling network design model-to identify optimal pumping and sampling strategies. The methodology consists of four steps: (1) The optimal ground-water management strategy for the present level of model uncertainty is determined using the chance-constrained management model; (2) for a specified data collection budget, the monitoring network design model identifies, prior to data collection, the sampling strategy that will minimize model uncertainty; (3) the optimal ground-water management strategy is recalculated on the basis of the projected model uncertainty after sampling; and (4) the worth of the monitoring strategy is assessed by comparing the value of the sample information-i.e., the projected reduction in management costs-with the cost of data collection. Steps 2-4 are repeated for a series of data collection budgets, producing a suite of management/monitoring alternatives, from which the best alternative can be selected. A hypothetical example demonstrates the methodology‘s ability to identify the ground-water sampling strategy with greatest net economic benefit for ground-water management.A decision framework is presented for assessing the value of ground-water sampling within the context of ground-water management under uncertainty. The framework couples two optimization models - a chance-constrained ground-water management model and an integer-programming sampling network design model - to identify optimal pumping and sampling strategies. The methodology consists of four steps: (1) The optimal ground-water management strategy for the present level of model uncertainty is determined using the chance-constrained management model; (2) for a specified data collection budget, the monitoring network design model identifies, prior to data collection, the sampling strategy that will minimize model uncertainty; (3) the optimal ground-water management strategy is recalculated on the basis of the projected model uncertainty after sampling; and (4) the worth of the monitoring strategy is assessed by comparing the value of the sample information - i.e., the projected reduction in management costs - with the cost of data collection. Steps 2-4 are repeated for a series of data collection budgets, producing a suite of management/monitoring alternatives, from which the best alternative can be selected. A hypothetical example demonstrates the methodology‘s ability to identify the ground-water sampling strategy with greatest net economic benefit for ground-water management. |
| Genre: | Article |
| ProdID: | 70021443 |
| Citation Author: | Wagner, B. J. |
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| Citation End Page: | 288 |
| Citation Issue: | 5 |
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| Citation Language: | English |
| Citation Larger Work Title: | Journal of Water Resources Planning and Management |
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| Citation Number Of Pages: | 8 |
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| Citation Publisher: | ASCE |
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| Citation Search Results Text: | Evaluating data worth for ground-water management under uncertainty; 1999; Article; Journal; Journal of Water Resources Planning and Management; Wagner, B. J. |
| Citation Start Page: | 281 |
| Citation Volume: | 125 |
| Citation Year: | 1999 |
| Type: | citation/reference |
| Text: | Evaluating data worth for ground-water management under uncertainty; 1999; Article; Journal; Journal of Water Resources Planning and Management; Wagner, B. J. |
| URL (THUMBNAIL): | http://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg |
| URL (DIGITAL OBJECT IDENTIFIER): | http://dx.doi.org/10.1061/(ASCE)0733-9496(1999)125:5(281) |
| Date Other: | Fri, 1 Jan 1999 00:00 -0600 |
| Publisher: | ASCE |
