 |
| Titel |
Calibration of a crop model to irrigated water use using a genetic algorithm |
| VerfasserIn |
T. Bulatewicz, W. Jin, S. Staggenborg, S. Lauwo, M. Miller, S. Das, D. Andresen, J. Peterson, D. R. Steward, S. M. Welch |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1027-5606
|
| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 13, no. 8 ; Nr. 13, no. 8 (2009-08-14), S.1467-1483 |
| Datensatznummer |
250011970
|
| Publikation (Nr.) |
 copernicus.org/hess-13-1467-2009.pdf |
|
|
|
|
|
| Zusammenfassung |
| Near-term consumption of groundwater for irrigated agriculture in the High
Plains Aquifer supports a dynamic bio-socio-economic system, all parts of
which will be impacted by a future transition to sustainable usage that
matches natural recharge rates. Plants are the foundation of this system and
so generic plant models suitable for coupling to representations of other
component processes (hydrologic, economic, etc.) are key elements of needed
stakeholder decision support systems. This study explores utilization of the
Environmental Policy Integrated Climate (EPIC) model to serve in this role.
Calibration required many facilities of a fully deployed decision support
system: geo-referenced databases of crop (corn, sorghum, alfalfa, and
soybean), soil, weather, and water-use data (4931 well-years), interfacing
heterogeneous software components, and massively parallel processing
(3.8×109 model runs). Bootstrap probability distributions for ten
model parameters were obtained for each crop by entropy maximization via the
genetic algorithm. The relative errors in yield and water estimates based on
the parameters are analyzed by crop, the level of aggregation (county- or
well-level), and the degree of independence between the data set used for
estimation and the data being predicted. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|