A new methodology to assess the maximum irrigation rates at catchment scale using geostatistics and GIS
Derechos de accesoopenAccess
MetadataShow full item record
AuthorDe-Paz, José M.; Albert, C.; Visconti, Fernando; Jimenez, M. G.; Ingelmo, Florencio; Molina, María J.
Cita bibliográficaDe Paz, J.M., Albert, C., Visconti, F., Jimenez, M. G., Ingelmo, F., Molina, M.J. (2015). A new methodology to assess the maximum irrigation rates at catchment scale using geostatistics and GIS. Precision Agriculture, 16(5), 505-531.
Soil hydraulic parameters are important for irrigation scheduling. In the domain of “precision irrigation”, knowledge of the spatial distribution of these parameters is useful in determining the maximum irrigation rate for each field in a catchment. This study focuses on the development of a new methodology to assess the spatial distribution of the maximum irrigation rate depending on the available soil water holding capacity (ASWHC). This methodology combines geostatistical techniques with geographical information system (GIS) tools. A pilot zone of 12 400 ha in a Spanish Mediterranean area was selected to develop this methodology. The linear coregionalization model (LMCR), considering the percentage of sand, carbonates, and ASWHC at others soil depths as covariates, was the best option to model the ASWHC. Other required soil parameters were also spatially modeled. The percent of coarse fragments was modeled by regression kriging considering the soil map as an auxiliary variable. The bulk density was spatially modeled by LMCR, and extended to the rooting depth by linear regression. The spatial distributions modeled were implemented in a GIS with other spatial information layers of irrigation management parameters, such as the maximum allowable depletion of soil water content, the percent of wetted soil and the irrigation depth. The combination of these layers in the GIS was used to estimate the maximum irrigation rates for each field. A propagation error analysis was performed to know the uncertainties in the maximum irrigation rate estimation. Based on this information, the irrigation managers could optimize the irrigation rates for each field.