Abstract
A model for generating daily spatial correlated rainfall fields suitable for evaluating the impacts of climate change on water resources is presented. The model, termed Stochastic Rainfall Generating Process, is designed to incorporate two major nonstationarities: changes in the frequencies of different precipitation generating mechanisms (frontal and convective), and spatial nonstationarities caused by interactions of mesoscale atmospheric patterns with topography (orographic effects). These nonstationarities are approximated as discrete sets of the time-stationary Stochastic Rainfall Generating Process, each of which represents the different spatial patterns of rainfall (including its variation with topography) associated with different atmospheric circulation patterns and times of the year (seasons). Each discrete Stochastic Rainfall Generating Process generates daily correlated rainfall fields as the product of two random fields. First, the amount of rainfall is generated by a transformed Gaussian process applying sequential Gaussian simulation. Second, the delimitation of rain and no-rain areas (intermittence process) is defined by a binary random function simulated by sequential indicator simulations. To explore its applicability, the model is tested in the Upper Guadiana Basin in Spain. The result suggests that the model provides accurate reproduction of the major spatiotemporal features of rainfall needed for hydrological modeling and water resource evaluations. The results were significantly improved by incorporating spatial drift related to orographic precipitation into the model.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 621-645 |
| Number of pages | 25 |
| Journal | Mathematical Geosciences |
| Volume | 45 |
| Issue number | 5 |
| DOIs | |
| State | Published - Jul 2013 |
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Keywords
- Atmospheric circulation
- Downscaling
- Non-stationarity
- Rainfall
ASJC Scopus subject areas
- Mathematics (miscellaneous)
- Earth and Planetary Sciences(all)
Cite this
Stochastic Simulation of Nonstationary Rainfall Fields, Accounting for Seasonality and Atmospheric Circulation Pattern Evolution. / Sapriza Azuri, Gonzalo; Jódar, Jorge; Carrera, Jesús; Gupta, Hoshin Vijai.
In: Mathematical Geosciences, Vol. 45, No. 5, 07.2013, p. 621-645.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Stochastic Simulation of Nonstationary Rainfall Fields, Accounting for Seasonality and Atmospheric Circulation Pattern Evolution
AU - Sapriza Azuri, Gonzalo
AU - Jódar, Jorge
AU - Carrera, Jesús
AU - Gupta, Hoshin Vijai
PY - 2013/7
Y1 - 2013/7
N2 - A model for generating daily spatial correlated rainfall fields suitable for evaluating the impacts of climate change on water resources is presented. The model, termed Stochastic Rainfall Generating Process, is designed to incorporate two major nonstationarities: changes in the frequencies of different precipitation generating mechanisms (frontal and convective), and spatial nonstationarities caused by interactions of mesoscale atmospheric patterns with topography (orographic effects). These nonstationarities are approximated as discrete sets of the time-stationary Stochastic Rainfall Generating Process, each of which represents the different spatial patterns of rainfall (including its variation with topography) associated with different atmospheric circulation patterns and times of the year (seasons). Each discrete Stochastic Rainfall Generating Process generates daily correlated rainfall fields as the product of two random fields. First, the amount of rainfall is generated by a transformed Gaussian process applying sequential Gaussian simulation. Second, the delimitation of rain and no-rain areas (intermittence process) is defined by a binary random function simulated by sequential indicator simulations. To explore its applicability, the model is tested in the Upper Guadiana Basin in Spain. The result suggests that the model provides accurate reproduction of the major spatiotemporal features of rainfall needed for hydrological modeling and water resource evaluations. The results were significantly improved by incorporating spatial drift related to orographic precipitation into the model.
AB - A model for generating daily spatial correlated rainfall fields suitable for evaluating the impacts of climate change on water resources is presented. The model, termed Stochastic Rainfall Generating Process, is designed to incorporate two major nonstationarities: changes in the frequencies of different precipitation generating mechanisms (frontal and convective), and spatial nonstationarities caused by interactions of mesoscale atmospheric patterns with topography (orographic effects). These nonstationarities are approximated as discrete sets of the time-stationary Stochastic Rainfall Generating Process, each of which represents the different spatial patterns of rainfall (including its variation with topography) associated with different atmospheric circulation patterns and times of the year (seasons). Each discrete Stochastic Rainfall Generating Process generates daily correlated rainfall fields as the product of two random fields. First, the amount of rainfall is generated by a transformed Gaussian process applying sequential Gaussian simulation. Second, the delimitation of rain and no-rain areas (intermittence process) is defined by a binary random function simulated by sequential indicator simulations. To explore its applicability, the model is tested in the Upper Guadiana Basin in Spain. The result suggests that the model provides accurate reproduction of the major spatiotemporal features of rainfall needed for hydrological modeling and water resource evaluations. The results were significantly improved by incorporating spatial drift related to orographic precipitation into the model.
KW - Atmospheric circulation
KW - Downscaling
KW - Non-stationarity
KW - Rainfall
UR - http://www.scopus.com/inward/record.url?scp=84879894065&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84879894065&partnerID=8YFLogxK
U2 - 10.1007/s11004-013-9467-0
DO - 10.1007/s11004-013-9467-0
M3 - Article
AN - SCOPUS:84879894065
VL - 45
SP - 621
EP - 645
JO - Mathematical Geosciences
JF - Mathematical Geosciences
SN - 1874-8961
IS - 5
ER -