TY - JOUR
T1 - Gauge based precipitation estimation and associated model and product uncertainties
AU - Shao, Quanxi
AU - Lerat, Julien
AU - Brink, Heron
AU - Tomkins, Kerrie
AU - Yang, Ang
AU - Peeters, Luk
AU - Li, Ming
AU - Zhang, Lu
AU - Podger, Geoff
AU - Renzullo, Luigi J.
PY - 2012/6/11
Y1 - 2012/6/11
N2 - Estimating areal precipitation and quantifying the associated uncertainties are important for both hydrological research and water resource management. However, many, if not all, precipitation products provide only the precipitation at reasonable spatial scales without uncertainty attached. In this paper, we promote a double smoothing technique to derive the precipitation amounts at small grid size based on gauge observations and then propose a bootstrap method to quantify the rainfall model estimation uncertainty (the uncertainty of rainfall estimation by a given model; here our model is double smoothing) by the traditional bootstrap for parameter uncertainty and the rainfall product uncertainty in term of prediction. As the residuals by the direct use of smoothing approach are heterogeneous, making the direct use of bootstrapping method invalid, we use an empirical transformation to stabilise the residuals. Furthermore, by using bootstrapping method, we can easily upscale the precipitation and the associate uncertainty to any required scales. The product is easy to use in research and practice. We demonstrate our methods by applying it to Murray Darling Basin in the eastern Australia.
AB - Estimating areal precipitation and quantifying the associated uncertainties are important for both hydrological research and water resource management. However, many, if not all, precipitation products provide only the precipitation at reasonable spatial scales without uncertainty attached. In this paper, we promote a double smoothing technique to derive the precipitation amounts at small grid size based on gauge observations and then propose a bootstrap method to quantify the rainfall model estimation uncertainty (the uncertainty of rainfall estimation by a given model; here our model is double smoothing) by the traditional bootstrap for parameter uncertainty and the rainfall product uncertainty in term of prediction. As the residuals by the direct use of smoothing approach are heterogeneous, making the direct use of bootstrapping method invalid, we use an empirical transformation to stabilise the residuals. Furthermore, by using bootstrapping method, we can easily upscale the precipitation and the associate uncertainty to any required scales. The product is easy to use in research and practice. We demonstrate our methods by applying it to Murray Darling Basin in the eastern Australia.
KW - Bootstrap
KW - Nonparametric kernel smoothing
KW - Rainfall product
KW - Uncertainty estimation
KW - Upscaling
UR - http://www.scopus.com/inward/record.url?scp=84861221338&partnerID=8YFLogxK
U2 - 10.1016/j.jhydrol.2012.04.009
DO - 10.1016/j.jhydrol.2012.04.009
M3 - Article
AN - SCOPUS:84861221338
SN - 0022-1694
VL - 444-445
SP - 100
EP - 112
JO - Journal of Hydrology
JF - Journal of Hydrology
ER -