Bayesian networks
; Catchments
; Chirp modulation
; Gages
; Inference engines
; Interpolation
; Inverse problems
; Mean square error
; Runoff
; Approximate Bayesian inference
; Hydrological assessment
; Inverse distance weighting
; Leave-one-out cross-validation (LOOCV)
; Performance criterion
; Probability of detection
; Root mean squared errors
; Spatial interpolation
; Climate models
; accuracy assessment
; comparative study
; gauge
; interpolation
; model validation
; mountain region
; parameter estimation
; performance assessment
; precipitation (climatology)
; satellite data
; terrain
; time series analysis
; Aconcagua
; Andes
; Argentina
; Chile
; Cordillera Principal
英文摘要:
The accuracy of hydrological assessments in mountain regions is often hindered by the low density of gauges coupled with complex spatial variations in climate. Increasingly, spatial datasets (i.e. satellite and other products) and new computational tools are merged with ground observations to address this problem. This paper presents a comparison of approaches of different complexities to spatially interpolate monthly precipitation and daily temperature time series in the upper Aconcagua catchment in central Chile. A generalised linear mixed model (GLMM) whose parameters are estimated through approximate Bayesian inference is compared with simpler alternatives: inverse distance weighting (IDW), lapse rates (LRs), and two methods that analyse the residuals between observations and WorldClim (WC) data or Climate Hazards Group Infrared Precipitation with Station data (CHIRPS). The assessment is based on a leave-one-out cross validation (LOOCV), with the root-mean-squared error (RMSE) being the primary performance criterion for both climate variables, while the probability of detection (POD) and false-alarm ratio (FAR) are also used for precipitation. Results show that for spatial interpolation of temperature and precipitation, the approaches based on the WorldClim or CHIRPS residuals may be recommended as being more accurate, easy to apply and relatively robust to tested reductions in the number of estimation gauges. The GLMM has comparable performance when all gauges were included and is better for estimating occurrence of precipitation but is more sensitive to the reduction in the number of gauges used for estimation, which is a constraint in sparsely monitored catchments.
Ossa-Moreno, J., Centre for Water in the Minerals Industry, Sustainable Minerals Institute, University of Queensland, Brisbane, QLD, Australia; Keir, G., Centre for Water in the Minerals Industry, Sustainable Minerals Institute, University of Queensland, Brisbane, QLD, Australia; Mcintyre, N., Centre for Water in the Minerals Industry, Sustainable Minerals Institute, University of Queensland, Brisbane, QLD, Australia; Cameletti, M., Department of Management, Economics and Quantitative Methods, Università degli Studi di Bergamo, Bergamo, Italy; Rivera, D., Water Research Center for Agriculture and Mining (WARCAM), School of Agricultural Engineering, Universidad de Concepción, Concepción, Chile
Recommended Citation:
Ossa-Moreno J.,Keir G.,Mcintyre N.,et al. Comparison of approaches to interpolating climate observations in steep terrain with low-density gauging networks[J]. Hydrology and Earth System Sciences,2019-01-01,23(11)