气候变化领域集成服务门户(CCPortal): Probabilistic inference of ecohydrological parameters using observations from point to satellite scales

CCPortal

DOI	10.5194/hess-22-3229-2018
	Probabilistic inference of ecohydrological parameters using observations from point to satellite scales
	Bassiouni M.; Higgins C.W.; Still C.J.; Good S.P.
发表日期	2018
ISSN	1027-5606
起始页码	3229
结束页码	3243
卷号	22 期号:6
英文摘要	Vegetation controls on soil moisture dynamics are challenging to measure and translate into scale- and site-specific ecohydrological parameters for simple soil water balance models. We hypothesize that empirical probability density functions (pdfs) of relative soil moisture or soil saturation encode sufficient information to determine these ecohydrological parameters. Further, these parameters can be estimated through inverse modeling of the analytical equation for soil saturation pdfs, derived from the commonly used stochastic soil water balance framework. We developed a generalizable Bayesian inference framework to estimate ecohydrological parameters consistent with empirical soil saturation pdfs derived from observations at point, footprint, and satellite scales. We applied the inference method to four sites with different land cover and climate assuming (i) an annual rainfall pattern and (ii) a wet season rainfall pattern with a dry season of negligible rainfall. The Nash-Sutcliffe efficiencies of the analytical model's fit to soil observations ranged from 0.89 to 0.99. The coefficient of variation of posterior parameter distributions ranged from < ĝ€1 to 15ĝ€%. The parameter identifiability was not significantly improved in the more complex seasonal model; however, small differences in parameter values indicate that the annual model may have absorbed dry season dynamics. Parameter estimates were most constrained for scales and locations at which soil water dynamics are more sensitive to the fitted ecohydrological parameters of interest. In these cases, model inversion converged more slowly but ultimately provided better goodness of fit and lower uncertainty. Results were robust using as few as 100 daily observations randomly sampled from the full records, demonstrating the advantage of analyzing soil saturation pdfs instead of time series to estimate ecohydrological parameters from sparse records. Our work combines modeling and empirical approaches in ecohydrology and provides a simple framework to obtain scale- and site-specific analytical descriptions of soil moisture dynamics consistent with soil moisture observations. © 2018 Author(s).
语种	英语
scopus关键词	Analytical models; Bayesian networks; Drought; Dynamics; Inference engines; Inverse problems; Moisture control; Probability density function; Rain; Soil moisture; Soil surveys; Stochastic systems; Analytical description; Coefficient of variation; Empirical probabilities; Parameter distributions; Parameter identifiability; Probabilistic inference; Relative soil moistures; Soil water balance model; Parameter estimation; dry season; ecohydrology; land cover; parameterization; probability; satellite data; saturation; soil moisture; soil water; time series analysis; water budget; wet season
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160005
作者单位	Bassiouni, M., Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97331, United States; Higgins, C.W., Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97331, United States; Still, C.J., College of Forestry, Oregon State University, Corvallis, OR 97331, United States; Good, S.P., Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR 97331, United States
推荐引用方式 GB/T 7714	Bassiouni M.,Higgins C.W.,Still C.J.,et al. Probabilistic inference of ecohydrological parameters using observations from point to satellite scales[J],2018,22(6).
APA	Bassiouni M.,Higgins C.W.,Still C.J.,&Good S.P..(2018).Probabilistic inference of ecohydrological parameters using observations from point to satellite scales.Hydrology and Earth System Sciences,22(6).
MLA	Bassiouni M.,et al."Probabilistic inference of ecohydrological parameters using observations from point to satellite scales".Hydrology and Earth System Sciences 22.6(2018).