There are no hydrological monsters, just models and observations with large uncertainties!

被引:65
作者
Kuczera, George [1 ]
Renard, Benjamin [2 ]
Thyer, Mark [1 ]
Kavetski, Dmitri [1 ]
机构
[1] Univ Newcastle, Sch Engn, Newcastle, NSW 2308, Australia
[2] Irstea, UR HHLY, Hydrol Hydraul, F-69336 Lyon, France
来源
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES | 2010年 / 55卷 / 06期
关键词
Bayesian total error analysis; model structural error; data errors; rainfall-runoff models; ill-posedness;
D O I
10.1080/02626667.2010.504677
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
Catchments that do not behave in the way the hydrologist expects, expose the frailties of hydrological science, particularly its unduly simplistic treatment of input and model uncertainty. A conceptual rainfall-runoff model represents a highly simplified hypothesis of the transformation of rainfall into runoff. Sub-grid variability and mis-specification of processes introduce an irreducible model error, about which little is currently known. In addition, hydrological observation systems are far from perfect, with the principal catchment forcing (rainfall) often subject to large sampling errors. When ignored or treated simplistically, these errors develop into monsters that destroy our ability to model certain catchments. In this paper, these monsters are tackled using Bayesian Total Error Analysis, a framework that accounts for user-specified sources of error and yields quantitative insights into how prior knowledge of these uncertainties affects our ability to infer models and use them for predictive purposes. A case study involving a catchment with an apparent water balance anomaly (a hydrological monstrosity!) illustrates these concepts. It is found that, in the absence of additional information, the rainfall-runoff record is insufficient to explain this anomaly - it could be due to a large export of groundwater, systematic overestimation of catchment rainfall of the order of 40%, or a conspiracy of these factors. There is "no free lunch" in hydrology. The rainfall-runoff record on its own is insufficient to decompose the different sources of uncertainty affecting calibration, testing and prediction, and hydrological monstrosities will persist until additional independent knowledge of uncertainties is obtained.
引用
收藏
页码:980 / 991
页数:12
相关论文
共 19 条
[1]   Impact of spatial aggregation of inputs and parameters on the efficiency of rainfall-runoff models:: A theoretical study using chimera watersheds -: art. no. W05209 [J].
Andréassian, V ;
Oddos, A ;
Michel, C ;
Anctil, F ;
Perrin, C ;
Loumagne, C .
WATER RESOURCES RESEARCH, 2004, 40 (05) :W052091-W052099
[2]   The Court of Miracles of Hydrology: can failure stories contribute to hydrological science? [J].
Andreassian, Vazken ;
Perrin, Charles ;
Parent, Eric ;
Bardossy, Andras .
HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, 2010, 55 (06) :849-856
[3]  
[Anonymous], 2021, Bayesian data analysis
[4]   THE FUTURE OF DISTRIBUTED MODELS - MODEL CALIBRATION AND UNCERTAINTY PREDICTION [J].
BEVEN, K ;
BINLEY, A .
HYDROLOGICAL PROCESSES, 1992, 6 (03) :279-298
[5]  
Box GEP, 1987, Empirical model-building and response surfaces
[6]  
Bras R.L., 1984, Random Functions and Hydrology
[7]   On joint deterministic grid modeling and sub-grid variability conceptual framework for model evaluation [J].
Ching, Jason ;
Herwehe, Jerold ;
Swall, Jenise .
ATMOSPHERIC ENVIRONMENT, 2006, 40 (26) :4935-4945
[8]   Calibration of hydrological model GR2M using Bayesian uncertainty analysis [J].
Huard, David ;
Mailhot, Alain .
WATER RESOURCES RESEARCH, 2008, 44 (02)
[9]   Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory [J].
Kavetski, D ;
Kuczera, G ;
Franks, SW .
WATER RESOURCES RESEARCH, 2006, 42 (03)
[10]  
Kavetski D., 2002, CALIBRATION WATERSHE, P49, DOI [10.1029/WS006p0049, DOI 10.1029/WS006P0049]