A metric for attributing variability in modelled streamflows

Shoaib, Syed Abu; Marshall, Lucy; Sharma, Ashish

doi:10.1016/j.jhydrol.2016.08.050

Cited by 10 publications

(32 citation statements)

References 50 publications

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“…The superiority of the HEAVEN is further confirmed when we assess the capability of both approaches in predicting high flow events. This corroborates with the findings of others (Pathiraja, Moradkhani, et al, ; Pathiraja, Anghileri, et al, , ; Shoaib et al, ), who demonstrated that accounting for the model structural uncertainty is particularly important in predicting high flow events.…”

Section: Resultssupporting

confidence: 92%

The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework

Abbaszadeh

Moradkhani

Daescu

2019

Water Resources Research

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This article presents a novel approach to couple a deterministic four‐dimensional variational (4DVAR) assimilation method with the particle filter (PF) ensemble data assimilation system, to produce a robust approach for dual‐state‐parameter estimation. In our proposed method, the Hybrid Ensemble and Variational Data Assimilation framework for Environmental systems (HEAVEN), we characterize the model structural uncertainty in addition to model parameter and input uncertainties. The sequential PF is formulated within the 4DVAR system to design a computationally efficient feedback mechanism throughout the assimilation period. In this framework, the 4DVAR optimization produces the maximum a posteriori estimate of state variables at the beginning of the assimilation window without the need to develop the adjoint of the forecast model. The 4DVAR solution is then perturbed by a newly defined prior error covariance matrix to generate an initial condition ensemble for the PF system to provide more accurate and reliable posterior distributions within the same assimilation window. The prior error covariance matrix is updated from one cycle to another over the main assimilation period to account for model structural uncertainty resulting in an improved estimation of posterior distribution. The premise of the presented approach is that it (1) accounts for all sources of uncertainties involved in hydrologic predictions, (2) uses a small ensemble size, and (3) precludes the particle degeneracy and sample impoverishment. The proposed method is applied on a nonlinear hydrologic model and the effectiveness, robustness, and reliability of the method is demonstrated for several river basins across the United States.

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Section: Resultssupporting

confidence: 92%

The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework

Abbaszadeh

Moradkhani

Daescu

2019

Water Resources Research

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“…For this reason, this approach originally focused on the effects of different parameters on the model response, that is, parameter uncertainty. More recently, however, these strategies have also been extended to account for other sources of uncertainty (Baroni & Tarantola, 2014;Lilburne & Tarantola, 2009;Savage et al, 2016;Shoaib et al, 2016;Stahn et al, 2017). Thus, different model structures could be also evaluated in the assessment, especially when combined with flexible modeling frameworks (e.g., Clark et al, 2008Clark et al, , 2015Kneis, 2015).…”

Section: 1029/2018wr022813mentioning

confidence: 99%

What Did Really Improve Our Mesoscale Hydrological Model? A Multidimensional Analysis Based on Real Observations

Francke

Baroni

Brosinsky

et al. 2018

Water Resources Research

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Modelers can improve a model by addressing the causes for the model errors (data errors and structural errors). This leads to implementing model enhancements (MEs), for example, meteorological data based on more monitoring stations, improved calibration data, and/or modifications in process formulations. However, deciding on which MEs to implement remains a matter of expert knowledge. After implementing multiple MEs, any improvement in model performance is not easily attributed, especially when considering different objectives or aspects of this improvement (e.g., better dynamics vs. reduced bias). We present an approach for comparing the effect of multiple MEs based on real observations and considering multiple objectives (MMEMO). A stepwise selection approach and structured plots help to address the multidimensionality of the problem. Tailored analyses allow a differentiated view on the effect of MEs and their interactions. MMEMO is applied to a case study employing the mesoscale hydro‐sedimentological model WASA‐SED for the Mediterranean‐mountainous Isábena catchment, northeast Spain. The investigated seven MEs show diverse effects: some MEs (e.g., rainfall data) cause improvements for most objectives, while other MEs (e.g., land use data) only affect a few objectives or even decrease model performance. Interaction of MEs was observed for roughly half of the MEs, confirming the need to address them in the analysis. Calibration and increasing the temporal resolution showed by far stronger impact than any of the other MEs. The proposed framework can be adopted in other studies to analyze the effect of MEs and, thus, facilitate the identification and implementation of the most promising MEs for comparable cases.

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“…Concerns regarding data quality may linger if recent development in systematic modelling and data assimilation approaches (e.g. Clark et al, 2015;Shoaib et al, 2016;Hut et al, 2015) would be adopted at the water authorities in the Netherlands. Such frameworks would allow systematic tracing of the propagation of uncertainty of citizen data in decision support tools and may identify opportunities for citizen science in model structure determination, which can be the largest source of uncertainty (Shoaib et al, 2016).…”

Section: Reflection On the Four Themesmentioning

confidence: 99%

“…Clark et al, 2015;Shoaib et al, 2016;Hut et al, 2015) would be adopted at the water authorities in the Netherlands. Such frameworks would allow systematic tracing of the propagation of uncertainty of citizen data in decision support tools and may identify opportunities for citizen science in model structure determination, which can be the largest source of uncertainty (Shoaib et al, 2016). Ottinger (2010) stressed the need for standardized methods in citizen science and legal embedding to increase the acceptance of the data and actual use of citizen science in policy-making.…”

Section: Reflection On the Four Themesmentioning

confidence: 99%

“…Clark et al, 2015) and specifically uncertainty quantification (e.g. Shoaib et al, 2016) can allow for more effective design of citizen science campaigns to systematically reduce model uncertainty, as well as more effective use of citizen-collected data. These data are often considered highly uncertain by practitioners, as they indicated in the exploratory interviews held at the start of this research.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Practitioners' viewpoints on citizen science in water management: a case study in Dutch regional water resource management

Minkman

Sanden

Rutten

2017

Hydrol. Earth Syst. Sci.

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Abstract. In recent years, governmental institutes have started to use citizen science as a form of public participation. The Dutch water authorities are among them. They face pressure on the water governance system and a water awareness gap among the general public, and consider citizen science a possible solution. The reasons for practitioners to engage in citizen science, and in particular those of government practitioners, have seldom been studied. This article aims to pinpoint the various viewpoints of practitioners at Dutch regional water authorities on citizen science. A Qmethodological approach was used because it allows for exploration of viewpoints and statistical analysis using a small sample size. Practitioners (33) at eight different water authorities ranked 46 statements from agree to disagree. Three viewpoints were identified with a total explained variance of 67 %. Viewpoint A considers citizen science a potential solution that can serve several purposes, thereby encouraging citizen participation in data collection and analysis. Viewpoint B considers citizen science a method for additional, illustrative data. Viewpoint C views citizen science primarily as a means of education. These viewpoints show water practitioners in the Netherlands are willing to embrace citizen science at water authorities, although there is no support for higher levels of citizen engagement.

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A metric for attributing variability in modelled streamflows

Cited by 10 publications

References 50 publications

The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework

The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework

What Did Really Improve Our Mesoscale Hydrological Model? A Multidimensional Analysis Based on Real Observations

Practitioners' viewpoints on citizen science in water management: a case study in Dutch regional water resource management

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