Hydrologic post-processing of MOPEX streamflow simulations

Ye, Aizhong; Duan, Qingyun; Yuan, Xing; Wood, Eric F.; Schaake, John C.

doi:10.1016/j.jhydrol.2013.10.055

Cited by 53 publications

(71 citation statements)

References 30 publications

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“…The expected improvement in IMERG in snow detection could not be verified in this study as India is mostly a tropical country which receives very scanty snowfall. The constant overestimation of low-flow magnitudes in the rainfall-runoff exercise suggest that IMERG may benefit from a post-forecast data assimilation scheme (or postprocessing; Ye et al, 2014), which is a worthy topic for further research.…”

Section: Discussionmentioning

confidence: 99%

“…IMERG overestimated low flows for the majority of time in both IMD and TRMM calibrated VIC model for both the basins, and thus was inferior in performance to TRMM. This suggests that the use of an appropriate post-processor for streamflow (Ye et al, 2014) could tremendously benefit the flow simulations, which might be an interesting study for the future.…”

Section: Rainfall-runoff Modelingmentioning

confidence: 99%

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Does the GPM mission improve the systematic error component in satellite rainfall estimates over TRMM? An evaluation at a pan-India scale

Beria¹,

Nanda²,

Bisht³

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. The last couple of decades have seen the outburst of a number of satellite-based precipitation products with Tropical Rainfall Measuring Mission (TRMM) as the most widely used for hydrologic applications. Transition of TRMM into the Global Precipitation Measurement (GPM) promises enhanced spatio-temporal resolution along with upgrades to sensors and rainfall estimation techniques. The dependence of systematic error components in rainfall estimates of the Integrated Multi-satellitE Retrievals for GPM (IMERG), and their variation with climatology and topography, was evaluated over 86 basins in India for year 2014 and compared with the corresponding (2014) and retrospective (1998-2013) TRMM estimates. IMERG outperformed TRMM for all rainfall intensities across a majority of Indian basins, with significant improvement in low rainfall estimates showing smaller negative biases in 75 out of 86 basins. Low rainfall estimates in TRMM showed a systematic dependence on basin climatology, with significant overprediction in semiarid basins, which gradually improved in the higher rainfall basins. Medium and high rainfall estimates of TRMM exhibited a strong dependence on basin topography, with declining skill in higher elevation basins. The systematic dependence of error components on basin climatology and topography was reduced in IMERG, especially in terms of topography. Rainfall-runoff modeling using the Variable Infiltration Capacity (VIC) model over two flood-prone basins (Mahanadi and Wainganga) revealed that improvement in rainfall estimates in IMERG did not translate into improvement in runoff simulations. More studies are required over basins in different hydroclimatic zones to evaluate the hydrologic significance of IMERG.

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Section: Discussionmentioning

confidence: 99%

Section: Rainfall-runoff Modelingmentioning

confidence: 99%

Does the GPM mission improve the systematic error component in satellite rainfall estimates over TRMM? An evaluation at a pan-India scale

Beria¹,

Nanda²,

Bisht³

et al. 2017

Hydrol. Earth Syst. Sci.

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“…We use the US Model Parameter Estimation Experiment (MOPEX) dataset, which is documented in Schaake et al (2006; see also Schaake et al 2000). This dataset comprises hydrometeorological and land-surfacecharacteristic data originating from US catchments of intermediate size, and has been extensively used in hydrological studies (see e.g., Kavetski et al 2006b;Sawicz et al 2011;Huang et al 2013;Evin et al 2014;Weijs et al 2013;Ye et al 2014;Ren et al 2016;Hernández-López and Francés 2017). All included catchments are unregulated; therefore, the modelling assumption of stationarity is reasonable on these real-world data (see e.g., Koutsoyiannis 2011; Montanari and Koutsoyiannis 2014;Koutsoyiannis and Montanari 2015).…”

Section: Rainfall-runoff Datasetmentioning

confidence: 99%

Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale

Papacharalampous

Tyralis

Koutsoyiannis

et al. 2020

Advances in Water Resources

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Predictive hydrological uncertainty can be quantified by using ensemble methods. If properly formulated, these methods can offer improved predictive performance by combining multiple predictions. In this work, we use 50-year-long monthly time series observed in 270 catchments in the United States to explore the performances provided by an ensemble learning post-processing methodology for issuing probabilistic hydrological predictions. This methodology allows the utilization of flexible quantile regression models for exploiting information about the hydrological model's error. Its key differences with respect to basic two-stage hydrological postprocessing methodologies using the same type of regression models are that: (a) instead of a single point hydrological prediction it generates a large number of "sister predictions" (yet using a single hydrological model), and that (b) it relies on the concept of combining probabilistic predictions via simple quantile averaging. A major hydrological modelling challenge is obtaining probabilistic predictions that are simultaneously reliable and associated to prediction bands that are as narrow as possible; therefore, we assess both these desired properties of the predictions by computing their coverage probabilities, average widths and average interval scores. The results confirm the usefulness of the proposed methodology and its larger robustness with respect to basic two-stage post-processing methodologies. Finally, this methodology is empirically proven to harness the "wisdom of the crowd" in terms of average interval score, i.e., the average of the individual predictions combined by this methodology scores no worse -usually better− than the average of the scores of the individual predictions.

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“…For ephemeral rivers, this approach violates the assumptions of normal and symmetrical errors as pointed out by Smith et al (). It has nonetheless been used as a simple “pragmatic” approach in some studies that include ephemeral rivers (McInerney et al, ; Woldemeskel et al, ; Ye et al, ). Predictive uncertainty is generated only with the method used for Condition in section , including when

\overset{z}{true} ()(, t) \leq {\overset{true˜}{z}}_{C}

. o‐censored.…”

Section: Error Model Experimentsmentioning

confidence: 99%

A Data Censoring Approach for Predictive Error Modeling of Flow in Ephemeral Rivers

Wang

Bennett

Li³

2020

Water Resources Research

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Flow simulations of ephemeral rivers are often highly uncertain. Therefore, error models that can reliably quantify predictive uncertainty are particularly important. Existing error models are incapable of producing predictive distributions that contain >50% zeros, making them unsuitable for use in highly ephemeral rivers. We propose a new method to produce reliable predictions in highly ephemeral rivers. The method uses data censoring of observed and simulated flow to estimate model parameters by maximum likelihood. Predictive uncertainty is conditioned on the simulation in such a way that it can generate >50% zeros. Our method allows the setting of a censoring threshold above zero. Many conceptual hydrological models can only approach, but never equal, zero. For these hydrological models, we show that setting a censoring threshold slightly above zero is required to produce reliable predictive distributions in highly ephemeral catchments. Our new method allows reliable predictions to be generated even in highly ephemeral catchments.Plain Language Summary Many rivers cease to flow at various times. These rivers are difficult to model well, meaning that the models have a high level of uncertainty. There are no existing methods to correctly quantify the uncertainty in models of rivers that cease to flow >50% of the time. We propose a new method that can quantify the uncertainty of these models, even for rivers that cease to flow very often.Key Points:• Current hydrological error models cannot handle cases where >50% flow is zero • We introduce a new error modeling method that uses data censoring of simulations and observations • Our model produces reliable predictions even in highly ephemeral catchmentsSupporting Information:• Supporting Information S1

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Hydrologic post-processing of MOPEX streamflow simulations

Cited by 53 publications

References 30 publications

Does the GPM mission improve the systematic error component in satellite rainfall estimates over TRMM? An evaluation at a pan-India scale

Does the GPM mission improve the systematic error component in satellite rainfall estimates over TRMM? An evaluation at a pan-India scale

Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale

A Data Censoring Approach for Predictive Error Modeling of Flow in Ephemeral Rivers

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