Model bias and complexity: Understanding the effects of structural deficits and input errors on runoff predictions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F15%3A00225856" target="_blank" >RIV/68407700:21110/15:00225856 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.envsoft.2014.11.006" target="_blank" >http://dx.doi.org/10.1016/j.envsoft.2014.11.006</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.envsoft.2014.11.006" target="_blank" >10.1016/j.envsoft.2014.11.006</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Model bias and complexity: Understanding the effects of structural deficits and input errors on runoff predictions

Popis výsledku v původním jazyce

Oversimplified models and erroneous inputs play a significant role in impairing environmental predictions. To assess the contribution of these errors to model uncertainties is still challenging. Our objective is to understand the effect of model complexity on systematic modeling errors. Our method consists of formulating alternative models with increasing detail and flexibility and describing their systematic deviations by an autoregressive bias process. We test the approach in an urban catchment with five drainage models. Our results show that a single bias description produces reliable predictions for all models. The bias decreases with increasing model complexity and then stabilizes. The bias decline can be associated with reduced structural deficits, while the remaining bias is probably dominated by input errors. Combining a bias description with a multimodel comparison is an effective way to assess the influence of structural and rainfall errors on flow forecasts.

Název v anglickém jazyce

Model bias and complexity: Understanding the effects of structural deficits and input errors on runoff predictions

Popis výsledku anglicky

Oversimplified models and erroneous inputs play a significant role in impairing environmental predictions. To assess the contribution of these errors to model uncertainties is still challenging. Our objective is to understand the effect of model complexity on systematic modeling errors. Our method consists of formulating alternative models with increasing detail and flexibility and describing their systematic deviations by an autoregressive bias process. We test the approach in an urban catchment with five drainage models. Our results show that a single bias description produces reliable predictions for all models. The bias decreases with increasing model complexity and then stabilizes. The bias decline can be associated with reduced structural deficits, while the remaining bias is probably dominated by input errors. Combining a bias description with a multimodel comparison is an effective way to assess the influence of structural and rainfall errors on flow forecasts.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

DA - Hydrologie a limnologie

OECD FORD obor

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Projekt

<a href="/cs/project/GA14-22978S" target="_blank" >GA14-22978S: Predikce srážkového odtoku v urbanizovaných povodích na základě deštěm generovaného útlumu mikrovlnných spojů telekomunikační sítě</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Environmental Modelling & Software

ISSN

1364-8152

e-ISSN

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Svazek periodika

64

Číslo periodika v rámci svazku

64

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

205-214

Kód UT WoS článku

000349429800017

EID výsledku v databázi Scopus

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