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Open AccessJournal ArticleDOI

Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation

David R. Legates, +1 more
- 01 Jan 1999 - 
- Vol. 35, Iss: 1, pp 233-241
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TLDR
In this paper, the goodness-of-fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation-based measures are discussed.
Abstract
Correlation and correlation-based measures (e.g., the coefficient of determination) have been widely used to evaluate the “goodness-of-fit” of hydrologic and hydroclimatic models. These measures are oversensitive to extreme values (outliers) and are insensitive to additive and proportional differences between model predictions and observations. Because of these limitations, correlation-based measures can indicate that a model is a good predictor, even when it is not. In this paper, useful alternative goodness-of-fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation-based measures are discussed. Modifications to these statistics to aid in interpretation are presented. It is concluded that correlation and correlation-based measures should not be used to assess the goodness-of-fit of a hydrologic or hydroclimatic model and that additional evaluation measures (such as summary statistics and absolute error measures) should supplement model evaluation tools.

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Linear regressions and neural approaches to water demand forecasting in irrigation districts with telemetry systems

TL;DR: In this article, the authors evaluated the performance of linear multiple regressions and feed forward computational neural networks (CNNs) trained with the Levenberg-Marquardt algorithm for the purpose of irrigation demand modelling.
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Analyzing the future climate change of Upper Blue Nile River basin using statistical downscaling techniques

TL;DR: The authors evaluated the performance of two widely used statistical downscaling techniques, namely the Long Ashton Research Station Weather Generator (LARS-WG) and the Statistical Downscaling Model (SDSM), to downscale future climate scenarios of precipitation, maximum temperature (Tmax ) and minimum temperature ( Tmin ) of the Upper Blue Nile River basin at finer spatial and temporal scales to suit further hydrological impact studies.
Journal ArticleDOI

Climate change impacts on maritime mountain snowpack in the Oregon Cascades

Abstract: . This study investigates the effect of projected temperature increases on maritime mountain snowpack in the McKenzie River Basin (MRB; 3041 km2) in the Cascades Mountains of Oregon, USA. We simulated the spatial distribution of snow water equivalent (SWE) in the MRB for the period of 1989–2009 with SnowModel, a spatially-distributed, process-based model (Liston and Elder, 2006b). Simulations were evaluated using point-based measurements of SWE, precipitation, and temperature that showed Nash-Sutcliffe Efficiency coefficients of 0.83, 0.97, and 0.80, respectively. Spatial accuracy was shown to be 82% using snow cover extent from the Landsat Thematic Mapper. The validated model then evaluated the inter- and intra-year sensitivity of basin wide snowpack to projected temperature increases (2 °C) and variability in precipitation (±10%). Results show that a 2 °C increase in temperature would shift the average date of peak snowpack 12 days earlier and decrease basin-wide volumetric snow water storage by 56%. Snowpack between the elevations of 1000 and 2000 m is the most sensitive to increases in temperature. Upper elevations were also affected, but to a lesser degree. Temperature increases are the primary driver of diminished snowpack accumulation, however variability in precipitation produce discernible changes in the timing and volumetric storage of snowpack. The results of this study are regionally relevant as melt water from the MRB's snowpack provides critical water supply for agriculture, ecosystems, and municipalities throughout the region especially in summer when water demand is high. While this research focused on one watershed, it serves as a case study examining the effects of climate change on maritime snow, which comprises 10% of the Earth's seasonal snow cover.
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Short‐term inflow forecasting using an artificial neural network model

TL;DR: The primary objective of this study is to investigate the possibility of including more temporal and spatial information on short-term inflow forecasting, which is not easily attained in the traditional time-series models or conceptual hydrological models.
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Continuous simulation for flood estimation in ungauged mesoscale catchments of Switzerland - part I: modelling framework and calibration results

TL;DR: Viviroli et al. as discussed by the authors introduced a straightforward yet robust automatic calibration procedure, which presented a tradeoff between computational time and algorithm complexity to identify, with reasonable effort, a parameter set that is well representative of the catchment's dynamics.
References
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Journal ArticleDOI

River flow forecasting through conceptual models part I — A discussion of principles☆

TL;DR: In this article, the principles governing the application of the conceptual model technique to river flow forecasting are discussed and the necessity for a systematic approach to the development and testing of the model is explained and some preliminary ideas suggested.
Journal ArticleDOI

On the validation of models

TL;DR: In this paper, it is suggested that the correlation coefficieness between observed and simulated variates is not as good as observed variates, and that correlation can be improved.
Journal ArticleDOI

A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation

TL;DR: This paper reviewed the nonparametric estimation of statistical error, mainly the bias and standard error of an estimator, or the error rate of a prediction rule, at a relaxed mathematical level, omitting most proofs, regularity conditions and technical details.
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