Computer-oriented method of optimizing hydrologic model parameters

DeCoursey, Donn G.; Snyder, Willard M.

doi:10.1016/0022-1694(69)90013-4

Cited by 32 publications

(12 citation statements)

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“…Dawdy and O'Donnell (1965) have attempted to account for known uncertainties in models by improving methods for parameter optimization. Similar developments were continued by Labadie and Dracup (1969), Decoursey and Snyder (1969), and Douglas et 01. (1976).…”

Section: Introductionsupporting

confidence: 68%

A residual noise model for streamflow prediction

Logan¹,

Lennox²,

Unny³

1983

Can. J. Civ. Eng.

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A nonlinear, conceptual hydrologic modcl is formulated for usc in prcdicting flows under conditions of controlled input. The model has time-invariant parameters and a rcsiduai noisc model is incorporated for sequential prediction of hourly flows using a closed loop feedback scheme with hypothetically controlled inputs which are adapted to the residuals. The residual model derived comprises error terms which are correlated in time and variable.It is concluded that the residual noise model approach using an input control adjustment algorithm with adaptation to the model residual, produces satisfactory improvement in flow prediction compared with deterministic prcdictions using physically measured inputs.Un modkle hydrologiquc conceptuel non-lineairc est present6 pour unc utilisation dans la prevision des debits avcc des inputs contr6lts. Les paramktres du modkle sont invariables dans le temps et le modkle de bruit residue1 cst incorpord pour la prevision sequentielle des debits horaires utilisant un schema de retroaction en bouclcs fermees avec des inputs hypothdtiques contr6lCs qui sont adapt& aux rksidus. Le modkle residue1 proposd comprend dcs termes d'erreur qui sont corrkl&s dans le temps ct avec la variable.On peut conclure que l'approchc du modkle de bruit residue1 comprcnant un algorithmc dc contr6le ajustt pour I'input et une adaptation pour le rnodkle residue1 donne des amCliorations acceptables pour la prdvision dcs ddbits comparkes aux previsions dkterministes qui ellcs utilisent des inputs physiques mesures.Mot.r-c1i.s: Prediction des debits, modklc non-lindaire, bruit residucl, bruit con%lC, contralc des inputs, ajustemcnt, algorithme, mesure, amelioration acceptable.[Traduit par la revue]Can.

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Section: Introductionsupporting

confidence: 68%

A residual noise model for streamflow prediction

Logan¹,

Lennox²,

Unny³

1983

Can. J. Civ. Eng.

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“…Williams (1975) used data collected from 18 small unequal area watersheds with considerably varying land use and collection periods for a total of 778 individual storms. To determine the most accurate prediction equation, each form of the runoff and sediment yield factors were substituted into the universal soil loss equation and evaluated by optimization [55]. The equation that best fits the data (Equation (2)) gave α and β factor values of 11.8 and 0.56, respectively.…”

Section: Model Descriptionmentioning

confidence: 99%

Streamflow and Sediment Yield Prediction for Watershed Prioritization in the Upper Blue Nile River Basin, Ethiopia

Ayele

Teshale

et al. 2017

Water

127

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Inappropriate use of land and poor ecosystem management have accelerated land degradation and reduced the storage capacity of reservoirs. To mitigate the effect of the increased sediment yield, it is important to identify erosion-prone areas in a 287 km 2 catchment in Ethiopia. The objectives of this study were to: (1) assess the spatial variability of sediment yield; (2) quantify the amount of sediment delivered into the reservoir; and (3) prioritize sub-catchments for watershed management using the Soil and Water Assessment Tool (SWAT). The SWAT model was calibrated and validated using SUFI-2, GLUE, ParaSol, and PSO SWAT-CUP optimization algorithms. For most of the SWAT-CUP simulations, the observed and simulated river discharge were not significantly different at the 95% level of confidence (95PPU), and sources of uncertainties were captured by bracketing more than 70% of the observed data. This catchment prioritization study indicated that more than 85% of the sediment was sourced from lowland areas (slope range: 0-8%) and the variation in sediment yield was more sensitive to the land use and soil type prevailing in the area regardless of the terrain slope. Contrary to the perception of the upland as an important source of sediment, the lowland in fact was the most important source of sediment and should be the focus area for improved land management practice to reduce sediment delivery into storage reservoirs. The research also showed that lowland erosion-prone areas are typified by extensive agriculture, which causes significant modification of the landscape. Tillage practice changes the infiltration and runoff characteristics of the land surface and interaction of shallow groundwater table and saturation excess runoff, which in turn affects the delivery of water and sediment to the reservoir and catchment evapotranspiration.

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“…For the areas where the equation was developed, a and b were 11.8 and 0.56, respectively, for metric system units. The optimization technique suggested by DeCoursey and Snyder (1969) was used for the development of the prediction equation and designating a and b. A disagreement with the principle of dimensional analysis of the MUSLE has been explained by Cardei (2010).…”

Section: Introductionmentioning

confidence: 99%

A review of the application of the MUSLE model worldwide

Sadeghi

Gholami

Darvishan

et al. 2013

Hydrological Sciences Journal

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The sediment yield model of the MUSLE (modified universal soil loss equation) is applied extensively throughout the world, but different performances have been reported of its success relative to measured data. A review of all the available literature is presented to assess the application of the model under different conditions and, ultimately, make a comprehensive judgement on the different aspects to allow readers to adjust their further research. A review of 49 papers showed the variable accuracy of the model, which depends on the manner of calculation and determination of the input and output, and the study time and space scales. There were differences in land use, in correspondence of the physiographic characteristics with those of the original conditions of model development, and even in the experience of researchers in applying the model. The results also show the need to consider the original application of the model, as proposed by its developers, to achieve comparable results.

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Computer-oriented method of optimizing hydrologic model parameters

Cited by 32 publications

References 1 publication

A residual noise model for streamflow prediction

A residual noise model for streamflow prediction

Streamflow and Sediment Yield Prediction for Watershed Prioritization in the Upper Blue Nile River Basin, Ethiopia

A review of the application of the MUSLE model worldwide

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