Watershed model parameter estimation and uncertainty in data-limited environments

Fonseca, André; Ames, Daniel P.; Yang, P.; Botelho, Cidália M.S.; Boaventura, Rui A.R.; Vilar, Vítor J.P.

doi:10.1016/j.envsoft.2013.09.023

Cited by 52 publications

(26 citation statements)

References 30 publications

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“…Water quality data were available at all eight stations for a period of 4 years from 2003 to 2006 at a monthly time step interval. The hydrology was previously calibrated for a 5‐year period (1985–1989) and validated for a 4‐year period (2003–2006) at station 15E03 (Fonseca et al , ). Because there were no observed flow data for the other stations addressed in this paper, the same hydrology behaviour was assumed for the entire basin in order to perform water quality assessment.…”

Section: Methodsmentioning

confidence: 99%

Global Warming Effects on Faecal Coliform Bacterium Watershed Impairments in Portugal

Fonseca

Botelho

Boaventura

et al. 2014

River Research & Apps

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Impairment of surface water quality by faecal coliform bacteria is an issue of great importance across the globe. A water quality model, Hydrological Simulation Program FORTRAN, was used to predict the impacts of farming and climate change on faecal coliform loads and concentrations in streams of the Lis River watershed, in the Leiria region, Portugal. The calibrated faecal coliform model simulated well the patterns and range of observed faecal coliform concentrations. The accuracy of the model was evaluated by the per cent bias coefficient and the coefficient of determination. The results indicate a general deterioration of the water quality regarding faecal contamination in Lis River. Maximum daily loads were calculated for each of the impaired streams; an average of 77% reduction in the current faecal coliform load from the watershed is necessary to achieve the established water quality goals by the Council Directive 75/440/EEC (). Climate change scenarios (increments on temperature and precipitation) were assumed to predict the behaviour of faecal coliform bacteria in the watershed. The simulated results showed that an increase of 1°C in air daily temperature results in an increase of water temperature of 1.1°C and a 1.5% decrease on faecal coliform bacteria in stream concentration. The combined effect of air temperature (+1°C) and precipitation (+7%) increment leads to an increase of ~2% in bacteria inflow to the basin. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: Methodsmentioning

confidence: 99%

Global Warming Effects on Faecal Coliform Bacterium Watershed Impairments in Portugal

Fonseca

Botelho

Boaventura

et al. 2014

River Research & Apps

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“…Sensitivity analysis on the HSPF model has been performed in many previous studies (e.g., Jacomino and Fields, 1997; Fonseca et al, 2014) [34,35]. However, due to the use of different values of the HSPF model parameters, the major factors affecting model output for each watershed may be different.…”

Section: Sensitivity Analysis For the Hydrology Simulationmentioning

confidence: 99%

“…However, due to the use of different values of the HSPF model parameters, the major factors affecting model output for each watershed may be different. Al-Abed and Whiteley [28] and Fonseca et al [35] found that LZSN is the major factor, INFILT is the most sensitive parameter in a complex geomorphological river basin (Kourgialas et al [36]) and in the South National watershed in Eastern Ontario (Iskra and Droste, [37]), and DEEPFR is the most sensitive parameter in mountainous basins containing coal mines (Atkins et al [38]). However, we found that UZSN and INFILT are more sensitive.…”

Section: Sensitivity Analysis For the Hydrology Simulationmentioning

confidence: 99%

Using the HSPF and SWMM Models in a High Pervious Watershed and Estimating Their Parameter Sensitivity

Tsai

Chen

Fan

et al. 2017

Water

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Models are necessary tools for watershed management. However, applying watershed models is time consuming and requires technical knowledge, including model selection and validation. The objective of this study is to assess two commonly used watershed models and their parameter sensitivity to reduce model loadings and to gain a better understanding of the model performances. The Hydrological Simulation Program-Fortran (HSPF) model and Storm Water Management Model (SWMM) were applied to a mostly forested Taiwanese reservoir watershed with pollution from tea plantations. Statistical analysis showed that both models are suitable for the studied watershed, but the performances of the flow and water quality simulations are different. The mean flow simulated by SWMM was lower than the experimental observations. The HSPF model performed better, possibly because the soil in the study area is highly permeable and the HSPF model has more precise soil layer calculations. SWMM may underestimate the total phosphorous (TP) and suspended solid (SS) loads following small storm events in highly permeable watersheds. The Latin Hypercube-One factor At a Time (LH-OAT) method was used to determine the parameter sensitivity of the HSPF model and SWMM. In both of the models, the parameters related to infiltration and soil characteristics strongly affected the flow simulation, except when using the Horton infiltration method in the SWMM. Manning's roughness coefficient for pervious areas was more sensitive in SWMM than in the HSPF model because SWMM has fewer parameters.

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“…For example, infiltration capacity (INFILT) is one of the parameters to which modeled streamflow is the most sensitive [33], and initial values were assigned based on a spatially distributed map of the hydrologic soil group (HSG). Hydrologic soil groups range from "A" to "D", where "A" soils are characterized by the highest infiltration rate and the lowest runoff potential and "D" soils have a very slow infiltration rate and the highest runoff potential.…”

Section: Hydrologic and Sediment Calibrationmentioning

confidence: 99%

Characterizing Changes in Streamflow and Sediment Supply in the Sacramento River Basin, California, Using Hydrological Simulation Program—FORTRAN (HSPF)

Stern¹,

Flint²,

Minear³

et al. 2016

Water

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A daily watershed model of the Sacramento River Basin of northern California was developed to simulate streamflow and suspended sediment transport to the San Francisco Bay-Delta. To compensate for sparse data, a unique combination of model inputs was developed, including meteorological variables, potential evapotranspiration, and parameters defining hydraulic geometry. A slight decreasing trend of sediment loads and concentrations was statistically significant in the lowest 50% of flows, supporting the observed historical sediment decline. Historical changes in climate, including seasonality and decline of snowpack, contribute to changes in streamflow, and are a significant component describing the mechanisms responsible for the decline in sediment. Several wet and dry hypothetical climate change scenarios with temperature changes of 1.5 • C and 4.5 • C were applied to the base historical conditions to assess the model sensitivity of streamflow and sediment to changes in climate. Of the scenarios evaluated, sediment discharge for the Sacramento River Basin increased the most with increased storm magnitude and frequency and decreased the most with increases in air temperature, regardless of changes in precipitation. The model will be used to develop projections of potential hydrologic and sediment trends to the Bay-Delta in response to potential future climate scenarios, which will help assess the hydrological and ecological health of the Bay-Delta into the next century.

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Watershed model parameter estimation and uncertainty in data-limited environments

Cited by 52 publications

References 30 publications

Global Warming Effects on Faecal Coliform Bacterium Watershed Impairments in Portugal

Global Warming Effects on Faecal Coliform Bacterium Watershed Impairments in Portugal

Using the HSPF and SWMM Models in a High Pervious Watershed and Estimating Their Parameter Sensitivity

Characterizing Changes in Streamflow and Sediment Supply in the Sacramento River Basin, California, Using Hydrological Simulation Program—FORTRAN (HSPF)

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