scholarly journals Sensitivity analysis of a simplified precipitation-runoff model to estimate water availability in Southern Portuguese watersheds

2021 ◽  
Vol 10 (2) ◽  
pp. 33-47
Author(s):  
Tiago N. Martins ◽  
Manuel Mendes Oliveira ◽  
Maria M. Portela ◽  
Teresa Eira Leitão
Keyword(s):  
Sensitivity Analysis ◽  
Water Availability ◽  
Surface Flow ◽  
Model Parameters ◽  
Southern Portugal ◽  
Measured Flow ◽  
Management Policies ◽  
Runoff Model ◽  
Potential Maximum ◽  
Maximum Retention

The water availability estimation in large regions is a relevant procedure to define broad water resources management policies but may prove difficult due to the lack of data and uncertainty to related regional hydrological and hydrogeological characterization. BALSEQ, a daily sequential water budget model, was applied in a set of twenty-two watersheds in southern Portugal, aiming to understand the possible relations between the model parameters and watershed characteristics that may allow assembling calibration functions for non-monitored watersheds. A sensitivity analysis was conducted by comparing BALSEQ results with measured surface flow, focusing specifically on the fraction of the potential maximum retention (φ) and the maximum amount of water available in the soil for evapotranspiration (AGUT) parameters and the underlying hydrogeological conceptual model that ultimately controls the surface-groundwater interactions. The overall results did not allow to identify clear relations that permit extrapolation to other regions without data as the sensitivity analysis procedures returned similar results for wide intervals of parameters for the majority of watersheds. The results confirmed that the groundwater discharge is an important component for the total measured surface flow and that the φ parameter should not be overlooked when calculating direct runoff. Poor adjustments between the model results and measured flow were observed in watersheds with a low Surface flow – Rainfall ratio.

scholarly journals Uncertainty and sensitivity analysis of GIS based continuous hydrological modelling

2021 ◽  
Author(s):  
Harry R. Manson
Keyword(s):  
Sensitivity Analysis ◽  
Curve Number ◽  
Monte Carlo Analysis ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Lumped Model ◽  
Daily Average ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

The impact of uncertainty in spatial and a-spatial lumped model parameters for a continuous rainfall-runoff model is evaluated with respect to model prediction. The model uses a modified SCS-Curve Number approach that is loosely coupled with a geographic information system (GIS). The rainfall-runoff model uses daily average inputs and is calibrated using a daily average streamflow record for the study site. A Monte Carlo analysis is used to identify total model uncertainty while sensitivity analysis is applied using both a one-at-a-time (OAT) approach as well as through application of the extended Fourier Amplitude Sensitivity Technique (FAST). Conclusions suggest that the model is highly followed by model inputs and finally the Curve Number. While the model does not indicate a high degree of sensitivity to the Curve Number at present conditions, uncertainties in Curve Number estimation can potentially be the cause of high predictive errors when future development scenarios are evaluated.

scholarly journals Climatic and basin factors affecting the flood frequency curve: PART I – A simple sensitivity analysis based on the continuous simulation approach

2000 ◽  
Vol 4 (3) ◽  
pp. 463-482 ◽  
Author(s):  
A. M. Hashemi ◽  
M. Franchini ◽  
P. E. O’Connell
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Flood Frequency ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Frequency Curve ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Insight Into ◽  
Frequency Curves

Abstract. Regionalized and at-site flood frequency curves exhibit considerable variability in their shapes, but the factors controlling the variability (other than sampling effects) are not well understood. An application of the Monte Carlo simulation-based derived distribution approach is presented in this two-part paper to explore the influence of climate, described by simulated rainfall and evapotranspiration time series, and basin factors on the flood frequency curve (ffc). The sensitivity analysis conducted in the paper should not be interpreted as reflecting possible climate changes, but the results can provide an indication of the changes to which the flood frequency curve might be sensitive. A single site Neyman Scott point process model of rainfall, with convective and stratiform cells (Cowpertwait, 1994; 1995), has been employed to generate synthetic rainfall inputs to a rainfall runoff model. The time series of the potential evapotranspiration (ETp) demand has been represented through an AR(n) model with seasonal component, while a simplified version of the ARNO rainfall-runoff model (Todini, 1996) has been employed to simulate the continuous discharge time series. All these models have been parameterised in a realistic manner using observed data and results from previous applications, to obtain ‘reference’ parameter sets for a synthetic case study. Subsequently, perturbations to the model parameters have been made one-at-a-time and the sensitivities of the generated annual maximum rainfall and flood frequency curves (unstandardised, and standardised by the mean) have been assessed. Overall, the sensitivity analysis described in this paper suggests that the soil moisture regime, and, in particular, the probability distribution of soil moisture content at the storm arrival time, can be considered as a unifying link between the perturbations to the several parameters and their effects on the standardised and unstandardised ffcs, thus revealing the physical mechanism through which their influence is exercised. However, perturbations to the parameters of the linear routing component affect only the unstandardised ffc. In Franchini et al. (2000), the sensitivity analysis of the model parameters has been assessed through an analysis of variance (ANOVA) of the results obtained from a formal experimental design, where all the parameters are allowed to vary simultaneously, thus providing deeper insight into the interactions between the different factors. This approach allows a wider range of climatic and basin conditions to be analysed and reinforces the results presented in this paper, which provide valuable new insight into the climatic and basin factors controlling the ffc. Keywords: stochastic rainfall model; rainfall runoff model; simulation; derived distribution; flood frequency; sensitivity analysis

scholarly journals Sensitivity of Direct Runoff to Curve Number Using the SCS-CN Method

2019 ◽  
Vol 5 (12) ◽  
pp. 2738-2746
Author(s):  
Abdul Ghani Soomro ◽  
Muhammad Munir Babar ◽  
Anila Hameem Memon ◽  
Arjumand Zehra Zaidi ◽  
Arshad Ashraf ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Water Availability ◽  
Curve Number ◽  
Resource Planning ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Direct Runoff ◽  
Wide Range ◽  
The Impact ◽  
Scs Cn

This study explores the impact of runoff curve number (CN) on the hydrological model outputs for the Morai watershed, Sindh-Pakistan, using the Soil Conservation Service Curve Number (SCS-CN) method. The SCS-CN method is an empirical technique used to estimate rainfall-runoff volume from precipitation in small watersheds, and CN is an empirically derived parameter used to calculate direct runoff from a rainfall event. CN depends on soil type, its condition, and the land use and land cover (LULC) of an area. Precise knowledge of these factors was not available for the study area, and therefore, a range of values was selected to analyze the sensitivity of the model to the changing CN values. Sensitivity analysis involves a methodological manipulation of model parameters to understand their impacts on model outputs. A range of CN values from 40-90 was selected to determine their effects on model results at the sub-catchment level during the historic flood year of 2010. The model simulated 362 cumecs of peak discharge for CN=90; however, for CN=40, the discharge reduced substantially to 78 cumecs (a 78.46% reduction). Event-based comparison of water volumes for different groups of CN values—90-75, 80-75, 75-70, and 90-40 —showed reductions in water availability of 8.88%, 3.39%, 3.82%, and 41.81%, respectively. Although it is known that the higher the CN, the greater the discharge from direct runoff and the less initial losses, the sensitivity analysis quantifies that impact and determines the amount of associated discharges with changing CN values. The results of the case study suggest that CN is one of the most influential parameters in the simulation of direct runoff. Knowledge of accurate runoff is important in both wet (flood management) and dry periods (water availability). A wide range in the resulting water discharges highlights the importance of precise CN selection. Sensitivity analysis is an essential facet of establishing hydrological models in limited data watersheds. The range of CNs demonstrates an enormous quantitative consequence on direct runoff, the exactness of which is necessary for effective water resource planning and management. The method itself is not novel, but the way it is proposed here can justify investments in determining the accurate CN before initiating mega projects involving rainfall-runoff simulations. Even a small error in CN value may lead to serious consequences. In the current study, the sensitivity analysis challenges the strength of the results of a model in the presence of ambiguity regarding CN value.

scholarly journals Determination of Tank Model Parameters with Four Series Structure in Rokan Watershed

2019 ◽  
Vol 5 (2) ◽  
pp. 85-92
Author(s):  
Manyuk Fauzi ◽  
Yohanna Lilis Handayani ◽  
Annisa Destiany
Keyword(s):  
Water Resources ◽  
Water Availability ◽  
Flow Analysis ◽  
Daily Rainfall ◽  
Surface Flow ◽  
Base Flow ◽  
Low Flow ◽  
Model Parameters ◽  
Tank Model ◽  
Discharge Data

Information about low flow and water availability is one of the important factors in the management of water resources. The Rokan River Basin as one of the water resources in Riau Province is very important to know the condition of its water availability. One conceptual hydrological model for low flow analysis is the Tank Model developed by Sugawara. Data input needed in this research is daily rainfall data at Pasar Tangun Station, climatology data at Rambah Utama station and discharge data at AWLR Pasir Pengaraian. Model parameter search in the calibration stage is by trial and error. Using a 90% confidence interval a range of parameter values for the tank model is obtained, which is special for production store are surface flow 150.81 mm ≤ H1 ≤ 204.75 mm, intermediate flow 156.74 mm ≤ H2 ≤ 194.37 mm, sub base flow 141.24 mm ≤ H3 ≤ 176, 54 mm and base flow 139.43 mm ≤ H4 ≤ 176.12 mm.

scholarly journals Uncertainty and sensitivity analysis of GIS based continuous hydrological modelling

2021 ◽  
Author(s):  
Harry R. Manson
Keyword(s):  
Sensitivity Analysis ◽  
Curve Number ◽  
Monte Carlo Analysis ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Lumped Model ◽  
Daily Average ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

The impact of uncertainty in spatial and a-spatial lumped model parameters for a continuous rainfall-runoff model is evaluated with respect to model prediction. The model uses a modified SCS-Curve Number approach that is loosely coupled with a geographic information system (GIS). The rainfall-runoff model uses daily average inputs and is calibrated using a daily average streamflow record for the study site. A Monte Carlo analysis is used to identify total model uncertainty while sensitivity analysis is applied using both a one-at-a-time (OAT) approach as well as through application of the extended Fourier Amplitude Sensitivity Technique (FAST). Conclusions suggest that the model is highly followed by model inputs and finally the Curve Number. While the model does not indicate a high degree of sensitivity to the Curve Number at present conditions, uncertainties in Curve Number estimation can potentially be the cause of high predictive errors when future development scenarios are evaluated.

scholarly journals Updating the neural network sediment load models using different sensitivity analysis methods: a regional application

2020 ◽  
Vol 22 (3) ◽  
pp. 562-577 ◽  
Author(s):  
Reza Asheghi ◽  
Seyed Abbas Hosseini ◽  
Mojtaba Saneie ◽  
Abbas Abbaszadeh Shahri
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Sediment Load ◽  
Suspended Load ◽  
Model Parameters ◽  
Bed Load ◽  
Sensitivity Indices ◽  
The Neural Network ◽  
Measured Flow ◽  
Artificial Neural Network Ann

Abstract The amount of transported sediment load by streams is a vital but high nonlinear dynamic process in water resources management. In the current paper, two optimum predictive models subjected to artificial neural network (ANN) were developed. The employed inputs were then prioritized using diverse sensitivity analysis (SA) methods to address new updated but more efficient ANN structures. The models were found through the 263 processed datasets of three rivers in Idaho, USA using nine different measured flow and sediment variables (e.g., channel geometry, geomorphology, hydraulic) for a period of 11 years. The used parameters were selected based on the prior knowledge of the conventional analyses in which the effect of suspended load on bed load was also investigated. Analyzed accuracy performances using different criteria exhibited improved predictability in updated models which can lead to an advanced understanding of used parameters. Despite different SA methods being employed in evaluating model parameters, almost similar results were observed and then verified using relevant sensitivity indices. It was demonstrated that the ranked parameters using SA due to covering more uncertainties can be more reliable. Evaluated models using sensitivity indices showed that contribution of suspended load on predicted bed load is not significant.

Multi-Criteria and Time Dependent Sensitivity Analysis of an Event-Oriented and Physically-Based Distributed Sediment and Runoff Model

2021 ◽  
pp. 126268
Author(s):  
Menberu B. Meles ◽  
Dave C. Goodrich ◽  
Hoshin V. Gupta ◽  
I. Shea Burns ◽  
Carl L. Unkrich ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Time Dependent ◽  
Physically Based ◽  
Runoff Model

scholarly journals A Sensitivity Analysis of the SPACSYS Model

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 624
Author(s):  
Yan Shan ◽  
Mingbin Huang ◽  
Paul Harris ◽  
Lianhai Wu
Keyword(s):  
Sensitivity Analysis ◽  
Wheat Yield ◽  
Climatic Conditions ◽  
Water Dynamics ◽  
Ecological Modelling ◽  
Soil Conditions ◽  
Model Parameters ◽  
Nutrient Inputs ◽  
Nutrient Pool ◽  
Wheat Field

A sensitivity analysis is critical for determining the relative importance of model parameters to their influence on the simulated outputs from a process-based model. In this study, a sensitivity analysis for the SPACSYS model, first published in Ecological Modelling (Wu, et al., 2007), was conducted with respect to changes in 61 input parameters and their influence on 27 output variables. Parameter sensitivity was conducted in a ‘one at a time’ manner and objectively assessed through a single statistical diagnostic (normalized root mean square deviation) which ranked parameters according to their influence of each output variable in turn. A winter wheat field experiment provided the case study data. Two sets of weather elements to represent different climatic conditions and four different soil types were specified, where results indicated little influence on these specifications for the identification of the most sensitive parameters. Soil conditions and management were found to affect the ranking of parameter sensitivities more strongly than weather conditions for the selected outputs. Parameters related to drainage were strongly influential for simulations of soil water dynamics, yield and biomass of wheat, runoff, and leaching from soil during individual and consecutive growing years. Wheat yield and biomass simulations were sensitive to the ‘ammonium immobilised fraction’ parameter that related to soil mineralization and immobilisation. Simulations of CO2 release from the soil and soil nutrient pool changes were most sensitive to external nutrient inputs and the process of denitrification, mineralization, and decomposition. This study provides important evidence of which SPACSYS parameters require the most care in their specification. Moving forward, this evidence can help direct efficient sampling and lab analyses for increased accuracy of such parameters. Results provide a useful reference for model users on which parameters are most influential for different simulation goals, which in turn provides better informed decision making for farmers and government policy alike.

scholarly journals Global Sensitivity Analysis of a Homogenized Constrained Mixture Model of Arterial Growth and Remodeling

2021 ◽  
Author(s):  
Sebastian Brandstaeter ◽  
Sebastian L. Fuchs ◽  
Jonas Biehler ◽  
Roland C. Aydin ◽  
Wolfgang A. Wall ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Model Parameters ◽  
Computational Studies ◽  
Growth And Remodeling ◽  
Global Sensitivity ◽  
Output Variability ◽  
Constrained Mixture ◽  
The One ◽  
Influential Parameters

AbstractGrowth and remodeling in arterial tissue have attracted considerable attention over the last decade. Mathematical models have been proposed, and computational studies with these have helped to understand the role of the different model parameters. So far it remains, however, poorly understood how much of the model output variability can be attributed to the individual input parameters and their interactions. To clarify this, we propose herein a global sensitivity analysis, based on Sobol indices, for a homogenized constrained mixture model of aortic growth and remodeling. In two representative examples, we found that 54–80% of the long term output variability resulted from only three model parameters. In our study, the two most influential parameters were the one characterizing the ability of the tissue to increase collagen production under increased stress and the one characterizing the collagen half-life time. The third most influential parameter was the one characterizing the strain-stiffening of collagen under large deformation. Our results suggest that in future computational studies it may - at least in scenarios similar to the ones studied herein - suffice to use population average values for the other parameters. Moreover, our results suggest that developing methods to measure the said three most influential parameters may be an important step towards reliable patient-specific predictions of the enlargement of abdominal aortic aneurysms in clinical practice.

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