How to improve the representation of hydrological processes in SWAT for a lowland catchment - temporal analysis of parameter sensitivity and model performance

2013 ◽  
Vol 28 (4) ◽  
pp. 2651-2670 ◽  
Author(s):  
Björn Guse ◽  
Dominik E. Reusser ◽  
Nicola Fohrer
Keyword(s):  
Model Performance ◽  
Temporal Analysis ◽  
Parameter Sensitivity ◽  
Hydrological Processes

Evaluating a Sewershed Urban Storm Water Model for Variability in Parameter Sensitivity and Resolution Effects

2020 ◽  
Author(s):  
Zhaokai Dong ◽  
Daniel Bain ◽  
Murat Akcakaya ◽  
Carla Ng
Keyword(s):  
Pipe Flow ◽  
Peak Flow ◽  
Model Performance ◽  
Parameter Sensitivity ◽  
Geometric Parameters ◽  
Storm Water ◽  
Parameter Estimates ◽  
Kappa Statistics ◽  
Thiessen Polygon ◽  
Urban Storm

A high-quality parameter set is essential for reliable stormwater models. Model performance can be improved by optimizing initial parameter estimates. Parameter sensitivity analysis is a robust way to distinguish the influence of parameters on model output and efficiently target the most important parameters to modify. This study evaluates efficient construction of a sewershed model using relatively low-resolution (e.g., 30 meter DEM) data and explores model sensitivity to parameters and regional characteristics using the EPA’s Storm Water Management Model (SWMM). A SWMM model was developed for a sewershed in the City of Pittsburgh, where stormwater management is a critical concern. We assumed uniform or log-normal distributions for parameters and used Monte Carlo simulations to explore and rank the influence of parameters on predicted surface runoff, peak flow, maximum pipe flow and model performance, as measured using the Nash–Sutcliffe efficiency metric. By using the Thiessen polygon approach for sub-catchment delineations, we substantially simplified the parameterization of the areas and hydraulic parameters. Despite this simplification, our approach provided good agreement with monitored pipe flow (Nash–Sutcliffe efficiency: 0.41 – 0.85). Total runoff and peak flow were very sensitive to the model discretization. The size of the polygons (modeled subcatchment areas) and imperviousness had the most influence on both outputs. The imperviousness, infiltration and Manning’s roughness (in the pervious area) contributed strongly to the Nash-Sutcliffe efficiency (70%), as did pipe geometric parameters (92%). Parameter rank sets were compared by using kappa statistics between any two model elements to identify generalities. Within our relatively large (9.7 km^2) sewershed, optimizing parameters for the highly impervious (>50%) areas and larger pipes lower in the network contributed most to improving Nash–Sutcliffe efficiency. The geometric parameters influence the water quantity distribution and flow conveyance, while imperviousness determines the subcatchment subdivision and influences surface water generation. Application of the Thiessen polygon approach can simplify the construction of large-scale urban storm water models, but the model is sensitive to the sewer network configuration and care must be taken in parameterizing areas (polygons) with heterogenous land uses.

scholarly journals Analyzing catchment behavior through catchment modeling in the Gilgel Abay, Upper Blue Nile River Basin, Ethiopia

2010 ◽  
Vol 14 (10) ◽  
pp. 2153-2165 ◽  
Author(s):  
S. Uhlenbrook ◽  
Y. Mohamed ◽  
A. S. Gragne
Keyword(s):  
Water Resources ◽  
Model Performance ◽  
Computation Time ◽  
Hydrological Processes ◽  
Runoff Generation ◽  
Time Step ◽  
Blue Nile ◽  
Model Transferability ◽  
Catchment Modeling ◽  
Data Limitations

Abstract. Understanding catchment hydrological processes is essential for water resources management, in particular in data scarce regions. The Gilgel Abay catchment (a major tributary into Lake Tana, source of the Blue Nile) is undergoing intensive plans for water management, which is part of larger development plans in the Blue Nile basin in Ethiopia. To obtain a better understanding of the water balance dynamics and runoff generation mechanisms and to evaluate model transferability, catchment modeling has been conducted using the conceptual hydrological model HBV. Accordingly, the catchment of the Gilgel Abay has been divided into two gauged sub-catchments (Upper Gilgel Abay and Koga) and the un-gauged part of the catchment. All available data sets were tested for stationarity, consistency and homogeneity and the data limitations (quality and quantity) are discussed. Manual calibration of the daily models for three different catchment representations, i.e. (i) lumped, (ii) lumped with multiple vegetation zones, and (iii) semi-distributed with multiple vegetation and elevation zones, showed good to satisfactory model performances with Nash-Sutcliffe efficiencies Reff > 0.75 and > 0.6 for the Upper Gilgel Abay and Koga sub-catchments, respectively. Better model results could not be obtained with manual calibration, very likely due to the limited data quality and model insufficiencies. Increasing the computation time step to 15 and 30 days improved the model performance in both sub-catchments to Reff > 0.8. Model parameter transferability tests have been conducted by interchanging parameters sets between the two gauged sub-catchments. Results showed poor performances for the daily models (0.30 < Reff < 0.67), but better performances for the 15 and 30 days models, Reff > 0.80. The transferability tests together with a sensitivity analysis using Monte Carlo simulations (more than 1 million model runs per catchment representation) explained the different hydrologic responses of the two sub-catchments, which seems to be mainly caused by the presence of dambos in Koga sub-catchment. It is concluded that daily model transferability is not feasible, while it can produce acceptable results for the 15 and 30 days models. This is very useful for water resources planning and management, but not sufficient to capture detailed hydrological processes in an ungauged area.

scholarly journals Four-dimensional variational (4DVAR) performance test with assimilation satellite and radar data (case study of a heavy rainfall Bengkulu March 4, 2019)

2021 ◽  
Vol 893 (1) ◽  
pp. 012031
Author(s):  
D I Syafitri J ◽  
F P Sari
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Performance Test ◽  
Time Window ◽  
Time Integration ◽  
Model Performance ◽  
Temporal Analysis ◽  
Radar Data ◽  
Initial Time ◽  
Statistical Verification

Abstract Four-dimensional variational (4DVAR) is one of the assimilation techniques considering time integration to distribute observational data at time window intervals. In this study, we aim to evaluate the 4DVAR assimilation technique using satellite and radar data to simulate a heavy rainfall case in Bengkulu on March 4, 2019. The result shows that radar data assimilation (DA-RAD) can improve rainfall pattern over Bengkulu mainland areas, while the satellite data assimilation (DA-SAT) enhances rainfall over the ocean. In addition, for temporal analysis, the DA-RAD successfully correct the initial time of the event, producing the smallest error and the best correlation in statistical verification, also a small bias and higher accuracy for discrete verification. However, DA-SAT is more capable to improve rainfall accumulation with the lowest FAR value. In conclusion, compared to others, both satellite and radar can be used as assimilation data for 4DVAR methods as they have different roles in increasing the quality of model performance.

scholarly journals Performance evaluation of global hydrological models in six large Pan-Arctic watersheds

2020 ◽  
Vol 163 (3) ◽  
pp. 1329-1351 ◽  
Author(s):  
Anne Gädeke ◽  
Valentina Krysanova ◽  
Aashutosh Aryal ◽  
Jinfeng Chang ◽  
Manolis Grillakis ◽  
...  
Keyword(s):  
Climate Change ◽  
Performance Evaluation ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Explanatory Power ◽  
Model Performance ◽  
Climate Change Impacts ◽  
Hydrological Processes ◽  
Climate Data ◽  
Seasonal Discharge

AbstractGlobal Water Models (GWMs), which include Global Hydrological, Land Surface, and Dynamic Global Vegetation Models, present valuable tools for quantifying climate change impacts on hydrological processes in the data scarce high latitudes. Here we performed a systematic model performance evaluation in six major Pan-Arctic watersheds for different hydrological indicators (monthly and seasonal discharge, extremes, trends (or lack of), and snow water equivalent (SWE)) via a novel Aggregated Performance Index (API) that is based on commonly used statistical evaluation metrics. The machine learning Boruta feature selection algorithm was used to evaluate the explanatory power of the API attributes. Our results show that the majority of the nine GWMs included in the study exhibit considerable difficulties in realistically representing Pan-Arctic hydrological processes. Average APIdischarge (monthly and seasonal discharge) over nine GWMs is > 50% only in the Kolyma basin (55%), as low as 30% in the Yukon basin and averaged over all watersheds APIdischarge is 43%. WATERGAP2 and MATSIRO present the highest (APIdischarge > 55%) while ORCHIDEE and JULES-W1 the lowest (APIdischarge ≤ 25%) performing GWMs over all watersheds. For the high and low flows, average APIextreme is 35% and 26%, respectively, and over six GWMs APISWE is 57%. The Boruta algorithm suggests that using different observation-based climate data sets does not influence the total score of the APIs in all watersheds. Ultimately, only satisfactory to good performing GWMs that effectively represent cold-region hydrological processes (including snow-related processes, permafrost) should be included in multi-model climate change impact assessments in Pan-Arctic watersheds.

scholarly journals Parameter sensitivity and uncertainty analysis for a storm surge and wave model

2016 ◽  
Vol 16 (10) ◽  
pp. 2195-2210 ◽  
Author(s):  
Luis A. Bastidas ◽  
James Knighton ◽  
Shaun W. Kline
Keyword(s):  
Numerical Models ◽  
Model Performance ◽  
Wave Model ◽  
Parameter Sensitivity ◽  
Sensitivity Threshold ◽  
Model Parameters ◽  
Hurricane Wind ◽  
Output Uncertainty ◽  
Model Response ◽  
Wave Models

Abstract. Development and simulation of synthetic hurricane tracks is a common methodology used to estimate hurricane hazards in the absence of empirical coastal surge and wave observations. Such methods typically rely on numerical models to translate stochastically generated hurricane wind and pressure forcing into coastal surge and wave estimates. The model output uncertainty associated with selection of appropriate model parameters must therefore be addressed. The computational overburden of probabilistic surge hazard estimates is exacerbated by the high dimensionality of numerical surge and wave models. We present a model parameter sensitivity analysis of the Delft3D model for the simulation of hazards posed by Hurricane Bob (1991) utilizing three theoretical wind distributions (NWS23, modified Rankine, and Holland). The sensitive model parameters (of 11 total considered) include wind drag, the depth-induced breaking γB, and the bottom roughness. Several parameters show no sensitivity (threshold depth, eddy viscosity, wave triad parameters, and depth-induced breaking αB) and can therefore be excluded to reduce the computational overburden of probabilistic surge hazard estimates. The sensitive model parameters also demonstrate a large number of interactions between parameters and a nonlinear model response. While model outputs showed sensitivity to several parameters, the ability of these parameters to act as tuning parameters for calibration is somewhat limited as proper model calibration is strongly reliant on accurate wind and pressure forcing data. A comparison of the model performance with forcings from the different wind models is also presented.

scholarly journals Parameter sensitivity and uncertainty analysis for a storm surge and wave model

2015 ◽  
Vol 3 (10) ◽  
pp. 6491-6534 ◽  
Author(s):  
L. A. Bastidas ◽  
J. Knighton ◽  
S. W. Kline
Keyword(s):  
Numerical Models ◽  
Model Performance ◽  
Wave Model ◽  
Parameter Sensitivity ◽  
Sensitivity Threshold ◽  
Model Parameters ◽  
Hurricane Wind ◽  
Output Uncertainty ◽  
Model Response ◽  
Wave Models

Abstract. Development and simulation of synthetic hurricane tracks is a common methodology used to estimate hurricane hazards in the absence of empirical coastal surge and wave observations. Such methods typically rely on numerical models to translate stochastically generated hurricane wind and pressure forcing into coastal surge and wave estimates. The model output uncertainty associated with selection of appropriate model parameters must therefore be addressed. The computational overburden of probabilistic surge hazard estimates is exacerbated by the high dimensionality of numerical surge and wave models. We present a model parameter sensitivity analysis of the Delft3D model for the simulation of hazards posed by Hurricane Bob (1991) utilizing three theoretical wind distributions (NWS23, modified Rankine, and Holland). The sensitive model parameters (of eleven total considered) include wind drag, the depth-induced breaking γB, and the bottom roughness. Several parameters show no sensitivity (threshold depth, eddy viscosity, wave triad parameters and depth-induced breaking αB) and can therefore be excluded to reduce the computational overburden of probabilistic surge hazard estimates. The sensitive model parameters also demonstrate a large amount of interactions between parameters and a non-linear model response. While model outputs showed sensitivity to several parameters, the ability of these parameters to act as tuning parameters for calibration is somewhat limited as proper model calibration is strongly reliant on accurate wind and pressure forcing data. A comparison of the model performance with forcings from the different wind models is also presented.

scholarly journals Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea

2017 ◽  
Vol 18 (1) ◽  
pp. 22 ◽  
Author(s):  
D. KASSIS ◽  
G. KORRES ◽  
A. KONSTANTINIDOU ◽  
L. PERIVOLIOTIS
Keyword(s):  
High Resolution ◽  
Hydrodynamic Model ◽  
Numerical Models ◽  
Regional Scale ◽  
Model Performance ◽  
Temporal Analysis ◽  
In Situ Monitoring ◽  
Ionian Sea ◽  
Nested Models

In-situ monitoring is an essential component for the development of hydrodynamic numerical models. Argo expansion into marginal seas has enabled the advancement of high resolution regional nested models through initialization, assimilation and validation processes. The SANI (Southern Adriatic-Northern Ionian) hydrodynamic model is a regional nested model producing high resolution outputs for the period 2008-2012. For the corresponding time period, 21 free drifting Argo floats recorded Temperature –Salinity (T/S) profiles throughout the region. This study presents the inter-comparison of the two data sets whilst noting interesting aspects of the model performance regarding the representation of the major water masses characteristics of the SANI area. Aside from the inter-comparison in a basin’s scale, a spatio-temporal analysis is also performed. The results indicate an adequate response of the model simulations regarding the basic hydrographic features of the region. Nevertheless, important differences are highlighted mainly in the upper and deep layers of the study area. At a regional scale, inter-annual variability of the model’s performance is observed reflecting the hydrographic changes occurred in the wider area during the study period. Overall, the results present the strong points but also highlight the weaknesses of the model. They also confirm the challenging task of producing high resolution numerical simulations in transitional areas such as the Ionian Sea.

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