Sensitivity analysis of SWAT model on changes of landscape pattern: a case study from Lao Guanhe watershed in Danjiangkou Reservoir area

2014 ◽  
Vol 34 (2) ◽  
Author(s):  
魏冲 WEI Chong ◽  
宋轩 SONG Xuan ◽  
陈杰 CHEN Jie
Keyword(s):  
Sensitivity Analysis ◽  
Landscape Pattern ◽  
Swat Model ◽  
Reservoir Area ◽  
Danjiangkou Reservoir

scholarly journals Sensitivity analysis of the DEM resolution and effective parameters of runoff yield in the SWAT model: a case study

2019 ◽  
Vol 69 (1) ◽  
pp. 39-54 ◽  
Author(s):  
Mohammad Nazari-Sharabian ◽  
Masoud Taheriyoun ◽  
Moses Karakouzian
Keyword(s):  
Sensitivity Analysis ◽  
Assessment Tool ◽  
Swat Model ◽  
Computation Time ◽  
Effective Parameters ◽  
Digital Elevation ◽  
Elevation Model ◽  
The Impact ◽  
Reduce Computation Time

Abstract This study investigates the impact of different digital elevation model (DEM) resolutions on the topological attributes and simulated runoff, as well as the sensitivity of runoff parameters in the Mahabad Dam watershed in Iran. The watershed and streamlines were delineated in ArcGIS, and the hydrologic analyses were performed using the Soil and Water Assessment Tool (SWAT). The sensitivity analysis on runoff parameters was performed, using the Sequential Uncertainties FItting Ver. 2 algorithm, in the SWAT Calibration and Uncertainty Procedures (SWAT-CUP) program. The results indicated that the sensitivity of runoff parameters, watershed surface area, and elevations changed under different DEM resolutions. As the distribution of slopes changed using different DEMs, surface parameters were most affected. Furthermore, higher amounts of runoff were generated when DEMs with finer resolutions were implemented. In comparison with the observed value of 8 m3/s at the watershed outlet, the 12.5 m DEM showed more realistic results (6.77 m3/s). Comparatively, the 12.5 m DEM generated 0.74% and 2.73% more runoff compared with the 30 and 90 m DEMs, respectively. The findings of this study indicate that in order to reduce computation time, researchers may use DEMs with coarser resolutions at the expense of minor decreases in accuracy.

scholarly journals A Regional Sensitivity Analysis of a Multi-Variable Hydrological Model: A Case Study of a Greek Catchment

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 9
Author(s):  
Panagiota Venetsanou ◽  
Christina Anagnostopoulou ◽  
Athanasios Loukas ◽  
Konstantinos Voudouris
Keyword(s):  
Sensitivity Analysis ◽  
Wind Speed ◽  
Relative Humidity ◽  
River Basin ◽  
Hydrological Model ◽  
Swat Model ◽  
Future Climate Change ◽  
Hydrological Process ◽  
Climate Data

The importance of climate data in hydrological process simulation is widely recognized. Evaluation of the hydrological budget response to climate variability is required, especially in water resource management. The present paper illustrates a case study of a sensitivity analysis for the hydrological model SWAT (Soil and Water Assessment Tool) using climate data from the Havrias river basin in northern Greece. The ERA-Interim reanalysis daily climate data were used as input data to drive the SWAT model. The SWAT model was calibrated for the period from 1981 to 2000. The sensitivity of the hydrological parameters to the alteration of the climate data was analyzed by using eleven hypothetical scenarios. These scenarios regard different combinations of temperature, wind speed, precipitation, and relative humidity. The results show that the changes of precipitation temperature and relative humidity have a significant influence in evapotranspiration and percolation (and consequently recharge) in the study region. On the contrary, the wind speed negligibly affects the hydrological components. Overall, the Havrias river basin hydrological budget is sensitive to shifts in climate data and the utilization of reliable and accurate climate models outputs is necessary in order for water managers to be able to build scenarios providing sustainability against potential future climate change impacts.

scholarly journals Evaluation of ESV Change under Urban Expansion Based on Ecological Sensitivity: A Case Study of Three Gorges Reservoir Area in China

2021 ◽  
Vol 13 (15) ◽  
pp. 8490
Author(s):  
Hongjie Peng ◽  
Lei Hua ◽  
Xuesong Zhang ◽  
Xuying Yuan ◽  
Jianhao Li
Keyword(s):  
Three Gorges Reservoir ◽  
Urban Expansion ◽  
Urban Land ◽  
Three Gorges Reservoir Area ◽  
Three Gorges ◽  
Ecological Sensitivity ◽  
Reservoir Area ◽  
Green Development ◽  
Source Data

In recent years, ecosystem service values (ESV) have attracted much attention. However, studies that use ecological sensitivity methods as a basis for predicting future urban expansion and thus analyzing spatial-temporal change of ESV are scarce in the region. In this study, we used the CA-Markov model to predict the 2030 urban expansion under ecological sensitivity in the Three Gorges reservoir area based on multi-source data, estimations of ESV from 2000 to 2018 and predictions of ESV losses from 2018 to 2030. Research results: (i) In the concept of green development, the ecological sensitive zone has been identified in Three Gorges reservoir area; it accounts for about 35.86% of the study area. (ii) It is predicted that the 2030 urban land will reach 211,412.51 ha by overlaying the ecological sensitive zone. (iii) The total ESV of Three Gorges Reservoir area showed an increasing trend from 2000 to 2018 with growth values of about USD 3644.26 million, but the ESVs of 16 districts were decreasing, with Dadukou and Jiangbei having the highest reductions. (iv) New urban land increases by 80,026.02 ha from 2018 to 2030. The overall ESV losses are about USD 268.75 million. Jiulongpo, Banan and Shapingba had the highest ESV losses.

scholarly journals Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Markus J. Ankenbrand ◽  
Liliia Shainberg ◽  
Michael Hock ◽  
David Lohr ◽  
Laura M. Schreiber
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiac Mri ◽  
Magnetic Resonance Images ◽  
Model Interpretation ◽  
First Case ◽  
Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

scholarly journals Sensitivity Analysis of Fuzzy Activity-Based Life Cycle Costing

2018 ◽  
Vol 225 ◽  
pp. 05002
Author(s):  
Freselam Mulubrhan ◽  
Ainul Akmar Mokhtar ◽  
Masdi Muhammad
Keyword(s):  
Sensitivity Analysis ◽  
Life Cycle ◽  
Interest Rate ◽  
Life Cycle Costing ◽  
High Impact ◽  
Final Output ◽  
Left And Right ◽  
The Interest Rate ◽  
Operation Cost

A sensitivity analysis is typically conducted to identify how sensitive the output is to changes in the input. In this paper, the use of sensitivity analysis in the fuzzy activity based life cycle costing (LCC) is shown. LCC is the most frequently used economic model for decision making that considers all costs in the life of a system or equipment. The sensitivity analysis is done by varying the interest rate and time 15% and 45%, respectively, to the left and right, and varying 25% of the maintenance and operation cost. It is found that the operation cost and the interest rate give a high impact on the final output of the LCC. A case study of pumps is used in this study.

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