Application of Energy Concepts to Groundwater Flow: Time Step Control and Integrated Sensitivity Analysis

1991 ◽  
Vol 27 (12) ◽  
pp. 3225-3235 ◽  
Author(s):  
Bryan W. Karney ◽  
Asitha Seneviratne
Keyword(s):  
Sensitivity Analysis ◽  
Groundwater Flow ◽  
Flow Time ◽  
Time Step ◽  
Step Control ◽  
Integrated Sensitivity

scholarly journals The Dependence of Water Consumption on the Pressure Conditions and Sensitivity Analysis of the Input Parameters

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 592 ◽  
Author(s):  
Ladislav Tuhovcak ◽  
Tomas Suchacek ◽  
Jan Rucka
Keyword(s):  
Sensitivity Analysis ◽  
Water Consumption ◽  
Office Building ◽  
Time Step ◽  
Input Parameters ◽  
Water Meter

The paper presents results and sensitivity analysis of the results of a real detailed study focused on changes in water consumption and its unevenness with changing pressure conditions in a particular observed office building. The dependence of water consumption on pressure is expressed using the FAVAD equation using the N3 coefficient. Parameters for sensitivity analysis are number of workers in the building, pulse value from water meter and length of time step for expressing unevenness of water consumption during the day.

scholarly journals Technical note: Analytical sensitivity analysis and uncertainty estimation of baseflow index calculated by a two-component hydrograph separation method with conductivity as a tracer

2019 ◽  
Vol 23 (2) ◽  
pp. 1103-1112 ◽  
Author(s):  
Weifei Yang ◽  
Changlai Xiao ◽  
Xiujuan Liang
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Measurement Errors ◽  
Uncertainty Estimation ◽  
Separation Method ◽  
Analytical Sensitivity ◽  
Hydrograph Separation ◽  
Time Step ◽  
Sensitivity Indices ◽  
Two Component

Abstract. The two-component hydrograph separation method with conductivity as a tracer is favored by hydrologists owing to its low cost and easy application. This study analyzes the sensitivity of the baseflow index (BFI, long-term ratio of baseflow to streamflow) calculated using this method to errors or uncertainties in two parameters (BFC, the conductivity of baseflow, and ROC, the conductivity of surface runoff) and two variables (yk, streamflow, and SCk, specific conductance of streamflow, where k is the time step) and then estimates the uncertainty in BFI. The analysis shows that for time series longer than 365 days, random measurement errors in yk or SCk will cancel each other out, and their influence on BFI can be neglected. An uncertainty estimation method of BFI is derived on the basis of the sensitivity analysis. Representative sensitivity indices (the ratio of the relative error in BFI to that of BFC or ROC) and BFI′ uncertainties are determined by applying the resulting equations to 24 watersheds in the US. These dimensionless sensitivity indices can well express the propagation of errors or uncertainties in BFC or ROC into BFI. The results indicate that BFI is more sensitive to BFC, and the conductivity two-component hydrograph separation method may be more suitable for the long time series in a small watershed. When the mutual offset of the measurement errors in conductivity and streamflow is considered, the uncertainty in BFI is reduced by half.

scholarly journals Integrated sensitivity analysis of a macroscale hydrologic model in the north of the Iberian Peninsula

2020 ◽  
Vol 590 ◽  
pp. 125230
Author(s):  
Patricio Yeste ◽  
Matilde García-Valdecasas Ojeda ◽  
Sonia R. Gámiz-Fortis ◽  
Yolanda Castro-Díez ◽  
María Jesús Esteban-Parra
Keyword(s):  
Sensitivity Analysis ◽  
Iberian Peninsula ◽  
Hydrologic Model ◽  
The North ◽  
Integrated Sensitivity

High-resolution pan-European multi-model simulations of hydrologic states and fluxes

2020 ◽  
Author(s):  
Bibi S Naz ◽  
Wendy Sharples ◽  
Klaus Goergen ◽  
Stefan Kollet
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Groundwater Flow ◽  
Hydrologic Model ◽  
Hydrological Processes ◽  
Model Parameters ◽  
Time Step ◽  
European Continent ◽  
Model Simulations ◽  
Community Land Model

<p> <span>High-resolution large-scale predictions of hydrologic states and fluxes are important for many regional-scale applications and water resource management. However, because of uncertainties related to forcing data, model structural errors arising from simplified representations of hydrological processes or uncertain model parameters, model simulations remain uncertain. To quantify this uncertainty, multi-model simulations were performed at 3km resolution over the European continent using the Community Land Model (CLM3.5) and the ParFlow hydrologic model. While Parflow uses a similar approach as CLM in simulating the snow, vegetation and land-atmosphere exchange processes, it simulates three-dimensional variably saturated groundwater flow solving Richards equation and overland flow with a two-dimensional kinematic wave approximation. </span><span>The </span><span>CLM</span><span>3.5</span><span> uses a simple groundwater model to account for groundwater recharge and discharge processes. Both models were driven with the COSMO-REA6 reanalysis dataset at 6km resolution for the time period from 2000 to 2006 at an hourly time step, and both used the same datasets for the static input variables (such as topography, vegetation and soil properties). The performance of both models was analyzed through comparisons with independent observations including satellite-derived and in-situ soil moisture, evapotranspiration, river discharge, water table depth and total water storage datasets. Overall, both models capture the interannual variability in the hydrologic states and fluxes well, however differences in performance between models showed the uncertainty associated with the representation of hydrological processes, such as groundwater flow and soil moisture and its control on latent and sensible heat fluxes at the surface.</span></p>

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