scholarly journals Marching in step: The importance of matching model complexity to data availability in terrestrial biosphere models

2020 ◽  
Vol 26 (6) ◽  
pp. 3190-3192
Author(s):  
Xue Feng
Keyword(s):  
Data Availability ◽  
Model Complexity ◽  
Matching Model ◽  
Terrestrial Biosphere

scholarly journals Development of an Event-Based Water Quality Model for Sparsely Gauged Catchments

2019 ◽  
Vol 11 (6) ◽  
pp. 1773
Author(s):  
Hong Nguyen ◽  
Gunter Meon ◽  
Van Nguyen
Keyword(s):  
Water Quality ◽  
Nutrient Dynamics ◽  
Water Quality Model ◽  
Data Availability ◽  
Model Complexity ◽  
Quality Model ◽  
Small Catchment ◽  
Modeling Systems ◽  
Model Components ◽  
Event Based

This paper describes an event-based water quality model for sparsely gauged catchments. The model was cultivated in a robust way to cope with practical issues, such as limited available data and error propagation. A simplified model structure and fewer input parameters are the most appealing features of this model. All model components are coupled and controlled within an Excel Spreadsheet Macro as an operational tool. Herein, the geomorphological instantaneous unit hydrograph (GIUH), the simplified process erosion and sedimentation component, the loading function, and the river routing from different existing modeling systems are adopted and linked together. Furthermore, an add-on Monte Carlo simulation tool is provided to deliver an uncertainty analysis for calibration of the output obtained from the model results. The model was successfully applied to simulate nutrient dynamics for small catchment scales during flood events in Vietnam. The success of the model application shows the ability of our model, which can adapt the model complexity to the data availability, i.e., the dominant processes in the system should be captured, whereas the minor processes may be neglected or treated in a less complex manner.

scholarly journals Process-based modelling to evaluate simulated groundwater levels and frequencies in a Chalk catchment in south-western England

2018 ◽  
Vol 18 (2) ◽  
pp. 445-461 ◽  
Author(s):  
Simon Brenner ◽  
Gemma Coxon ◽  
Nicholas J. K. Howden ◽  
Jim Freer ◽  
Andreas Hartmann
Keyword(s):  
Groundwater Level ◽  
Climate Changes ◽  
Data Availability ◽  
Model Complexity ◽  
High Porosity ◽  
Groundwater Levels ◽  
Subsurface Heterogeneity ◽  
Split Sample ◽  
Sample Test ◽  
Simulated Groundwater

Abstract. Chalk aquifers are an important source of drinking water in the UK. Due to their properties, they are particularly vulnerable to groundwater-related hazards like floods and droughts. Understanding and predicting groundwater levels is therefore important for effective and safe water management. Chalk is known for its high porosity and, due to its dissolvability, exposed to karstification and strong subsurface heterogeneity. To cope with the karstic heterogeneity and limited data availability, specialised modelling approaches are required that balance model complexity and data availability. In this study, we present a novel approach to evaluate simulated groundwater level frequencies derived from a semi-distributed karst model that represents subsurface heterogeneity by distribution functions. Simulated groundwater storages are transferred into groundwater levels using evidence from different observations wells. Using a percentile approach we can assess the number of days exceeding or falling below selected groundwater level percentiles. Firstly, we evaluate the performance of the model when simulating groundwater level time series using a spilt sample test and parameter identifiability analysis. Secondly, we apply a split sample test to the simulated groundwater level percentiles to explore the performance in predicting groundwater level exceedances. We show that the model provides robust simulations of discharge and groundwater levels at three observation wells at a test site in a chalk-dominated catchment in south-western England. The second split sample test also indicates that the percentile approach is able to reliably predict groundwater level exceedances across all considered timescales up to their 75th percentile. However, when looking at the 90th percentile, it only provides acceptable predictions for long time periods and it fails when the 95th percentile of groundwater exceedance levels is considered. By modifying the historic forcings of our model according to expected future climate changes, we create simple climate scenarios and we show that the projected climate changes may lead to generally lower groundwater levels and a reduction of exceedances of high groundwater level percentiles.

THE EFFECT OF DATA AVAILABILITY AND MODEL COMPLEXITY ON BUXAHATCHEE CREEK TMDL

2003 ◽  
Vol 2003 (4) ◽  
pp. 393-423
Author(s):  
Jim Palumbo ◽  
Van Maltby ◽  
Rick Oates ◽  
David Dilks
Keyword(s):  
Data Availability ◽  
Model Complexity

scholarly journals A percentile approach to evaluate simulated groundwater levels and frequencies in a Chalk catchment in Southwest England

2016 ◽  
Author(s):  
Simon Brenner ◽  
Gemma Coxon ◽  
Nicholas J. K. Howden ◽  
Jim Freer ◽  
Andreas Hartmann
Keyword(s):  
Groundwater Level ◽  
Climate Changes ◽  
Data Availability ◽  
Model Complexity ◽  
High Porosity ◽  
Groundwater Levels ◽  
Subsurface Heterogeneity ◽  
Split Sample ◽  
Sample Test ◽  
Simulated Groundwater

Abstract. Chalk aquifers are an important source of drinking water in the UK. Understanding and predicting groundwater levels is therefore important for effective water management of this resource. Chalk is known for its high porosity and, due to its dissolvability, exposed to karstification and strong subsurface heterogeneity. To cope with the karstic heterogeneity and limited data availability, specialised modelling approaches are required that balance model complexity and data availability. In this study we present a novel approach to simulate groundwater level frequency distributions with a semi-distributed karst model that represents subsurface heterogeneity by distribution functions. Simulated groundwater storages are transferred into groundwater levels using evidence from different observations wells. Using a newly developed percentile approach we can simulate the number of days exceeding or falling below selected groundwater level percentiles. Firstly, we evaluate the performance of the model to simulate three groundwater time series by a spilt sample test and parameter identifiability analysis. Secondly, we apply a split sample test on the simulated groundwater level percentiles to explore the performance in predicting groundwater level exceedances. We show that the model provides robust simulations of discharge and groundwater levels at 3 observation wells at a test site in chalk dominated catchment in Southwest England. The second split sample test also indicates that percentile approach is able to reliably predict groundwater level exceedances across all considered time scales up to their 75th percentile. However, when looking at the 90th percentile, it only provides acceptable predictions for the longest available time scale and it fails when the 95th percentile of groundwater exceedance levels is considered. Modifying the historic forcings of our model according to expected future climate changes, we create simple climate scenarios and we show that the projected climate changes may lead to generally lower groundwater levels and a reduction of exceedances of high groundwater level percentiles.

Data availability and model complexity, generality, and utility: a reply to Lonergan

2014 ◽  
Vol 29 (6) ◽  
pp. 302-303 ◽  
Author(s):  
Matthew R. Evans ◽  
Tim G. Benton ◽  
Volker Grimm ◽  
Catherine M. Lessells ◽  
Maureen A. O’Malley ◽  
...  
Keyword(s):  
Data Availability ◽  
Model Complexity

scholarly journals Modeling public opinion over time: A simulation study of latent trend models

2021 ◽  
Author(s):  
Marta Kolczynska ◽  
Paul - Christian Bürkner
Keyword(s):  
Public Opinion ◽  
High Frequency ◽  
Simulated Data ◽  
Data Availability ◽  
Autoregressive Models ◽  
Model Complexity ◽  
Accurate Estimation ◽  
Social Scientists ◽  
Trend Model ◽  
Low Amplitude

Analyzing trends in public opinion is important for monitoring social change and for testing theories aimed at explaining this change. With growing availability of multi-wave surveys, social scientists are increasingly turning to latent trend models applied to survey data for examining changes in social and political attitudes. With the aim of facilitating this research, our study compares different approaches to modeling latent trends of aggregate public opinion: splines, Gaussian processes, and discrete autoregressive models. We examine the ability of these models to recover latent trends with simulated data that vary with regard to the frequency and magnitude of changes in the true trend, model complexity and data availability. Overall, we find that all three latent trend models perform well in all scenarios, even the most difficult ones with frequent and weak changes of the latent trend and sparse data. The two main performance differences we find include the relatively higher squared errors of autoregressive models compared to the other models, and the under-coverage of posterior intervals in high-frequency low-amplitude trends with splines. For all models and across all scenarios performance improves with increased data availability, which emphasizes the need of supplying sufficient data for accurate estimation of latent trends.

Sign in / Sign up

Export Citation Format

Share Document