scholarly journals Improving Hillslope Link Model Performance from Non-Linear Representation of Natural and Artificially Drained Subsurface Flows

Hydrology ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 187
Author(s):  
Nicolás Velásquez ◽  
Ricardo Mantilla ◽  
Witold Krajewski ◽  
Morgan Fonley ◽  
Felipe Quintero
Keyword(s):  
Model Performance ◽  
Linear Representation ◽  
Tile Drainage ◽  
Diagnostic Approach ◽  
Scale Parameters ◽  
Performance Improvements ◽  
Total Runoff ◽  
Recession Curves ◽  
Non Linear ◽  
Link Model

This study evaluates the potential for a newly proposed non-linear subsurface flux equation to improve the performance of the hydrological Hillslope Link Model (HLM). The equation contains parameters that are functionally related to the hillslope steepness and the presence of tile drainage. As a result, the equation provides better representation of hydrograph recession curves, hydrograph timing, and total runoff volume. The authors explore the new parameterization’s potential by comparing a set of diagnostic and prognostic setups in HLM. In the diagnostic approach, they configure 12 different scenarios with spatially uniform parameters over the state of Iowa. In the prognostic case, they use information from topographical maps and known locations of tile drainage to distribute parameter values. To assess performance improvements, they compare simulation results to streamflow observations during a 17-year period (2002–2018) at 140 U.S. Geological Survey (USGS) gauging stations. The operational setup of the HLM model used at the Iowa Flood Center (IFC) serves as a benchmark to quantify the overall improvement of the model. In particular, the new equation provides better representation of recession curves and the total streamflow volumes. However, when comparing the diagnostic and prognostic setups, the authors found discrepancies in the spatial distribution of hillslope scale parameters. The results suggest that more work is required when using maps of physical attributes to parameterize hydrological models. The findings also demonstrate that the diagnostic approach is a useful strategy to evaluate models and assess changes in their formulations.

Assessment of nominal data requirements for robust estimation of fractional snow cover in alpine-forested terrain

2021 ◽  
Author(s):  
Elzbieta Wisniewski ◽  
Wit Wisniewski
Keyword(s):  
Snow Cover ◽  
Input Data ◽  
Model Performance ◽  
Slope Aspect ◽  
Ann Model ◽  
Linear Modeling ◽  
Nominal Data ◽  
Fractional Snow Cover ◽  
Non Linear ◽  
Input Variables

<p>The presented research examines what minimum combination of input variables are required to obtain state-of-the-art fractional snow cover (FSC) estimates for heterogeneous alpine-forested terrains. Currently, one of the most accurate FSC estimators for alpine regions is based on training an Artificial Neural Network (ANN) that can deconvolve the relationships among numerous compounded and possibly non-linear bio-geophysical relations encountered in alpine terrain. Under the assumption that the ANN optimally extracts available information from its input data, we can exploit the ANN as a tool to assess the contributions toward FSC estimation of each of the data sources, and combinations thereof. By assessing the quality of the modeled FSC estimates versus ground equivalent data, suitable combinations of input variables can be identified. High spatial resolution IKONOS images are used to estimate snow cover for ANN training and validation, and also for error assessment of the ANN FSC results. Input variables are initially chosen representing information already incorporated into leading snow cover estimators (ex. two multispectral bands for NDSI, etc.). Additional variables such as topographic slope, aspect, and shadow distribution are evaluated to observe the ANN as it accounts for illumination incidence and directional reflectance of surfaces affecting the viewed radiance in complex terrain. Snow usually covers vegetation and underlying geology partially, therefore the ANN also has to resolve spectral mixtures of unobscured surfaces surrounded by snow. Multispectral imagery if therefore acquired in the fall prior to the first snow of the season and are included in the ANN analyses for assessing the baseline reflectance values of the environment that later become modified by the snow. In this study, nine representative scenarios of input data are selected to analyze the FSC performance. Numerous selections of input data combinations produced good results attesting to the powerful ability of ANNs to extract information and utilize redundancy. The best ANN FSC model performance was achieved when all 15 pre-selected inputs were used. The need for non-linear modeling to estimate FSC was verified by forcing the ANN to behave linearly. The linear ANN model exhibited profoundly decreased FSC performance, indicating that non-linear processing more optimally estimates FSC in alpine-forested environments.</p>

scholarly journals A Comparison of Continuous and Event-Based Rainfall–Runoff (RR) Modelling Using EPA-SWMM

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 611 ◽  
Author(s):  
Sharif Hossain ◽  
Guna Alankarage Hewa ◽  
Subhashini Wella-Hewage
Keyword(s):  
Goodness Of Fit ◽  
Model Performance ◽  
South Australia ◽  
Comparative Performance ◽  
Direct Runoff ◽  
Continuous Simulation ◽  
Runoff Hydrograph ◽  
Total Runoff ◽  
Event Based ◽  
Runoff Hydrographs

This study investigates the comparative performance of event-based and continuous simulation modelling of a stormwater management model (EPA-SWMM) in calculating total runoff hydrographs and direct runoff hydrographs. Myponga upstream and Scott Creek catchments in South Australia were selected as the case study catchments and model performance was assessed using a total of 36 streamflow events from the period of 2001 to 2004. Goodness-of-fit of the EPA-SWMM models developed using automatic calibration were assessed using eight goodness-of-fit measures including Nash–Sutcliff efficiency (NSE), NSE of daily high flows (ANSE), Kling–Gupta efficiency (KGE), etc. The results of this study suggest that event-based modelling of EPA-SWMM outperforms the continuous simulation approach in producing both total runoff hydrograph (TRH) and direct runoff hydrograph (DRH).

scholarly journals Impacts of Tile Drainage on Phosphorus Losses from Edge-of-Field Plots in the Lake Champlain Basin of New York

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 328 ◽  
Author(s):  
Laura B. Klaiber ◽  
Stephen R. Kramer ◽  
Eric O. Young
Keyword(s):  
New York ◽  
Surface Runoff ◽  
Soluble Reactive Phosphorus ◽  
Tile Drainage ◽  
Total Runoff ◽  
Tile Drains ◽  
Reactive Phosphorus ◽  
Phosphorus Losses ◽  
Field Plots ◽  
Reduced Surface

Quantifying the influence of tile drainage on phosphorus (P) transport risk is important where eutrophication is a concern. The objective of this study was to compare P exports from tile-drained (TD) and undrained (UD) edge-of-field plots in northern New York. Four plots (46 by 23 m) were established with tile drainage and surface runoff collection during 2012–2013. Grass sod was terminated in fall 2013 and corn (Zea mays L.) for silage was grown in 2014 and 2015. Runoff, total phosphorus (TP), soluble reactive phosphorus (SRP), and total suspended solids (TSS) exports were measured from April 2014 through June 2015. Mean total runoff was 396% greater for TD, however, surface runoff for TD was reduced by 84% compared to UD. There was no difference in mean cumulative TP export, while SRP and TSS exports were 55% and 158% greater for UD, respectively. A three day rain/snowmelt event resulted in 61% and 84% of cumulative SRP exports for TD and UD, respectively, with over 100% greater TP, SRP and TSS exports for UD. Results indicate that tile drainage substantially reduced surface runoff, TSS and SRP exports while having no impact on TP exports, suggesting tile drains may not increase the overall P export risk.

scholarly journals Multi-step streamflow forecasting using data-driven non-linear methods in contrasting climate regimes

2013 ◽  
Vol 16 (3) ◽  
pp. 671-689 ◽  
Author(s):  
Daniel J. Karran ◽  
Efrat Morin ◽  
Jan Adamowski
Keyword(s):  
Wavelet Transforms ◽  
Model Performance ◽  
Probability Of Detection ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Non Linear ◽  
Using Data

Considering the popularity of using data-driven non-linear methods for forecasting streamflow, there has been no exploration of how well such models perform in climate regimes with differing hydrological characteristics, nor has the performance of these models, coupled with wavelet transforms, been compared for lead times of less than 1 month. This study compares the use of four different models, namely artificial neural networks (ANNs), support vector regression (SVR), wavelet-ANN, and wavelet-SVR in a Mediterranean, Oceanic, and Hemiboreal watershed. Model performance was tested for 1, 2 and 3 day forecasting lead times, measured by fractional standard error, the coefficient of determination, Nash–Sutcliffe model efficiency, multiplicative bias, probability of detection and false alarm rate. SVR based models performed best overall, but no one model outperformed the others in more than one watershed, suggesting that some models may be more suitable for certain types of data. Overall model performance varied greatly between climate regimes, suggesting that higher persistence and slower hydrological processes (i.e. snowmelt, glacial runoff, and subsurface flow) support reliable forecasting using daily and multi-day lead times.

scholarly journals Psychophysical evidence for a non-linear representation of facial identity

2009 ◽  
Vol 49 (18) ◽  
pp. 2285-2296 ◽  
Author(s):  
Steven C. Dakin ◽  
Diana Omigie
Keyword(s):  
Linear Representation ◽  
Facial Identity ◽  
Non Linear

NIMG-32. THE FEDERATED TUMOR SEGMENTATION (FETS) INITIATIVE: THE FIRST REAL-WORLD LARGE-SCALE DATA-PRIVATE COLLABORATION FOCUSING ON NEURO-ONCOLOGY

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi135-vi136
Author(s):  
Ujjwal Baid ◽  
Sarthak Pati ◽  
Siddhesh Thakur ◽  
Brandon Edwards ◽  
Micah Sheller ◽  
...  
Keyword(s):  
Large Scale ◽  
Model Performance ◽  
Training Data ◽  
Tumor Segmentation ◽  
Consensus Model ◽  
Local Data ◽  
Validation Data ◽  
Performance Improvements ◽  
Brain Extraction ◽  
Model Training

Abstract PURPOSE Robustness and generalizability of artificial intelligent (AI) methods is reliant on the training data size and diversity, which are currently hindered in multi-institutional healthcare collaborations by data ownership and legal concerns. To address these, we introduce the Federated Tumor Segmentation (FeTS) Initiative, as an international consortium using federated learning (FL) for data-private multi-institutional collaborations, where AI models leverage data at participating institutions, without sharing data between them. The initial FeTS use-case focused on detecting brain tumor boundaries in MRI. METHODS The FeTS tool incorporates: 1) MRI pre-processing, including image registration and brain extraction; 2) automatic delineation of tumor sub-regions, by label fusion of pretrained top-performing BraTS methods; 3) tools for manual delineation refinements; 4) model training. 55 international institutions identified local retrospective cohorts of glioblastoma patients. Ground truth was generated using the first 3 FeTS functionality modes as mentioned earlier. Finally, the FL training mode comprises of i) an AI model trained on local data, ii) local model updates shared with an aggregator, which iii) combines updates from all collaborators to generate a consensus model, and iv) circulates the consensus model back to all collaborators for iterative performance improvements. RESULTS The first FeTS consensus model, from 23 institutions with data of 2,200 patients, showed an average improvement of 11.1% in the performance of the model on each collaborator’s validation data, when compared to a model trained on the publicly available BraTS data (n=231). CONCLUSION Our findings support that data increase alone would lead to AI performance improvements without any algorithmic development, hence indicating that the model performance would improve further when trained with all 55 collaborating institutions. FL enables AI model training with knowledge from data of geographically-distinct collaborators, without ever having to share any data, hence overcoming hurdles relating to legal, ownership, and technical concerns of data sharing.

scholarly journals Towards non-linear representation

2011 ◽  
Vol 15 (2) ◽  
pp. 93-95
Author(s):  
Paul Richens
Keyword(s):  
Linear Representation ◽  
Non Linear

scholarly journals The effects of spatial discretization on performances and parameters of urban hydrological model

2019 ◽  
Vol 80 (3) ◽  
pp. 517-528 ◽  
Author(s):  
Qing Chang ◽  
So Kazama ◽  
Yoshiya Touge ◽  
Shunsuke Aita
Keyword(s):  
Spatial Resolution ◽  
Scale Effects ◽  
Model Performance ◽  
Computation Time ◽  
Rainfall Runoff ◽  
Total Runoff ◽  
Sensing Technology ◽  
Impervious Area ◽  
Runoff Modeling ◽  
The Impact

Abstract Selecting a proper spatial resolution for urban rainfall runoff modeling was not a trivial issue because it could affect the model outputs. Recently, the development of remote sensing technology and increasingly available data source had enabled rainfall runoff process to be modeled at detailed and microscales. However, the models with less complexity might have equally good performance with less model establishment and computation time. This study attempted to explore the impact of model spatial resolution on model performance and parameters. Models with different discretization degree were built up on the basis of actual drainage networks, urban parcels and specific land use. The results showed that there was very little difference in the total runoff volumes while peak flows showed obvious scale effects which could be up to 30%. Generally, model calibration could compensate the scale effect. The calibrated models with different resolution showed similar performances. The consideration of effective impervious area (EIA) as a calibration parameter marginally increased performance of the calibration period but also slightly decreased performance in the validation period which indicated the importance of detailed EIA identification.

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