Application of a hydrological-hydraulic modelling cascade in lowlands for investigating water and sediment fluxes in catchment, channel and reach

Abstract This study shows a comprehensive simulation of water and sediment fluxes from the catchment to the reach scale. We describe the application of a modelling cascade in a well researched study catchment through connecting stateof- the-art public domain models in ArcGIS. Three models are used consecutively: (1) the hydrological model SWAT to evaluate water balances, sediment input from fields and tile drains as a function of catchment characteristics; (2) the onedimensional hydraulic model HEC-RAS to depict channel erosion and sedimentation along a 9 km channel onedimensionally; and (3) the two-dimensional hydraulic model AdH for simulating detailed substrate changes in a 230 m long reach section over the course of one year. Model performance for the water fluxes is very good, sediment fluxes and substrate changes are simulated with good agreement to observed data. Improvement of tile drain sediment load, simulation of different substrate deposition events and carrying out data sensitivity tests are suggested as future work. Main advantages that can be deduced from this study are separate representation of field, drain and bank erosion processes; shown adaptability to lowland catchments and transferability to other catchments; usability of the model’s output for habitat assessments.

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Analyzing Compositionality-Sensitivity of NLI Models

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33016867 ◽

2019 ◽

Vol 33 ◽

pp. 6867-6874 ◽

Cited By ~ 1

Author(s):

Yixin Nie ◽

Yicheng Wang ◽

Mohit Bansal

Keyword(s):

Natural Language ◽

High Probability ◽

State Of The Art ◽

Model Performance ◽

Analysis Tool ◽

Bag Of Words ◽

Compositional Semantics ◽

Sensitivity Testing ◽

Performance Loss ◽

Future Work

Success in natural language inference (NLI) should require a model to understand both lexical and compositional semantics. However, through adversarial evaluation, we find that several state-of-the-art models with diverse architectures are over-relying on the former and fail to use the latter. Further, this compositionality unawareness is not reflected via standard evaluation on current datasets. We show that removing RNNs in existing models or shuffling input words during training does not induce large performance loss despite the explicit removal of compositional information. Therefore, we propose a compositionality-sensitivity testing setup that analyzes models on natural examples from existing datasets that cannot be solved via lexical features alone (i.e., on which a bag-of-words model gives a high probability to one wrong label), hence revealing the models’ actual compositionality awareness. We show that this setup not only highlights the limited compositional ability of current NLI models, but also differentiates model performance based on design, e.g., separating shallow bag-of-words models from deeper, linguistically-grounded tree-based models. Our evaluation setup is an important analysis tool: complementing currently existing adversarial and linguistically driven diagnostic evaluations, and exposing opportunities for future work on evaluating models’ compositional understanding.

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Comments on “Vertical Hydraulic Gradient and Run-On Water and Sediment Effects on Erosion Processes and Sediment Regimes”

Soil Science Society of America Journal ◽

10.2136/sssaj2001.653953x ◽

2001 ◽

Vol 65 (3) ◽

pp. 953-956 ◽

Cited By ~ 18

Author(s):

P.I.A. Kinnell

Keyword(s):

Hydraulic Gradient ◽

Erosion Processes ◽

Water And Sediment ◽

Vertical Hydraulic Gradient

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A Seasonally Coherent Calibration (SCC) Model for Postprocessing Numerical Weather Predictions

Monthly Weather Review ◽

10.1175/mwr-d-19-0108.1 ◽

2019 ◽

Vol 147 (10) ◽

pp. 3633-3647 ◽

Cited By ~ 6

Author(s):

Q. J. Wang ◽

Tony Zhao ◽

Qichun Yang ◽

David Robertson

Keyword(s):

Weather Prediction ◽

Model Performance ◽

Statistical Characteristics ◽

Lead Times ◽

Model Parameters ◽

Calibration Model ◽

Numerical Weather ◽

Calibration Models ◽

Short Period ◽

One Year

Abstract Statistical calibration of forecasts from numerical weather prediction (NWP) models aims to produce forecasts that are unbiased, reliable in ensemble spread, and as skillful as possible. We suggest that the calibrated forecasts should also be coherent in climatology, including seasonality, consistent with observations. This is especially important when forecasts approach climatology as forecast skill becomes low, such as at long lead times. However, it is challenging to achieve these aims when data available to establish sophisticated calibration models are limited. Many NWP models have only a short period of archived data, typically one year or less, when they become officially operational. In this paper, we introduce a seasonally coherent calibration (SCC) model for working effectively with limited archived NWP data. Detailed rationale and mathematical formulations are presented. In the development of the model, three issues are resolved. These are 1) constructing a calibration model that is sophisticated enough to allow for seasonal variation in the statistical characteristics of raw forecasts and observations, 2) bringing climatology that is representative of long-term statistics into the calibration model, and 3) reducing the number of model parameters through sensible reparameterization to make the model workable with short NWP dataset. A case study is conducted to examine model assumptions and evaluate model performance. We find that the model assumptions are sound, and the developed SCC model produces well-calibrated forecasts.

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Evaluation of European air quality modelled by CAMx including the volatility basis set scheme

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-10313-2016 ◽

2016 ◽

Vol 16 (16) ◽

pp. 10313-10332 ◽

Cited By ~ 25

Author(s):

Giancarlo Ciarelli ◽

Sebnem Aksoyoglu ◽

Monica Crippa ◽

Jose-Luis Jimenez ◽

Eriko Nemitz ◽

...

Keyword(s):

Air Quality ◽

Measurement Data ◽

Model Performance ◽

Monitoring And Evaluation ◽

Basis Set ◽

Air Quality Model ◽

Quality Model ◽

Aerosol Mass ◽

Sensitivity Tests ◽

Compensation Of Errors

Abstract. Four periods of EMEP (European Monitoring and Evaluation Programme) intensive measurement campaigns (June 2006, January 2007, September–October 2008 and February–March 2009) were modelled using the regional air quality model CAMx with VBS (volatility basis set) approach for the first time in Europe within the framework of the EURODELTA-III model intercomparison exercise. More detailed analysis and sensitivity tests were performed for the period of February–March 2009 and June 2006 to investigate the uncertainties in emissions as well as to improve the modelling of organic aerosol (OA). Model performance for selected gas phase species and PM2.5 was evaluated using the European air quality database AirBase. Sulfur dioxide (SO2) and ozone (O3) were found to be overestimated for all the four periods, with O3 having the largest mean bias during June 2006 and January–February 2007 periods (8.9 pbb and 12.3 ppb mean biases respectively). In contrast, nitrogen dioxide (NO2) and carbon monoxide (CO) were found to be underestimated for all the four periods. CAMx reproduced both total concentrations and monthly variations of PM2.5 for all the four periods with average biases ranging from −2.1 to 1.0 µg m−3. Comparisons with AMS (aerosol mass spectrometer) measurements at different sites in Europe during February–March 2009 showed that in general the model overpredicts the inorganic aerosol fraction and underpredicts the organic one, such that the good agreement for PM2.5 is partly due to compensation of errors. The effect of the choice of VBS scheme on OA was investigated as well. Two sensitivity tests with volatility distributions based on previous chamber and ambient measurements data were performed. For February–March 2009 the chamber case reduced the total OA concentrations by about 42 % on average. In contrast, a test based on ambient measurement data increased OA concentrations by about 42 % for the same period bringing model and observations into better agreement. Comparison with the AMS data at the rural Swiss site Payerne in June 2006 shows no significant improvement in modelled OA concentration. Further sensitivity tests with increased biogenic and anthropogenic emissions suggest that OA in Payerne was affected by changes in emissions from residential heating during the February–March 2009 whereas it was more sensitive to biogenic precursors in June 2006.

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Effect of length of the observed dataset on the calibration of a distributed hydrological model

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-368-305-2015 ◽

2015 ◽

Vol 368 ◽

pp. 305-311 ◽

Cited By ~ 5

Author(s):

X. Cui ◽

W. Sun ◽

J. Teng ◽

H. Song ◽

X. Yao

Keyword(s):

Hydrological Model ◽

Assessment Tool ◽

Swat Model ◽

Model Performance ◽

Hydrological Models ◽

Distributed Hydrological Model ◽

Discharge Data ◽

One Year ◽

Parameter Values ◽

Discharge Measurements

Abstract. Calibration of hydrological models in ungauged basins is now a hot research topic in the field of hydrology. In addition to the traditional method of parameter regionalization, using discontinuous flow observations to calibrate hydrological models has gradually become popular in recent years. In this study, the possibility of using a limited number of river discharge data to calibrate a distributed hydrological model, the Soil and Water Assessment Tool (SWAT), was explored. The influence of the quantity of discharge measurements on model calibration in the upper Heihe Basin was analysed. Calibration using only one year of daily discharge measurements was compared with calibration using three years of discharge data. The results showed that the parameter values derived from calibration using one year’s data could achieve similar model performance with calibration using three years’ data, indicating that there is a possibility of using limited numbers of discharge data to calibrate the SWAT model effectively in poorly gauged basins.

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MODELING LONG-TERM BEACH CHANGE UNDER INTERACTING LONGSHORE AND CROSS-SHORE PROCESSES

Coastal Engineering Proceedings ◽

10.9753/icce.v32.sediment.62 ◽

2011 ◽

Vol 1 (32) ◽

pp. 62

Author(s):

Hans Hanson ◽

Magnus Larson ◽

Nicholas C Kraus

Keyword(s):

New York ◽

Numerical Model ◽

Model Performance ◽

Sand Transport ◽

Shoreline Position ◽

Coupled Equations ◽

Sand Supply ◽

Sensitivity Tests ◽

Improve Reliability

This paper presents mathematical formulations and a new numerical model GenCade that simulates beach and dune change in response to cross-shore processes of dune growth by wind and dune erosion by storms, and by gradients in longshore sand transport that will also alter shoreline position. The berm plays a central role since the potential for sand to be transported to the dune by wind depends on berm width, and sand lost in erosion of the dune during storms will widen the berm. Morphologic equilibrium considerations are introduced to improve reliability of predictions and stability of the non-linear model. An analytical solution is given to illustrate properties of the model under simplified conditions. Sensitivity tests with the numerical solution of the coupled equations demonstrate model performance. Finally, the numerical model is applied to examine the consequences of groin shortening at Westhampton Beach, Long Island, New York, as an alternative for providing a sand supply to the down-drift beach. Results indicate that the sand will be released over several decades as the shoreline and dune move landward in adjustment to the new equilibrium condition with the shortened groins.

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Estimating Real World Performance of a Predictive Model: A Case-Study in Predicting End-of-Life

10.1101/19008821 ◽

2019 ◽

Author(s):

Vincent J Major ◽

Neil Jethani ◽

Yindalon Aphinyanaphongs

Keyword(s):

Experimental Design ◽

End Of Life ◽

Real World ◽

Hospital Admissions ◽

Model Performance ◽

Training Data ◽

Real World Data ◽

Test Set ◽

Subsequent Effect ◽

One Year

AbstractObjectiveThe main criteria for choosing how models are built is the subsequent effect on future (estimated) model performance. In this work, we evaluate the effects of experimental design choices on both estimated and actual model performance.Materials and MethodsFour years of hospital admissions are used to develop a 1 year end-of-life prediction model. Two common methods to select appropriate prediction timepoints (backwards-from-outcome and forwards-from-admission) are introduced and combined with two ways of separating cohorts for training and testing (internal and temporal). Two models are trained in identical conditions, and their performances are compared. Finally, operating thresholds are selected in each test set and applied in a final, ‘real-world’ cohort consisting of one year of admissions.ResultsBackwards-from-outcome cohort selection discards 75% of candidate admissions (n=23,579), whereas forwards-from-admission selection includes many more (n=92,148). Both selection methods produce similar global performances when applied to an internal test set. However, when applied to the temporally defined ‘real-world’ set, forwards-from-admission yields higher areas under the ROC and precision recall curves (88.3 and 56.5% vs. 83.2 and 41.6%).DiscussionA backwards-from-outcome experiment effectively transforms the training data such that it no longer resembles real-world data. This results in optimistic estimates of test set performance, especially at high precision. In contrast, a forwards-from-admission experiment with a temporally separated test set consistently and conservatively estimates real-world performance.ConclusionExperimental design choices impose bias upon selected cohorts. A forwards-from-admission experiment, validated temporally, can conservatively estimate real-world performance.

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Incorporating Robustness to Imaging Physics into Radiomic Feature Selection for Breast Cancer Risk Estimation

Cancers ◽

10.3390/cancers13215497 ◽

2021 ◽

Vol 13 (21) ◽

pp. 5497

Author(s):

Raymond J. Acciavatti ◽

Eric A. Cohen ◽

Omid Haji Maghsoudi ◽

Aimilia Gastounioti ◽

Lauren Pantalone ◽

...

Keyword(s):

Risk Estimation ◽

Characteristic Curve ◽

Model Performance ◽

Image Sharpness ◽

Data Set ◽

Cancer Occurrence ◽

Measure Model ◽

Left And Right ◽

The Difference ◽

One Year

Digital mammography has seen an explosion in the number of radiomic features used for risk-assessment modeling. However, having more features is not necessarily beneficial, as some features may be overly sensitive to imaging physics (contrast, noise, and image sharpness). To measure the effects of imaging physics, we analyzed the feature variation across imaging acquisition settings (kV, mAs) using an anthropomorphic phantom. We also analyzed the intra-woman variation (IWV), a measure of how much a feature varies between breasts with similar parenchymal patterns—a woman’s left and right breasts. From 341 features, we identified “robust” features that minimized the effects of imaging physics and IWV. We also investigated whether robust features offered better case-control classification in an independent data set of 575 images, all with an overall BI-RADS® assessment of 1 (negative) or 2 (benign); 115 images (cases) were of women who developed cancer at least one year after that screening image, matched to 460 controls. We modeled cancer occurrence via logistic regression, using cross‑validated area under the receiver-operating-characteristic curve (AUC) to measure model performance. Models using features from the most-robust quartile of features yielded an AUC = 0.59, versus 0.54 for the least-robust, with p < 0.005 for the difference among the quartiles.

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On the consistency between global and regional methane emissions inferred from SCIAMACHY, TANSO-FTS, IASI and surface measurements

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-13-8023-2013 ◽

2013 ◽

Vol 13 (3) ◽

pp. 8023-8064

Author(s):

C. Cressot ◽

F. Chevallier ◽

P. Bousquet ◽

C. Crevoisier ◽

E. J. Dlugokencky ◽

...

Keyword(s):

Transport Model ◽

Methane Emissions ◽

Error Statistics ◽

Sensitivity Tests ◽

Global Budgets ◽

Satellite Retrievals ◽

Surface Measurements ◽

One Year ◽

The One

Abstract. Satellite retrievals of methane weighted atmospheric columns are studied within a Bayesian inversion system to infer the global and regional methane emissions and sinks. 19-month inversions from June 2009 to December 2010 are independently computed from three different space-borne observing systems under various hypotheses for prior-flux and observation errors. Posterior methane emissions are inter-compared and evaluated with surface mole fraction measurements, via a chemistry-transport model. Sensitivity tests show that refining the assigned error statistics has a larger impact on the quality of the inverted fluxes than correcting for residual airmass-factor-dependent biases in the satellite retrievals. Improved configurations using TANSO-FTS, SCIAMACHY, IASI and surface measurements induce posterior methane global budgets of respectively, 568 ± 17 Tg yr−1, 603 ± 28 yr−1, 524 ± 16 yr−1 and 538 ± 20 yr−1 over the one-year period August 2009–July 2010. This consistency between some of these satellite retrievals and surface measurements is promising for future improvement of CH4 emission estimates by inversions.

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