The search for orthogonal hydrological modelling metrics: a case study of 20 monitoring stations in Colombia

Domínguez, Encarnación Sueiro; Dawson, Christian W.; Ramírez, A; Abrahart, Robert J.

doi:10.2166/hydro.2010.116

Cited by 23 publications

(10 citation statements)

References 14 publications

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“…The varimax method rotates the significant axes of resulting factors orthogonally in order to force the loadings of the original components of each factor to be either as large as possible, or near zero [31]. This procedure has the advantage of simplifying the interpretation of the resulting factors, and has been utilized in research related to soil, water and meteorology [32], [33]. The factor analysis was run in SPSS v17 (IBM Corp., New York).…”

Section: Methodsmentioning

confidence: 99%

Soil CO2 Dynamics in a Tree Island Soil of the Pantanal: The Role of Soil Water Potential

et al. 2013

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The Pantanal is a biodiversity hotspot comprised of a mosaic of landforms that differ in vegetative assemblages and flooding dynamics. Tree islands provide refuge for terrestrial fauna during the flooding period and are particularly important to the regional ecosystem structure. Little soil CO2 research has been conducted in this region. We evaluated soil CO2 dynamics in relation to primary controlling environmental parameters (soil temperature and soil water). Soil respiration was computed using the gradient method using in situ infrared gas analyzers to directly measure CO2 concentration within the soil profile. Due to the cost of the sensors and associated equipment, this study was unreplicated. Rather, we focus on the temporal relationships between soil CO2 efflux and related environmental parameters. Soil CO2 efflux during the study averaged 3.53 µmol CO2 m−2 s−1, and was equivalent to an annual soil respiration of 1220 g C m−2 y−1. This efflux value, integrated over a year, is comparable to soil C stocks for 0–20 cm. Soil water potential was the measured parameter most strongly associated with soil CO2 concentrations, with high CO2 values observed only once soil water potential at the 10 cm depth approached zero. This relationship was exhibited across a spectrum of timescales and was found to be significant at a daily timescale across all seasons using conditional nonparametric spectral Granger causality analysis. Hydrology plays a significant role in controlling CO2 efflux from the tree island soil, with soil CO2 dynamics differing by wetting mechanism. During the wet-up period, direct precipitation infiltrates soil from above and results in pulses of CO2 efflux from soil. The annual flood arrives later, and saturates soil from below. While CO2 concentrations in soil grew very high under both wetting mechanisms, the change in soil CO2 efflux was only significant when soils were wet from above.

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Section: Methodsmentioning

confidence: 99%

Soil CO2 Dynamics in a Tree Island Soil of the Pantanal: The Role of Soil Water Potential

et al. 2013

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“…To assess the forecasts' efficiency against the persistence forecast, we used a normalized error skill score, common to operational forecasts' verification in Russia (Borsch and Simonov, 2016), also referred to as "Inertial RMSE" or IRMSE [30], calculated as…”

Section: Model Training and Testing Methodologymentioning

confidence: 99%

How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting

Moreido

Гарцман

Solomatine

et al. 2021

Water

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With more machine learning methods being involved in social and environmental research activities, we are addressing the role of available information for model training in model performance. We tested the abilities of several machine learning models for short-term hydrological forecasting by inferring linkages with all available predictors or only with those pre-selected by a hydrologist. The models used in this study were multivariate linear regression, the M5 model tree, multilayer perceptron (MLP) artificial neural network, and the long short-term memory (LSTM) model. We used two river catchments in contrasting runoff generation conditions to try to infer the ability of different model structures to automatically select the best predictor set from all those available in the dataset and compared models’ performance with that of a model operating on predictors prescribed by a hydrologist. Additionally, we tested how shuffling of the initial dataset improved model performance. We can conclude that in rainfall-driven catchments, the models performed generally better on a dataset prescribed by a hydrologist, while in mixed-snowmelt and baseflow-driven catchments, the automatic selection of predictors was preferable.

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“…However, it is difficult to determine exact model performance since analyzed statistical criteria provide different performance ratings from very good to fair performance depending on the selected criteria. Therefore, the integrated criteria of model performance, such as the ideal point error (IPE) metric [59][60][61][62] or the standardized ranking performance index (sRPI) [63] could be additional measures to evaluate more robust model performance in a future study. Our simulation model results show reliable model simulation performance based on the evaluation criteria.…”

Section: Hspf Model Performancementioning

confidence: 99%

Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study

Kim

Ryu

2019

Water

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The research features how parallel computing can advance hydrological performances associated with different calibration schemes (SCOs). The result shows that parallel computing can save up to 90% execution time, while achieving 81% simulation improvement. Basic statistics, including (1) index of agreement (D), (2) coefficient of determination (R2), (3) root mean square error (RMSE), and (4) percentage of bias (PBIAS) are used to evaluate simulation performances after model calibration in computer parallelism. Once the best calibration scheme is selected, additional efforts are made to improve model performances at the selected calibration target points, while the Rescaled Adjusted Partial Sums (RAPS) is used to evaluate the trend in annual streamflow. The qualitative result of reducing execution time by 86% on average indicates that parallel computing is another avenue to advance hydrologic simulations in the urban-rural interface, such as the Boise River Watershed, Idaho. Therefore, this research will provide useful insights for hydrologists to design and set up their own hydrological modeling exercises using the cost-effective parallel computing described in this case study.

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The search for orthogonal hydrological modelling metrics: a case study of 20 monitoring stations in Colombia

Cited by 23 publications

References 14 publications

Soil CO2 Dynamics in a Tree Island Soil of the Pantanal: The Role of Soil Water Potential

Soil CO2 Dynamics in a Tree Island Soil of the Pantanal: The Role of Soil Water Potential

How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting

Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study

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