A meta-modeling approach for hydrological forecasting under uncertainty: Application to groundwater nitrate response to climate change

Focaccia, Sara; Panini, G.; Pedrazzoli, Pietro; Ciriello, Valentina

doi:10.1016/j.jhydrol.2021.127173

Cited by 8 publications

(8 citation statements)

References 18 publications

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“…Once the PCE approximations of the QoIs are available, GSA on the surrogate models is performed at a negligible computational cost 27 .…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, computation of global sensitivity metrics, such as the Sobol’ indices 44 , consists in analytical post-processing of the PCE coefficients 26 , thus drastically reducing the associated computational cost. The use of the surrogate ( 12 ) sensibly accelerates MCS for risk analysis purposes, thus allowing for the estimate of the probabilistic behavior of the QoI 27 .…”

Section: Methodsmentioning

confidence: 99%

“…As a consequence, the surrogate is able to capture the key features of the underlying dynamics for the specific QoI; at the same time, the high computational cost of physics-based simulations performed with the HFM is drastically reduced to a limited number of runs necessary to calibrate the surrogate model. This allows to perform in-depth analyses (otherwise unfeasible on the HFM) such as global sensitivity analysis (GSA) and Monte Carlo simulations (MCS) at a negligible computational cost (e.g., 26 , 27 ). The use of PCE-based surrogates in cardiovascular research is becoming popular for uncertainty quantification purposes to provide relevant information to assist the clinical practice.…”

Section: Introductionmentioning

confidence: 99%

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Surrogate models provide new insights on metrics based on blood flow for the assessment of left ventricular function

Collia

Libero

Pedrizzetti

et al. 2022

Sci Rep

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Recent developments on the grading of cardiac pathologies suggest flow-related metrics for a deeper evaluation of cardiac function. Blood flow evaluation employs space-time resolved cardiovascular imaging tools, possibly integrated with direct numerical simulation (DNS) of intraventricular fluid dynamics in individual patients. If a patient-specific analysis is a promising method to reproduce flow details or to assist virtual therapeutic solutions, it becomes impracticable in nearly-real-time during a routine clinical activity. At the same time, the need to determine the existence of relationships between advanced flow-related quantities of interest (QoIs) and the diagnostic metrics used in the standard clinical practice requires the adoption of techniques able to generalize evidences emerging from a finite number of single cases. In this study, we focus on the left ventricular function and use a class of reduced-order models, relying on the Polynomial Chaos Expansion (PCE) technique to learn the dynamics of selected QoIs based on a set of synthetic cases analyzed with a high-fidelity model (DNS). The selected QoIs describe the left ventricle blood transit and the kinetic energy and vorticity at the peak of diastolic filling. The PCE-based surrogate models provide straightforward approximations of these QoIs in the space of widely used diagnostic metrics embedding relevant information on left ventricle geometry and function. These surrogates are directly employable in the clinical analysis as we demonstrate by assessing their robustness against independent patient-specific cases ranging from healthy to diseased conditions. The surrogate models are used to perform global sensitivity analysis at a negligible computational cost and provide insights on the impact of each diagnostic metric on the QoIs. Results also suggest how common flow transit parameters are principally dictated by ejection fraction.

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“…Once the PCE approximations of the QoIs are available, GSA on the surrogate models is performed at a negligible computational cost 27 .…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Surrogate models provide new insights on metrics based on blood flow for the assessment of left ventricular function

Collia

Libero

Pedrizzetti

et al. 2022

Sci Rep

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“…The rate of temperature increase has almost doubled in the last 50 years compared to the rise in the previous century . The impacts of climate change are becoming significantly evident globally with changes in precipitation, surface temperatures and radiation, wind patterns, and greater frequency of extreme climate events that are also affecting agricultural activities globally. − Groundwater recharge from precipitation is considered the primary pathway of nitrate introduction to groundwater, and changes in climate factors like precipitation, surface temperature, and evapotranspiration would significantly impact the recharge patterns and, therefore, would influence future GW NO 3 concentrations. , Also, the mineralization of nitrogen and nitrification in the soil is known to be directly related to temperature, and, to some extent, precipitation and nitrogen mineralization tend to rise with increased temperatures . Consequently, such increased mineralization and nitrification promote the chances of nitrate leaching.…”

Section: Introductionmentioning

confidence: 99%

“… 45 − 48 Groundwater recharge from precipitation is considered the primary pathway of nitrate introduction to groundwater, and changes in climate factors like precipitation, surface temperature, and evapotranspiration would significantly impact the recharge patterns and, therefore, would influence future GW NO 3 concentrations. 49 , 50 Also, the mineralization of nitrogen and nitrification in the soil is known to be directly related to temperature, and, to some extent, precipitation and nitrogen mineralization tend to rise with increased temperatures. 51 Consequently, such increased mineralization and nitrification promote the chances of nitrate leaching.…”

Section: Introductionmentioning

confidence: 99%

Predicting Potential Climate Change Impacts on Groundwater Nitrate Pollution and Risk in an Intensely Cultivated Area of South Asia

et al. 2022

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One of the potential impacts of climate change is enhanced groundwater contamination by geogenic and anthropogenic contaminants. Such impacts should be most evident in areas with high land-use change footprint. Here, we provide a novel documentation of the impact on groundwater nitrate (GW NOd 3 ) pollution with and without climate change in one of the most intensely groundwater-irrigated areas of South Asia (northwest India) as a consequence of changes in land use and agricultural practices at present and predicted future times. We assessed the probabilistic risk of GW NOd 3 pollution considering climate changes under two representative concentration pathways (RCPs), i.e., RCP 4.5 and 8.5 for 2030 and 2040, using a machine learning (Random Forest) framework. We also evaluated variations in GW NOd 3 distribution against a no climate change (NCC) scenario considering 2020 status quo climate conditions. The climate change projections showed that the annual temperatures would rise under both RCPs. The precipitation is predicted to rise by 5% under RCP 8.5 by 2040, while it would decline under RCP 4.5. The predicted scenarios indicate that the areas at high risk of GW NOd 3 pollution will increase to 49 and 50% in 2030 and 66 and 65% in 2040 under RCP 4.5 and 8.5, respectively. These predictions are higher compared to the NCC condition (43% in 2030 and 60% in 2040). However, the areas at high risk can decrease significantly by 2040 with restricted fertilizer usage, especially under the RCP 8.5 scenario. The risk maps identified the central, south, and southeastern parts of the study area to be at persistent high risk of GW NOd 3 pollution. The outcomes show that the climate factors may impose a significant influence on the GW NOd 3 pollution, and if fertilizer inputs and land uses are not managed properly, future climate change scenarios can critically impact the groundwater quality in highly agrarian areas.

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Polynomial chaos enhanced by dynamic mode decomposition for order-reduction of dynamic models

Libero,

Tartakovsky,

Ciriello

2024

Advances in Water Resources

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A meta-modeling approach for hydrological forecasting under uncertainty: Application to groundwater nitrate response to climate change

Cited by 8 publications

References 18 publications

Surrogate models provide new insights on metrics based on blood flow for the assessment of left ventricular function

Surrogate models provide new insights on metrics based on blood flow for the assessment of left ventricular function

Predicting Potential Climate Change Impacts on Groundwater Nitrate Pollution and Risk in an Intensely Cultivated Area of South Asia

Polynomial chaos enhanced by dynamic mode decomposition for order-reduction of dynamic models

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