Potential evaporation at eddy-covariance sites across the globe

Abstract. Potential evaporation (Ep) is a crucial variable for hydrological forecasting and drought monitoring. However, multiple interpretations of Ep exist, and these reflect a diverse range of methods to calculate it. As such, a comparison of the performance of these methods against field observations in different global ecosystems is urgently needed. In this study, potential evaporation was defined as the rate of evaporation (or evapotranspiration – sum of transpiration and soil evaporation) that the actual ecosystem would attain if it evaporates at maximal rate. We use eddy-covariance measurements from the FLUXNET2015 database, covering eleven different biomes, to parameterize and inter-compare the most widely used Ep methods and to uncover their relative performance. For each site, we isolate the days for which ecosystems can be considered as unstressed based on both an energy balance approach and a soil water content approach. Evaporation measurements during these days are used as reference to calibrate and validate the different methods to estimate Ep. Our results indicate that a simple radiation-driven method calibrated per biome consistently performs best, with a mean correlation of 0.93, unbiased RMSE of 0.56 mm day−1, and bias of −0.02 mm day−1 against in situ measurements of unstressed evaporation. A Priestley and Taylor method, calibrated per biome, performed just slightly worse, yet substantially and consistently better than more complex Penman, Penman–Monteith-based or temperature-driven approaches. We show that the poor performance of Penman–Monteith-based approaches relates largely to the fact that the unstressed stomatal conductance cannot be assumed to be constant in time at the ecosystem scale. Contrastingly, the biome-specific parameters required for the simple radiation-driven methods are relatively constant in time and per biome type. This makes these methods a robust way to estimate Ep and a suitable tool to investigate the impact of water use and demand, drought severity and biome productivity.

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Potential evaporation at eddy-covariance sites across the globe

10.5194/hess-2017-682 ◽

2018 ◽

Cited By ~ 4

Author(s):

Wouter H. Maes ◽

Pierre Gentine ◽

Niko E. C. Verhoest ◽

Diego G. Miralles

Keyword(s):

Eddy Covariance ◽

Poor Performance ◽

Drought Severity ◽

Potential Evaporation ◽

Diverse Range ◽

Eddy Covariance Measurements ◽

Priestley And Taylor Method ◽

The Impact ◽

Energy Balance Approach

Abstract. Potential evaporation (Ep) is a crucial variable for hydrological forecast and in drought monitoring systems. However, multiple interpretations of Ep exist, and these reflect a diverse range of methods to calculate Ep. As such, a comparison of the performance of these methods against field observations in different global ecosystems is badly needed. In this study, we used eddy-covariance measurements from 107 sites of the FLUXNET2015 database, covering 11 different biomes, to parameterize and compare the main Ep methods and uncover their relative performance. For each site, we extracted the days for which ecosystems are unstressed based on both an energy balance approach and on a soil water content approach. The evaporation measurements during these days were used as reference to validate the different methods to estimate Ep. Our results indicate that a simple radiation-driven method calibrated per biome consistently performed best, with a mean correlation of 0.93, an unbiased RMSE of 0.56 mm day−1, and a bias of −0.02 mm day−1 against in situ measurements of unstressed evaporation. A Priestley and Taylor method, calibrated per biome, performed just slightly worse, yet substantially and consistently better than more complex Penman, Penman-Monteith-based or temperature-based approaches. We show that the poor performance of Penman-Monteith based approaches relates largely to the fact that the unstressed stomatal conductance was assumed constant. Further analysis showed that the biome-specific parameters required for the simple radiation-driven methods are relatively constant per biome. This makes this simple radiation-driven method calibrated per biome a robust method that can be incorporated into models for improving our understanding of the impact of global warming on future global water use and demand, drought severity and ecosystem productivity.

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Potential evaporation at eddy-covariance sites across the globe

Hydrology and Earth System Sciences ◽

10.5194/hess-23-925-2019 ◽

2019 ◽

Vol 23 (2) ◽

pp. 925-948 ◽

Cited By ~ 15

Author(s):

Wouter H. Maes ◽

Pierre Gentine ◽

Niko E. C. Verhoest ◽

Diego G. Miralles

Keyword(s):

Eddy Covariance ◽

Poor Performance ◽

Drought Severity ◽

Atmospheric Conditions ◽

Potential Evaporation ◽

Diverse Range ◽

Eddy Covariance Measurements ◽

Priestley And Taylor Method ◽

The Impact ◽

The Given

Abstract. Potential evaporation (Ep) is a crucial variable for hydrological forecasting and drought monitoring. However, multiple interpretations of Ep exist, which reflect a diverse range of methods to calculate it. A comparison of the performance of these methods against field observations in different global ecosystems is urgently needed. In this study, potential evaporation was defined as the rate of terrestrial evaporation (or evapotranspiration) that the actual ecosystem would attain if it were to evaporate at maximal rate for the given atmospheric conditions. We use eddy-covariance measurements from the FLUXNET2015 database, covering 11 different biomes, to parameterise and inter-compare the most widely used Ep methods and to uncover their relative performance. For each of the 107 sites, we isolate days for which ecosystems can be considered unstressed, based on both an energy balance and a soil water content approach. Evaporation measurements during these days are used as reference to calibrate and validate the different methods to estimate Ep. Our results indicate that a simple radiation-driven method, calibrated per biome, consistently performs best against in situ measurements (mean correlation of 0.93; unbiased RMSE of 0.56 mm day−1; and bias of −0.02 mm day−1). A Priestley and Taylor method, calibrated per biome, performed just slightly worse, yet substantially and consistently better than more complex Penman-based, Penman–Monteith-based or temperature-driven approaches. We show that the poor performance of Penman–Monteith-based approaches largely relates to the fact that the unstressed stomatal conductance cannot be assumed to be constant in time at the ecosystem scale. On the contrary, the biome-specific parameters required by simpler radiation-driven methods are relatively constant in time and per biome type. This makes these methods a robust way to estimate Ep and a suitable tool to investigate the impact of water use and demand, drought severity and biome productivity.

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The Oxford Handbook of Adult Cognitive Disorders

10.1093/oxfordhb/9780190664121.001.0001 ◽

2019 ◽

Keyword(s):

Cognitive Impairment ◽

Cognitive Function ◽

Neurodevelopmental Disorders ◽

Brain Aging ◽

Cognitive Disorders ◽

Diverse Range ◽

Medical Specialties ◽

Psychiatric Illnesses ◽

Medical Disorders ◽

The Impact

The prevalence of cognitive impairment caused by neurodegenerative diseases and other neurologic disorders associated with aging is expected to rise dramatically between now and year 2050, when the population of Americans aged 65 or older will nearly double. Cognitive impairment also commonly occurs in other neurologic conditions, as well as in non-neurologic medical disorders (and their treatments), idiopathic psychiatric illnesses, and adult neurodevelopmental disorders. Cognitive impairment can thus infiltrate all aspects of healthcare, making it necessary for clinicians and clinical researchers to have an integrated knowledge of the spectrum of adult cognitive disorders. The Oxford Handbook of Adult Cognitive Disorders is meant to serve as an up-to-date, scholarly, and comprehensive volume covering most diseases, conditions, and injuries resulting in impairments in cognitive function in adults. Topics covered include normal cognitive and brain aging, the impact of medical disorders (e.g., cardiovascular, liver, pulmonary) and psychiatric illnesses (e.g., depression and bipolar disorder) on cognitive function, adult neurodevelopmental disorders (e.g., Down Syndrome, Attention Deficit/Hyperactivity Disorder), as well as the various neurological conditions (e.g., Alzheimer’s disease, chronic traumatic encephalopathy, concussion). A section of the Handbook is also dedicated to unique perspectives and special considerations for the clinicians and clinical researchers, covering topics such as cognitive reserve, genetics, diversity, and neuroethics. The target audience of this Handbook includes: (1) clinicians, particularly psychologists, neuropsychologists, neurologists (including behavioral and cognitive neurologists), geriatricians, and psychiatrists (including neuropsychiatrists), who provide clinical care and management for adults with a diverse range of cognitive disorders; (2) clinical researchers who investigate cognitive outcomes and functioning in adult populations; and (3) graduate level students and post-doctoral trainees studying psychology, clinical neuroscience, and various medical specialties.

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Modeling regional impacts and resilience to water service disruptions in urban economies

Environment and Planning B Urban Analytics and City Science ◽

10.1177/2399808321998703 ◽

2021 ◽

pp. 239980832199870

Author(s):

Sheree A Pagsuyoin ◽

Joost R Santos

Keyword(s):

Case Studies ◽

Large Scale ◽

Urban Region ◽

Economic Losses ◽

Drought Severity ◽

Severe Drought ◽

Time Varying ◽

National Capital Region ◽

Economic Sectors ◽

The Impact

Water is a critical natural resource that sustains the productivity of many economic sectors, whether directly or indirectly. Climate change alongside rapid growth and development are a threat to water sustainability and regional productivity. In this paper, we develop an extension to the economic input-output model to assess the impact of water supply disruptions to regional economies. The model utilizes the inoperability variable, which measures the extent to which an infrastructure system or economic sector is unable to deliver its intended output. While the inoperability concept has been utilized in previous applications, this paper offers extensions that capture the time-varying nature of inoperability as the sectors recover from a disruptive event, such as drought. The model extension is capable of inserting inoperability adjustments within the drought timeline to capture time-varying likelihoods and severities, as well as the dependencies of various economic sectors on water. The model was applied to case studies of severe drought in two regions: (1) the state of Massachusetts (MA) and (2) the US National Capital Region (NCR). These regions were selected to contrast drought resilience between a mixed urban–rural region (MA) and a highly urban region (NCR). These regions also have comparable overall gross domestic products despite significant differences in the distribution and share of the economic sectors comprising each region. The results of the case studies indicate that in both regions, the utility and real estate sectors suffer the largest economic loss; nonetheless, results also identify region-specific sectors that incur significant losses. For the NCR, three sectors in the top 10 ranking of highest economic losses are government-related, whereas in the MA, four sectors in the top 10 are manufacturing sectors. Furthermore, the accommodation sector has also been included in the NCR case intuitively because of the high concentration of museums and famous landmarks. In contrast, the Wholesale Trade sector was among the sectors with the highest economic losses in the MA case study because of its large geographic size conducive for warehouses used as nodes for large-scale supply chain networks. Future modeling extensions could potentially include analysis of water demand and supply management strategies that can enhance regional resilience against droughts. Other regional case studies can also be pursued in future efforts to analyze various categories of drought severity beyond the case studies featured in this paper.

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Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models

Scientific Reports ◽

10.1038/s41598-021-92155-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Sarv Priya ◽

Tanya Aggarwal ◽

Caitlin Ward ◽

Girish Bathla ◽

Mathews Jacob ◽

...

Keyword(s):

Machine Learning ◽

Pulmonary Hypertension ◽

Feature Selection ◽

Subgroup Analysis ◽

Cardiac Mri ◽

Intraclass Correlation ◽

Poor Performance ◽

Superior Performance ◽

Feature Filtering ◽

The Impact

AbstractSide experiments are performed on radiomics models to improve their reproducibility. We measure the impact of myocardial masks, radiomic side experiments and data augmentation for information transfer (DAFIT) approach to differentiate patients with and without pulmonary hypertension (PH) using cardiac MRI (CMRI) derived radiomics. Feature extraction was performed from the left ventricle (LV) and right ventricle (RV) myocardial masks using CMRI in 82 patients (42 PH and 40 controls). Various side study experiments were evaluated: Original data without and with intraclass correlation (ICC) feature-filtering and DAFIT approach (without and with ICC feature-filtering). Multiple machine learning and feature selection strategies were evaluated. Primary analysis included all PH patients with subgroup analysis including PH patients with preserved LVEF (≥ 50%). For both primary and subgroup analysis, DAFIT approach without feature-filtering was the highest performer (AUC 0.957–0.958). ICC approaches showed poor performance compared to DAFIT approach. The performance of combined LV and RV masks was superior to individual masks alone. There was variation in top performing models across all approaches (AUC 0.862–0.958). DAFIT approach with features from combined LV and RV masks provide superior performance with poor performance of feature filtering approaches. Model performance varies based upon the feature selection and model combination.

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