The Experts below are selected from a list of 4752 Experts worldwide ranked by ideXlab platform
John O Roads - One of the best experts on this subject based on the ideXlab platform.
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evaluation of the land surface water budget in ncep ncar and ncep doe reanalyses using an off line hydrologic model
Journal of Geophysical Research, 2001Co-Authors: Edwin P. Maurer, Greg Odonnell, Dennis P Lettenmaier, John O RoadsAbstract:The ability of the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis (NRA1) and the follow-up NCEP/Department of Energy (DOE) reanalysis (NRA2), to reproduce the hydrologic budgets over the Mississippi River basin is evaluated using a macroscale hydrology model. This diagnosis is aided by a relatively unconstrained global climate simulation using the NCEP global spectral model, and a more highly constrained regional climate simulation using the NCEP regional spectral model, both employing the same land surface parameterization (LSP) as the reanalyses. The hydrology model is the variable infiltration capacity (VIC) model, which is forced by gridded observed precipitation and temperature. It reproduces observed streamflow, and by closure is constrained to balance other terms in the surface water and energy budgets. The VIC-simulated surface fluxes therefore provide a benchmark for evaluating the predictions from the reanalyses and the climate models. The comparisons, conducted for the 10-year period 1988–1997, show the well-known overestimation of summer precipitation in the southeastern Mississippi River basin, a consistent overestimation of evapotranspiration, and an underprediction of snow in NRA1. These biases are generally lower in NRA2, though a large Overprediction of snow water equivalent exists. NRA1 is subject to errors in the surface water budget due to nudging of modeled soil moisture to an assumed climatology. The nudging and precipitation bias alone do not explain the consistent Overprediction of evapotranspiration throughout the basin. Another source of error is the gravitational drainage term in the NCEP LSP, which produces the majority of the model's reported runoff. This may contribute to an Overprediction of persistence of surface water anomalies in much of the basin. Residual evapotranspiration inferred from an atmospheric balance of NRA1, which is more directly related to observed atmospheric variables, matches the VIC prediction much more closely than the coupled models. However, the persistence of the residual evapotranspiration is much less than is predicted by the hydrological model or the climate models.
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Evaluation of the land surface water budget in NCEP/NCAR and NCEP/DOE reanalyses using an off‐line hydrologic model
Journal of Geophysical Research: Atmospheres, 2001Co-Authors: Edwin P. Maurer, Dennis P Lettenmaier, Greg O'donnell, John O RoadsAbstract:The ability of the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis (NRA1) and the follow-up NCEP/Department of Energy (DOE) reanalysis (NRA2), to reproduce the hydrologic budgets over the Mississippi River basin is evaluated using a macroscale hydrology model. This diagnosis is aided by a relatively unconstrained global climate simulation using the NCEP global spectral model, and a more highly constrained regional climate simulation using the NCEP regional spectral model, both employing the same land surface parameterization (LSP) as the reanalyses. The hydrology model is the variable infiltration capacity (VIC) model, which is forced by gridded observed precipitation and temperature. It reproduces observed streamflow, and by closure is constrained to balance other terms in the surface water and energy budgets. The VIC-simulated surface fluxes therefore provide a benchmark for evaluating the predictions from the reanalyses and the climate models. The comparisons, conducted for the 10-year period 1988–1997, show the well-known overestimation of summer precipitation in the southeastern Mississippi River basin, a consistent overestimation of evapotranspiration, and an underprediction of snow in NRA1. These biases are generally lower in NRA2, though a large Overprediction of snow water equivalent exists. NRA1 is subject to errors in the surface water budget due to nudging of modeled soil moisture to an assumed climatology. The nudging and precipitation bias alone do not explain the consistent Overprediction of evapotranspiration throughout the basin. Another source of error is the gravitational drainage term in the NCEP LSP, which produces the majority of the model's reported runoff. This may contribute to an Overprediction of persistence of surface water anomalies in much of the basin. Residual evapotranspiration inferred from an atmospheric balance of NRA1, which is more directly related to observed atmospheric variables, matches the VIC prediction much more closely than the coupled models. However, the persistence of the residual evapotranspiration is much less than is predicted by the hydrological model or the climate models.
Paulo De Marco Júnior - One of the best experts on this subject based on the ideXlab platform.
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Overprediction of species distribution models in conservation planning: A still neglected issue with strong effects
Biological Conservation, 2020Co-Authors: Santiago José Elías Velazco, Bruno R. Ribeiro, Livia Maira Orlandi Laureto, Paulo De Marco JúniorAbstract:Abstract Species distribution models (SDM) are increasingly used in conservation planning to identify priority areas for the establishment of protected areas. Nevertheless, the quality of SDM varies widely and may compromise the effectiveness of protected areas. Here we reviewed whether SDM Overprediction is considered in spatial conservation prioritization exercises and evaluated how model Overprediction influences the effectiveness and the spatial arrangement of priority areas. To do so, we carried out a systematic review to analyze how researchers have handled SDM Overprediction when identifying priority areas for conservation. To show how spatial conservation prioritization outcomes are affected by SDM Overprediction, we used SDM of native palm at three geographic scales (Neotropics, Amazon ecoregion, and Ecuadorian Amazon). We found that only 10% of the evaluated manuscripts accounted for model Overprediction. Our spatial conservation prioritization based on SDM with Overprediction conferred high priority rank values in a region where species do not occur, underestimated the efficiency of selected priority areas, and over or underestimated the efficiency of current protected areas. Such effects were lower at smaller geographic extents. Our findings highlight the importance of improving future spatial conservation prioritization studies through the correction of SDM Overprediction, resulting in the detection of more adequate areas for species conservation, especially at broader extents.
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Dealing with Overprediction in species distribution models: How adding distance constraints can improve model accuracy
Ecological Modelling, 2020Co-Authors: Poliana Mendes, Santiago José Elías Velazco, André Felipe Alves De Andrade, Paulo De Marco JúniorAbstract:Abstract Species distribution models can be affected by Overprediction when dispersal movement is not incorporated into the modelling process. We compared the efficiency of seven methods that take into account spatial constraints to reduce Overprediction when using four algorithms for species distribution models. By using a virtual ecologist approach, we were able to measure the accuracy of each model in predicting actual species distributions. We built 40 virtual species distributions within the Neotropical realm. Then, we randomly sampled 50 occurrences that were used in seven spatially restricted species distribution models (hereafter called M-SDMs) and a non-spatially restricted ecological niche model (ENM). We used four algorithms; Maximum Entropy, Generalized Linear Models, Random Forest, and Support Vector Machine. M-SDM methods were divided into a priori methods, in which spatial restrictions were inserted with environmental variables in the modelling process, and a posteriori methods, in which reachable and suitable areas were overlapped. M-SDM efficiency was obtained by calculating the difference in commission and omission errors between M-SDMs and ENMs. We used linear mixed-effects models to test if differences in commission and omission errors varied among the M-SDMs and algorithms. Our results indicate that overall M-SDMs reduce Overprediction with no increase in underprediction compared to ENMs with few exceptions, such as a priori methods combined with the Support Vector Machine algorithm. There is a high variation in modelling performance among species, but there were only a few cases in which Overprediction or underprediction increased. We only compared methods that do not require species dispersal data, guaranteeing that they can be applied to less-studied species. We advocate that species distribution modellers should not ignore spatial constraints, especially because they can be included in models at low costs but high benefits in terms of Overprediction reduction.
Stanley Rachman - One of the best experts on this subject based on the ideXlab platform.
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Stimulus estimation and the Overprediction of fear
British Journal of Clinical Psychology, 1994Co-Authors: Steven Taylor, Stanley RachmanAbstract:Overprediction of fear is the tendency to overestimate the amount of fear that one will experience in a subjectively threatening situation. Little is known about this bias, despite the important role it appears to play in producing phobic avoidance. The present study proposed a stimulus estimation hypothesis of Overprediction, which states that Overprediction of fear arises from the Overprediction of the danger features of the feared stimulus and the underprediction of safety features. This model was supported by the results of structural equation modelling based on the responses of 224 snake-fearful subjects exposed to a live harmless snake. The determinants of the stimulus estimation bias are considered and directions for further investigation are discussed.
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The Overprediction of fear: A review.
Behaviour research and therapy, 1994Co-Authors: Stanley RachmanAbstract:Abstract There is converging evidence that many people overestimate how frightened they will be when faced by a fear-provoking situation (Arntz & van den Hout, 1988, Behaviour Research and Therapy, 26, 207–223; Rachman & Bichard, 1988, Clinical Psychology Review, 8, 303–313; Rachman, 1990, Fear and courage (2nd edn). New York: W.H. Freeman). This Overprediction of fear is commonly seen in people who are troubled by excessive fear (e.g. claustrophobics, panic patients), but is not confined to them. Anecdotal, clinical, and research evidence suggests that the tendency to overestimate the subjective impact of an aversive event is a common psychological phenomenon. This review will present examples of Overpredictions, put forward some explanations of why people might overpredict, consider the function that overpredicting might serve, and the possible consequences of overpredicting. The process by which Overpredictions are reduced is also considered and an attempt will be made to relate this strong tendency to overpredict fear to other types of psychological overestimation.
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Role of selective recall in the Overprediction of fear
Behaviour research and therapy, 1994Co-Authors: Steven Taylor, Stanley RachmanAbstract:Overprediction of fear is a bias in which phobic individuals tend to overestimate the amount of fear they will experience in a subjectively threatening situation. The selective recall model states that this bias arises because memories of highly fearful experiences are more easily retrieved than memories of nonfearful experiences. The model predicts that phobics should show a greater magnitude of Overprediction if they receive fear-relevant priming compared with fear-irrelevant priming. A study of 100 spider-fearful Ss found that the magnitude of Overprediction was smallest after fear-relevant priming, thus refuting the model. Alternative models are considered, and directions for further investigation are set out.
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The Overprediction and underprediction of pain
Clinical Psychology Review, 1991Co-Authors: Stanley Rachman, A. ArntzAbstract:Abstract There is a consistent pattern in the way that people predict painful experiences, natural or contrived, and this pattern resembles the way in which people predict frightening experiences. People tend to overpredict how much pain they will experience. These predictions of intensity tend to decrease after people have made an Overprediction, and to increase if they underpredict the pain. After a correct prediction, the subsequent prediction tends to be unchanged. An underpredicted pain is experienced as being more aversive than a correctly or overpredicted pain. It is probable that people tend to overpredict the intensity of a variety of aversive experiences, and that underpredictions are followed by immediate and prolonged increases in the predictions of subsequent aversive events, unless there is a superordinate predictive pattern. The functional value of Overpredictions of pain is considered, as are the clinical implications of these findings.
Edwin P. Maurer - One of the best experts on this subject based on the ideXlab platform.
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evaluation of the land surface water budget in ncep ncar and ncep doe reanalyses using an off line hydrologic model
Journal of Geophysical Research, 2001Co-Authors: Edwin P. Maurer, Greg Odonnell, Dennis P Lettenmaier, John O RoadsAbstract:The ability of the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis (NRA1) and the follow-up NCEP/Department of Energy (DOE) reanalysis (NRA2), to reproduce the hydrologic budgets over the Mississippi River basin is evaluated using a macroscale hydrology model. This diagnosis is aided by a relatively unconstrained global climate simulation using the NCEP global spectral model, and a more highly constrained regional climate simulation using the NCEP regional spectral model, both employing the same land surface parameterization (LSP) as the reanalyses. The hydrology model is the variable infiltration capacity (VIC) model, which is forced by gridded observed precipitation and temperature. It reproduces observed streamflow, and by closure is constrained to balance other terms in the surface water and energy budgets. The VIC-simulated surface fluxes therefore provide a benchmark for evaluating the predictions from the reanalyses and the climate models. The comparisons, conducted for the 10-year period 1988–1997, show the well-known overestimation of summer precipitation in the southeastern Mississippi River basin, a consistent overestimation of evapotranspiration, and an underprediction of snow in NRA1. These biases are generally lower in NRA2, though a large Overprediction of snow water equivalent exists. NRA1 is subject to errors in the surface water budget due to nudging of modeled soil moisture to an assumed climatology. The nudging and precipitation bias alone do not explain the consistent Overprediction of evapotranspiration throughout the basin. Another source of error is the gravitational drainage term in the NCEP LSP, which produces the majority of the model's reported runoff. This may contribute to an Overprediction of persistence of surface water anomalies in much of the basin. Residual evapotranspiration inferred from an atmospheric balance of NRA1, which is more directly related to observed atmospheric variables, matches the VIC prediction much more closely than the coupled models. However, the persistence of the residual evapotranspiration is much less than is predicted by the hydrological model or the climate models.
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Evaluation of the land surface water budget in NCEP/NCAR and NCEP/DOE reanalyses using an off‐line hydrologic model
Journal of Geophysical Research: Atmospheres, 2001Co-Authors: Edwin P. Maurer, Dennis P Lettenmaier, Greg O'donnell, John O RoadsAbstract:The ability of the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis (NRA1) and the follow-up NCEP/Department of Energy (DOE) reanalysis (NRA2), to reproduce the hydrologic budgets over the Mississippi River basin is evaluated using a macroscale hydrology model. This diagnosis is aided by a relatively unconstrained global climate simulation using the NCEP global spectral model, and a more highly constrained regional climate simulation using the NCEP regional spectral model, both employing the same land surface parameterization (LSP) as the reanalyses. The hydrology model is the variable infiltration capacity (VIC) model, which is forced by gridded observed precipitation and temperature. It reproduces observed streamflow, and by closure is constrained to balance other terms in the surface water and energy budgets. The VIC-simulated surface fluxes therefore provide a benchmark for evaluating the predictions from the reanalyses and the climate models. The comparisons, conducted for the 10-year period 1988–1997, show the well-known overestimation of summer precipitation in the southeastern Mississippi River basin, a consistent overestimation of evapotranspiration, and an underprediction of snow in NRA1. These biases are generally lower in NRA2, though a large Overprediction of snow water equivalent exists. NRA1 is subject to errors in the surface water budget due to nudging of modeled soil moisture to an assumed climatology. The nudging and precipitation bias alone do not explain the consistent Overprediction of evapotranspiration throughout the basin. Another source of error is the gravitational drainage term in the NCEP LSP, which produces the majority of the model's reported runoff. This may contribute to an Overprediction of persistence of surface water anomalies in much of the basin. Residual evapotranspiration inferred from an atmospheric balance of NRA1, which is more directly related to observed atmospheric variables, matches the VIC prediction much more closely than the coupled models. However, the persistence of the residual evapotranspiration is much less than is predicted by the hydrological model or the climate models.
Santiago José Elías Velazco - One of the best experts on this subject based on the ideXlab platform.
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Overprediction of species distribution models in conservation planning: A still neglected issue with strong effects
Biological Conservation, 2020Co-Authors: Santiago José Elías Velazco, Bruno R. Ribeiro, Livia Maira Orlandi Laureto, Paulo De Marco JúniorAbstract:Abstract Species distribution models (SDM) are increasingly used in conservation planning to identify priority areas for the establishment of protected areas. Nevertheless, the quality of SDM varies widely and may compromise the effectiveness of protected areas. Here we reviewed whether SDM Overprediction is considered in spatial conservation prioritization exercises and evaluated how model Overprediction influences the effectiveness and the spatial arrangement of priority areas. To do so, we carried out a systematic review to analyze how researchers have handled SDM Overprediction when identifying priority areas for conservation. To show how spatial conservation prioritization outcomes are affected by SDM Overprediction, we used SDM of native palm at three geographic scales (Neotropics, Amazon ecoregion, and Ecuadorian Amazon). We found that only 10% of the evaluated manuscripts accounted for model Overprediction. Our spatial conservation prioritization based on SDM with Overprediction conferred high priority rank values in a region where species do not occur, underestimated the efficiency of selected priority areas, and over or underestimated the efficiency of current protected areas. Such effects were lower at smaller geographic extents. Our findings highlight the importance of improving future spatial conservation prioritization studies through the correction of SDM Overprediction, resulting in the detection of more adequate areas for species conservation, especially at broader extents.
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Dealing with Overprediction in species distribution models: How adding distance constraints can improve model accuracy
Ecological Modelling, 2020Co-Authors: Poliana Mendes, Santiago José Elías Velazco, André Felipe Alves De Andrade, Paulo De Marco JúniorAbstract:Abstract Species distribution models can be affected by Overprediction when dispersal movement is not incorporated into the modelling process. We compared the efficiency of seven methods that take into account spatial constraints to reduce Overprediction when using four algorithms for species distribution models. By using a virtual ecologist approach, we were able to measure the accuracy of each model in predicting actual species distributions. We built 40 virtual species distributions within the Neotropical realm. Then, we randomly sampled 50 occurrences that were used in seven spatially restricted species distribution models (hereafter called M-SDMs) and a non-spatially restricted ecological niche model (ENM). We used four algorithms; Maximum Entropy, Generalized Linear Models, Random Forest, and Support Vector Machine. M-SDM methods were divided into a priori methods, in which spatial restrictions were inserted with environmental variables in the modelling process, and a posteriori methods, in which reachable and suitable areas were overlapped. M-SDM efficiency was obtained by calculating the difference in commission and omission errors between M-SDMs and ENMs. We used linear mixed-effects models to test if differences in commission and omission errors varied among the M-SDMs and algorithms. Our results indicate that overall M-SDMs reduce Overprediction with no increase in underprediction compared to ENMs with few exceptions, such as a priori methods combined with the Support Vector Machine algorithm. There is a high variation in modelling performance among species, but there were only a few cases in which Overprediction or underprediction increased. We only compared methods that do not require species dispersal data, guaranteeing that they can be applied to less-studied species. We advocate that species distribution modellers should not ignore spatial constraints, especially because they can be included in models at low costs but high benefits in terms of Overprediction reduction.
