Evaluating stream health based environmental justice model performance at different spatial scales

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

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Effects of disturbance at two spatial scales on macroinvertebrate and fish metrics of stream health

Journal of Freshwater Ecology ◽

10.1080/02705060.2013.832707 ◽

2013 ◽

Vol 29 (1) ◽

pp. 83-100 ◽

Cited By ~ 7

Author(s):

Michael H. Paller ◽

Sean C. Sterrett ◽

Tracey D. Tuberville ◽

Dean E. Fletcher ◽

Andrew M. Grosse

Keyword(s):

Spatial Scales ◽

Stream Health

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Evaluation of numerical models by FerryBox and fixed platform in situ data in the southern North Sea

Ocean Science ◽

10.5194/os-11-879-2015 ◽

2015 ◽

Vol 11 (6) ◽

pp. 879-896 ◽

Cited By ~ 6

Author(s):

M. Haller ◽

F. Janssen ◽

J. Siddorn ◽

W. Petersen ◽

S. Dick

Keyword(s):

North Sea ◽

High Frequency ◽

Numerical Models ◽

Spatial Scales ◽

Model Performance ◽

Eastern Coast ◽

Southern North Sea ◽

In Situ Data ◽

Oyster Ground ◽

Study Results

Abstract. For understanding and forecasting of hydrodynamics in coastal regions, numerical models have served as an important tool for many years. In order to assess the model performance, we compared simulations to observational data of water temperature and salinity. Observations were available from FerryBox transects in the southern North Sea and, additionally, from a fixed platform of the MARNET network. More detailed analyses have been made at three different stations, located off the English eastern coast, at the Oyster Ground and in the German Bight. FerryBoxes installed on ships of opportunity (SoO) provide high-frequency surface measurements along selected tracks on a regular basis. The results of two operational hydrodynamic models have been evaluated for two different time periods: BSHcmod v4 (January 2009 to April 2012) and FOAM AMM7 NEMO (April 2011 to April 2012). While they adequately simulate temperature, both models underestimate salinity, especially near the coast in the southern North Sea. Statistical errors differ between the two models and between the measured parameters. The root mean square error (RMSE) of water temperatures amounts to 0.72 °C (BSHcmod v4) and 0.44 °C (AMM7), while for salinity the performance of BSHcmod is slightly better (0.68 compared to 1.1). The study results reveal weaknesses in both models, in terms of variability, absolute levels and limited spatial resolution. Simulation of the transition zone between the coasts and the open sea is still a demanding task for operational modelling. Thus, FerryBox data, combined with other observations with differing temporal and spatial scales, can serve as an invaluable tool not only for model evaluation, but also for model optimization by assimilation of such high-frequency observations.

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How are nature based solutions contributing to priority societal challenges surrounding human well-being in the United Kingdom: a systematic map protocol

Environmental Evidence ◽

10.1186/s13750-019-0180-4 ◽

2019 ◽

Vol 8 (1) ◽

Cited By ~ 2

Author(s):

Jan Dick ◽

James D. Miller ◽

Jonathan Carruthers-Jones ◽

Anne J. Dobel ◽

Steve Carver ◽

...

Keyword(s):

United Kingdom ◽

Environmental Justice ◽

Spatial Scales ◽

Research Question ◽

Well Being ◽

Acoustic Environment ◽

The United Kingdom ◽

Societal Challenges ◽

Cost Efficacy ◽

Management Issues

Abstract Background The concept of Nature-based Solutions (NBS) has evolved as an umbrella concept embracing concepts such as Green/Blue/Nature Infrastructure, Ecosystem Approach, Ecosystem Services, but at their core, they cluster into the general theme of learning from and using nature to create sustainable socio-ecological systems, which enhance human well-being (HWB). NBS address societal challenges across a broad range of spatial scales—local, regional and global—and temporal scales—medium to long-term. While there are many reviews and a clear evidence base linking certain NBS to various elements of HWB, particularly urban greenspace and human health, no comprehensive mapping exists of the links between NBS interventions and the associated multiple positive and negative HWB outcomes across a range of habitats. The initial research phase used a participatory co-design process to select four priority societal challenges facing the United Kingdom: three related to management issues i.e. NBS cost-efficacy, governance in planning, environmental justice, and the fourth threats to the acoustic environment. These challenges collectively address priority management issues which stakeholders requested be investigated widely i.e. across landscapes, cityscapes, seascapes and soundscapes. Results of the study are intended to identify and define potential future environmental evidence challenges for UK science. Methods This protocol describes the methodology for approaching the research question: What evidence is there for nature based solutions and their impacts on human wellbeing for societal challenges related to cost-efficacy, governance in planning, environmental justice, and the acoustic environment? Using systematic mapping, this study will search for and identify studies that seek to assess nature-based solutions on human well-being with regard to these four societal challenges. Systematic searches across a number of academic/online databases are tested against a number of test articles. Search results are refined using eligibility criteria through a three stage process: title, abstract, full text. Data from screened studies are extracted using a predefined coding strategy. Key trends in data will be synthesized according to a range of secondary questions and be presented in a graphical matrix illustrating the knowledge gaps and clusters for research into nature-based solutions and human well-being for each societal challenge.

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Comparison of generalized non-data-driven lake and reservoir routing models for global-scale hydrologic forecasting of reservoir outflow at diurnal time steps

Hydrology and Earth System Sciences ◽

10.5194/hess-24-2711-2020 ◽

2020 ◽

Vol 24 (5) ◽

pp. 2711-2729 ◽

Cited By ~ 2

Author(s):

Joseph L. Gutenson ◽

Ahmad A. Tavakoly ◽

Mark D. Wahl ◽

Michael L. Follum

Keyword(s):

Large Scale ◽

Spatial Scales ◽

Model Performance ◽

Global Scale ◽

Army Corps ◽

Time Step ◽

Large Spatial Scale ◽

Reservoir Routing ◽

Routing Methods ◽

Improved Model

Abstract. Large-scale hydrologic forecasts should account for attenuation through lakes and reservoirs when flow regulation is present. Globally generalized methods for approximating outflow are required but must contend with operational complexity and a dearth of information on dam characteristics at global spatial scales. There is currently no consensus on the best approach for approximating reservoir release rates in large spatial scale hydrologic forecasting, particularly at diurnal time steps. This research compares two parsimonious reservoir routing methods at daily steps: Döll et al. (2003) and Hanasaki et al. (2006). These reservoir routing methods have been previously implemented in large-scale hydrologic modeling applications and have been typically evaluated seasonally. These routing methods are compared across 60 reservoirs operated by the U.S. Army Corps of Engineers. The authors vary empirical coefficients for both reservoir routing methods as part of a sensitivity analysis. The method proposed by Döll et al. (2003) outperformed that presented by Hanasaki et al. (2006) at a daily time step and improved model skill over most run-of-the-river conditions. The temporal resolution of the model influences model performances. The optimal model coefficients varied across the reservoirs in this study and model performance fluctuates between wet years and dry years, and for different configurations such as dams in series. Overall, the method proposed by Döll et al. (2003) could enhance large-scale hydrologic forecasting, but can be subject to instability under certain conditions.

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Simulating carbon and water cycles of larch forests in East Asia by the BIOME-BGC model with AsiaFlux data

Biogeosciences ◽

10.5194/bg-7-959-2010 ◽

2010 ◽

Vol 7 (3) ◽

pp. 959-977 ◽

Cited By ~ 38

Author(s):

M. Ueyama ◽

K. Ichii ◽

R. Hirata ◽

K. Takagi ◽

J. Asanuma ◽

...

Keyword(s):

Air Temperature ◽

Ecosystem Respiration ◽

Spatial Scales ◽

Model Performance ◽

Limiting Factors ◽

Model Parameters ◽

Total Precipitation ◽

Water Conditions ◽

Larch Forests ◽

Water Cycles

Abstract. Larch forests are widely distributed across many cool-temperate and boreal regions, and they are expected to play an important role in global carbon and water cycles. Model parameterizations for larch forests still contain large uncertainties owing to a lack of validation. In this study, a process-based terrestrial biosphere model, BIOME-BGC, was tested for larch forests at six AsiaFlux sites and used to identify important environmental factors that affect the carbon and water cycles at both temporal and spatial scales. The model simulation performed with the default deciduous conifer parameters produced results that had large differences from the observed net ecosystem exchange (NEE), gross primary productivity (GPP), ecosystem respiration (RE), and evapotranspiration (ET). Therefore, we adjusted several model parameters in order to reproduce the observed rates of carbon and water cycle processes. This model calibration, performed using the AsiaFlux data, substantially improved the model performance. The simulated annual GPP, RE, NEE, and ET from the calibrated model were highly consistent with observed values. The observed and simulated GPP and RE across the six sites were positively correlated with the annual mean air temperature and annual total precipitation. On the other hand, the simulated carbon budget was partly explained by the stand disturbance history in addition to the climate. The sensitivity study indicated that spring warming enhanced the carbon sink, whereas summer warming decreased it across the larch forests. The summer radiation was the most important factor that controlled the carbon fluxes in the temperate site, but the VPD and water conditions were the limiting factors in the boreal sites. One model parameter, the allocation ratio of carbon between belowground and aboveground, was site-specific, and it was negatively correlated with the annual climate of annual mean air temperature and total precipitation. Although this study substantially improved the model performance, the uncertainties that remained in terms of the sensitivity to water conditions should be examined in ongoing and long-term observations.

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The impact of local surface changes in Borneo on atmospheric composition at wider spatial scales: coastal processes, land-use change and air quality

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2011.0060 ◽

2011 ◽

Vol 366 (1582) ◽

pp. 3210-3224 ◽

Cited By ~ 22

Author(s):

J. A. Pyle ◽

N. J. Warwick ◽

N. R. P. Harris ◽

Mohd Radzi Abas ◽

A. T. Archibald ◽

...

Keyword(s):

Land Use ◽

Air Quality ◽

Land Use Change ◽

Spatial Scales ◽

Model Performance ◽

Atmospheric Composition ◽

Model Studies ◽

Uncertainty Model ◽

Emission Changes ◽

The Impact

We present results from the OP3 campaign in Sabah during 2008 that allow us to study the impact of local emission changes over Borneo on atmospheric composition at the regional and wider scale. OP3 constituent data provide an important constraint on model performance. Treatment of boundary layer processes is highlighted as an important area of model uncertainty. Model studies of land-use change confirm earlier work, indicating that further changes to intensive oil palm agriculture in South East Asia, and the tropics in general, could have important impacts on air quality, with the biggest factor being the concomitant changes in NO x emissions. With the model scenarios used here, local increases in ozone of around 50 per cent could occur. We also report measurements of short-lived brominated compounds around Sabah suggesting that oceanic (and, especially, coastal) emission sources dominate locally. The concentration of bromine in short-lived halocarbons measured at the surface during OP3 amounted to about 7 ppt, setting an upper limit on the amount of these species that can reach the lower stratosphere.

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Climate and bark beetle effects on forest productivity — linking dendroecology with forest landscape modeling

Canadian Journal of Forest Research ◽

10.1139/cjfr-2016-0103 ◽

2016 ◽

Vol 46 (8) ◽

pp. 1026-1034 ◽

Cited By ~ 6

Author(s):

Alec M. Kretchun ◽

E. Louise Loudermilk ◽

Robert M. Scheller ◽

Matthew D. Hurteau ◽

Soumaya Belmecheri

Keyword(s):

Simulation Model ◽

Bark Beetle ◽

Bark Beetles ◽

Net Primary Productivity ◽

Spatial Scales ◽

Model Performance ◽

Moisture Stress ◽

Lake Tahoe Basin ◽

Aboveground Net Primary Productivity ◽

Tree Ring Data

In forested systems throughout the world, climate influences tree growth and aboveground net primary productivity (ANPP). The effects of extreme climate events (i.e., drought) on ANPP can be compounded by biotic factors (e.g., insect outbreaks). Understanding the contribution of each of these influences on growth requires information at multiple spatial scales and is essential for understanding regional forest response to changing climate. The mixed conifer forests of the Lake Tahoe Basin, California and Nevada, provide an opportunity to analyze biotic and abiotic influences on ANPP. Our objective was to evaluate the influence of moisture stress (climatic water deficit, CWD) and bark beetles on basin-wide ANPP from 1987 to 2006, estimated through tree core increments and a landscape simulation model (LANDIS-II). Tree ring data revealed that ANPP increased throughout this period and had a nonlinear relationship to water demand. Simulation model results showed that despite increased complexity, simulations that include moderate moisture sensitivity and bark beetle outbreaks most closely approximated the field-derived ANPP∼CWD relationship. Although bark beetle outbreaks and episodic drought-induced mortality events are often correlated, decoupling them within a simulation model offers insight into assessing model performance, as well as examining how each contributes to total declines in productivity.

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Tactical Predictions of Shoreline Oiling Probability via Machine Learning Models and Satellite-Derived Surface Oil Analysis Products

International Oil Spill Conference Proceedings ◽

10.7901/2169-3358-2014.1.660 ◽

2014 ◽

Vol 2014 (1) ◽

pp. 660-672

Author(s):

Zachary Nixon

Keyword(s):

Machine Learning ◽

Spatial Scales ◽

Information Service ◽

Habitat Type ◽

Model Performance ◽

Boosted Regression Trees ◽

Oil Analysis ◽

Spatial Indices ◽

Spatial And Temporal Scales ◽

Trajectory Models

ABSTRACT For significant oil spills in remote areas with complex shoreline geometry, apportioning Shoreline Cleanup Assessment Technique (SCAT) survey effort is a complicated and difficult task. Aerial surveys are often used to select shoreline areas for ground survey after an initial prioritization based upon anecdotal reports or trajectory models, but aerial observers may have difficulty locating cryptic surface shoreline oiling in vegetated or other complex environments. In dynamic beach environments, stranded shoreline oiling may be rapidly buried, making aerial observation difficult. A machine learning-based model is presented for estimating shoreline oiling probabilities via satellite-derived surface oil analysis products, wind summary data, and shoreline habitat type and geometry data. These inputs are increasingly available at spatial and temporal scales sufficient for tactical use, enabling model predictions to be generated within hours after satellite remote sensing products are available. The model was constructed using SCAT data from the Deepwater Horizon oil spill, satellite-derived surface oil analysis products generated during the spill by NOAA's National Environmental Satellite, Data, and Information Service (NESDIS) using a variety of satellite platforms of opportunity, and available shoreline geometry, character, and other preexisting data. The model involves the generation of set of spatial indices of relative over-water proximity of surface oil slicks based upon the satellite-derived analysis products. The model then uses boosted regression trees (BRT), a flexible and relatively recently developed modeling methodology, to generate calibrated estimates of probability of subsequent shoreline oiling based upon these indices, wind climatological data over the time period of interest, and other shoreline data. The model can be implemented via data preparation in any Geographic Information System (GIS) software coupled with the open-source statistical computing language, R. The model is entirely probabilistic and makes no attempt to reproduce the physics of oil moving through the environment, as do trajectory models. It is best used in concert with such models to make estimates at different spatial scales, or when time and data requirements make implementation of fine-scale trajectory modeling impractical for tactical use. The details of model development implementation and assessments of model performance and limitations are presented.

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Incorporating Shrub and Snag Specific LiDAR Data into GAP Wildlife Models

Journal of Fish and Wildlife Management ◽

10.3996/092013-jfwm-064 ◽

2015 ◽

Vol 6 (2) ◽

pp. 437-447

Author(s):

Teresa J Lorenz ◽

Kerri T Vierling ◽

Jody Vogeler ◽

Jeffrey Lonneker ◽

Jocelyn Aycrigg

Keyword(s):

Land Cover ◽

Spatial Scales ◽

Coniferous Forest ◽

Model Performance ◽

Lidar Data ◽

Gap Model ◽

Distribution Models ◽

Classification Rate ◽

3 Dimensional ◽

Gap Models

Abstract The U.S. Geological Survey’s Gap Analysis Program (hereafter, GAP) is a nationally based program that uses land cover, vertebrate distributions, and land ownership to identify locations where gaps in conservation coverage exist, and GAP products are commonly used by government agencies, nongovernmental organizations, and private citizens. The GAP land-cover designations are based on satellite-derived data, and although these data are widely available, these data do not capture the 3-dimensional vegetation architecture that may be important in describing vertebrate distributions. To date, no studies have examined how the inclusion of snag- or shrub-specific Light Detection and Ranging (LiDAR) data might influence GAP model performance. The objectives of this paper were 1) to assess the performance of the National GAP models and Northwest GAP models with independently collected field data, and 2) to assess whether the inclusion of 3-dimensional vegetation data from LiDAR improved the performance of National GAP and Northwest GAP models. We included only two parameters from the LiDAR data: presence or absence of shrubs and presence or absence of snags ≥25 cm diameter at breast height. We surveyed for birds at>150 points in a 20,000-ha coniferous forest in northern Idaho and used data for eight shrub- and cavity-nesting species for validation purposes. On a guild level, National GAP models performed only marginally better than Northwest GAP models in correct classification rate, and LiDAR data did not improve vertebrate distribution models. At the scale used in this study, GAP models had poor predictive power and this is important for managers interested in using GAP models for species distributions at scales similar to ours, such as a small park or preserve <200 km2 in size. Additionally, because the inclusion of LiDAR data did not consistently affect the performance of GAP models, future studies might consider whether LiDAR data affect GAP model performance by examining 1) different spatial scales, 2) different LiDAR metrics, and/or 3) species-specific habitat relationships not currently available in GAP models.

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