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The study watershed data were extracted from a provincial database in ArcMap (ArcGIS Version 8.1; ESRI, Redlands, CA, USA).
We used the measurement endpoints and data sources listed in Table 1 for the descriptors contained in Eqs. 4– 6. Watershed protection groups and environmental group chapters obtained from EPA watershed data (http://www.epa.gov/surf/) were used to quantify conservation support activities at the HUC-8 watershed scale or larger.
For this watershed, data availability is generally scarce.
In the next section, we will use the real watershed data as a case study to further illustrate the copula entropy theory as well as risk analysis.
With the availability of quantifiable watershed data (watershed descriptors and streamflow indices), variable selection can provide insight into the dominant watershed descriptors that drive different streamflow regimes.
Using the real watershed data (i.e., Flume 1 at Walnut Gulch, Arizona), the case study shows the appropriateness of POME-univariate distribution of scaled variable to model the univariate distribution for the observed variates.
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In contrast with watershed data-intensive models in one extreme and site level cost-savings calculators in the other, our model allows us to generalize principles for green infrastructure design and implementation at a neighborhood scale, to inform policy-making.
A sensitivity analysis and initial test of SPUR 2000 were performed using rainfall simulation plot and micro-watershed data from Idaho sagebrush rangeland.
The DSS model selected a data source for each metric, watershed by watershed, based on availability of data and a prioritization of data sources for each metric so that at each watershed the best data for that particular metric was used.
Based on statistical data, watershed N budgets were calculated by using watershed N balance model proposed by OECD (2001).
Land use classifications with higher resolution (based on SPOT 5) only exist for the upper part of the Guapi-Macacu watershed, but such data would permit our OC analyses to be extrapolated in more detail.
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