resolve conflict

Author: davemfish

requirements.txt

@@ -1,5 +1,5 @@
 git-lfs
-git+https://github.com/natcap/invest.git@release/3.15.0
+natcap.invest
 pint
 setuptools_scm
 sphinx

source/en/carbon_edge.rst

@@ -104,8 +104,10 @@ to a single result using a distance linear interpolation scheme. The
 model then converts biomass to carbon with a user provided conversion
 factor, defaulted to 0.47 (IPCC 2006). The user can designate the number
 of local models used in the interpolation scheme which is defaulted to
-10 but can range anywhere from 1 (only closest point) to 2635 (every
-regression model on the planet). Note that a selection of 1 may result
+10 but can range anywhere from 1 (only closest point) to 2313 (every
+regression model on the planet). Note that only those regression models
+that fall within 500km of the study area will be used, so the actual
+number of models averaged may be fewer. A selection of 1 may result
 in artificially large differences in carbon when moving from one pixel
 to the next where they fall in different regression grid cells. The
 higher the number of regression grid cells selected, the smoother the

source/en/crop_production.rst

@@ -25,14 +25,14 @@ Expanding agricultural production and closing yield gaps is a key strategy for m
 The Model
 =========
 
-The InVEST crop production model is divided into a percentile based yield model, covering 175 crops worldwide, and a regression based model that accounts for fertilization rates on 10 crops. These models are deployed to the end user as the "percentile" and "regression" models.
+The InVEST crop production model is divided into a percentile based yield model, covering 172 crops worldwide, and a regression based model that accounts for fertilization rates on 10 crops. These models are deployed to the end user as the "percentile" and "regression" models.
 
 All results are paired with observed results from the same region for quality control checks as well as nutrition information for 33 macro and micronutrients.
 
 Percentile Model
 ----------------
 
-The InVEST Crop Production Percentile model produces estimates of 175 crops' yield from existing data, percentile summaries, and observed yields. These observations are based on FAO and sub-national datasets for 175 crops, as metric tons per hectare (Monfreda et al. 2008) and nutrition information. The percentile yields are useful for exploring a range of intenstification levels, listing the yield for the 25th, 50th, 75th, and 95th percentiles, amongst observed yield data in each of the crop's climate bins.
+The InVEST Crop Production Percentile model produces estimates of 172 crops' yield from existing data, percentile summaries, and observed yields. These observations are based on FAO and sub-national datasets for 172 crops, as metric tons per hectare (Monfreda et al. 2008) and nutrition information. The percentile yields are useful for exploring a range of intenstification levels, listing the yield for the 25th, 50th, 75th, and 95th percentiles, amongst observed yield data in each of the crop's climate bins.
 
 Regression Model
 ----------------
@@ -112,7 +112,7 @@ The current version of the model is a coarse global model driven mostly by clima
 Data Needs
 ==========
 
-There are two InVEST crop production models available, a Percentile based observation model that operates on 175 crops, and a Regression model for exploring fertilziation rates that operates on 10 crops. The arguments below are for both models unless otherwise specified.
+There are two InVEST crop production models available, a Percentile based observation model that operates on 172 crops, and a Regression model for exploring fertilziation rates that operates on 10 crops. The arguments below are for both models unless otherwise specified.
 
 **Important**: You need to download the InVEST dataset for Crop Production, to get the Monfreda Dataset required as input to the models. If you choose to install the Crop Production sample data when you install InVEST, there will be a folder called ``sample_data\CropProduction`` in the installation folder for InVEST, where this data may be found. Or, you can download it from http://releases.naturalcapitalproject.org/?prefix=invest/, after going to that link, clicking on the target version, then navigating into the ``data`` directory and selecting ``CropProduction.zip``.
 

source/en/data_sources.rst

@@ -355,6 +355,12 @@ So you can assign these dual hydrologic group values to the first group (A, B or
 
 In the United States, free soil data is available from the NRCS gSSURGO, SSURGO and gNATSGO databases: https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/survey/geo/. They also provide ArcGIS tools (Soil Data Viewer for SSURGO and Soil Data Development Toolbox for gNATSGO) that help with processing these databases into spatial data that can be used by the model. The Soil Data Development Toolbox (available at https://www.nrcs.usda.gov/resources/data-and-reports/gridded-soil-survey-geographic-gssurgo-database) is easiest to use, and highly recommended if you use ArcGIS Desktop (it does not work in ArcGIS Pro or QGIS) and need to process U.S. soil data. Another option is SSURGO Portal (https://www.nrcs.usda.gov/resources/data-and-reports/ssurgo-portal), which is a new (beta) application independent from ArcGIS.
 
+**Note for soil hydrologic group values in urban areas**
+
+It is sometimes the case (as with SSURGO data in the United States) that soil group maps are missing data in urban areas. If the urban areas are known to be mostly development and impervious surface, you can set the soil group value for these pixels to 4 (D), which indicates the highest level of rainfall runoff. You could also refine these values based on whether the land use/land cover (LULC) map indicates low-, medium- or high-intensity development.
+
+**Estimating soil hydrologic groups from hydraulic conductivity and soil depth**
+
 If desired, soil groups may also be determined from hydraulic conductivity and soil depths. FutureWater’s Soil Hydraulic Properties dataset also contains hydraulic conductivity, as may other soil databases. Table 1 below can be used to convert soil conductivity into soil groups.
 
 |

source/en/getting_started.rst

@@ -160,12 +160,14 @@ See the :ref:`running-models` section of this chapter for more information about
 
 This screen also provides the ability to save parameters (and optionally data) to a file, through the "**Save as...**" link. Three options are available:
 
-+ **Parameters only**: Saves a JSON file that includes the paths to your input data, but it does not save the data itself. You can use the "**Load parameters from file**" option to bring this file into InVEST to restore your parameters.
++ **Parameters only**: Saves a JSON file that includes the paths to your input data, but it does not save the data itself. You can use the "**Load parameters from file**" option to bring this file into InVEST, or drag and drop the JSON file into the model interface, to restore your parameters.
 
-+ **Parameters and data**: Saves both parameters and data in a compressed archive (.tgz). This archive contains the same JSON file produced by the "**Parameters only**" option, plus the data. You can use the "**Load parameters from file**" option to bring this file into InVEST to restore your parameters. This option is useful for copying all of the necessary data for a model run to a different location. For example, you can send the archive to a colleague to reproduce your model run. If you post to the Community Forum asking for help with a problem, you may be asked to provide your input data, and this is the preferred way to package up your input data and parameters.
++ **Parameters and data**: Saves both parameters and data in a compressed archive (.tgz). This archive contains the same JSON file produced by the "**Parameters only**" option, plus the data. You can use the "**Load parameters from file**" option to bring this file into InVEST, or drag and drop the .tgz file into the model interface, to restore your parameters. This option is useful for copying all of the necessary data for a model run to a different location. For example, you can send the archive to a colleague to reproduce your model run. If you post to the Community Forum asking for help with a problem, you may be asked to provide your input data, and this is the preferred way to package up your input data and parameters.
 
 + **Python script**: Saves your parameters in a python script. This includes the paths to your input data, but not the data itself. Running the python script will run the model with your parameters. Use this as a starting point for batch scripts.
 
+If you have a saved parameter datastack (.tgz file = parameters + data) to bring into Workbench, you can either drag and drop it into the interface, or use the "**Load parameters from file**" option and choose the .tgz. After dropping the .tgz into the interface, or selecting it in the "**Load parameters from file**" option, a window will appear called "**Choose location to extract archive**". Where it says "**File name**", type the name of a new *folder*, which is where the contents of the .tgz will be extracted to. Note that it does not currently work to select a folder that has already been created, you must create a new one only through the "**File name**" entry. 
+
 The "**User's Guide**" link takes you to the User's Guide chapter for that model. The "**Frequently Asked Questions**" link takes you to the Natural Capital Project's Community Forum (https://community.naturalcapitalproject.org/), showing the posts that are related to that model.
 
 Once you have filled in all of the required input data, click "**Run**" to run the model. A logging screen will appear.

source/en/habitat_quality.rst

@@ -151,7 +151,7 @@ Data Needs
 
 - :investspec:`habitat_quality workspace_dir`
 - :investspec:`habitat_quality results_suffix`
-- :investspec:`habitat_quality lulc_cur_path` All spatial inputs are reprojected to this rasters SRS and this raster is used to define the geospatial extents for the corresponding threat rasters.
+- :investspec:`habitat_quality lulc_cur_path` All spatial inputs are reprojected to this raster's coordinate system and pixel size and this raster is used to define the geospatial extents for the corresponding threat rasters.
 
 - :investspec:`habitat_quality lulc_fut_path` If provided, the model will generate degradation, habitat quality, and habitat rarity (if baseline map is provided) outputs.
 

source/en/ndr.rst

@@ -224,7 +224,7 @@ The NDR model does not directly quantify the amount of nutrient retained on the
 
 To calculate nitrogen retention services within a single scenario, we recommend subtracting *n_total_export.tif* from the *modified_load_n.tif* result located in the *intermediate* output folder. Similarly, phosphorus retention services can be calculated by subtracting *p_surface_export.tif* from *modified_load_p.tif*. Use the .gpkg output to quantify watershed scale nutrient retention services by subtracting the *n_total_export* result from (*n_surface_load* + *n_subsurface_load*) for nitrogen and *p_surface_export* from *p_surface_load* for phosphorus.
 
-Monetary (or non-monetary) valuation of nutrient retention services is very context-specific. An important note about assigning a monetary value to any service is that valuation should only be done on model outputs that have been calibrated and validated. Otherwise, it is unknown how well the model is representing the area of interest, which may lead to misrepresentation of the exact value. If the model has not been calibrated, only relative results should be used (such as an increase of 10%) not absolute values (such as 1,523 kg, or 42,900 dollars.)
+Monetary (or non-monetary) valuation of nutrient retention services is very context-specific. An important note about assigning a monetary value to any service is that valuation should only be done on model outputs that have been calibrated and validated. Otherwise, it is unknown how well the model is representing the area of interest, which may lead to misrepresentation of the exact value. If the model has not been calibrated, only relative results should be used (such as an increase of 10%) not absolute values (such as 1,523 kg, or 42,900 dollars.) For more information on calibration and validation of the NDR model, see :ref:`comparison-to-observed-data`.
 
 Limitations and Simplifications
 ===============================
@@ -383,11 +383,15 @@ The InVEST model computes a nutrient mass balance over a watershed, subtracting
 
  * Potential contribution from point source pollution: domestic and industrial waste are often part of the nutrient budget and should be accounted for during calibration (for example, by adding point-source nutrient loads to modeled nutrient export, then comparing the sum to observed data).
 
+.. _comparison-to-observed-data:
+
 Comparison to observed data
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Despite the above uncertainties, the InVEST model provides a first-order assessment of the processes of nutrient retention and may be compared with observations. Time series of nutrient concentration used for model validation should span over a reasonably long period (preferably at least 10 years) to attenuate the effect of inter-annual variability. Time series should also be relatively complete throughout a year (without significant seasonal data gaps) to ensure comparison with total annual loads. If the observed data is expressed as a time series of nutrient concentration, they need to be converted to annual loads (LOADEST and FLUX32 are two software tools facilitating this conversion). Additional details on methods and model performance for relative predictions can be found in the study of Redhead et al 2018.
 
+A detailed study of NDR model calibration and validation was done by `Valladares-Castellanos et. al. <https://doi.org/10.1016/j.scitotenv.2024.175111>`_  (Valladares-Castellanos 2024) in Puerto Rico using open source monitoring data. In the referenced paper, they provide their framework, workflow and R code, which can be adapted to other locations, and is recommended reading when planning your own calibration and validation process. 
+
 If there are dams on streams in the analysis area, it is possible that they are retaining nutrient, such that it will not arrive at the outlet of the study area. In this case, it may be useful to adjust for this retention when comparing model results with observed data. For an example of how this was done for a study in the northeast U.S., see Griffin et al 2020. The dam retention methodology is described in the paper's Appendix, and requires knowing the nutrient trapping efficiency of the dam(s).
 
 
@@ -477,6 +481,8 @@ Scanlon, B.R., Jolly, I., Sophocleous, M., Zhang, L., 2007. Global impacts of co
 
 Tarboton, D., 1997. A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resour. Res. 33, 309–319.
 
+Valladares-Castellanos, M., de Jesús Crespo, R., Xu, Y. J., Douthat, T. H., 2024. A framework for validating watershed ecosystem service models in the United States using long-term water quality data: Applications with the InVEST Nutrient Delivery (NDR) model in Puerto Rico, Science of The Total Environment, Volume 949, 2024, 175111, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2024.175111.
+
 Vigiak, O., Borselli, L., Newham, L.T.H., Mcinnes, J., Roberts, A.M., 2012. Comparison of conceptual landscape metrics to define hillslope-scale sediment delivery ratio. Geomorphology 138, 74–88.
 
 Zhang, X., Liu, X., Zhang, M., Dahlgren, R. a, Eitzel, M., 2009. A review of vegetated buffers and a meta-analysis of their mitigation efficacy in reducing nonpoint source pollution. J. Environ. Qual. 39, 76–84.

source/en/recreation.rst

@@ -184,7 +184,6 @@ Model Outputs
 
     + If Compute Regression is selected, one field for each predictor given in the Predictor Table. The values of those fields are the metric calculated per response feature (:ref:`rec-data-needs`: Predictor Table).
 
-
 + **regression_summary.txt** (output if Compute Regression is selected):
 
   + This is a text file output of the regression analysis. It includes :math:`\beta_p` estimates for each predictor variable (see :ref:`rec-how-it-works`). It also contains a “server id hash” value which can be used to correlate the PUD and TUD result with the data available on the server. If these results are used in publication this hash should be included with the results for reproducibility.

source/en/urban_flood_mitigation.rst

@@ -132,7 +132,7 @@ Interpreting Results
 
  * **Parameter log**: Each time the model is run, a text (.txt) file will be created in the Workspace. The file will list the parameter values and output messages for that run and will be named according to the service, the date and time. When contacting NatCap about errors in a model run, please include the parameter log.
 
- * **Runoff_retention.tif**: raster with runoff retention index values (no unit, values of 0-1, relative to precipitation volume). Calculated from equation :eq:`runoff_retention`.
+ * **Runoff_retention_index.tif**: raster with runoff retention index values (no unit, values of 0-1, relative to precipitation volume). Calculated from equation :eq:`runoff_retention`.
 
  * **Runoff_retention_m3.tif**: raster with runoff retention volume values (in :math:`m^3`). Calculated from equation :eq:`runoff_retention_volume`.