l5d1l5 Goto Github PK

2018_riverbank_vegetation_river_inn

Data and code of Bauer et al. (2018) Riv Res Appl

2021-hector-ar6-optimization

Hector investigation for optimizing parameters related to better fitting AR6 RF.

2021-hector-ar6rf

Code and materials used to test and validate Hector v3 new radiative forcing implementation

2021_forte_agu

Materials used to prepare the materials for the FoRTE AGU talk.

2021_ipcc

2021fall.github.io

Website for Data Science Lab 2021 Fall

2022_danube_dike_survey

Data and code of Bauer et al. (submitted)

2022_denissen_eli_cmip6_nat_clim_change

3wme

Viennese Forest Growth Models

abm

agent_based_model

abrpmonitoring

absim_rat.1

An agent-based model of landscape change that uses a rational, long-term decision optimization strategy to simulate aggregate land owner decisions in a target geography (typically at the scale of a US county).

academic-kickstart

Source of my website (marco-mina.com) created with academic-hugo theme and Rblogdown

adaptation-map

adaptation-tracking

adgvm1_ccam

aDGVM1 with with CCAM downscaled GCM daily input data

advanced_nuclear_2021

advancedrasteranalysis

Advanced Raster Analysis assignment from TeamTropical

aez18_to_uscntyfips

The problem I attempt solve in this repository is how to identify which U.S. counties belong to each Agro-ecological Zone.

afforestation-map

afr-soil-prediction

Advances in rapid, low cost analysis of soil samples using infrared spectroscopy, georeferencing of soil samples, and greater availability of earth remote sensing data provide new opportunities for predicting soil functional properties at unsampled locations. Soil functional properties are those properties related to a soil’s capacity to support essential ecosystem services such as primary productivity, nutrient and water retention, and resistance to soil erosion. Digital mapping of soil functional properties, especially in data sparse regions such as Africa, is important for planning sustainable agricultural intensification and natural resources management.

africa_fertilizer_profitability

ag_ecosopt

Ag_EcoSOpt is an open-source software-based decision-support tool that incorporates the concepts of ecosystem modelling, ecosystem services tradeoffs, economic valuation and optimization, and land management and land use change decision making in agricultural production systems. It has two main components with several attached modules. The ecological qualification component is used to quantify changes in ecosystem functions (e.g. greenhouse gas emissions, soil organic matter, water loss, nitrogen leaching, crop yield, and crop residue) in response to changes in human decision making on land management practices or land uses. It integrates two core models: (1) DAYCENT, a biogeochemical model created by the Natural Resources and Ecology Laboratory at Colorado State University; and (2) The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model (GREET) created by Argonne National Laboratory. The optimization component includes a spreadsheet-based module or standalone MATLAB application for farm-gate optimization or supply chain optimization of end use products (e.g. grain for cattle feed or biofuel, biomass for electricity or biofuel). Ag_EcoSOpt supports multilevel decision making for crop lands in the United States such as individual farms or landscape, user-defined time frames (1-50 years), and farm-gate or supply chain analysis.

ages

Agricultural Ecosystems Service (AgES) spatial watershed model

agreement-index

A revisited symmetric index of agreement for continuous datasets.

agriculturalassistant

A tool which calculates the revenue generated by a farmer given the size of land, amount of crops, and type of crops. The types of crops are optimized for a California Farmer. Our project fits into big data because it requires extensive knowledge of each plant in order to be useful.

agrigee

These are some agricultural analyses I performed on Google Earth Engine.

agroserv

Folder for agroserv project stuff

aim_annual_report_code

Using AIM data (TerrADat) and R, generate tables and figures for surface cover, gap cover, dominant species, and more. Categorize by zones, allotments, strata or ecological sites! "Final..." is a compilation of all sections except "Summary_Code" and "bench...". For videos of running the code visit https://www.youtube.com/channel/UCsq1npX-D5wrg1qGlAJdOJA

airgap

Assessing Inequality in Risk from Global Air Pollution