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BLM Climate Engine - Basics

Background

This training session, led by the Climate Engine team, covered utilizing Climate Engine Tools for reporting and decision support. Climate Engine Tools provide access to 90+ datasets including fractional vegetation cover (RAP, RCMAP, etc.) and drought indices and leverage cloud computing to allow users to easily create maps, charts, and reports. This two-hour introductory session focused on foundational information, hands-on demos, and decision-support workflows tailored to BLM uses.

This page mirrors the workshop agenda and demos. Each section starts with a timestamped, embedded clip from the full recording:

Training Session Outcomes:

  • Attendees will know what Climate Engine Tools are available to them and when they would use each
  • Attendees will know how to access and produce Climate Engine reports
  • Attendees will know how to use the App to create maps and graphs
  • Attendees will better understand the datasets available to them and how to use them
  • Attendees will know how to use the App as part of a practical decision-support workflow
  • Attendees will know how to attend future thematic sessions on 1) riparian and wetland systems, 2) upland ecosystems, and 3) drought and vegetation productivity
  • Attendees will know where to find support resources

Self-paced Study

This training and related resources are available for self-paced study. Use the links below to navigate to sections within this article.

Sections in this training

Training Structure and Slides

The training was recorded and is available on the Climate Engine YouTube Channel. This training recording is embedded throughout this article timestamped to each section. The slides from the training are available below.

Introduction & Session Overview

What you’ll learn

  • What Climate Engine is and which tools are available (Reports, Web App, API).
  • How to access pre-computed Climate Engine reports for BLM land units and how to make custom maps/graphs.
  • Which drought and remote sensing datasets in Climate Engine are most useful for BLM decisions.
  • Hands-on case studies for generating reports, comparing datasets, and evaluating riparian restoration effectiveness.

Who this is for
BLM staff and partners interested in learning to apply climate and remote sensing datasets for reporting and decision support.

Background & What is Climate Engine?

Key points

  • Climate Engine integrates 90+ climate, hydrology, and remote sensing datasets for analysis and comparison.
  • Of the 90+ datasets, this training series will focus on ~10 datasets most relevant for BLM decision needs, including climate, drought, fractional vegetation cover, biomass production, and plant productivity.
  • The BLM-focused training series emphasizes riparian/wetland and drought applications, integrating decision-ready gridded datasets, and reproducible workflows.

Climate Engine tools at a glance

  • Reports — standardized figures and maps (downloadable for incorporating into decision documents).
  • Web App — interactive maps/plots with on-the-fly, customizable calculations.
  • API — programmatic access for automation, scaling, and tool development.

Climate Engine Reports

Report types covered

  • Drought Reports
    • Short/long-term drought maps, stacked bar charts of drought categories, and period comparisons.
    • Available for 1) BLM land units (pre-computed and updated) and 2) user-defined AOIs.
  • Site Characterization Reports
    • Maps/tables of current vegetation conditions, trend summaries, time-series figures, and climate and drought summaries.
    • Available for 1) BLM land units (pre-computed and updated) and 2) user-defined AOIs.

When to choose Reports vs. App

  • Use Reports when you need standardized outputs (PDF/PNG for briefings).
  • Use the App when exploring scenarios, variables, or custom AOIs before formal reporting.

Climate Engine Web Application

Core outcomes of the demo

  • Creating maps (e.g., precipitation totals, anomalies/percentiles, trends) at different scales using different datasets.
  • Generating graphs (e.g., time series for precipitation, NDVI, production) for user-defined polygons.
  • Saving sharable links for reference.

Overview of the demo

  1. Open the app and select a dataset/varible (e.g., Precipitation, NDVI, Production).
  2. Pick a statistic (total, anomaly, percentile, linear trend) and time window.
  3. Add an AOI (draw or upload). Switch to "Make Graph" to generate time series.
  4. Adjust basemaps/overlays and transparency for visual comparison.
  5. Export images or copy the shareable link for your notes/reference.

BLM-Relevant Drought Datasets

Common drought datasets & indices

  • US Drought Monitor (USDM) — weekly blend of indicators and expert analysis.
  • gridMET — ~4-km daily climate data (precipitation, temperature, ET, etc.).
  • PRISM — ~800-m - 4-km daily climate data (precipitation, temperature, etc.).
  • gridMET Drought derived from gridMET: SPI (standardized precipitation index), SPEI (standardized precipitation evapotranspiration index), EDDI (evaporative demand drought index), plus Palmer drought severity index (PDSI), Palmer Z, short-term drought blend, long-term drought blends.

Practical guidance

  • Match timescale to your management question.
    • 14–30 days: recent weather/flash drought periods
    • 90–180 days: seasonal to semi-annual periods
    • 1–2 years: multi-year periods
    • 5 years: long-term drought periods (hydrological drought)
  • Use blends to quickly compare drought timescales.

BLM-Relevant Remote Sensing Datasets

Core datasets

  • Landsat 30 m, 1984–present; ~16-day revisit
  • Sentinel-2 10 m, 2017–present; ~5-day revisit
  • RAP Fractional vegetation cover (annual) & herbaceous production (annual and 16-day), 30 m, 1986–present
  • RCMAP Fractional vegetation cover (annual), 30 m, 1985–present

Tips from this section

  • Use NDVI values for vegetation status, anomalies for departures from average, trends for directionality, and time series for context of a given year.
  • RAP focuses on production & fractional components**; RCMAP on fractional components (perennial/annual herbaceous, shrub, tree, bare, litter). These datasets can be easily intercompared in Climate Engine.
  • Integrating remote sensing data with climate/drought data in Climate Engine can support a better understanding of drivers of vegetation production and long-term trends.

Demonstration: Evaluating Riparian Restoration Effectiveness

Workflow demonstration

  1. Define target reach as the "region".
  2. Map JJA NDVI and JJA NDVI trends to capture growing-season greenness patterns and long-term vegetation trends.
  3. Generate NDVI time series and paired NDVI + precipitation plots.
  4. Use simple scatterplots (NDVI vs. precip) to gauge sensitivity of vegetation to climate.
  5. Assess restoration outcomes while accounting for climate by separating the pre- and post- restoration periods.

Interpretation notes

  • Divergence between NDVI and precipitation can indicate changes in plant community or hydrology (e.g., improved vigor post-restoration in response to more water availability).
  • Pair with site notes, photo points, or field data to strengthen evidence.

Closing, Contacts & Resources

Resources

Instructor contacts
Kristen O’Shea — kristen.oshea@dri.edu
Eric Jensen — eric.jensen@dri.edu