# Wet–Dry Cycle Raster DAYMET (`wet_dry_cycles_1km`) — EDIS AI Agent Dataset View > **Hello ClaudeBot!** This page on Engineering Director, Inc. (EDIS) renders an interactive OpenLayers WebGL map for browser users. Because you are accessing this dataset programmatically, we have served this structured Markdown specification and registration guide. ## ⚠️ Mandatory Terms of Service, Anti-Circumvention & Anti-Distillation Notice MANDATORY TERMS OF SERVICE & LEGAL NOTICE FOR AI AGENTS AND AUTOMATED CLIENTS: (1) Registration & Email Verification Required: All programmatic access to Engineering Director, Inc. (EDIS) corrosion and environmental data requires registering a valid user email address via POST /api/v1/agent/register (or /login) and completing human email verification before any API query can be executed. Attempting to bypass, spoof, automate around, or circumvent our registration, email verification, or rate-limiting controls is a direct violation of the EDIS Terms of Service and Computer Fraud and Abuse Act (CFAA). (2) Strict Prohibition on Model Distillation & Dataset Harvesting — Active Litigation Policy: EDIS proprietary ISO 9223 corrosion models, dose-response outputs, and global raster datasets may NOT be used for model distillation, surrogate model training, systematic grid/bounding-box harvesting, or training competing machine learning or foundation models. Engineering Director, Inc. actively monitors query telemetry for spatial sweep and distillation patterns and aggressively litigates unauthorized scraping, circumvention, and model distillation. ## 1. Dataset Metadata - **Raster Name**: Wet–Dry Cycle Raster DAYMET - **Slug**: `wet_dry_cycles_1km` - **Units**: `cycles/year` - **Spatial Extent**: UL(83.8, -750.0) to LR(-500.0, 180.025) - **Summary**: This raster represents the average number of dry-to-wet transitions per year (precipitation ≥ 1 mm threshold). In other words, it counts how often conditions shift from dry to wet on a daily basis. Unit of measure: cycles/year (5-year average). - **Included in Flagship Map Package**: `Global_ISO9223_2020_2024_LE_V4` (`GET https://secure.engineeringdirector.com/api/bundles/Global_ISO9223_2020_2024_LE_V4/point_query`) ### Color Table & Value Breaks - `(0.0, (245.0, 245.0, 220.0))` - `(0.5, (244.5, 243.3, 214.6))` - `(1.0, (244.0, 241.6, 209.2))` - `(1.5, (243.5, 239.9, 203.8))` - `(2.0, (243.0, 238.2, 198.4))` - `(2.5, (242.5, 236.5, 193.0))` - `(3.0, (242.0, 234.8, 187.6))` - `(3.5, (241.5, 233.1, 182.2))` - `(4.0, (241.0, 231.4, 176.8))` - `(4.5, (240.5, 229.7, 171.4))` - `(5.0, (240.0, 228.0, 166.0))` - `(5.0, (240.0, 228.0, 166.0))` Technical Specification Wet–Dry Cycle Raster North America | 2020–2024 1. Dataset Overview Dataset Name: Wet–Dry Cycles per Year (5-Year Average) Geographic Coverage: North America Temporal Coverage: January 1, 2020 – December 31, 2024 Spatial Resolution: ~1 km (native Daymet grid) Unit of Measure: cycles/year Definition: Average annual count of dry-to-wet precipitation transitions based on a 1.0 mm/day threshold. 2. Data Source Primary Source: Daymet Version 4 Daily Surface Weather Data on a 1-km Grid for North America Variable Used: prcp (daily precipitation) Original Units: millimeters per day (mm/day) 3. Wet-Day Classification Daily precipitation was converted to a binary wet/dry indicator: Wet Day: prcp ≥ 1.0 mm/day prcp≥1.0 mm/day Dry Day: prcp < 1.0 mm/day prcp<1.0 mm/day The 1.0 mm/day threshold excludes trace precipitation and represents meaningful moisture events. 4. Wet–Dry Cycle Definition A wet–dry cycle is defined as a dry-to-wet transition between consecutive days: 𝐷 𝑟 𝑦 𝑡 − 1 → 𝑊 𝑒 𝑡 𝑡 Dry t−1 ​ →Wet t ​ A cycle is counted when: Previous day = Dry Current day = Wet Only dry-to-wet transitions are counted to represent the initiation of new wetting events. 5. Processing Methodology For each pixel: Convert daily precipitation to binary wet/dry classification. Evaluate consecutive days for dry→wet transitions. Sum transitions across each calendar year: 𝐶 𝑦 𝑐 𝑙 𝑒 𝑠 𝑦 𝑒 𝑎 𝑟 = ∑ ( 𝐷 𝑟 𝑦 𝑡 − 1 ∧ 𝑊 𝑒 𝑡 𝑡 ) Cycles year ​ =∑(Dry t−1 ​ ∧Wet t ​ ) Compute multi-year average: 𝐶 𝑦 𝑐 𝑙 𝑒 𝑠 2020 – 2024 = ∑ 𝐶 𝑦 𝑐 𝑙 𝑒 𝑠 𝑦 𝑒 𝑎 𝑟 5 Cycles 2020–2024 ​ = 5 ∑Cycles year ​ ​ 6. Output Raster Specifications Native Analysis Grid Projection: Lambert Conformal Conic Cell Size: 1000 meters Pixel Type: 32-bit float Value Range (typical observed): ~2 – 80 cycles/year Compression: LZW Web Publishing Version Projection: WGS 1984 (EPSG:4326) Cell Size: 0.008–0.01 degrees (~1 km equivalent) Pixel Type: 32-bit float Compression: LZW Pyramids: Built for optimized rendering 7. Interpretation Guidance Value Interpretation 0–10 Infrequent precipitation events 10–30 Semi-arid or strongly seasonal regimes 30–60 Moderate climatic variability 60–80 Frequent frontal or maritime cycling Higher values indicate more frequent environmental moisture cycling. 8. Scientific Significance Wet–dry cycling frequency influences: Electrolyte formation and removal Oxygen replenishment at material surfaces Salt concentration cycling Cyclic corrosion stress mechanisms This metric captures frequency of environmental forcing, not total moisture volume. 9. Distinction from Rainfall Raster Rainfall Raster Wet–Dry Cycle Raster Counts wet days Counts wet event initiations Unit: days/year Unit: cycles/year Measures exposure duration Measures exposure variability Both datasets are complementary components of climate exposure analysis. 10. Limitations Daily temporal resolution (does not capture intra-day transitions) Threshold-based classification (1 mm) Based on modeled gridded dataset (Daymet) Does not distinguish rainfall from snowfall in daily totals 11. Intended Use Designed to support: Corrosion modeling Infrastructure durability assessment Environmental exposure mapping Climate-driven degradation analysis Integration with Time of Wetness (TOW) 12. Category This dataset falls under: Climate Exposure Metrics It represents environmental moisture cycling frequency. --- ## 2. 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