流域の水利用可能量推定を改善する水齢予測手法 (Predicting Water Age to Improve Estimates of Water Availability Within Watersheds)

2026-08-18 パシフィック・ノースウェスト国立研究所（PNNL）

＜関連情報＞

• https://www.pnnl.gov/publications/predicting-water-age-improve-estimates-water-availability-within-watersheds
• https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2024MS004765

水追跡機能を備えた水文モデルにおける水の移動時間と水文トレーサーの動態を推定するための逐次降水入力タグ付け（SPIT） Sequential Precipitation Input Tagging (SPIT) to Estimate Water Transit Times and Hydrologic Tracer Dynamics Within Water-Tagging Enabled Hydrologic Models

Zachariah Butler, Stephen Good, Huancui Hu, Xingyuan Chen, Aubrey Dugger
Journal Advances in Modeling Earth Systems  Published: 16 October 2025
DOI:https://doi.org/10.1029/2024MS004765

Abstract

Determining the age distribution of water exiting a catchment is important for understanding groundwater storage and mixing. New water-tagging capabilities within models track precipitation events as they move through simulated storages, yet forward modeling of individual events may not systematically capture the full transit time distribution (TTD). Here, we present a “sequential precipitation input tagging” (SPIT) framework to tag all input precipitation at regular intervals during extended model simulations. Monthly tags over 7 years were applied at six National Ecological Observatory Network sites to calculate TTDs and derive mean virtual tracer age,^ーT_V, fractions of young water, F_yw, and hydrologic tracer concentrations (water isotopes δ¹⁸O and δ²H) within a tagging enabled version of the Weather Research and Forecast hydrologic model (WRF-Hydro). Throughout seven simulation years, the fraction of simulated discharge derived from tagged events, F_tag, increased each year, with the final year’s F_tag ranging from 66% to 100% and highlights the need to apply SPIT over many years to understand TTDs. When the F_tag was >75%, simulated ^ーT_V ranged 179–923 days and F_yw 0.6%–23.9%, with daily values exhibiting a power-law relationship with precipitation, discharge, and groundwater. Through implementation of SPIT, we find this hydrologic model configuration performs poorly in estimation of ^ーT_V and F_yw (root mean squared error of 469 days and 14.4% respectively), suggesting it misrepresents subsurface mixing. Thus, the SPIT framework provides a reproducible approach to calculate watershed transit times within tagging enabled models and thereby assess and improve representation of hydrologic processes.