I Will diagnose your watershed's climate sensitivity using the SRF-SEI framework

## What I Do

I provide rapid climate-warming response diagnosis for mountain watersheds. Using my self-developed SRF-SEI anchoring framework, I identify watershed sensitivity to warming and detect whether a watershed is approaching a state transition ("anchor switch").

Traditional hydrology answers "where does water come from?" (source partitioning). My framework answers a different question: **How will the seasonal flow structure of this watershed change under warming?**

## What You Get

- Watershed anchor type diagnosis (snow anchor / mixed anchor / rain anchor / hidden anchor)
- SRF (Spring Runoff Fraction): fraction of annual runoff occurring during April–June, measuring snow reliance
- SEI (Seasonal Extremization Index): July–September mean flow divided by annual median flow
- Historical SEI trend over the past 30–60 years
- Historical winter snow depth trend over the past 30–40 years
- Snowmelt peak date trend
- Plain-language conclusion and actionable recommendations
- 5–10 page diagnostic report with time-series figures

## Who This Is For

- Consulting firms: rapid batch screening of watersheds for project pre-assessment
- Planning institutes: regional water security preliminary studies
- Research teams: watershed response pattern analysis
- Water resource agencies: internal research reference

## Two Case Studies

**Altai Mountains Western Sub-basin (high sensitivity)**
- SRF = 0.384
- 58-year SEI change: -37.6%
- Snow depth trend: -0.058 cm/year
- Conclusion: triple-consistent signal, rapidly shifting toward rain anchor

**Hutubi River, Tianshan North Slope (low sensitivity)**
- SRF = 0.377
- 58-year SEI change: -6.5%
- Snow depth trend: +0.009 cm/year (essentially stable)
- Conclusion: weak signal, possibly buffered by glacier melt

These two watersheds have almost identical SRF (0.384 vs 0.377), yet their system sensitivities are completely different. Traditional water source partitioning cannot distinguish them. This framework can.

## Method Source

Ou, Q. (2024). Snow-Dependence Modulates Warming-Induced Seasonal Extremization in Mountain Watersheds. EarthArXiv preprint.

The method has been validated across 78 watersheds in the western United States and 14,406 grid points in Chinese mountains. Out-of-sample prediction is significant (r = -0.405, p = 0.014).

## Workflow

1. You provide watershed coordinates (longitude/latitude range) or a USGS station ID
2. I retrieve streamflow, snow depth, and climate data from public databases
3. I calculate SRF, SEI, snowmelt peak date, and winter snow depth
4. I determine the anchor type and sensitivity level
5. I deliver a PDF diagnostic report

## Turnaround

5 business days.

## Important Note

This service is for academic research reference only. It does not constitute a legally valid hydrological survey or water resource assessment. It is not intended for engineering approval, water resource argumentation, or final insurance pricing. All conclusions should be cross-validated with local in-situ observations.

## Why Choose Me

- I am faster than traditional hydrology: I can screen dozens of watersheds in one week and identify the 10% that need detailed study
- I add a new dimension: not "how much snow water is there", but "how will the system behave under warming"
- I do not compete with traditional hydrology: I do pre-screening, they do detailed modeling. We are upstream and downstream.