Developing a stochastic hydrological model for informing lake water level drawdown management.

Area of Science:

• Environmental Hydrology
• Water Resource Management
• Limnology

Background:

• Winter drawdown (WD) is a common lake management practice for flood control, vegetation reduction, and infrastructure maintenance.
• Regulatory guidelines exist for WD timing and magnitude, but uncertainties in lake characteristics and hydrometeorology complicate target achievement.
• Modeling tools are essential for assessing the feasibility of meeting WD management targets.

Purpose of the Study:

• To develop and evaluate a hydrological modeling framework for lake water level drawdown management (HMF-Lake).
• To assess the ability of 15 Massachusetts lakes to meet state WD guidelines using stochastic simulations.
• To provide quantitative insights for lake managers regarding WD strategy feasibility.

Main Methods:

• Developed the HMF-Lake hydrological modeling framework based on daily lake water balance.
• Simulated inflows using the Cemaneige-GR4J rainfall-runoff model and calculated outflows using a modified Target Storage and Release Based Method (TSRB).
• Employed stochastic model executions to assess probabilities of meeting WD guidelines (drawdown by Dec 1, refill by Apr 1) for various drawdown magnitudes.

Main Results:

• The HMF-Lake model showed satisfactory performance in simulating historical lake water levels (NSE: 0.53–0.86).
• Stochastic analysis indicated that most lakes could not achieve a drawdown of 3 ft or more by Dec 1.
• Starting refills in March was feasible for most lakes to achieve a 95% probability of full refill by Apr 1 for drawdowns up to 6 ft.

Conclusions:

• The HMF-Lake model provides a quantitative tool for lake managers to understand WD feasibility against state guidelines.
• The model can support systematic evaluation of WD strategies at state or regional scales.
• Findings aid adaptive WD management, especially under changing climate conditions.