Modeling Rainwater Harvesting Systems with Covered Storage Tank on A Smartphone
Vikram Vyas
TL;DR
The paper addresses designing rainwater harvesting systems with covered storage under rainfall variability by developing a smartphone‑friendly water balance framework. It defines daily harvest as $R_i = k r_i A$ and updates tank storage with $W_i = max(min(W_{i-1}+R_i,V) - D_i, 0)$, using five years of rainfall to estimate reliability and forecast 30‑day performance via $P_{succ} = (N_s / N_d) * 100$. Implemented as the SimTanka app, the approach guides tank sizing decisions and drought strategies, validated through Bangalore and Thar Desert case studies that show reliability gains from larger tanks or higher runoff, and improved security via demand reduction or water purchases. The work offers a practical decision‑support tool for RWHS planning under climate‑driven rainfall variability, highlighting both the benefits and limitations of using historical records for near‑term forecasts. It also discusses future directions, including the potential role of machine learning in augmenting or replacing the probabilistic framework with automated rainfall and tank measurements.
Abstract
A mathematical model, suitable for smartphone implementation, is presented to simulate the performance of a rainwater harvesting system equipped with a covered storage tank. This model serves to determine the optimal tank size and develop strategies to mitigate drought conditions.