SIMPCCe - Climate Change Forecasting Simulator for Reservoirs

🌊 πŸ“Š #climate-change #reservoirs #hydrology #forecasting #machine-learning #data-science

Context

As climate change alters hydrological patterns, it is essential to have tools that allow evaluating and forecasting the impact on water availability in reservoirs. In this context, SIMPCCe is developed as a tool to perform simulations of climate change forecasts applied to reservoirs.

SIMPCCe Application Screenshots

Screenshots of the SIMPCCe application
Figure 1 - Screenshots of the SIMPCCe application

Description

The SIMPCCe application has been developed as an innovative tool for evaluating the effects of climate change on reservoirs. Its objective is to facilitate the implementation of advanced methodologies without requiring code execution or the installation of multiple dependencies.

Main Features:

  1. Hydrometeorological Data Analysis: Integration of projected climate data and historical measurements to estimate the impact on reservoirs.

  2. Use of Artificial Intelligence: Application of machine learning models and neural networks to identify patterns and perform hydrological forecasts.

  3. Generation of Climate Change Scenarios: Modeling of different scenarios to assess the resilience of reservoirs under future conditions.

  4. Intuitive Interface: Allows configuring and executing analyses without requiring advanced programming knowledge.

Perspectives

🏞️ πŸ“Š Water managers and hydrological planning authorities will be able to access detailed and real-time information on the evolution of reservoir storage.

🌍 πŸ” πŸ“‰ Integrating AI into climate forecasting analysis will enhance water management and help mitigate negative impacts.

Additional Notes

πŸ”— GitHub Repository: SIMPCCe on GitHub

πŸ“„ Scientific Publication: SIMPCCe: Climate Change Forecasting Simulator for Reservoirs

🌏 Implementation: Application used in water management projects in collaboration with IHCantabria.