Skip to main content

Optimal utilization of funds for watershed conservation

Point of Contention

How to rate a watershed ? How to estimate the level of degradation in a watershed ? Which parameters can represent the status of a watershed ? Is it level of erosion/deforestation or change in climatic pattern or decay in water quality or land conversion ? Which will be the most important parameter to represent the status of the watershed or will it take more than one or two parameters to estimate the health of the watershed ? 

Many questions are required to be answered before embargoing upon a watershed management plan.It is important to separate the needful watersheds from the already conserved catchments for optimal utilization of the available funds.But finding a full proof method is not very easy.

Do You Know ?

Spatial and Spectral Resolution: 

Spatial Resolution in Geographical Information System is defined as the pixel spacing on the real earth surface or Ground Sample Distance(GSD) of the region captured in the image.

In general the Spatial Resolution can be defined as the measure of how closely lines can be resolved in an image.

Spectral Resolution in the other hand is the number of distinguishable spectrum or wavelengths which reproduce colour in a multi-spectral image

Summary : It is important to find a method/media for representation of Watershed Status for optimal distribution of fund of conservation.

Comments

Popular posts from this blog

What was in news last week in the World of Water and Power Industry ?

Indian Government Approves INR 12,461 Cr Support for 31,350 MW Hydro Electric Projects The Indian government has recently approved a budgetary support of INR 12,461 crore to facilitate the development of 31,350 MW of Hydroelectric projects, aiming to boost the hydro power sector by providing funding for necessary infrastructure development like roads and bridges in remote locations where these projects are often situated; this scheme will run from FY 2024-25 to FY 2031-32 Click here to learn more. "The U.S. Department of Energy (DOE) announced nearly $16.7 million for 25 small business-led hydropower and marine energy projects..." The US Department of Energy has allocated $16.7 million for 25 small business-led hydropower and marine energy projects through the Small Business Innovation Research and Small Business Technology Transfer programs. The projects, focusing on hydropower and marine energy, aim to advance water power technologies, balancing electricity grids with rene...

Five Most Suitable Scholarship Opportunities for the prospective students of Water related Masters or Doctoral programs

Enter your email address and become a subscriber of Energy in Style powered by TinyLetter The Rotary Scholarships for Water and Sanitation Professionals, PhD Spatial Governance of Climate Change Adaptation and Water Management, Earmarked Scholarships Scheme To Support Category 1 Project Grants 2023, Japan Foundation for UNU (jfUNU) Scholarship, and Zema Energy Studies Scholarship are scholarships for students pursuing MSc in water-related programs at IHE Delft University of Netherlands, Australia, Japan, and Australia. The scholarships offer a living stipend of $32,192 per annum, tuition fees covered, and Single Overseas Student Health Cover (OSHC). 1) Name of Scholarship: Rotary Scholarships for Water and Sanitation Professionals Scholarship Amount: Entire course fee Country of Origin: Global Program: MSc in some Water-related programs offered by IHE Delft University of Netherlands Deadline: April(Annual) Course starts July onward Link for Morte Info:  https://www.un-ihe....

Hydrology Meets AI: Using Neural Networks to Predict Time‑Dependent Variables

Most hydrological variables are strongly time‑dependent. River discharge, groundwater level, rainfall, evapotranspiration, even reservoir storage – all of these change over hours, days, seasons, and years. Capturing this temporal behaviour accurately is at the heart of modern water resources modelling and management. Artificial Neural Networks (ANNs) have emerged as a powerful tool for dealing with such time‑dependent problems. They are data‑driven, flexible, and capable of learning complex nonlinear relationships that are often difficult to express analytically. Even though the exact theoretical relationship between time dependence in hydrological variables and neural network architecture is still an open research question, experience shows that ANNs can deliver more accurate predictions than many traditional hydrological models in a wide range of applications. This post outlines a practical methodology for applying neural networks to develop hydrological prediction models, especially...