National-Scale Dynamic Water Resources Assessment Model in China National-Scale Dynamic Water Resources Assessment Model in China

National-Scale Dynamic Water Resources Assessment Model in China

Huan Liu and Others
    • €97.99
    • €97.99

Publisher Description

This detailed book introduces China's national dynamic water resources assessment model, presenting its construction, application scenarios, and modern modeling approach to replace traditional statistics-based methods.

The book thoroughly explores the applications of distributed hydrological modeling techniques in national water resources assessment. It presents the successful development of the China Water Assessment Model (CWAM), which is based on the WEP-L hydrological model. CWAM demonstrates broad potential in supporting national water information acquisition, uniquely covering both surveyed and un-surveyed regions. The work highlights the model's ability to account for China's diverse climatic and geological characteristics, while improving the efficiency of water resources assessment and providing solutions to dynamic assessment challenges, especially under climate change and human impacts.

This work will serve as an essential reference for scholars and students in hydrology and water resources, as well as policy makers and engineers involved in water resources management and assessment.

Chapter 1 of this book is freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons Attribution-Non Commercial-No Derivatives (CC BY-NC-ND) 4.0 license.

GENRE
Professional & Technical
RELEASED
2025
11 September
LANGUAGE
EN
English
LENGTH
178
Pages
PUBLISHER
CRC Press
SIZE
11.1
MB
SOCIALLY RESPONSIBLE AI: THEORIES AND PRACTICES SOCIALLY RESPONSIBLE AI: THEORIES AND PRACTICES
2023
Combating Online Hostile Posts in Regional Languages during Emergency Situation Combating Online Hostile Posts in Regional Languages during Emergency Situation
2021
Disinformation, Misinformation, and Fake News in Social Media Disinformation, Misinformation, and Fake News in Social Media
2020
Feature Engineering for Machine Learning and Data Analytics Feature Engineering for Machine Learning and Data Analytics
2018
Social Media Processing Social Media Processing
2017
Twitter Data Analytics Twitter Data Analytics
2013