Development of A Distributed Modeling Framework Considering Spatiotemporally Varying Hydrological Processes for Sub-Daily Flood Forecasting in Semi-Humid and Semi-Arid Watersheds-Reference-Cited by-同舟云学术

Development of A Distributed Modeling Framework Considering Spatiotemporally Varying Hydrological Processes for Sub-Daily Flood Forecasting in Semi-Humid and Semi-Arid Watersheds

Published:2024-04-24 Issue:10 Volume:38 Page:3725-3754
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Li Xiaoyang,Ye Lei,Gu Xuezhi,Chu Jinggang,Wang Jin,Zhang Chi,Zhou Huicheng

Funder

National Natural Science Foundation of China

Major Science and Technology Program of the Ministry of Water Resources of China

National Key Research and Development Program of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11269-024-03837-5.pdf

Reference37 articles.

1. Apip, Sayama T, Tachikawa Y, Takara K (2012) Spatial lumping of a distributed rainfall-sediment-runoff model and its effective lumping scale. Hydrol Process 26(6):855–871. https://doi.org/10.1002/hyp.8300

2. Chang C et al (2019) A model-aided satellite-altimetry-based flood forecasting system for the Mekong River. Environ Model Softw 112:112–127. https://doi.org/10.1016/j.envsoft.2018.11.017

3. Chao L, Zhang K, Li Z, Wang J, Yao C, Li Q (2019) Applicability assessment of the CASCade two Dimensional SEDiment (CASC2D-SED) distributed hydrological model for flood forecasting across four typical medium and small watersheds in China. J Flood Risk Manag 12:e12518. https://doi.org/10.1111/jfr3.12518

4. Dang TD, Vu DT, Chowdhury AFMK, Galelli S (2020) A software package for the representation and optimization of water reservoir operations in the VIC hydrologic model. Environ Model Softw 126:104673. https://doi.org/10.1016/j.envsoft.2020.104673

5. Ensaniyat NH, Shahkarami N, Jafarinia R, Rezaei J (2021) Investigating uniqueness and identifiability in auto-calibration of the ARNO daily rainfall-runoff model using the PSO algorithm. Int J River Basin Manage 19(4):481–492. https://doi.org/10.1080/15715124.2020.1760290