Types of Rainfall–Runoff Models Explained | Processes, Space, Time, and Uncertainty

Описание: Rainfall–runoff modelling is at the core of hydrology, flood estimation, and water resources engineering. But with so many different models available, choosing the right one can be confusing.

In this video, I provide a clear and structured overview of rainfall–runoff models, starting with a brief explanation of the rainfall–runoff process and then introducing four key dimensions used to classify hydrological models.
For each classification, I explain:
• The key characteristics
• Typical applications
• Strengths and limitations
• Examples of well-known hydrological models
This video is ideal for hydrology students, civil and water engineers, researchers, and practitioners who want to understand how rainfall–runoff models differ and when each type should be used.
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📌 Topics Covered
•Rainfall–runoff process fundamentals
•Process Representation: Empirical, conceptual, and physically-based models
•Spatial Discretisation: Lumped, semi-distributed, and distributed models
•Temporal Scope: Event-based vs continuous modelling
•Treatment of Uncertainty: Deterministic vs stochastic hydrological models
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⏱️ Timestamps
00:00 Introduction and motivation
00:35 What is the rainfall–runoff process?
01:28 Classification by Process Representation
01:37 Empirical (black-box) models
02:14 Conceptual (grey-box) models
03:00 Physically-based (white-box) models
03:37 Classification by Spatial Discretisation
03:47 Lumped models
04:13 Semi-distributed models
04:53 Distributed models
05:27 Classification by Temporal Scope
05:35 Event-based models
05:51 Continuous models
06:11 Classification by Treatment of Uncertainty
06:19 Deterministic models
06:38 Stochastic models
07:02 Concluding notes

Keywords:
#rainfallrunoffmodel #hydrology #floodmodelling #hydrologicalmodelling #waterresourcemanagement #hydrologytutorial #empiricalmodels #conceptualmodels #physically-basedmodel #lumpedmodel #deterministicmodel #stochasticmodel #event-based #continuousmodelling #distributedmodel #semi-distributedmodel