River flow forecasting through conceptual models part I — A discussion of principles☆
Abstract
The principles governing the application of the conceptual model technique to river flow forecasting are discussed. The necessity for a systematic approach to the development and testing of the model is explained and some preliminary ideas suggested.
References (3)
- N.H. Crawford et al.
A conceptual model of the hydrologic cycle
Int. Assoc. Sci. Hydr., Publ. No. 63
(1964)
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Integrating conceptual and machine learning models to enhance daily-Scale streamflow simulation and assessing climate change impact in the watersheds of the Godavari basin, India
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Effects of multi-year droughts on the precipitation-runoff relationship: An integrated analysis of meteorological, hydrological, and compound droughts
2024, Journal of HydrologyMulti-year droughts cause severe water scarcity, ecosystem collapse, and significant socio-economic losses. Understanding the impact of these droughts on the precipitation-runoff (PR) relationship is crucial for managing water resources and mitigating drought-related damage. However, previous research focuses mainly on the influence of multi-year meteorological droughts on the PR relationship, neglecting multi-year hydrological droughts and their compound effects. Studies also generally overlook the speed and volume of precipitation-to-runoff conversion, considering only the quality of PR relationship. Furthermore, there is a lack of effective simulation methods using the intensity of multi-year droughts to understand the PR relationship. To address these gaps, this study defines multi-year compound droughts and employs three key parameters, namely, the determination of coefficient (R2), linear regression slope, and runoff coefficient, to assess the impact of multi-year droughts on changes in the PR relationship's quality, precipitation-to-runoff conversion speed, and runoff volume. A random forest model is then employed to simulate these parameters based on the intensity of multi-year droughts. Case studies conducted in three basins within the arid-semiarid region of northern China as well as in three basins in the humid region of southern China reveal that multi-year droughts result in water shortages and have a significant impact on the PR relationship. Among different types of multi-year droughts, hydrological droughts have the greatest impact on the PR relationship, followed by compound and meteorological droughts, respectively. During meteorological, hydrological, and compound drought periods, the average rainfall (runoff) is lower by 26 – 30% (33 – 58%), 18 – 28% (36 – 60%), and 18 – 26% (30 – 58%) respectively compared to non-drought periods. Furthermore, the PR relationship remains stable in the rivers of the humid region in southern China. In contrast, the Loess Plateau region in northern China exhibits a substantial decrease in both the speed and volume of precipitation-to-runoff conversion. Interestingly, this decline is not accompanied by a significant increase in the R2 of the PR relationship. The random forest approach effectively simulates key parameters of the PR relationship based on the intensity of multi-year droughts. These findings hold crucial implications for the long-term regional water resource planning.
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This is the first of a series of papers which it is hoped to publish from time to time reporting the results of the continuing work in this field of the Institute of Hydrology, Wallingford, Berkshire, U.K.