Multiple spatio-temporal scale runoff forecasting and driving mechanism exploration by K-means optimized XGBoost and SHAP

Journal of Hydrology, Год журнала: 2024, Номер 630, С. 130650 - 130650

Опубликована: Янв. 19, 2024

Язык: Английский

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Streamflow prediction in ungauged catchments through use of catchment classification and deep learning

Journal of Hydrology, Год журнала: 2024, Номер 639, С. 131638 - 131638

Опубликована: Июль 3, 2024

Язык: Английский

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Controls of Climate and Catchment Behaviour on Runoff Response Across Large‐Scale Sample

International Journal of Climatology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 16, 2025

ABSTRACT Recent research has extensively examined the response of runoff to climate change. However, physical mechanisms underlying responses in changing conditions remain poorly understood. To address this gap, study uses measured streamflow and meteorological data from public GAGES‐II database investigate controls influencing catchment behaviour across more than 1000 catchments contiguous United States. Eighteen flow signatures 56 indicators related attributes were analysed grouped using a hierarchical clustering method, resulting classification 1000+ into ten clusters, each with distinct characteristics. Within cluster, we explored patterns response, focusing on changes sensitivity for signature attribute. Our findings indicate that such as ratio, annual runoff, 95th percentile significantly affect total changes. Evapotranspiration displays trade‐off relationship overall but shows synergistic Richard Baker's rapid runoff. Furthermore, driven by align changes, suggesting predominantly influences generation processes. Climate factors tend exert greater influence arid semi‐arid catchments.

Язык: Английский

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Toward improved deep learning-based regionalized streamflow modeling : Exploiting the power of basin similarity

Environmental Modelling & Software, Год журнала: 2025, Номер unknown, С. 106374 - 106374

Опубликована: Март 1, 2025

Язык: Английский

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Evolutionary characteristics and attributions of ecological drought in river: A case study in the Yellow River Basin

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 59, С. 102409 - 102409

Опубликована: Апрель 25, 2025

Язык: Английский

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Precise ecological restoration under water diversions-groundwater-ecosystem interactions in drylands

Journal of Hydrology, Год журнала: 2023, Номер 628, С. 130601 - 130601

Опубликована: Дек. 7, 2023

Язык: Английский

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A Nonlinear Local Approximation Approach for Catchment Classification

Entropy, Год журнала: 2024, Номер 26(3), С. 218 - 218

Опубликована: Фев. 29, 2024

Catchment classification plays an important role in many applications associated with water resources and environment. In recent years, several studies have applied the concepts of nonlinear dynamics chaos for catchment classification, mainly using dimensionality measures. The present study explores prediction as a measure through application local approximation method. method uses concept phase-space reconstruction time series to represent underlying system identifies nearest neighbors phase space evolution prediction. accuracy measures, well optimum values parameters involved (e.g., or embedding dimension, number neighbors), are used classification. For implementation, is daily streamflow data from 218 catchments Australia, predictions made different dimensions neighbors. results suggest that alone can provide good predictions. also indicate better achieved lower smaller numbers neighbors, suggesting possible low dynamics. based on found be useful identification regions/stations higher predictability, which has implications interpolation extrapolation data.

Язык: Английский

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Clustering Similar Ungauged Hydrologic Basins in Saudi Arabia by Message Passing Algorithms

Earth Systems and Environment, Год журнала: 2024, Номер 8(2), С. 325 - 345

Опубликована: Март 5, 2024

Язык: Английский

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Location, Location, Location – Considering Relative Catchment Location to Understand Subsurface Losses

Опубликована: Янв. 1, 2024

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Язык: Английский

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Community Detection Using Modularity Optimization Method For Catchment Classification

Journal of Advanced Research in Applied Sciences and Engineering Technology, Год журнала: 2024, Номер 45(2), С. 78 - 89

Опубликована: Май 24, 2024

A general framework for catchment classification may be helpful more accurate and efficient modeling of hydrologic systems, as well to improve communication between hydrology researchers those in other disciplines. There are plethora numbers methods applied classification, but these years, recent studies implementing the complex networks concept purposes. The community structure which networks-based focus mainly classify catchments. Hence, efficiency network ideas, especially using is examined this study. Specifically, modularity optimization method that one 218 stream-gauges stations entire Australia covers a large variety hydroclimatic, topographic, geomorphic, soil usage, climatic parameters. In present study, applicability validated by proposed method. Australian catchments was further assessed with threshold value 0.8, resulted formation nine communities at least 9 combine have almost 77% total number (165 out 218). All selected were also terms flow characteristics (i.e. mean covariance) drainage area, elevation stream length). behaviors each interpreted distance correlation relationship, give some useful insights towards generalization framework.

Язык: Английский

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