Dynamic runoff simulation in a changing environment: A data stream approach

Yang, Qinli; Zhang, Heng; Wang, Guoqing; Luo, Shasha; Chen, Dongzi; Peng, Wanshan; Shao, Jun

doi:10.1016/j.envsoft.2018.11.007

Cited by 29 publications

(15 citation statements)

References 49 publications

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“…The study area belongs to a humid subtropical climate zone that is hot and rainy in summer while mild and dry in winter. The mean annual temperature ranges from 11.1 • C to 17.2 • C, and the mean annual precipitation was about 1053 mm between 1960 and 2012 [34]. Seven rain gauges and one hydrological station were set up in the study area.…”

Section: Study Areamentioning

confidence: 99%

Attribution Analysis for Runoff Change on Multiple Scales in a Humid Subtropical Basin Dominated by Forest, East China

Yang

Luo

et al. 2019

Forests

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Attributing runoff change to different drivers is vital in order to better understand how and why runoff varies, and to further support decision makers on water resources planning and management. Most previous works attributed runoff change in the arid or semi-arid areas to climate variability and human activity on an annual scale. However, attribution results may differ greatly according to different climatic zones, decades, temporal scales, and different contributors. This study aims to quantitatively attribute runoff change in a humid subtropical basin (the Qingliu River basin, East China) to climate variability, land-use change, and human activity on multiple scales over different periods by using the Soil and Water Assessment Tool (SWAT) model. The results show that runoff increased during 1960–2012 with an abrupt change occurring in 1984. Annual runoff in the post-change period (1985–2012) increased by 16.05% (38.05 mm) relative to the pre-change period (1960–1984), most of which occurred in the winter and early spring (March). On the annual scale, climate variability, human activity, and land-use change (mainly for forest cover decrease) contributed 95.36%, 4.64%, and 12.23% to runoff increase during 1985–2012, respectively. On the seasonal scale, human activity dominated runoff change (accounting for 72.11%) in the dry season during 1985–2012, while climate variability contributed the most to runoff change in the wet season. On the monthly scale, human activity was the dominant contributor to runoff variation in all of the months except for January, May, July, and August during 1985–2012. Impacts of climate variability and human activity on runoff during 2001–2012 both became stronger than those during 1985–2000, but counteracted each other. The findings should help understandings of runoff behavior in the Qingliu River and provide scientific support for local water resources management.

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Section: Study Areamentioning

confidence: 99%

Attribution Analysis for Runoff Change on Multiple Scales in a Humid Subtropical Basin Dominated by Forest, East China

Yang

Luo

et al. 2019

Forests

Self Cite

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“…A hydrological model is supposed not only to have a good predictive power but also the ability of capturing relationships among the forcing factors and catchment response so that an accurate estimate of rainfall-runoff could be made (Shortridge et al, 2016). However, until now, there is no hydrological model that can simulate basin-behaviour universally well against all the hydrological challenges inflicted from climate change and human-interventions (Yang et al, 2019). As a result, many hydrological models have been devised considering functioning and robustness of models in explaining underlying complexity in quantifying basin-scale response to small-scale spatial complexity of physical processes (Shortridge et al, 2016;Herath et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Machine learning based streamflow prediction in a hilly catchment for future scenarios using CMIP6 data

Singh

Vardhan

Sahu

et al. 2022

Preprint

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Abstract. The alteration in river flow patterns, particularly those that originate in the Himalayas, has been caused by the increased temperature and rainfall variability brought on by climate change. Due to the impending intensification of extreme climate events, as predicted by the Intergovernmental Panel on Climate Change (IPCC) in its sixth assessment report, it is more essential than ever to predict changes in streamflow for future periods. Despite the fact that some research has utilised machine learning-based models to predict streamflow patterns in response to climate change, very few studies have been undertaken for a mountainous catchment, with the number of studies for the western Himalaya being so small as to be considered insignificant. This study investigates the capability of different machine learning (ML) models, including the Gaussian Linear Regression Model (GLM), Gaussian Generalized Additive Model (GAM), Multivariate Adaptive Regression Splines (MARS), Artificial Neural Network (ANN), and Random Forest (RF), in streamflow prediction over the Sutlej River Basin in western Himalaya during the periods 2041–2070 (2050s) and 2071–2100 (2080s) for two greenhouse gas trajectories (SSP245 and SSP585). Coupled Model Intercomparison Project Phase 6 (CMIP6) bias corrected data downscaled at grid resolution of 0.25°× 0.25° for 6 General Circulation Models (GCMs) were used for this purpose. Four different rainfall scenarios (R0, R1, R2, and R3) were applied to the models trained with daily data (1979–2009) at Kasol (the outlet of the basin) in order to better understand how catchment size and the geo-hydro-morphological aspects of the basin affect runoff. RF model with rainfall scenario R3 which outperformed other models during the training and testing period therefore was chosen to simulate streamflow in the Sutlej River in the 2050s and 2080s under the SSP245 and SSP585 scenarios. The mean ensemble of model results show that the mean annual streamflow of the Sutlej River is expected to rise between 2050s and 2080s by 5.51 to 6.04 % for SSP585 and by 6.65 to 6.75 % for SSP245. The seasonal streamflow also is expected to increase in the 2050s and 2080s under both emission scenarios, with the exception of the pre-monsoon, where a decline in streamflow is anticipated for SSP585 in the 2080s. However, under both the emission scenarios, there seemed to be significant variation in the streamflow simulations among the individual models for various time periods. It has been found that the pattern of this variability is highly correlated with the pattern of precipitation and temperature predicted by various GCMs for future emission scenarios. The present study will therefore assist in strategy planning for ensuring the sustainable use of water resources downstream by acquiring a knowledge of the nature and causes of unpredictable streamflow patterns.

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“…To address these problems, a variety of models and approaches have been developed. These approaches can be divided into three categories: statistical methods (Valipour et al, 2013), physical methods (Duan et al, 1992;Wang et al, 2011;Robertson et al, 2013) and machine-learning methods (Chau et al, 2005;Liu et al, 2015;Rajaee et al, 2019;Zhang et al, 2018;Yaseen et al, 2019;Fotovatikhah et al, 2018;Mosavi et al, 2018;Chau, 2017;Ghorbani et al, 2018). Each method has its own conditions and scope of application.…”

Section: Introductionmentioning

confidence: 99%

Multistep-ahead daily inflow forecasting using the ERA-Interim reanalysis data set based on gradient-boosting regression trees

Liao

Liu

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Inflow forecasting plays an essential role in reservoir management and operation. The impacts of climate change and human activities have made accurate inflow prediction increasingly difficult, especially for longer lead times. In this study, a new hybrid inflow forecast framework – using the ERA-Interim reanalysis data set as input and adopting gradient-boosting regression trees (GBRT) and the maximal information coefficient (MIC) – is developed for multistep-ahead daily inflow forecasting. Firstly, the ERA-Interim reanalysis data set provides more information for the framework, allowing it to discover inflow for longer lead times. Secondly, MIC can identify an effective feature subset from massive features that significantly affects inflow; therefore, the framework can reduce computational burden, distinguish key attributes from unimportant ones and provide a concise understanding of inflow. Lastly, GBRT is a prediction model in the form of an ensemble of decision trees, and it has a strong ability to more fully capture nonlinear relationships between input and output at longer lead times. The Xiaowan hydropower station, located in Yunnan Province, China, was selected as the study area. Six evaluation criteria, namely the mean absolute error (MAE), the root-mean-squared error (RMSE), the Pearson correlation coefficient (CORR), Kling–Gupta efficiency (KGE) scores, the percent bias in the flow duration curve high-segment volume (BHV) and the index of agreement (IA) are used to evaluate the established models utilizing historical daily inflow data (1 January 2017–31 December 2018). The performance of the presented framework is compared to that of artificial neural network (ANN), support vector regression (SVR) and multiple linear regression (MLR) models. The results indicate that reanalysis data enhance the accuracy of inflow forecasting for all of the lead times studied (1–10 d), and the method developed generally performs better than other models, especially for extreme values and longer lead times (4–10 d).

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Dynamic runoff simulation in a changing environment: A data stream approach

Cited by 29 publications

References 49 publications

Attribution Analysis for Runoff Change on Multiple Scales in a Humid Subtropical Basin Dominated by Forest, East China

Attribution Analysis for Runoff Change on Multiple Scales in a Humid Subtropical Basin Dominated by Forest, East China

Machine learning based streamflow prediction in a hilly catchment for future scenarios using CMIP6 data

Multistep-ahead daily inflow forecasting using the ERA-Interim reanalysis data set based on gradient-boosting regression trees

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