A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data

Yang, Shuyu; Yang, Dawen; Chen, Jinsong; Santisirisomboon, Jerasorn; Lü, Weiwei; Zhao, Baoxu

doi:10.1016/j.jhydrol.2020.125206

Cited by 138 publications

(67 citation statements)

References 73 publications

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“…For example, after a simple coupling, we can ensure that the energy and water balance of the entire model remains unchanged, and then determine model parameters in a simple manner through a data-driven method. Meanwhile, physical process models can also add monitoring data to data-driven models [100].…”

Section: Data-driven Modelmentioning

confidence: 99%

A Review on Snowmelt Models: Progress and Prospect

et al. 2021

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The frequency and intensity of flood events have been increasing recently under the warming climate, with snowmelt floods being a significant part. As an effective manner of simulating snowmelt flood, snowmelt models have attracted more and more attention. Through comprehensive analysis of the literature, this paper reviewed the characteristics and current status of different types of snowmelt models, as well as the different coupling methods of models for runoff generation and confluence. We then discussed key issues in snowmelt modelling, including blowing snow model, frozen ground model, and rain-on-snow model. Finally, we give some perspectives from four aspects: data, model structure, forecast and early warning, and forecast and estimation. At present, most of the snowmelt models do not have blowing snow or frozen ground modules. Explicit consideration of blowing snow and soil freezing/thawing processes can improve the accuracy of snowmelt runoff simulations. With climate warming, rain-on-snow events have increased, but the mechanism of enhanced rain and snow mixed flooding is still unclear, particularly for the mechanism of rain-snow-ice mixed runoff generation. The observation and simulation of rain and snow processes urgently need further study. A distributed physical snowmelt model based on energy balance is an advanced tool for snowmelt simulation, but the model structure and parameter schemes still need further improvements. Moreover, the integration of satellite-based snow products, isotopes, and terrestrial water storage change, monitored by gravity satellites, can help improve the calibration and validation of snowmelt models.

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Section: Data-driven Modelmentioning

confidence: 99%

A Review on Snowmelt Models: Progress and Prospect

et al. 2021

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“…Sustainability 2021, 13,4627 The goodness-of-fit (GOF) of each distribution was computed by using RMSE and AIC values to select the most appropriate distribution for fitting each individual variable. The results of GOF are presented in Table 3.…”

Section: Marginal Probability Distribution Functions Of C-vine Model Variablesmentioning

confidence: 99%

“…Process-based modeling methods are based on the principle of water cycle balance coupling various physical processes, such as precipitation, evaporation, infiltration, and other processes [10,11]. These models use large amounts of data (e.g., hydrometeorology, topography, and land use/cover) and robust calibration techniques, while data-driven models can be easily built in practice without considering physical process information from hydrological models and have been extensively used [12][13][14]. Therefore, data-driven technology is very useful and valuable as an option for streamflow forecasting.…”

Section: Introductionmentioning

confidence: 99%

A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China

Huang

et al. 2021

Sustainability

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In this study, a C-vine copula-based quantile regression (CVQR) model is proposed for forecasting monthly streamflow. The CVQR model integrates techniques for vine copulas and quantile regression into a framework that can effectively establish relationships between the multidimensional response-independent variables as well as capture the upper tail or asymmetric dependence (i.e., upper extreme values). The CVQR model is applied to the Xiangxi River basin that is located in the Three Gorges Reservoir area in China for monthly streamflow forecasting. Multiple linear regression (MLR) and artificial neural network (ANN) are also compared to illustrate the applicability of CVQR. The results show that the CVQR model performs best in the calibration period for monthly streamflow prediction. The results also indicate that MLR has the worst effects in extreme quantile (flood events) and confidence interval predictions. Moreover, the performance of ANN tends to be overestimated in the process of peak prediction. Notably, CVQR is the most effective at capturing upper tail dependences among the hydrometeorological variables (i.e., floods). These findings are very helpful to decision-makers in hydrological process identification and water resource management practices.

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“…Zhang et al (2013) used SHAWDHM in the Binggou watershed and the results showed that the model was able to predict soil freezing and thawing, unfrozen soil water content, and snow depth reasonably well. Yang et al (2015) developed a distributed scheme (GBHM) for eco-hydrological simulation in the upper Heihe River, which has been subsequently developed into the distributed Geomorphology-Based Eco-hydrological Model (GBEHM) (Gao et al, 2016) and a physical process and machine learning combined hydrological model GBHM-ANN-CA-CV (Yang et al, 2020). The results identified that the soil freezing and thawing process would significantly influence the runoff generation.…”

Section: Watershed Scalementioning

confidence: 99%

Integrated Hydrologic Modelling of Groundwater-Surface Water Interactions in Cold Regions

Yang

et al. 2021

Front. Earth Sci.

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Groundwater-surface water (GW-SW) interaction, as a key component in the cold region hydrologic cycle, is extremely sensitive to seasonal and climate change. Specifically, the dynamic change of snow cover and frozen soil bring additional challenges in observing and simulating hydrologic processes under GW-SW interactions in cold regions. Integrated hydrologic models are promising tools to simulate such complex processes and study the system behaviours as well as its responses to perturbations. The cold region integrated hydrologic models should be physically representative and fully considering the thermal-hydrologic processes under snow cover variations, freeze-thaw cycles in frozen soils and GW-SW interactions. Benchmarking and integration with scarce field observations are also critical in developing cold region integrated hydrologic models. This review summarizes the current status of hydrologic models suitable for cold environment, including distributed hydrologic models, cryo-hydrogeologic models, and fully-coupled cold region GW-SW models, with a specific focus on their concepts, numerical methods, benchmarking, and applications across scales. The current research can provide implications for cold region hydrologic model development and advance our understanding of altered environments in cold regions disturbed by climate change, such as permafrost degradation, early snow melt and water shortage.

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A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data

Cited by 138 publications

References 73 publications

A Review on Snowmelt Models: Progress and Prospect

A Review on Snowmelt Models: Progress and Prospect

A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China

Integrated Hydrologic Modelling of Groundwater-Surface Water Interactions in Cold Regions

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