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Study on rainfall-runoff simulation and prediction in lake basin based on dynamic data-driven deep recurrent network

  • role： First author第一作者
  • Affiliation：

    Wuhan University, Wuhan 430079, China

    Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, National Administration of Surveying, Mapping and Geoinformation, Nanchang 330209, China

    Jiangxi Provincial Geomatics Center, Nanchang 330209, China

  • Email：liaomingjxgc@foxmail.com
  • Introduction：廖明，1980年生，男，博士研究生，研究方向为地理信息服务与湖泊流域生态监测。E-mail：liaomingjxgc@foxmail.com
LIAO Ming123,  
  • role： Corresponding author通信作者
  • Affiliation：

    Wuhan University, Wuhan 430079, China

  • Email：zqzhan@sgg.whu.edu.cn
  • Introduction：詹总谦，1978年生，男，副教授，研究方向为摄影测量与遥感。E-mail：zqzhan@sgg.whu.edu.cn
ZHAN Zongqian1*,  
  • Affiliation：

    Wuhan University, Wuhan 430079, China

GUO Wei1,  
  • Affiliation：

    Wuhan University, Wuhan 430079, China

PANG Chao1,  
  • Affiliation：

    Wuhan University, Wuhan 430079, China

LIU Yi1

résumé

Performing rainfall-runoff simulation and prediction in a lake basin is a time-series analysis problem in complex systems. Existing mechanistic and identification methods for model selection have advantages and disadvantages. Mechanism model has a clear physical explanation, but it requires professional data for support and the model-solving process is complicated. Identification model is flexible and its solution is simple, but it has difficulty building a universal model and the accuracy of the model is low. Existing simulation models are also based mostly on static data, which cannot effectively use real-time observation data from sensor networks to improve simulation uncertainty. This study aims to improve the problem of traditional identification models being incapable of effectively using timing information, which results in low simulation accuracy. Moreover, it establishes a simulation and simulation framework for dynamic feedback and adaptive adjustment between observation and numerical simulation. This research proposes a dynamic data-driven model based on deep recurrent neural network, named dynamic data-driven time sequence model, which consists of a multilayered long short-term memory loop body and a fully connected layer. The proposed model incorporates runoff remote sensing rainfall data and ground station observation rainfall data as static data input and recent ground station actual runoff observation data as dynamic data input to simulate catchment runoff process. Several cases of multiple sub-river runoff into Poyang Lake indicate that in static data-driven mode, with TRMM_3B42_V7 precipitation as input, the ENS accuracy of DTSM is 10 percentage points higher or more than that of the mechanism model. The cases also indicate that in static data-driven mode, with the fusion precipitation from TRMM_3B42_V7 and ground station as input, the ENS accuracy of DTSM is 29 percentage points higher or more than that of the mechanism model. Lastly, the dynamic data-driven model can further improve the accuracy of simulation compared with the static-driven model, and the improvement is substantial in the basin with lower accuracy in static data-driven mode. The model based on deep recurrent neural network can effectively extract the timing information from the data. The dynamic data-driven model can make adaptive adjustments to improve simulation uncertainty. Based on the above two reasons, the DTSM proposed in this paper can achieve the same or a better simulation accuracy than existing representative mechanism models. At the same time, DTSM is flexible and the solving process is simple.

mots-clés

remote sensing;data-driven simulation;deep learning;runoff simulation;TRMM rainfall;sensor network

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