Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution

Yin, Zhaokai; Liao, Weihong; Lei, Xiaohui; Wang, Hao; Wang, Ruojia

doi:10.3390/su10093209

Cited by 16 publications

(6 citation statements)

References 62 publications

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“…The proposed approach can enable policy makers to design an optimal and robust rain gauge network that can tackle the uncertainties by selecting optimal sites without the need of additional instrumentation. This result is in accordance with (Yin et al. , 2018), i.e., that for certain areas the rain gauge network configuration is more important than its quantity or density.…”

Section: Discussionsupporting

confidence: 91%

Optimal rain gauge network to reduce rainfall impacts on urban mobility – a spatial sensitivity analysis

Simoyama¹,

Tomás²,

Pinto³

et al. 2022

IMDS

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PurposeA sustainable transportation system should represent a win-win situation: minimizing transport's impact on the environment and reducing natural disasters' effects on transportation. A well-distributed set of rain gauges is crucial for monitoring services in smart cities. However, those services should consider the uncertainties about the registers of rainfall impacts. In this paper, the authors present a case study of optimal rain gauge location based on an actual database of rainfall events with impacts on urban mobility in the city of Sao Paulo (Brazil).Design/methodology/approachThis paper presents a maximal covering location formulation and proposes a robustness analysis considering spatial location perturbations.FindingsIn this case study, the robustness of the objective function is above 99.99%. The robustness for the number of covered demand points is 88.93%, and the frequency associated with every candidate is between 11.71% and 69.49%.Originality/valueIncorporating spatial uncertainties on coverage problems is essential to provide stakeholders more realistic supporting tools and to draw different possible scenarios.

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Section: Discussionsupporting

confidence: 91%

Optimal rain gauge network to reduce rainfall impacts on urban mobility – a spatial sensitivity analysis

Simoyama¹,

Tomás²,

Pinto³

et al. 2022

IMDS

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show abstract

“…Therefore, the model can not only simulate the dynamic change of runoff process at each point, but also can be used to analyze the impact of changing environment on hydrological processes. Global parameters need to be set to run the model [18]. We used daily-scale simulation results of runoff to compare with the Conv-TALSTM model.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Due to the high degree of non-linearity, uncertainty and variability of the hydrological process, even if the model is improved, the runoff simulation may not meet expectations. It will also encounter other problems, such as the same effect with different parameters, difficulty in obtaining data and expensive calculations [17,18]. Data-driven models make predictions by mining the relevant information between input and output variables without studying physical processes.…”

Section: Introductionmentioning

confidence: 99%

Research on Runoff Simulations Using Deep-Learning Methods

et al. 2021

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Runoff simulations are of great significance to the planning management of water resources. Here, we discussed the influence of the model component, model parameters and model input on runoff modeling, taking Hanjiang River Basin as the research area. Convolution kernel and attention mechanism were introduced into an LSTM network, and a new data-driven model Conv-TALSTM was developed. The model parameters were analyzed based on the Conv-TALSTM, and the results suggested that the optimal parameters were greatly affected by the correlation between the input data and output data. We compared the performance of Conv-TALSTM and variant models (TALSTM, Conv-LSTM, LSTM), and found that Conv-TALSTM can reproduce high flow more accurately. Moreover, the results were comparable when the model was trained with meteorological or hydrological variables, whereas the peak values with hydrological data were closer to the observations. When the two datasets were combined, the performance of the model was better. Additionally, Conv-TALSTM was also compared with an ANN (artificial neural network) and Wetspa (a distributed model for Water and Energy Transfer between Soil, Plants and Atmosphere), which verified the advantages of Conv-TALSTM in peak simulations. This study provides a direction for improving the accuracy, simplifying model structure and shortening calculation time in runoff simulations.

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“…In this paper, the main study area is the drainage basin of Gaoan hydrological station, which is located on Jinjiang River, with an area of 6,215 km 2 (Figure 1). The elevation of the catchment is higher in the northwest, where most tributaries of the Jinjiang River originate, ranging from 18 to 1,096 m (Yin et al 2018). There is no large reservoir in the basin, and it is less disturbed by human activities than other basins.…”

Section: Study Area and Datamentioning

confidence: 99%

Research and application of parameter estimation method in hydrological model based on dual ensemble Kalman filter

Lu²,

Liao

et al. 2021

Hydrology Research

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Although field measurements and using long hydrological datasets provide a reliable method for parameters' calibration, changes in the underlying basin surface and lack of hydrometeorological data may affect parameter accuracy in streamflow simulation. The ensemble Kalman filter (EnKF) can be used as a real-time parameter correction method to solve this problem. In this study, five representative Xin'anjiang model parameters are selected to study the effects of the initial parameter ensemble distribution and the specific function form of the parameter on the EnKF parameter estimation process for both single and multiple parameters. Results indicate: (1) the method of parameter calibration to determine the initial distribution mean can improve the assimilation efficiency; (2) there is mutual interference among the parameters during multiple parameters' estimation which invalidates some conclusions of single-parameter estimation. We applied and evaluated the EnKF method in Jinjiang River Basin, China. Compared to traditional approaches, our method showed a better performance in both basins with long hydrometeorological dataset (an increase of Kling–Gupta efficiency (KGE) from 0.810 to 0.887 and a decrease of bias from −1.08% to −0.74%); and in basins with a lack of hydrometeorological data (an increase of KGE from 0.536 to 0.849 and a decrease of bias from −15.55% to −11.42%).

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Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution

Cited by 16 publications

References 62 publications

Optimal rain gauge network to reduce rainfall impacts on urban mobility – a spatial sensitivity analysis

Optimal rain gauge network to reduce rainfall impacts on urban mobility – a spatial sensitivity analysis

Research on Runoff Simulations Using Deep-Learning Methods

Research and application of parameter estimation method in hydrological model based on dual ensemble Kalman filter

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