Hydrological simulation of Sperchios River basin in Central Greece using the MIKE SHE model and geographic information systems

Paparrizos, Spyridon; Maris, Fotios

doi:10.1007/s13201-015-0271-5

Cited by 25 publications

(13 citation statements)

References 22 publications

(16 reference statements)

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“…Some physics-based hydrological models, such as SWAT [7][8][9], TOPMODEL/BTOP-MC [10,11], and MIKE SHE [12,13], have already been widely used to simulate the rainfall-runoff relationship for runoff prediction because of the definite and explainable mechanisms and the theoretically high accuracy of the modelling results, in addition to the user-friendly interfaces of the modelling software. However, the above-mentioned physics-based models usually require the observed rainfall as the input and the observed runoff data for calibration/validation and some additional data, like the digital elevation model (DEM), soil types, and land use information.…”

Section: Introductionmentioning

confidence: 99%

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Wang

Liu

Yue

et al. 2020

Water

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A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model.

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Section: Introductionmentioning

confidence: 99%

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Wang

Liu

Yue

et al. 2020

Water

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“…The MIKE SHE model has been widely used to determine the hydrological responses in land-use changes and climate variability (Im et al 2009;Sumarauw and Ohgushi 2012;Zhang 2008), the operation of irrigation (Jayatilaka et al 1998;Singh et al 1999), hydrological simulation of surface runoff and infiltration into saturated and unsaturated zones (Paparrizos and Maris 2017).…”

Section: Description Of Watershed Modelmentioning

confidence: 99%

Impacts of future climate variability on hydrological processes in the upstream catchment of Kase River basin, Japan

2019

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The purpose of this study is to predict the hydrological responses to climate change in the upstream catchment of Kase River basin having an area of 225 km 2 by using MIKE SHE model and GIS. Meteorological and hydrological data for the period of 5 years were available in Kase River basin. Average daily discharge data from 5-year period (1991)(1992)(1993)(1994)(1995) were used for the calibration effort, and average daily discharge was measured at Kawakami station. The Nash-Sutcliffe efficiency, NSE = 0.588, suggests that the model can predict the runoff mechanism for different periods satisfactorily. To assess the climate change impacts, two scenarios were taken under consideration. The data generated from RCM 20 (Regional Climate Model) were utilized under scenario 1 to predict future period 2081-2100 in the river basin. On the other hand, CC-scenario 2 assumes an increase in average monthly air temperature about 3 °C and no change in precipitation in future at the end of twenty-first century. The results revealed that future climate variability under scenario 1 caused an increase of 408.16 mm in surface flow; 46.77 mm in terms of base flow and interflow; and an increase of 41.74 mm actual ET (evapotranspiration). The increase in surface runoff may be mostly due to increase in precipitation. Future climate variability under scenario 2 caused a decrease of 38.43 mm in surface flow; 2.25 mm in terms of base flow and interflow; and an increase of 46.62 mm actual ET. The decrease in surface runoff may be due to unchanged precipitation and increase in temperature.

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“…10 mm D e C h c = (11) Where: hc: High water in a capillary tube (mm) C: Constants (C varies between 10-50 mm2) D 10 : Effective diameter (mm) e: Soil pore number Capillary water height for various soils mentioned by Hansbo [12], presented in Table 1 At equilibrium, the upward force (F↑), must equal the downward force (F↓). Hence, σ.cosα.2πr = πr 2 C.ρ.g (14) Then gives an equation for the height of the capillary rise,…”

Section: A Materialsmentioning

confidence: 99%

Capillary Shock Phenomenon of Groundwater at the Beginning of Rainy Season

Panguriseng¹,

Nanda²

2018

International Journal on Advanced Science, Engineering and Information Technology

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This study aims to determine the causes of decreased water level phenomenon that occurred in the early period of the rainy season. This phenomenon became anti-logic because when the rainwater has entered the soil surface layer, instead of groundwater in the saturated layer decreased (decline). This study is experimental field research conducted in Takalar. This research found that the phenomenon of decreased water level in the early period of the rainy season triggered by increasing of capillary pressure due to shrinking pore diameter after water began to infiltrate into the surface soil layer, i.e., the layer of the vadose zone. Increasing of capillary pressure caused the attraction of groundwater in the saturated zone to the vertical direction, so that the groundwater level has decreased significantly. Therefore, this phenomenon was called by the researcher as "capillary shock phenomenon." (2) The lowest groundwater level did not occur in the climax period of the dry season, but it happened in the early period of the rainy season. The phenomenon that took place during the climax of the dry season was the capillary pressure conditions in the lowest layer of the soil because soil pore diameter reached maximum conditions due to the high evaporation process. Groundwater pumping did not solely cause saltwater intrusion into fresh groundwater zone, but it also could be caused by groundwater decrease for each early period of the rainy season.

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Hydrological simulation of Sperchios River basin in Central Greece using the MIKE SHE model and geographic information systems

Cited by 25 publications

References 22 publications

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Impacts of future climate variability on hydrological processes in the upstream catchment of Kase River basin, Japan

Capillary Shock Phenomenon of Groundwater at the Beginning of Rainy Season

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