A finite element one-dimensional kinematic wave rainfall-runoff model

Abstract. This paper introduces TOPKAPI (TOPographic Kinematic APproximation and Integration), a new physically-based distributed rainfall-runoff model deriving from the integration in space of the kinematic wave model. The TOPKAPI approach transforms the rainfall-runoff and runoff routing processes into three ‘structurally-similar’ non-linear reservoir differential equations describing different hydrological and hydraulic processes. The geometry of the catchment is described by a lattice of cells over which the equations are integrated to lead to a cascade of non-linear reservoirs. The parameter values of the TOPKAPI model are shown to be scale independent and obtainable from digital elevation maps, soil maps and vegetation or land use maps in terms of slope, soil permeability, roughness and topology. It can be shown, under simplifying assumptions, that the non-linear reservoirs aggregate into three reservoir cascades at the basin scale representing the soil, the surface and the drainage network, following the topographic and geomorphologic elements of the catchment, with parameter values which can be estimated directly from the small scale ones. The main advantage of this approach lies in its capability of being applied at increasing spatial scales without losing model and parameter physical interpretation. The model is foreseen to be suitable for land-use and climate change impact assessment; for extreme flood analysis, given the possibility of its extension to ungauged catchments; and last but not least as a promising tool for use with General Circulation Models (GCMs). To demonstrate the quality of the comprehensive distributed/lumped TOPKAPI approach, this paper presents a case study application to the Upper Reno river basin with an area of 1051 km2 based on a DEM grid scale of 200 m. In addition, a real-world case of applying the TOPKAPI model to the Arno river basin, with an area of 8135 km2 and using a DEM grid scale of 1000 m, for the development of the real-time flood forecasting system of the Arno river will be described. The TOPKAPI model results demonstrate good agreement between observed and simulated responses in the two catchments, which encourages further developments of the model. Keywords: rainfall-runoff modelling, topographic, kinematic wave approximation, spatial integration, physical meaning, non-linear reservoir model, distributed and lumped

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Sensitivity Analysis For Control Parameters Of Hybrid And Standard PSO Algorithms: Application Via A Rainfall-runoff Model Calibration

4th International Symposium on Innovative Approaches in Engineering and Natural Sciences Proceedings ◽

10.36287/setsci.4.6.085 ◽

2019 ◽

Author(s):

Umut Okkan ◽

Umut Kırdemir

Keyword(s):

Sensitivity Analysis ◽

Model Calibration ◽

Control Parameters ◽

Rainfall Runoff ◽

Rainfall Runoff Model ◽

Runoff Model

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Effects of streamflow isotope sampling strategies on the calibration of a tracer‐aided rainfall‐runoff model

Hydrological Processes ◽

10.1002/hyp.14223 ◽

2021 ◽

Author(s):

Jamie Lee Stevenson ◽

Christian Birkel ◽

Aaron J. Neill ◽

Doerthe Tetzlaff ◽

Chris Soulsby

Keyword(s):

Sampling Strategies ◽

Rainfall Runoff ◽

Rainfall Runoff Model ◽

Runoff Model

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Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Water ◽

10.3390/w13091226 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1226

Author(s):

Pakorn Ditthakit ◽

Sirimon Pinthong ◽

Nureehan Salaeh ◽

Fadilah Binnui ◽

Laksanara Khwanchum ◽

...

Keyword(s):

Exchange Rate ◽

Rainfall Runoff ◽

Management Planning ◽

Runoff Variation ◽

Groundwater Exchange ◽

Monthly Runoff ◽

Rainfall Runoff Model ◽

Two Parameters ◽

Runoff Model

Accurate monthly runoff estimation is crucial in water resources management, planning, and development, preventing and reducing water-related problems, such as flooding and droughts. This article evaluates the monthly hydrological rainfall-runoff model’s performance, the GR2M model, in Thailand’s southern basins. The GR2M model requires only two parameters: production store (X1) and groundwater exchange rate (X2). Moreover, no prior research has been reported on its application in this region. The 37 runoff stations, which are located in three sub-watersheds of Thailand’s southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. The Thornthwaite method was utilized for the determination of evapotranspiration. The model’s performance was conducted using three statistical indices: Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The model’s calibration results for 37 runoff stations gave the average NSE, r, and OI of 0.657, 0.825, and 0.757, respectively. Moreover, the NSE, r, and OI values for the model’s verification were 0.472, 0.750, and 0.639, respectively. Hence, the GR2M model was qualified and reliable to apply for determining monthly runoff variation in this region. The spatial distribution of production store (X1) and groundwater exchange rate (X2) values was conducted using the IDW method. It was susceptible to the X1, and X2 values of approximately more than 0.90, gave the higher model’s performance.

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