Hydrologic comparison between a lowland catchment (Kielstau, Germany) and a mountainous catchment (XitaoXi, China) using KIDS model in PCRaster

Abstract. The KIDS model (Kielstau Discharge Simulation model) is a simple rainfall-runoff model developed originally for the Kielstau catchment. To extend its range of application we applied it to a completely different catchment, the XitaoXi catchment in China. Kielstau is a small (51 km2) lowland basin in Northern Germany, with large proportion of wetland area. And XitaoXi is a mesoscale (2271 km2) mountainous basin in the south of China. Both catchments differ greatly in size, topography, landuse, soil properties, and weather conditions. We compared two catchments in these features and stress on the analysis how the specific catchment characteristics could guide the adaptation of KIDS model and the parameter estimation for streamflow simulation. The Nash and Sutcliffe coefficient was 0.73 for Kielstau and 0.65 for XitaoXi. The results suggest that the application of KIDS model may require adjustments according to the specific physical background of the study basin.

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Rainfall-Discharge Simulation in Bah Bolon Catchment Area by Mock Method, NRECA Method, and GR2M Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.845.24 ◽

2016 ◽

Vol 845 ◽

pp. 24-29 ◽

Cited By ~ 1

Author(s):

Hadiani Rintis ◽

Suyanto ◽

Yosephina Puspa Setyoasri

Keyword(s):

Catchment Area ◽

The Other ◽

Series Data ◽

Flow Duration Curve ◽

Rainfall Runoff ◽

Flow Duration ◽

Discharge Simulation ◽

Rainfall Runoff Model ◽

North Sumatra ◽

Runoff Model

Rainfall-discharge simulation is a process transformation from rainfall to discharge in a catchment area by modelling. The most popular models are Mock method and NRECA method. It is according to the handbook of irrigation that is written by government (Indonesia). GR2M (Global Rainfall-Runoff Model) is a new model that is not usual to be used in Indonesia. GR2M is a simulation model that needs less parameter than Mock and NRECA methods. This research was conducted in the Bah Bolon catchment area, Simalungun, North Sumatra. It will analyze the simulation of rainfall-discharge by three methods, Mock, NRECA, and GR2M without considering whether the watershed was wet or dry watershed. The analysis was computed the dependable discharge by flow duration curve (fdc) in a series data on each method. The parameter that compared was the dependable discharge, i.e. the discharge with probability 70% (Q70), probability 80% (Q80), and probability 90% (Q90). GR2M will compared with Mock, then compared with NRECA. The results show that the discharge simulation by GR2M methods and the discharge simulation by Mock method has correlation 0.968. The discharge simulation by GR2M method and the discharge simulation by NRECA method has correlation 0,955. It means that GR2M close to the both of them, but GR2M can used easily because it has less parameter than the other. Based on the graphic, GR2M close to the Mock method for probability more than 50%. So, if the probability is 70%, 80%, and 90%, then GR2M method close to Mock method.

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Assessment of Satellite and Reanalysis Precipitation Products for Rainfall–Runoff Modelling in a Mountainous Basin

Environmental Sciences Proceedings ◽

10.3390/ecas2021-10345 ◽

2021 ◽

Vol 8 (1) ◽

pp. 25

Author(s):

Hamed Hafizi ◽

Ali Arda Sorman

Keyword(s):

Weather Prediction ◽

Numerical Weather Prediction Model ◽

Rainfall Runoff ◽

Precipitation Measurement ◽

Gauge Data ◽

Low Performance ◽

Rainfall Runoff Model ◽

Mountainous Basin ◽

Runoff Model ◽

Precipitation Events

Precipitation measurement over a complex topography and highly elevated regions has always been a great challenge in recent decades. On the other hand, satellite-based and numerical weather prediction model outputs can be an alternative to fill this gap. Hence, the goal of this study is to evaluate the spatiotemporal stability and hydrologic utility of four precipitation products (TMPA-3B42v7, IMERGHHFv06, ERA5, and PERSIANN) over a mountainous basin (Karasu basin) located in the eastern part of Turkey. Moreover, the Kling–Gupta efficiency (KGE), including its correlation, bias, and variability ratio components, are used for a direct comparison of precipitation products (PPs) with observed gauge data, and the Hansen–Kuiper (HK) score is utilized to assess the detectability strength of PPs for different precipitation events. In the same way, the hydrologic utility of PPs is tested by exploiting a conceptual rainfall–runoff model under Kling–Gupta efficiency (KGE) and Nash–Sutcliffe efficiency (NSE) metrics. Generally, all PPs show low performance for a direct comparison with observed data while their performance considerably increases for streamflow simulation. TMPA-3B42v7 has high reproducibility in streamflow (KGE = 0.84), followed by IMERGHHFv06 (KGE = 0.76), ERA5 (KGE = 0.75), and PERSIANN (KGE = 0.70), for the entire period (2015–2019) of this study.

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Ensemble flood simulation for a small dam catchment in Japan using 10 and 2 km resolution nonhydrostatic model rainfalls

Natural Hazards and Earth System Science ◽

10.5194/nhess-16-1821-2016 ◽

2016 ◽

Vol 16 (8) ◽

pp. 1821-1839 ◽

Cited By ~ 5

Author(s):

Kenichiro Kobayashi ◽

Shigenori Otsuka ◽

Kazuo Saito ◽

Keyword(s):

Flood Control ◽

Threshold Value ◽

Ensemble Member ◽

Japan Meteorological Agency ◽

Rainfall Runoff ◽

Runoff Simulation ◽

Nonhydrostatic Model ◽

Discharge Simulation ◽

Rainfall Runoff Model ◽

Runoff Model

Abstract. This paper presents a study on short-term ensemble flood forecasting specifically for small dam catchments in Japan. Numerical ensemble simulations of rainfall from the Japan Meteorological Agency nonhydrostatic model (JMA-NHM) are used as the input data to a rainfall–runoff model for predicting river discharge into a dam. The ensemble weather simulations use a conventional 10 km and a high-resolution 2 km spatial resolutions. A distributed rainfall–runoff model is constructed for the Kasahori dam catchment (approx. 70 km2) and applied with the ensemble rainfalls. The results show that the hourly maximum and cumulative catchment-average rainfalls of the 2 km resolution JMA-NHM ensemble simulation are more appropriate than the 10 km resolution rainfalls. All the simulated inflows based on the 2 and 10 km rainfalls become larger than the flood discharge of 140 m3 s−1, a threshold value for flood control. The inflows with the 10 km resolution ensemble rainfall are all considerably smaller than the observations, while at least one simulated discharge out of 11 ensemble members with the 2 km resolution rainfalls reproduces the first peak of the inflow at the Kasahori dam with similar amplitude to observations, although there are spatiotemporal lags between simulation and observation. To take positional lags into account of the ensemble discharge simulation, the rainfall distribution in each ensemble member is shifted so that the catchment-averaged cumulative rainfall of the Kasahori dam maximizes. The runoff simulation with the position-shifted rainfalls shows much better results than the original ensemble discharge simulations.

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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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