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

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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Uncertainty Evaluation in a Flood Forecasting Model Using JMA Numerical Weather Prediction

Journal of Disaster Research ◽

10.20965/jdr.2009.p0272 ◽

2009 ◽

Vol 4 (4) ◽

pp. 600-605 ◽

Cited By ~ 5

Author(s):

Hadi Kardhana ◽

◽

Akira Mano ◽

Keyword(s):

Numerical Weather Prediction ◽

Lead Time ◽

Weather Prediction ◽

Japan Meteorological Agency ◽

Rainfall Runoff ◽

Flood Prediction ◽

Numerical Weather ◽

Forecast Lead Time ◽

Rainfall Runoff Model ◽

Runoff Model

Numerical weather prediction (NWP) is useful in flood prediction using a rainfall-runoff model. Uncertainty occurring in the forecast, however, adversely affects flood prediction accuracy, in addition to uncertainty inherent in the rainfall-runoff model. Clarifying this uncertainty and its magnitude is expected to lead to wider forecast applications. Taking the case of Japan’s Shichikashuku Dam, 6 flood events between 2002 and 2007 were analyzed. NWP was based on short-range forecasts by the Japan Meteorological Agency (JMA). The rainfall-runoff model is based on a distributed tank model. This research calculates uncertainty by identifying and quantifying the relative error of forecasts by a) NWP and b) the runoff model. Results showed that NAP is the main cause of flood forecast uncertainty. They also showed the correlation between forecast lead time and uncertainty. Uncertainty rises with longer lead time, corresponding to the magnitude of observed discharge and precipitation.

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Multistage stochastic linear programming model for daily coordinated multi-reservoir operation

Journal of Hydroinformatics ◽

10.2166/hydro.2008.007 ◽

2008 ◽

Vol 10 (1) ◽

pp. 23-41 ◽

Cited By ~ 9

Author(s):

Yongdae Lee ◽

Sheung-Kown Kim ◽

Ick Hwan Ko

Keyword(s):

Stochastic Model ◽

Reservoir Operation ◽

Lead Time ◽

Weather Prediction ◽

Prediction System ◽

Operation Planning ◽

Rainfall Runoff ◽

Multi Stage ◽

Rainfall Runoff Model ◽

Runoff Model

Operation planning for a coordinated multi-reservoir is a complex and challenging task due to the inherent uncertainty in inflow. In this study, we suggest the use of a new, multi-stage and scenario-based stochastic linear program with a recourse model incorporating the meteorological weather prediction information for daily, coordinated, multi-reservoir operation planning. Stages are defined as prediction lead-time spans of the weather prediction system. The multi-stage scenarios of the stochastic model are formed considering the reliability of rainfall prediction for each lead-time span. Future inflow scenarios are generated by a rainfall–runoff model based on the rainfall forecast. For short-term stage (2 days) scenarios, the regional data assimilation and prediction system (RDAPS) information is employed, and for mid-term stage (more than 2 days) scenarios, precipitation from the global data assimilation and prediction system (GDAPS) is used as an input for the rainfall–runoff model. After the 10th day (third stage), the daily historical rainfall data are used following the ensemble streamflow prediction (ESP) procedure. The model is applied to simulate the daily reservoir operation of the Nakdong River basin in Korea in a real-time operational environment. The expected benefit of the stochastic model is markedly superior to that of the deterministic model with average rainfall information. Our study results confirm the effectiveness of the stochastic model in real-time operation with meteorological forecasts and the presence of inflow uncertainty.

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

Advances in Geosciences ◽

10.5194/adgeo-21-125-2009 ◽

2009 ◽

Vol 21 ◽

pp. 125-130 ◽

Cited By ~ 1

Author(s):

X. Zhang ◽

G. Hörmann ◽

N. Fohrer

Keyword(s):

Weather Conditions ◽

Rainfall Runoff ◽

Northern Germany ◽

Wetland Area ◽

Mountainous Catchment ◽

Discharge Simulation ◽

Physical Background ◽

Rainfall Runoff Model ◽

Mountainous Basin ◽

Runoff Model

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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Assessment of TOPKAPI-X Applicability for Flood Events Simulation in Two Small Catchments in Saxony

Hydrology ◽

10.3390/hydrology8030109 ◽

2021 ◽

Vol 8 (3) ◽

pp. 109

Author(s):

Firas Al Janabi ◽

Nurlan Ongdas ◽

Christian Bernhofer ◽

Julian David Reyes Silva ◽

Jakob Benisch ◽

...

Keyword(s):

Rapid Development ◽

Soil Layer ◽

Data Availability ◽

Rainfall Runoff ◽

Flood Events ◽

Small Watersheds ◽

Physically Based ◽

Low Performance ◽

Rainfall Runoff Model ◽

Runoff Model

Numerical simulations of rainfall-runoff processes are useful tools for understanding hydrological processes and performing impact assessment studies. The advancements in computer technology and data availability have assisted their rapid development and wide use. This project aims to evaluate the applicability of a physically based, fully distributed rainfall-runoff model TOPKAPI-X for the simulation of flood events in two small watersheds of Saxony, Germany. The results indicate that the model was calibrated well for 4.88 km2 Wernersbach catchment (NSE 0.89), whereas 276 km2 Wesenitz catchment calibration was only satisfactory (NSE 0.7). The addition of the second soil layer improved the model’s performance in comparison to the simulations with only one soil layer for Wernersbach (NSE increase from 0.83 to 0.89). During the validation process, the model showed a variable performance. The best performance was achieved for Wernersbach for the year with the highest runoff (NSE 0.95) in the last decade. The lowest performance for the Wernersbach and Wesenitz catchments was 0.64 for both. The reasons for the model’s low performance in some years are discussed, and include: (i) input data quality and data insufficiency, (ii) methods used within the simulations (interpolation, ETP estimation, etc.), and (iii) assumptions made during the calibration (manual calibration, parameter selection, etc.).

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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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