scholarly journals Calibration of a rainfall-runoff model using radar and raingauge data

2005 ◽  
Vol 2 ◽  
pp. 41-46 ◽  
Author(s):  
V. Lopez ◽  
F. Napolitano ◽  
F. Russo
Keyword(s):  
Measurement System ◽  
Small Scale ◽  
Rainfall Runoff ◽  
Radar Rainfall ◽  
Measurement Systems ◽  
Raingauge Data ◽  
Temporal And Spatial ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Rainfall Estimates

Abstract. Since raingauges give pointwise measurements the small scale variability of rainfall fields leads to biases on the estimation for the rainfall over the whole basin. In this context meteorological radars have several advantages since a single site is able to obtain coverage over a wide area with high temporal and spatial resolution. The purpose of this study is to compare the capability of the two different measurement systems in order to give correct input to drive rainfall-runoff models. Therefore a geomorphological model was calibrated, using firstly raingauge data and secondly radar rainfall estimates, for the Treja river basin. In this way it is possible to determine different sets of parameters and the influence of measurement system in hydrological modelling. The results shown that radar rainfall data is able to improve significantly hydrographs reconstructions.

scholarly journals Use of digital map for a radar rainfall-runoff model

1992 ◽  
Vol 36 ◽  
pp. 671-676
Author(s):  
Akira ITO ◽  
Makoto SASAMOTO ◽  
Shigeki SAKAI ◽  
Ken-ichi HIRAYAMA
Keyword(s):  
Rainfall Runoff ◽  
Radar Rainfall ◽  
Digital Map ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals From runoff to rainfall: inverse rainfall–runoff modelling in a high temporal resolution

2014 ◽  
Vol 11 (12) ◽  
pp. 13259-13309 ◽  
Author(s):  
M. Herrnegger ◽  
H. P. Nachtnebel ◽  
K. Schulz
Keyword(s):  
Temporal Resolution ◽  
Numerical Experiments ◽  
Inverse Model ◽  
High Temporal Resolution ◽  
Rainfall Runoff ◽  
Time Step ◽  
Areal Rainfall ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Rainfall Estimates

Abstract. This paper presents a novel technique to calculate mean areal rainfall in a high temporal resolution of 60 min on the basis of an inverse conceptual rainfall–runoff model and runoff observations. Rainfall exhibits a large spatio-temporal variability, especially in complex alpine terrain. Additionally, the density of the monitoring network in mountainous regions is low and measurements are subjected to major errors, which lead to significant uncertainties in areal rainfall estimates. The most reliable hydrological information available refers to runoff, which in the presented work is used as input for a rainfall–runoff model. Thereby a conceptual, HBV-type model is embedded in an iteration algorithm. For every time step a rainfall value is determined, which results in a simulated runoff value that corresponds to the observation. To verify the existence, uniqueness and stability of the inverse rainfall, numerical experiments with synthetic hydrographs as inputs into the inverse model are carried out successfully. The application of the inverse model with runoff observations as driving input is performed for the Krems catchment (38.4 km2), situated in the northern Austrian Alpine foothills. Compared to station observations in the proximity of the catchment, the inverse rainfall sums and time series have a similar goodness of fit, as the independent INCA rainfall analysis of Austrian Central Institute for Meteorology and Geodynamics (ZAMG). Compared to observations, the inverse rainfall estimates show larger rainfall intensities. Numerical experiments show, that cold state conditions in the inverse model do not influence the inverse rainfall estimates, when considering an adequate spin-up time. The application of the inverse model is a feasible approach to obtain improved estimates of mean areal rainfall. These can be used to enhance interpolated rainfall fields, e.g. for the estimation of rainfall correction factors, the parameterisation of elevation dependency or the application in real-time flood forecasting systems.

scholarly journals Operational Hydrological Modelling of Small Watershed using QPE from Dual-Pol Radar in Brazil

2019 ◽  
Author(s):  
Cesar Beneti ◽  
Roberto V. Calheiros ◽  
Mino Sorribas ◽  
Leonardo Calvetti ◽  
Camila Oliveira ◽  
...  
Keyword(s):  
Land Surface ◽  
Rain Gauge ◽  
Water Levels ◽  
Rainfall Runoff ◽  
Radar Rainfall ◽  
Flood Alert ◽  
Precipitation Estimation ◽  
Radar Satellite ◽  
Rainfall Runoff Model ◽  
Runoff Model

Among other applications, radar-rainfall (RR) and QPE (Quantitative Precipitation Estimation) based on radar reflectivity, dual polarization variables, and multi-sensor information, provide important information for land surface hydrology, such as flood forecasting. Therefore, we developed a flood alert system using rainfall-runoff model forced with RR and QPE, and tipping-bucket observations to forecast river water levels (using rating-curves). In this study, we used an hourly dataset from an S-Band dual-polarimetric radar with two tropical R(Z) relations based distrometer data, a polarimetric R(Z,ZDR) algorithm from the literature and a multi-sensor approach using radar, satellite and rain gauge. Two hydrological models were used and calibrated using observed discharge time-series. Although our previous studies indicated accurate RR-based simulations, in some cases floods were not detected when using catchment-lumped rainfall derived from multi-sensor QPE. In this study, we advance further in this subject using improved R(Z,ZDR) relations and QPE for the period of 2016-2017 and flood event-based rainfall-runoff calibration. Thus, we focused on the development (and timing) of floods in the Marrecas River can be complex and strongly related to storms spatiotemporal distribution. To explore this aspect, we also perform a first analysis in using RR in rainfall-runoff model with a nested catchment discretization.

scholarly journals Flood modelling with a distributed event-based parsimonious rainfall-runoff model: case of the karstic Lez river catchment

2012 ◽  
Vol 12 (4) ◽  
pp. 1119-1133 ◽  
Author(s):  
M. Coustau ◽  
C. Bouvier ◽  
V. Borrell-Estupina ◽  
H. Jourde
Keyword(s):  
Statistical Prediction ◽  
Storm Event ◽  
Initial Condition ◽  
Rainfall Runoff ◽  
Time Step ◽  
Radar Rainfall ◽  
Flood Modelling ◽  
Rainfall Runoff Model ◽  
Event Based ◽  
Runoff Model

Abstract. Rainfall-runoff models are crucial tools for the statistical prediction of flash floods and real-time forecasting. This paper focuses on a karstic basin in the South of France and proposes a distributed parsimonious event-based rainfall-runoff model, coherent with the poor knowledge of both evaporative and underground fluxes. The model combines a SCS runoff model and a Lag and Route routing model for each cell of a regular grid mesh. The efficiency of the model is discussed not only to satisfactorily simulate floods but also to get powerful relationships between the initial condition of the model and various predictors of the initial wetness state of the basin, such as the base flow, the Hu2 index from the Meteo-France SIM model and the piezometric levels of the aquifer. The advantage of using meteorological radar rainfall in flood modelling is also assessed. Model calibration proved to be satisfactory by using an hourly time step with Nash criterion values, ranging between 0.66 and 0.94 for eighteen of the twenty-one selected events. The radar rainfall inputs significantly improved the simulations or the assessment of the initial condition of the model for 5 events at the beginning of autumn, mostly in September–October (mean improvement of Nash is 0.09; correction in the initial condition ranges from −205 to 124 mm), but were less efficient for the events at the end of autumn. In this period, the weak vertical extension of the precipitation system and the low altitude of the 0 °C isotherm could affect the efficiency of radar measurements due to the distance between the basin and the radar (~60 km). The model initial condition S is correlated with the three tested predictors (R2 > 0.6). The interpretation of the model suggests that groundwater does not affect the first peaks of the flood, but can strongly impact subsequent peaks in the case of a multi-storm event. Because this kind of model is based on a limited amount of readily available data, it should be suitable for operational applications.

Radar–raingauge data combination techniques: a revision and analysis of their suitability for urban hydrology

2013 ◽  
Vol 68 (4) ◽  
pp. 737-747 ◽  
Author(s):  
Li-Pen Wang ◽  
Susana Ochoa-Rodríguez ◽  
Nuno Eduardo Simões ◽  
Christian Onof ◽  
Čedo Maksimović
Keyword(s):  
Error Variance ◽  
Bias Reduction ◽  
Spatiotemporal Variability ◽  
Urban Hydrology ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Radar Rainfall ◽  
North East ◽  
Raingauge Data ◽  
Rainfall Estimates

The applicability of the operational radar and raingauge networks for urban hydrology is insufficient. Radar rainfall estimates provide a good description of the spatiotemporal variability of rainfall; however, their accuracy is in general insufficient. It is therefore necessary to adjust radar measurements using raingauge data, which provide accurate point rainfall information. Several gauge-based radar rainfall adjustment techniques have been developed and mainly applied at coarser spatial and temporal scales; however, their suitability for small-scale urban hydrology is seldom explored. In this paper a review of gauge-based adjustment techniques is first provided. After that, two techniques, respectively based upon the ideas of mean bias reduction and error variance minimisation, were selected and tested using as case study an urban catchment (∼8.65 km2) in North-East London. The radar rainfall estimates of four historical events (2010–2012) were adjusted using in situ raingauge estimates and the adjusted rainfall fields were applied to the hydraulic model of the study area. The results show that both techniques can effectively reduce mean bias; however, the technique based upon error variance minimisation can in general better reproduce the spatial and temporal variability of rainfall, which proved to have a significant impact on the subsequent hydraulic outputs. This suggests that error variance minimisation based methods may be more appropriate for urban-scale hydrological applications.

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

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

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

Water ◽  
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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