scholarly journals Improving Efficiency of Hydrological Prediction Based on Meteorological Classification: A Case Study of GR4J Model

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2546
Author(s):  
Xiaojing Wei ◽  
Shenglian Guo ◽  
Lihua Xiong
Keyword(s):  
Prediction Method ◽  
Meteorological Condition ◽  
Meteorological Conditions ◽  
Rainfall Runoff ◽  
Glue Method ◽  
Hydrological Prediction ◽  
Rainfall Runoff Model ◽  
One Year ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Runoff Model

Distribution of hydrological parameters is varied under contrasting meteorological conditions. However, how to determine the most suitable parameters on a predefined meteorological condition is challenging. To address this issue, a hydrological prediction method based on meteorological classification is established, which is conducted by using the standardized runoff index (SRI) value to identify three categories, i.e., the dry, normal and wet years. Three different simulation schemes are then adopted for these categories. In each category, two years hydrological data with similar SRI values are divided into a set; then, one-year data are used as the calibration period while the other year is for testing. The Génie Rural à 4 paramètres Journalier (GR4J) rainfall-runoff model, with four parameters x1, x2, x3 and x4, was selected as an experimental model. The generalized likelihood uncertainty estimation (GLUE) method is used to avoid parameter equifinality. Three basins in Australia were used as case studies. As expected, the results show that the distribution of the four parameters of GR4J model is significantly different under varied meteorological conditions. The prediction efficiency in the testing period based on meteorological classification is greater than that of the traditional model under all meteorological conditions. It is indicated that the rainfall-runoff model should be calibrated with a similar SRI year rather than all years. This study provides a new method to improve efficiency of hydrological prediction for the basin.

scholarly journals The Wageningen Lowland Runoff Simulator (WALRUS): application to the Hupsel Brook catchment and Cabauw polder

2014 ◽  
Vol 11 (2) ◽  
pp. 2091-2148 ◽  
Author(s):  
C. C. Brauer ◽  
P. J. J. F. Torfs ◽  
A. J. Teuling ◽  
R. Uijlenhoet
Keyword(s):  
Surface Water ◽  
Initial Conditions ◽  
Companion Paper ◽  
Water Levels ◽  
Rainfall Runoff ◽  
Wetness Index ◽  
Temporal And Spatial Variability ◽  
Rainfall Runoff Model ◽  
One Year ◽  
Runoff Model

Abstract. The Wageningen Lowland Runoff Simulator (WALRUS) is a new parametric (conceptual) rainfall-runoff model which accounts explicitly for processes that are important in lowland areas, such as groundwater-unsaturated zone coupling, wetness-dependent flowroutes, groundwater–surface water feedbacks, and seepage and surface water supply (see companion paper by Brauer et al., 2014). Lowland catchments can be divided into slightly sloping, freely draining catchments and flat polders with controlled water levels. Here, we apply WALRUS to two contrasting Dutch catchments: the Hupsel Brook catchment and Cabauw polder. In both catchments, WALRUS performs well: Nash–Sutcliffe efficiencies obtained after calibration on one year of discharge observations are 0.87 for the Hupsel Brook catchment and 0.83 for the Cabauw polder, with values of 0.74 and 0.76 for validation. The model also performs well during floods and droughts and can forecast the effect of control operations. Through the dynamic division between quick and slow flowroutes controlled by a wetness index, temporal and spatial variability in groundwater depths can be accounted for, which results in adequate simulation of discharge peaks as well as low flows. The performance of WALRUS is most sensitive to the parameter controlling the wetness index and the groundwater reservoir constant, and to a lesser extent to the quickflow reservoir constant. The effects of these three parameters can be identified in the discharge time series, which indicates that the model is not overparameterised (parsimonious). Forcing uncertainty was found to have a larger effect on modelled discharge than parameter uncertainty and uncertainty in initial conditions.

A Study on Parameters of Rainfall Runoff Model and The Prediction Method of Landslide Disaster in Mountainous Area

2020 ◽  
Author(s):  
Huimyeong yoo ◽  
Naoki koyama ◽  
Tadashi yamada
Keyword(s):  
Prediction Method ◽  
Heavy Rain ◽  
Mountainous Area ◽  
Rainfall Runoff ◽  
Mountainous Areas ◽  
Average Amount ◽  
Tank Model ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Landslide Disaster

<p>This study is to analyze the evacuation behavior of residents living in the mountainous area and predict landslide disasters during heavy rain. 70% of Japan has are mountainous areas, and landslide disasters have occurred due to heavy rains caused by typhoons and heavy rainfall, etc. the annual average amount of damage caused by landslide disasters is 1000 in recent years. Also, landslide disaster warning information and evacuation information are important, it is difficult to predict landslide disasters, however, if we issued the evacuation advisory when the disasters already happened, there will be not enough time for the evacuation. In order to protect residents from such disasters, it is important to clarify "what information is effective for evacuation" and "when should those information be released?" Therefore, we conducted a survey on the residents in the mountainous areas which suffered from the heavy rain disaster in 2017 and analyzed the answers.</p><p>As a result, some residents evacuated before the evacuation information was issued. Because some landslide disasters occurred even before the first evacuation information was transmitted, and they felt danger. This result shows that the early information based on the prediction of the disasters is important in mountainous areas.</p><p>Therefore, we suggested a method for predicting landslide disasters, the method uses a rainfall and runoff tank model with high reproducibility and robustness of geological characteristics and uses the cumulative rainfall at the time of disaster occurrence as an index. As a result, this model predicted the occurrence of the landslide disaster 3 hours earlier by using forecasted rainfall. it is an effective method.</p>

scholarly journals Estimation of River Discharge from Satellite Observations

2021 ◽  
Vol 12 (4) ◽  
pp. 1072-1083
Author(s):  
Dhanendra Bahekar, Et. al.
Keyword(s):  
River Discharge ◽  
Structure Design ◽  
Flood Routing ◽  
Rainfall Runoff ◽  
Daily Streamflow ◽  
Rainfall Runoff Model ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Gauged Basins ◽  
Runoff Model

The role of streamflow is very important in any type of hydrologic. For very effective flood routing and hydraulic structure design, it is important to have a large dataset of past years. We now have a conceptual rainfall-runoff model that can predict streamflow based on pre-existing datasets. Because there is no or very little observed data in un-gauged basins, calibrating these models to predict daily streamflow becomes difficult. Nowadays, parameters for example river width can be observed using satellite images, and some studies show a promising associated relation between discharge and river width. The suggested study demonstrates a method for calculating streamflow from river width extracted with the help of satellite imagery. To predict streamflow, hydrological models are calibrated using river width instead of in site observed streamflow, and for estimating uncertainty Generalized Likelihood Uncertainty Estimation (GLUE) is used. For validation, the suggested method is implemented in the Kharun river basin situated in the Chhattisgarh state of India. The obtained Nash-Sutcliffe efficiency is 92.6 % for simulated river discharge in 2019-2020 at the 50% quantile, which is promising.

scholarly journals A conceptual grey rainfall-runoff model for simulation with uncertainty

2012 ◽  
Vol 15 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Stefano Alvisi ◽  
Anna Bernini ◽  
Marco Franchini
Keyword(s):  
Model Uncertainty ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Total Uncertainty ◽  
Computing Procedure ◽  
Glue Method ◽  
Grey Number ◽  
Rainfall Runoff Model ◽  
Simplified Procedure ◽  
Runoff Model

This paper presents an approach based on grey numbers to represent the total uncertainty of a conceptual rainfall-runoff model. Using this approach, once the grey numbers representing the model parameters have been properly defined, it is possible to obtain simulated discharges in the form of intervals (grey numbers) whose envelope defines a band which represents the total model uncertainty. The application to a real case showed that the construction of this band, according to a rigorous application of grey number theory, involves long computational times. However, these times can be significantly reduced using a simplified computing procedure with minimal approximations in the quantification of the simulated grey discharge. Relying on this simplified procedure, the conceptual rainfall-runoff grey model was then calibrated in order to respect a predefined level of model uncertainty, i.e. the band obtained from the envelope of simulated grey discharges had to include an assigned percentage of observed discharges and was at the same time as narrow as possible. Finally, the uncertainty bands were compared with the ones obtained using a well-established approach for characterising uncertainty, the Generalised Likelihood Uncertainty Estimation (GLUE) method. The results of the comparison showed that the proposed approach may represent a valid tool for characterising the total uncertainty of a rainfall-runoff model.

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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