scholarly journals Calibrating hourly rainfall-runoff models with daily forcings for streamflow forecasting applications in meso-scale catchments

2016 ◽  
Vol 76 ◽  
pp. 20-36 ◽  
Author(s):  
James C. Bennett ◽  
David E. Robertson ◽  
Phillip G.D. Ward ◽  
H.A. Prasantha Hapuarachchi ◽  
Q.J. Wang
Keyword(s):  
Rainfall Runoff ◽  
Streamflow Forecasting ◽  
Hourly Rainfall ◽  
Meso Scale

Estimation of the added value of using rainfall–runoff transformation and statistical models for seasonal streamflow forecasting

2018 ◽  
Vol 63 (4) ◽  
pp. 630-645 ◽  
Author(s):  
Ketvara Sittichok ◽  
Ousmane Seidou ◽  
Abdouramane Gado Djibo ◽  
Neeranat Kaewprasert Rakangthong
Keyword(s):  
Statistical Models ◽  
Added Value ◽  
Rainfall Runoff ◽  
Streamflow Forecasting ◽  
Seasonal Streamflow

Development and Integration of Sub-hourly Rainfall–Runoff Modeling Capability Within a Watershed Model

2010 ◽  
Vol 24 (15) ◽  
pp. 4505-4527 ◽  
Author(s):  
Jaehak Jeong ◽  
Narayanan Kannan ◽  
Jeff Arnold ◽  
Roger Glick ◽  
Leila Gosselink ◽  
...  
Keyword(s):  
Rainfall Runoff ◽  
Watershed Model ◽  
Runoff Modeling ◽  
Hourly Rainfall

Stochastic generation and disaggregation of hourly rainfall series for continuous hydrological modelling and flood control reservoir design

2002 ◽  
Vol 45 (2) ◽  
pp. 113-119 ◽  
Author(s):  
B. Hingray ◽  
E. Monbaron ◽  
I. Jarrar ◽  
A.C. Favre ◽  
D. Consuegra ◽  
...  
Keyword(s):  
Flood Control ◽  
Statistical Information ◽  
Rainfall Series ◽  
Rainfall Runoff ◽  
Time Step ◽  
Detention Basin ◽  
Hourly Rainfall ◽  
State Frequency ◽  
Detention Basins

In the urban environment, stormwater detention basins are a powerful means to limit the frequency of sewer system failures and consecutive urban flooding. To design such waterworks or to check their efficiency, it is possible to carry out continuous rainfall-runoff modelling. A long-term discharge series obtained from a long-term rainfall series is used as input for a storage model describing the detention basin behaviour: the basin behaviour may be consequently studied over a long period. The provided statistical information on the working state frequency, failure frequency, … of the detention basin is of high interest for the basin diagnostic or for its design. This paper presents the whole methodology which leads to production of such statistical information and especially: the models used to generate long term rainfall series with a short time step, the rainfall-runoff model used to transform the later series into a long term discharge series, and the model used to describe the behaviour of the detention basin. This methodology was applied to evaluate the efficiency of 4 detention basins built for stormwater control and flood mitigation. They are situated on a Swiss urban catchment (Chamberonne catchment – 40 km2) collecting water from the Mèbre and Sorge rivers.

RAINFALL RUNOFF MODELING BY MULTILAYER PERCEPTRON NEURAL NETWORK FOR LUI RIVER CATCHMENT

2016 ◽  
Vol 78 (6-12) ◽  
Author(s):  
Nadeem Nawaz ◽  
Sobri Harun ◽  
Rawshan Othman ◽  
Arien Heryansyah
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Moving Average ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Hourly Rainfall ◽  
Physically Based ◽  
Auto Regressive ◽  
Event Based ◽  
Relationship Of

Reliable modeling for the rainfall-runoff processes embedded with high complexity and non-linearity can overcome the problems associated with managing a watershed. Physically based rainfall-runoff models need many realistic physical components and parameters which are sometime missing and hard to be estimated. In last decades the artificial intelligence (AI) has gained much popularity for calibrating the nonlinear relationships of rainfall–runoff processes. The AI models have the ability to provide direct relationship of the input to the desired output without considering any internal processes. This study presents an application of Multilayer Perceptron neural network (MLPNN) for the continuous and event based rainfall-runoff modeling to evaluate its performance for a tropical catchment of Lui River in Malaysia. Five years (1999-2013) daily and hourly rainfall and runoff data was used in this study. Rainfall-runoff processes were also simulated with a traditionally used statistical modeling technique known as auto-regressive moving average with exogenous inputs (ARMAX). The study has found that MLPNN model can be used as reliable rainfall-runoff modeling tool in tropical catchments.  

Copula-based interpretation of continuous rainfall–runoff simulations of a watershed in northern Iran

2012 ◽  
Vol 49 (5) ◽  
pp. 681-691 ◽  
Author(s):  
Saeed Golian ◽  
Bahram Saghafian ◽  
Ashkan Farokhnia
Keyword(s):  
Soil Moisture ◽  
Peak Discharge ◽  
Rainfall Runoff ◽  
Antecedent Soil Moisture ◽  
Rainfall Duration ◽  
Initial Soil Moisture ◽  
Hourly Rainfall ◽  
Copula Method ◽  
Initial Soil ◽  
Rainfall Depth

In the present work, the joint response of key hydrologic variables, including total precipitation depths and the corresponding simulated peak discharges, are investigated for different antecedent soil moisture conditions using the copula method. The procedure started with the calibration and validation of the soil moisture accounting (SMA) loss rate algorithm incorporated in the Hydrologic Engineering Center – hydrologic modeling system (HEC–HMS) model for the study watershed. A 1000 year long time series of hourly rainfall was then generated by the Neyman–Scott rectangular pulses (NSRP) rainfall generator, which was then transformed into the runoff rate by the HEC–HMS model. This long-term continuous hydrological simulation resulted in characterizing the response of the watershed for various input conditions such as initial soil moisture content (AMC), total rainfall depth, and rainfall duration. For each initial soil moisture class, the copula method was employed to determine the joint probability distribution of rainfall depth and peak discharge. For instance, for dry AMC condition and 1 h rainfall duration, the Joe family fitted best to the data, compared with six other one-parameter families of copulas. Results showed that the bivariate analysis of rainfall–runoff using the copula method can well characterize the watershed hydrological behavior. The derived offline curves could provide a probabilistic real-time peak discharge forecast.

scholarly journals Modelling rainfall–runoff processes of the Chemoga and Jedeb meso-scale catchments in the Abay/Upper Blue Nile basin, Ethiopia

2015 ◽  
pp. 1-18 ◽  
Author(s):  
Sirak Tekleab ◽  
Stefan Uhlenbrook ◽  
Hubert H.G. Savenije ◽  
Yasir Mohamed ◽  
Jochen Wenninger
Keyword(s):  
Rainfall Runoff ◽  
Nile Basin ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Meso Scale ◽  
Upper Blue Nile Basin

scholarly journals A strategy to overcome adverse effects of autoregressive updating of streamflow predictions

2014 ◽  
Vol 11 (6) ◽  
pp. 6035-6063
Author(s):  
M. Li ◽  
Q. J. Wang ◽  
J. C. Bennett ◽  
D. E. Robertson
Keyword(s):  
Adverse Effects ◽  
Hydrological Model ◽  
Model Simulation ◽  
Ar Model ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Streamflow Forecasting ◽  
Popular Approach ◽  
Ar Models ◽  
Simulation Errors

Abstract. For streamflow forecasting applications, rainfall–runoff hydrological models are often augmented with updating procedures that correct streamflow predictions based on the latest available observations of streamflow and their departures from model simulations. The most popular approach uses autoregressive (AR) models that exploit the "memory" in hydrological model simulation errors. AR models may be applied to raw errors directly or to normalised errors. In this study, we demonstrate that AR models applied in either way can sometimes cause over-correction of predictions. In using an AR model applied to raw errors, the over-correction usually occurs when streamflow is rapidly receding. In applying an AR model to normalised errors, the over-correction usually occurs when streamflow is rapidly rising. Furthermore, when parameters of a hydrological model and an AR model are estimated jointly, the AR model applied to normalised errors sometimes degrades the stand-alone performance of the base hydrological model. This is not desirable for forecasting applications, as predictions should rely as much as possible on the base hydrological model, and updating should be applied only to correct minor errors. To overcome the adverse effects of the ordinary AR models, a restricted AR model applied to normalised errors is introduced. The new model is evaluated on a number of catchments and is shown to reduce over-correction and to improve the performance of the base hydrological model considerably.

scholarly journals Fully distributed rainfall-runoff modeling using spatial-temporal graph neural network

2022 ◽  
Author(s):  
Zhongrun Xiang ◽  
Ibrahim Demir
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Spatial Information ◽  
Model Performance ◽  
Grid Cell ◽  
Great Promise ◽  
Rainfall Runoff ◽  
Streamflow Forecasting ◽  
Runoff Modeling ◽  
Runoff Model

Recent studies using latest deep learning algorithms such as LSTM (Long Short-Term Memory) have shown great promise in time-series modeling. There are many studies focusing on the watershed-scale rainfall-runoff modeling or streamflow forecasting, often considering a single watershed with limited generalization capabilities. To improve the model performance, several studies explored an integrated approach by decomposing a large watershed into multiple sub-watersheds with semi-distributed structure. In this study, we propose an innovative physics-informed fully-distributed rainfall-runoff model, NRM-Graph (Neural Runoff Model-Graph), using Graph Neural Networks (GNN) to make full use of spatial information including the flow direction and geographic data. Specifically, we applied a time-series model on each grid cell for its runoff production. The output of each grid cell is then aggregated by a GNN as the final runoff at the watershed outlet. The case study shows that our GNN based model successfully represents the spatial information in predictions. NRM-Graph network has shown less over-fitting and a significant improvement on the model performance compared to the baselines with spatial information. Our research further confirms the importance of spatially distributed hydrological information in rainfall-runoff modeling using deep learning, and we encourage researchers to incorporate more domain knowledge in modeling.

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