A Precipitation-Runoff Simulation Model for Flood Forecasting of River Tel, Odisha, India

2016 ◽  
pp. 665-670
Author(s):  
Prabeer Kumar Parhi
Keyword(s):  
Simulation Model ◽  
Flood Forecasting ◽  
Runoff Simulation

Urban Runoff Simulation Model

1988 ◽  
Vol 114 (4) ◽  
pp. 399-413 ◽  
Author(s):  
Alfred Garcia ◽  
Wesley P. James
Keyword(s):  
Simulation Model ◽  
Urban Runoff ◽  
Runoff Simulation

Towards deep learning based flood forecasting for ungauged basins

2020 ◽  
Author(s):  
Frederik Kratzert ◽  
Daniel Klotz ◽  
Guy Shalev ◽  
Sella Nevo ◽  
Günter Klambauer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Low Income ◽  
Short Term Memory ◽  
Flood Forecasting ◽  
Low Income Countries ◽  
Water Model ◽  
Ungauged Basins ◽  
Streamflow Forecasting ◽  
Runoff Simulation ◽  
Streamflow Forecasts

<p>Floods are among the most destructive natural hazards in the world. To reduce flood induced damages and casualties, streamflow forecasts should be as accurate as possible.</p><p>As of today, streamflow forecasts are usually made with either conceptual or process-based hydrological models. The problem these models usually have is that they perform best when calibrated for a specific basin, and performance degrades drastically if the models are used in places without historic streamflow measurements. To make things worse, some of the most devastating floods occur in developing and low-income countries, where historic records of streamflow measurements are scarce. Therefore, a central task for enhancing flood forecasts and helping local authorities to manage these areas is to provide high-quality streamflow forecasts in ungauged rivers. Although the IAHS dedicated an entire decade (2003-2012) to advance the problem of Prediction in Ungauged Basins the central goal remains largely a challenge.</p><p>In this talk, we will present a novel approach for tackling the problem of prediction in ungauged basins using a data-driven approach. More concretely, we show that the Long Short-Term Memory network (LSTM), which is a special type of a deep learning model, can serve as a generalizable rainfall-runoff simulation model. We will present recent results indicating that the LSTM gives on average better out-of-sample predictions (ungauged prediction) than e.g. the SAC-SMA in-sample (gauged) or the US National Water Model (Kratzert et al., 2019).</p><p>One place where these research results are already finding their way into operation is Google’s Flood Forecasting Initiative. The goal of this initiative is to provide (enhanced) flood warnings, where needed, starting with a pilot project in India. And as mentioned above, historic streamflow records in those regions are scarce, which motivates new and innovative approaches for enhanced streamflow forecasting.</p><p>References:</p><p>Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., & Nearing, G. S.: Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resources Research, 55. https://doi.org/10.1029/2019WR026065, 2019.</p>

Comparison of recorded rainfall with quantitative precipitation forecast in a rainfall-runoff simulation for the Langat River Basin, Malaysia

2011 ◽  
Vol 3 (3) ◽  
Author(s):  
Lawal Billa ◽  
Hamid Assilzadeh ◽  
Shattri Mansor ◽  
Ahmed Mahmud ◽  
Abdul Ghazali
Keyword(s):  
Response Time ◽  
River Basin ◽  
Flood Forecasting ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Noaa Avhrr ◽  
Quantitative Precipitation Forecast ◽  
Langat River

AbstractObserved rainfall is used for runoff modeling in flood forecasting where possible, however in cases where the response time of the watershed is too short for flood warning activities, a deterministic quantitative precipitation forecast (QPF) can be used. This is based on a limited-area meteorological model and can provide a forecasting horizon in the order of six hours or less. This study applies the results of a previously developed QPF based on a 1D cloud model using hourly NOAA-AVHRR (Advanced Very High Resolution Radiometer) and GMS (Geostationary Meteorological Satellite) datasets. Rainfall intensity values in the range of 3–12 mm/hr were extracted from these datasets based on the relation between cloud top temperature (CTT), cloud reflectance (CTR) and cloud height (CTH) using defined thresholds. The QPF, prepared for the rainstorm event of 27 September to 8 October 2000 was tested for rainfall runoff on the Langat River Basin, Malaysia, using a suitable NAM rainfall-runoff model. The response of the basin both to the rainfall-runoff simulation using the QPF estimate and the recorded observed rainfall is compared here, based on their corresponding discharge hydrographs. The comparison of the QPF and recorded rainfall showed R2 = 0.9028 for the entire basin. The runoff hydrograph for the recorded rainfall in the Kajang sub-catchment showed R2 = 0.9263 between the observed and the simulated, while that of the QPF rainfall was R2 = 0.819. This similarity in runoff suggests there is a high level of accuracy shown in the improved QPF, and that significant improvement of flood forecasting can be achieved through ‘Nowcasting’, thus increasing the response time for flood early warnings.

scholarly journals A DISTRIBUTED RUNOFF SIMULATION MODEL OF WATER QUALITY IN COMBINED SEWER PIPE SYSTEMS

2005 ◽  
Vol 49 ◽  
pp. 1591-1596
Author(s):  
Koichi NAGAYOSHI ◽  
Kazuhiro ISHIDA ◽  
Masahiro WATANBABE ◽  
Daimin RI
Keyword(s):  
Water Quality ◽  
Simulation Model ◽  
Sewer Pipe ◽  
Runoff Simulation ◽  
Combined Sewer ◽  
Pipe Systems

scholarly journals Model Calibration Parameter Using Optimization Trial in HEC-HMS for Unda Watershed

2021 ◽  
Vol 930 (1) ◽  
pp. 012040
Author(s):  
G A P Eryani ◽  
I M S Amerta ◽  
M W Jayantari
Keyword(s):  
Simulation Model ◽  
Water Availability ◽  
Resource Planning ◽  
Rainfall Simulation ◽  
Calibration Model ◽  
Hydrological Process ◽  
Rainfall Runoff ◽  
Initial Model ◽  
Runoff Simulation ◽  
Resources Management

Abstract In water resource planning, information on water availability is needed. Nowadays, data on water availability is still difficult to obtain. With technology in the form of a rainfall-runoff simulation model that can predict water availability in the Unda watershed. It can add information about the potential for water in the Unda watershed. It can be used to prepare water resources management in the Unda watershed so that the existing potential can be used sustainably. Based on the rainfall simulation model results in the Unda watershed, it can be concluded that after running the initial model and calibration. The results are obtained R2 value was 0.68 and increased by 9.81% to 0.754. Both the initial model and the calibration model show an efficient R2 value, NASH value increases by 49.93% to 0.713, which includes satisfactory criteria, RMSE value of 1.135 and decreased by 49.47% to 0.758, and the PBIAS value was 44.70% which was classified as unsatisfactory and decreased from 80.24% to 24.80% at the time of calibration which was classified as satisfactory. In general, the overall simulation results are quite good for representing the watershed’s efficient hydrological process.

New Runoff Simulation Model for Small Urban Catchments

2007 ◽  
Vol 12 (5) ◽  
pp. 540-544 ◽  
Author(s):  
S. Bennis ◽  
E. Crobeddu
Keyword(s):  
Simulation Model ◽  
Runoff Simulation ◽  
Urban Catchments

scholarly journals Real-time correction of antecedent precipitation for the Xinanjiang model using the genetic algorithm

2016 ◽  
Vol 18 (5) ◽  
pp. 803-815 ◽  
Author(s):  
Sheng-Li Liao ◽  
Gang Li ◽  
Qian-Ying Sun ◽  
Zhi-Fu Li
Keyword(s):  
Genetic Algorithm ◽  
Mean Squared Error ◽  
Fitness Function ◽  
Practical Method ◽  
Flood Forecasting ◽  
Optimization Process ◽  
Hunan Province ◽  
Runoff Simulation ◽  
Xinanjiang Model ◽  
Antecedent Precipitation

The Xinanjiang model has been successfully and widely applied in humid and semi-humid regions of China for rainfall–runoff simulation and flood forecasting. However, its forecasting precision is seriously affected by antecedent precipitation (Pa). Commonly applied methods relying on the experience of individual modelers are not standardized and difficult to transfer. In particular, the Xinanjiang daily model may result in obvious errors in the determination of Pa. Thus, a practical method for estimating Pa is proposed in this paper, which is based on a genetic algorithm (GA) and is estimated during a rising flood period. In the optimization process of a GA, Pa values form a chromosome, the root-mean-squared error between the observed and simulated streamflow is chosen as the fitness function. Simultaneously, the best individual reserved strategy is adopted between correction periods to avoid complete independence between each optimization process as well as to ensure the stability of the algorithm. Twenty-seven historical floods observed at the gauge station of the Shuangpai reservoir in Hunan Province of China are used to test the presented algorithm for estimation of Pa, and the results demonstrate that the proposed method significantly improves the quality of flood forecasting in the Xinanjiang model.

Sign in / Sign up

Export Citation Format

Share Document