scholarly journals Evaluating the Temporal Dynamics of Uncertainty Contribution from Satellite Precipitation Input in Rainfall-Runoff Modeling Using the Variance Decomposition Method

2018 ◽  
Vol 10 (12) ◽  
pp. 1876 ◽  
Author(s):  
Qiumei Ma ◽  
Lihua Xiong ◽  
Dedi Liu ◽  
Chong-Yu Xu ◽  
Shenglian Guo
Keyword(s):  
Real Time ◽  
Decomposition Method ◽  
Hydrological Modeling ◽  
Variance Decomposition ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Total Uncertainty ◽  
Input Uncertainty ◽  
Satellite Precipitation ◽  
Uncertainty Contribution

Satellite precipitation estimates (SPE), characterized by high spatial-temporal resolution, have been increasingly applied to hydrological modeling. However, the errors and bias inherent in SPE are broadly recognized. Yet, it remains unclear to what extent input uncertainty in hydrological models driven by SPE contributes to the total prediction uncertainty, resulting from difficulties in uncertainty partitioning. This study comprehensively quantified the input uncertainty contribution of three precipitation inputs (Tropical Rainfall Measurement Mission (TRMM) near-real-time 3B42RTv7 product, TRMM post-real-time 3B42v7 product and gauge-based precipitation) in rainfall-runoff simulation, using two hydrological models, the lumped daily Ge´nie Rural (GR) and distributed Coupled Routing and Excess STorage (CREST) models. For this purpose, the variance decomposition method was applied to disaggregate the total streamflow modeling uncertainty into seven components (uncertainties in model input, parameter, structure and their three first-order interaction effects, and residual error). The results showed that the total uncertainty in GR was lowest, moderate and highest when forced by gauge precipitation, 3B42v7 and 3B42RTv7, respectively. While the total uncertainty in CREST driven by 3B42v7 was lowest among the three input data sources. These results highlighted the superiority of post-real-time 3B42v7 in hydrological modeling as compared to real-time 3B42RTv7. All the input uncertainties in CREST driven by 3B42v7, 3B42RTv7 and gauge-based precipitation were lower than those in GR correspondingly. In addition, the input uncertainty was lowest in 3B42v7-driven CREST model while highest in gauge precipitation-driven GR model among the six combination schemes (two models combined with three precipitation inputs abovementioned). The distributed CREST model was capable of making better use of the spatial distribution advantage of SPE especially for the TRMM post-real-time 3B42v7 product. This study provided new insights into the SPE’s hydrological utility in the context of uncertainty, being significant for improving the suitability and adequacy of SPE to hydrological application.

scholarly journals Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting

Hydrology ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 188
Author(s):  
Rodrigo Valdés-Pineda ◽  
Juan B. Valdés ◽  
Sungwook Wi ◽  
Aleix Serrat-Capdevila ◽  
Tirthankar Roy
Keyword(s):  
Real Time ◽  
Optimal Parameter ◽  
Ensemble Forecasting ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Satellite Precipitation ◽  
Daily Streamflow ◽  
Medium Range ◽  
Streamflow Forecasts ◽  
Forecasting System

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.

scholarly journals Correcting Satellite Precipitation Data and Assimilating Satellite-Derived Soil Moisture Data to Generate Ensemble Hydrological Forecasts within the HBV Rainfall-Runoff Model

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2138
Author(s):  
Maurycy Ciupak ◽  
Bogdan Ozga-Zielinski ◽  
Jan Adamowski ◽  
Ravinesh C Deo ◽  
Krzysztof Kochanek
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
Snow Water Equivalent ◽  
Model Updating ◽  
Rainfall Runoff ◽  
Satellite Precipitation ◽  
Hbv Model

An implementation of bias correction and data assimilation using the ensemble Kalman filter (EnKF) as a procedure, dynamically coupled with the conceptual rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning (HBV) model, was assessed for the hydrological modeling of seasonal hydrographs. The enhanced HBV model generated ensemble hydrographs and an average stream-flow simulation. The proposed approach was developed to examine the possibility of using data (e.g., precipitation and soil moisture) from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Support to Operational Hydrology and Water Management (H-SAF), and to explore its usefulness in improving model updating and forecasting. Data from the Sola mountain catchment in southern Poland between 1 January 2008 and 31 July 2014 were used to calibrate the HBV model, while data from 1 August 2014 to 30 April 2015 were used for validation. A bias correction algorithm for a distribution-derived transformation method was developed by exploring generalized exponential (GE) theoretical distributions, along with gamma (GA) and Weibull (WE) distributions for the different data used in this study. When using the ensemble Kalman filter, the stochastically-generated ensemble of the model states generally induced bias in the estimation of non-linear hydrologic processes, thus influencing the accuracy of the Kalman analysis. In order to reduce the bias produced by the assimilation procedure, a post-processing bias correction (BC) procedure was coupled with the ensemble Kalman filter (EnKF), resulting in an ensemble Kalman filter with bias correction (EnKF-BC). The EnKF-BC, dynamically coupled with the HBV model for the assimilation of the satellite soil moisture observations, improved the accuracy of the simulated hydrographs significantly in the summer season, whereas, a positive effect from bias corrected (BC) satellite precipitation, as forcing data, was observed in the winter. Ensemble forecasts generated from the assimilation procedure are shown to be less uncertain. In future studies, the EnKF-BC algorithm proposed in the current study could be applied to a diverse array of practical forecasting problems (e.g., an operational assimilation of snowpack and snow water equivalent in forecasting models).

scholarly journals Evaluation of Satellite Precipitation Products and Their Potential Influence on Hydrological Modeling over the Ganzi River Basin of the Tibetan Plateau

2017 ◽  
Vol 2017 ◽  
pp. 1-23 ◽  
Author(s):  
Alaa Alden Alazzy ◽  
Haishen Lü ◽  
Rensheng Chen ◽  
Abubaker B. Ali ◽  
Yonghua Zhu ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
River Basin ◽  
Hydrological Modeling ◽  
The Tibetan Plateau ◽  
Hydrological Models ◽  
Alternative Source ◽  
Satellite Precipitation ◽  
Potential Alternative ◽  
Relative Potential ◽  
High Precipitation

In the last few years, satellite-based precipitation datasets are believed to be a potential source for forcing inputs in driving hydrological models, which are important especially in complex terrain areas or ungauged basins where ground gauges are generally sparse or nonexistent. This study aims to comprehensively evaluate the satellite precipitation products, CMORPH-CRT, PERSIANN-CDR, 3B42RT, and 3B42 against gauge-based datasets and to infer their relative potential impacts on hydrological processes simulation using the HEC-HMS model in the Ganzi River Basin (GRB) of the Tibetan Plateau. Results from a quantitative statistical comparison reveal that, at annual and seasonal scales, both CMORPH-CRT and 3B42 perform better than PERSIANN-CDR and 3B42RT. The CMORPH-CRT and 3B42 tend to underestimate values at the medium and high precipitation intensities ranges, whereas the opposite tendency is found for PERSIANN-CDR and 3B42RT. Overall, 3B42 exhibits the best performance for streamflow simulations over GRB and even outperforms simulation driven by gauge data during the validation period. PERSIANN-CDR shows the worst overall performance. After recalibrating with input-specific precipitation data, the performance of all satellite precipitation forced simulations is substantially improved, except for PERSIANN-CDR. Furthermore, 3B42 is more suitable to drive hydrological models and can be a potential alternative source of sparse data in Tibetan Plateau basins.

scholarly journals Assessing Near Real-Time Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Peruvian Andes

2021 ◽  
Vol 13 (4) ◽  
pp. 826 ◽  
Author(s):  
Harold Llauca ◽  
Waldo Lavado-Casimiro ◽  
Karen León ◽  
Juan Jimenez ◽  
Kevin Traverso ◽  
...  
Keyword(s):  
Real Time ◽  
Hydrological Modeling ◽  
Early Warning Systems ◽  
Rain Gauge ◽  
Peruvian Andes ◽  
Satellite Precipitation ◽  
Daily Precipitation Data ◽  
Flood Simulations ◽  
Hydrometric Station

This study investigates the applicability of Satellite Precipitation Products (SPPs) in near real-time for the simulation of sub-daily runoff in the Vilcanota River basin, located in the southeastern Andes of Peru. The data from rain gauge stations are used to evaluate the quality of Integrated Multi-satellite Retrievals for GPM–Early (IMERG-E), Global Satellite Mapping of Precipitation–Near Real-Time (GSMaP-NRT), Climate Prediction Center Morphing Method (CMORPH), and HydroEstimator (HE) at the pixel-station level; and these SPPs are used as meteorological inputs for the hourly hydrological modeling. The GR4H model is calibrated with the hydrometric station of the longest record, and model simulations are also verified at one station upstream and two stations downstream of the calibration point. Comparing the sub-daily precipitation data observed, the results show that the IMERG-E product generally presents higher quality, followed by GSMaP-NRT, CMORPH, and HE. Although the SPPs present positive and negative biases, ranging from mild to moderate, they do represent the diurnal and seasonal variability of the hourly precipitation in the study area. In terms of the average of Kling-Gupta metric (KGE), the GR4H_GSMaP-NRT’ yielded the best representation of hourly discharges (0.686), followed by GR4H_IMERG-E’ (0.623), GR4H_Ensemble-Mean (0.617) and GR4H_CMORPH’ (0.606), and GR4H_HE’ (0.516). Finally, the SPPs showed a high potential for monitoring floods in the Vilcanota basin in near real-time at the operational level. The results obtained in this research are very useful for implementing flood early warning systems in the Vilcanota basin and will allow the monitoring and short-term hydrological forecasting of floods by the Peruvian National Weather and Hydrological Service.

scholarly journals Estimating Uncertainties in High-Resolution Satellite Precipitation Products: Systematic or Random Error?

2016 ◽  
Vol 17 (4) ◽  
pp. 1119-1129 ◽  
Author(s):  
Viviana Maggioni ◽  
Mathew R. P. Sapiano ◽  
Robert F. Adler
Keyword(s):  
Real Time ◽  
Random Error ◽  
Hydrological Modeling ◽  
Strong Dependence ◽  
Management Policy ◽  
Satellite Precipitation ◽  
Bias Adjustment ◽  
Error Components ◽  
Random Components ◽  
Rain Rates

Abstract This study proposes a method to quantify systematic and random components of the error associated with satellite precipitation products. Specifically, the Precipitation Uncertainties for Satellite Hydrology (PUSH) model is expanded to provide an estimate of those components of the root-mean-square error. The framework is tested on the TRMM Multisatellite Precipitation Analysis (TMPA) 3B42, real time (3B42RT), and 3B42, version 7 (3B42V7), products over the contiguous United States, using the NOAA Climate Prediction Center (CPC) Unified gauge product as reference. Results show that 3B42V7 exhibits much smaller errors than the real-time product and that the major component of the error associated with both TMPA 3B42 products is random, as the systematic error is almost completely removed by the bias adjustment applied to the two products. A strong dependence of both systematic and random error components on satellite rain rates—with larger error components at larger rain rates—is observed for both satellite products, which suggests that future satellite bias adjustment procedures should account for this dependence. The resulting error estimates and their random and systematic components allow inferences about the accuracy of these datasets and will enhance their deployment in numerous applications, from hydrological modeling and hazard mitigation to climate change studies and water management policy.

scholarly journals Hydrological modeling of rainfall-runoff of the semi-arid Aguibat Ezziar watershed through the GR4J model

2021 ◽  
Vol 21 (3) ◽  
pp. 119-126
Author(s):  
Fatima-Zehrae Elhallaoui Oueldkaddour ◽  
Fatima Wariaghli ◽  
Hassane Brirhet ◽  
Ahmed Yahyaoui
Keyword(s):  
Simple Structure ◽  
Conceptual Models ◽  
Hydrological Modeling ◽  
Resource Assessment ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Satisfactory Performance ◽  
Enormous Amount ◽  
Water Resource Assessment ◽  
Limited Usefulness

Abstract The management of water resources requires as a first step the modeling of rainfall-runoff. It allows simulating the hydrological behavior of the basin for a good evaluation of the potentiality of this in terms of water production. There are different hydrological models used for water resource assessment, but conceptual models are still the most used due to their simple structure and satisfactory performance. In this study, t he performances of the conceptual model of rainfall and runoff (GR4J) modeled under R with the AirGR package, are used to Aguibat Ezziar the subbasin of the Bouregreg basin in Morocco. The enormous amount of data required and the uncertainty of some of the m makes these models of limited usefulness. The GR4J model allows evaluation of the runoff rates and describes the hydrological behavior of the Aguibat Ezziar watershed, which presents the aim behind writing this paper. A period from 2003 to 2017 has been selected. This period has been divided into two parts: one for calibration (2003-2006), and one for validation (2013-2016). After the calibration of the model and following the performance obtained (Nash higher than 0.72) we can say that the GR4J model behaves well in the Aguibat Ezziar catchment area.

scholarly journals Rainfall-Runoff Modelling of a River Basin Using HEC HMS: A Review Study

2021 ◽  
Vol 9 (9) ◽  
pp. 506-508
Author(s):  
Padala Raja Shekar
Keyword(s):  
Water Resources ◽  
River Basin ◽  
Soil Type ◽  
Hydrological Modeling ◽  
Hydrological Cycle ◽  
Meteorological Data ◽  
Physical Parameters ◽  
Army Corps ◽  
Hydrological Models ◽  
Rainfall Runoff

Abstract: A hydrological model helps in understanding of the hydrological processes and useful to measure water resources for effective water resources management. Hydrological cycle describes evaporation, condensation, precipitation and collection of earth water and on again. Hydrological models have been used in different watersheds across the world. The runoff estimation process is the most complex in nature that depends on the meteorological data and also on the various watershed physical parameters. To generate runoff data for a particular watershed it is needed to find out various parameters related to precipitation models. The HEC HMS (a Centre for Hydrological Engineering and Hydrological Modelling Systems introduced by the US Army Corps of Engineers) is a popularly used watershed model to simulate rainfall runoff process. The input variables used by hydrological models are rainfall data, runoff data, wind speed, relative humidity, soil type, catchment properties, hydrogeology and other properties. The Hydrological Modeling can also be an event based or may be continuous. This model is used to predict future impacts of the climate changes on the runoff of River basin and it is used to simulate runoff in ungauged watershed. This literature review represents that application of rainfall runoff modelling using HEC HMS is helpful in prediction of flood, water management and socio-economic development as well as food security. Keywords: HEC-HMS, hydrological modeling, rainfall-runoff simulation, soil type.

scholarly journals Scenario-Based Real-Time Flood Prediction with Logistic Regression

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1191
Author(s):  
Jaeyeong Lee ◽  
Byunghyun Kim
Keyword(s):  
Logistic Regression ◽  
Real Time ◽  
Prediction Method ◽  
Flood Inundation ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Flood Prediction ◽  
Flood Probability ◽  
Period Duration ◽  
Great Damage

This study proposed a real-time flood extent prediction method to shorten the time it takes from the flood occurrence to an alert issuance. This method uses logistic regression to generate a flood probability discriminant for each grid constituting the study area, and then predicts the flood extent with the amount of runoff caused by rainfall. In order to generate the flood probability discriminant for each grid, a two-dimensional (2D) flood inundation model was verified by applying the Typhoon Chaba, which caused great damage to the study area in 2016. Then, 100 probability rainfall scenarios were created by combining the return period, duration, and time distribution using past observation rainfall data, and rainfall-runoff–inundation relation databases were built for each scenario by applying hydrodynamic and hydrological models. A flood probability discriminant based on logistic regression was generated for each grid by using whether the grid was flooded (1 or 0) for the runoff amount in the database. When the runoff amount is input to the generated discriminant, the flood probability on the target grid is calculated by the coefficients, so that the flood extent is quickly predicted. The proposed method predicted the flood extent in a few seconds in both cases and showed high accuracy with 83.6~98.4% and 74.4~99.1%, respectively, in the application of scenario rainfall and actual rainfall.

Modifications of rainfall runoff and decision finding models for on-line simulation in real time control

1999 ◽  
Vol 39 (9) ◽  
pp. 201-207
Author(s):  
Andreas Cassar ◽  
Hans-Reinhard Verworn
Keyword(s):  
Real Time ◽  
Urban Drainage ◽  
Rain Event ◽  
Rainfall Runoff ◽  
Real Time Control ◽  
Design Storm ◽  
Time Control ◽  
Timing Data ◽  
On Line ◽  
Rainfall Runoff Model

Most of the existing rainfall runoff models for urban drainage systems have been designed for off-line calculations. With a design storm or a historical rain event and the model system the rainfall runoff processes are simulated, the faster the better. Since very recently, hydrodynamic models have been considered to be much too slow for real time applications. However, with the computing power of today - and even more so of tomorrow - very complex and detailed models may be run on-line and in real time. While the algorithms basically remain the same as for off-line simulations, problems concerning timing, data management and inter process communication have to be identified and solved. This paper describes the upgrading of the existing hydrodynamic rainfall runoff model HYSTEM/EXTRAN and the decision finding model INTL for real time performance, their implementation on a network of UNIX stations and the experiences from running them within an urban drainage real time control project. The main focus is not on what the models do but how they are put into action and made to run smoothly embedded in all the processes necessary in operational real time control.

Disintegration of uncertainties associated with real-time multi-satellite precipitation products in diverse topographic and climatic area in Pakistan

2021 ◽  
Vol 18 (3) ◽  
pp. 716-734
Author(s):  
Muhammad Masood ◽  
Ghulam Nabi ◽  
Muhammad Babur ◽  
Aftab Hussain Azhar ◽  
Muhammad Kaleem Ullah
Keyword(s):  
Real Time ◽  
Satellite Precipitation
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