scholarly journals Hydrological Evaluation of Satellite Soil Moisture Data in Two Basins of Different Climate and Vegetation Density Conditions

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Lu Zhuo ◽  
Dawei Han
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Spatial Difference ◽  
Vegetation Density ◽  
Modis Ndvi ◽  
Basin Scale ◽  
Microwave Sensors ◽  
Validation Tool

Accurate soil moisture information is very important for real-time flood forecasting. Although satellite soil moisture observations are useful information, their validations are generally hindered by the large spatial difference with the point-based measurements, and hence they cannot be directly applied in hydrological modelling. This study adopts a widely applied operational hydrological model Xinanjiang (XAJ) as a hydrological validation tool. Two widely used microwave sensors (SMOS and AMSR-E) are evaluated, over two basins (French Broad and Pontiac) with different climate types and vegetation covers. The results demonstrate SMOS outperforms AMSR-E in the Pontiac basin (cropland), while both products perform poorly in the French Broad basin (forest). The MODIS NDVI thresholds of 0.81 and 0.64 (for cropland and forest basins, resp.) are very effective in dividing soil moisture datasets into “denser” and “thinner” vegetation periods. As a result, in the cropland, the statistical performance is further improved for both satellites (i.e., improved to NSE = 0.74, RMSE = 0.0059 m and NSE = 0.58, RMSE = 0.0066 m for SMOS and AMER-E, resp.). The overall assessment suggests that SMOS is of reasonable quality in estimating basin-scale soil moisture at moderate-vegetated areas, and NDVI is a useful indicator for further improving the performance.

scholarly journals The suitability of remotely sensed soil moisture for improving operational flood forecasting

2014 ◽  
Vol 18 (6) ◽  
pp. 2343-2357 ◽  
Author(s):  
N. Wanders ◽  
D. Karssenberg ◽  
A. de Roo ◽  
S. M. de Jong ◽  
M. F. P. Bierkens
Keyword(s):  
Soil Moisture ◽  
Satellite Data ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Remotely Sensed ◽  
Added Value ◽  
Lead Times ◽  
Validation Data ◽  
Data Set ◽  
Discharge Data

Abstract. We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5–10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

scholarly journals Accuracy assessment of real-time flood forecasting of coupled hydrological and mesoscale meteorological models

2018 ◽  
Author(s):  
Aida Jabbari ◽  
Jae-Min So ◽  
Deg-Hyo Bae
Keyword(s):  
Real Time ◽  
Spatial Resolution ◽  
Lead Time ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Atmospheric Model ◽  
Flood Forecasting ◽  
Coupled Models ◽  
Temporal And Spatial ◽  
Meteorological Models

Abstract. Hydro-meteorological predictions are important for water management plans, which include providing early flood warnings and preventing flood damages. This study evaluates the real-time precipitation of an atmospheric model at the point and catchment scales to select the proper hydrological model to couple with the atmospheric model. Furthermore, a variety of tests were conducted to quantify the accuracy assessments of coupled models to provide details on the maximum spatial and temporal resolutions and lead times in a real-time forecasting system. As a major limitation of previous studies, the temporal and spatial resolutions of the hydrological model are smaller than those of the meteorological model. Here, through ultra-fine scale of temporal (10 min) and spatial resolution (1 km × 1 km), we determined the optimal resolution. A numerical weather prediction model and a rainfall runoff model were employed to evaluate real-time flood forecasting for the Imjin River (South and North Korea). The comparison of the forecasted precipitation and the observed precipitation indicated that the Weather Research and Forecasting (WRF) model underestimated precipitation. The skill of the model was relatively higher for the catchment than for the point scale, as illustrated by the lower RMSE value, which is important for a semi-distributed hydrological model. The variations in temporal and spatial resolutions illustrated a decrease in accuracy; additionally, the optimal spatial resolution obtained at 8 km and the temporal resolution did not affect the inherent inaccuracy of the results. Lead time variation demonstrated that lead time dependency was almost negligible below 36 h. With reference to our case study, comparisons of model performance provided quantitative knowledge for understanding the credibility and restrictions of hydro-meteorological models.

Use of Radar Quantitative Precipitation Estimates (QPEs) for Improved Hydrological Model Calibration and Flood Forecasting

2021 ◽  
Author(s):  
Dayal Wijayarathne ◽  
Paulin Coulibaly ◽  
Sudesh Boodoo ◽  
David Sills
Keyword(s):  
Real Time ◽  
Model Calibration ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Hydrological Models ◽  
Radar Rainfall ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates ◽  
Streamflow Simulation ◽  
Rainfall Estimates

AbstractFlood forecasting is essential to minimize the impacts and costs of floods, especially in urbanized watersheds. Radar rainfall estimates are becoming increasingly popular in flood forecasting because they provide the much-needed real-time spatially distributed precipitation information. The current study evaluates the use of radar Quantitative Precipitation Estimates (QPEs) in hydrological model calibration for streamflow simulation and flood mapping in an urban setting. Firstly, S-band and C-band radar QPEs were integrated into event-based hydrological models to improve the calibration of model parameters. Then, rain gauge and radar precipitation estimates’ performances were compared for hydrological modeling in an urban watershed to assess radar QPE's effects on streamflow simulation accuracy. Finally, flood extent maps were produced using coupled hydrological-hydraulic models integrated within the Hydrologic Engineering Center- Real-Time Simulation (HEC-RTS) framework. It is shown that the bias correction of radar QPEs can enhance the hydrological model calibration. The radar-gauge merging obtained a KGE, MPFC, NSE, and VE improvement of about + 0.42, + 0.12, + 0.78, and − 0.23, respectively for S-band and + 0.64, + 0.36, + 1.12, and − 0.34, respectively for C-band radar QPEs. Merged radar QPEs are also helpful in running hydrological models calibrated using gauge data. The HEC-RTS framework can be used to produce flood forecast maps using the bias-corrected radar QPEs. Therefore, radar rainfall estimates could be efficiently used to forecast floods in urbanized areas for effective flood management and mitigation. Canadian flood forecasting systems could be efficiently updated by integrating bias-corrected radar QPEs to simulate streamflow and produce flood inundation maps.

scholarly journals Assimilation of SMOS soil moisture into a distributed hydrological model and impacts on the water cycle variables over the Ouémé catchment in Benin

2016 ◽  
Vol 20 (7) ◽  
pp. 2827-2840 ◽  
Author(s):  
Delphine J. Leroux ◽  
Thierry Pellarin ◽  
Théo Vischel ◽  
Jean-Martial Cohard ◽  
Tania Gascon ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Water Table ◽  
Hydrological Model ◽  
Water Cycle ◽  
Tropical Rainfall Measuring Mission ◽  
Water Table Depth ◽  
Distributed Hydrological Model ◽  
Model Soil ◽  
Precipitation Estimation

Abstract. Precipitation forcing is usually the main source of uncertainty in hydrology. It is of crucial importance to use accurate forcing in order to obtain a good distribution of the water throughout the basin. For real-time applications, satellite observations allow quasi-real-time precipitation monitoring like the products PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, TRMM (Tropical Rainfall Measuring Mission) or CMORPH (CPC (Climate Prediction Center) MORPHing). However, especially in West Africa, these precipitation satellite products are highly inaccurate and the water amount can vary by a factor of 2. A post-adjusted version of these products exists but is available with a 2 to 3 month delay, which is not suitable for real-time hydrologic applications. The purpose of this work is to show the possible synergy between quasi-real-time satellite precipitation and soil moisture by assimilating the latter into a hydrological model. Soil Moisture Ocean Salinity (SMOS) soil moisture is assimilated into the Distributed Hydrology Soil Vegetation Model (DHSVM) model. By adjusting the soil water content, water table depth and streamflow simulations are much improved compared to real-time precipitation without assimilation: soil moisture bias is decreased even at deeper soil layers, correlation of the water table depth is improved from 0.09–0.70 to 0.82–0.87, and the Nash coefficients of the streamflow go from negative to positive. Overall, the statistics tend to get closer to those from the reanalyzed precipitation. Soil moisture assimilation represents a fair alternative to reanalyzed rainfall products, which can take several months before being available, which could lead to a better management of available water resources and extreme events.

scholarly journals Flood Forecasting with Uncertainty Using a Fully Automated Flood Model Chain: a Case Study for the City of Kulmbach

10.29007/jb27 ◽  
2018 ◽  
Author(s):  
Md Nazmul Azim Beg ◽  
Jorge Leandro ◽  
Punit Bhola ◽  
Iris Konnerth ◽  
Kanwal Amin ◽  
...  
Keyword(s):  
Real Time ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Flood Forecast ◽  
The Public ◽  
Uncertainty Sources ◽  
Flood Model ◽  
The City ◽  
Model Chain

Real time flood forecasting can help authorities in providing reliable warnings to the public. This process is, however, non-deterministic such that uncertainty sources need to be accounted before issuing forecasts. In the FloodEvac project, we have developed a tool which takes as inputs rainfall forecasts and links a hydrological with a hydraulic model for producing flood forecasts. The tool is able to handle calibration/validation of the hydrological model (LARSIM) and produces real-time flood forecast with associated uncertainty of flood discharges and flood extents. In this case study, we focus on the linkage with the hydrological model and on the real-time discharge forecasts generated.

Research on classified real-time flood forecasting framework based on K-means cluster and rough set

2015 ◽  
Vol 71 (10) ◽  
pp. 1507-1515 ◽  
Author(s):  
Wei Xu ◽  
Yong Peng
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Rough Set ◽  
Hydrological Model ◽  
Temporal Distribution ◽  
Flood Forecasting ◽  
Spatial And Temporal Distribution ◽  
Time Variance ◽  
Forecasting Method ◽  
Conceptual Hydrological Model

This research presents a new classified real-time flood forecasting framework. In this framework, historical floods are classified by a K-means cluster according to the spatial and temporal distribution of precipitation, the time variance of precipitation intensity and other hydrological factors. Based on the classified results, a rough set is used to extract the identification rules for real-time flood forecasting. Then, the parameters of different categories within the conceptual hydrological model are calibrated using a genetic algorithm. In real-time forecasting, the corresponding category of parameters is selected for flood forecasting according to the obtained flood information. This research tests the new classified framework on Guanyinge Reservoir and compares the framework with the traditional flood forecasting method. It finds that the performance of the new classified framework is significantly better in terms of accuracy. Furthermore, the framework can be considered in a catchment with fewer historical floods.

scholarly journals Validation of the AROME, ALADIN and WRF Meteorological Models for Flood Forecasting in Morocco

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 437 ◽  
Author(s):  
El Mahdi El Khalki ◽  
Yves Tramblay ◽  
Arnau Amengual ◽  
Victor Homar ◽  
Romualdo Romero ◽  
...  
Keyword(s):  
Developing Countries ◽  
Soil Moisture ◽  
International Development ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Added Value ◽  
Flood Events ◽  
African Countries ◽  
Antecedent Soil Moisture ◽  
Meteorological Models

Flash floods are common in small Mediterranean watersheds and the alerts provided by real-time monitoring systems provide too short anticipation times to warn the population. In this context, there is a strong need to develop flood forecasting systems in particular for developing countries such as Morocco where floods have severe socio-economic impacts. In this study, the AROME (Application of Research to Operations at Mesoscale), ALADIN (Aire Limited Dynamic Adaptation International Development) and WRF (Weather Research and Forecasting) meteorological models are evaluated to forecast flood events in the Rheraya and Ourika basin located in the High-Atlas Mountains of Morocco. The model evaluation is performed by comparing for a set of flood events the observed and simulated probabilities of exceedances for different precipitation thresholds. In addition, two different flood forecasting approaches are compared: the first one relies on the coupling of meteorological forecasts with a hydrological model and the second one is a based on a linear relationship between event rainfall, antecedent soil moisture and runoff. Three different soil moisture products (in-situ measurements, European Space Agency’s Climate Change Initiative ESA-CCI remote sensing data and ERA5 reanalysis) are compared to estimate the initial soil moisture conditions before flood events for both methods. Results showed that the WRF and AROME models better simulate precipitation amounts compared to ALADIN, indicating the added value of convection-permitting models. The regression-based flood forecasting method outperforms the hydrological model-based approach, and the maximum discharge is better reproduced when using the WRF forecasts in combination with ERA5. These results provide insights to implement robust flood forecasting approaches in the context of data scarcity that could be valuable for developing countries such as Morocco and other North African countries.

scholarly journals Assimilation of ASCAT near-surface soil moisture into the SIM hydrological model over France

2011 ◽  
Vol 15 (12) ◽  
pp. 3829-3841 ◽  
Author(s):  
C. Draper ◽  
J.-F. Mahfouf ◽  
J.-C. Calvet ◽  
E. Martin ◽  
W. Wagner
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Hydrological Model ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Model Soil ◽  
Dry Bias

Abstract. This study examines whether the assimilation of remotely sensed near-surface soil moisture observations might benefit an operational hydrological model, specifically Météo-France's SAFRAN-ISBA-MODCOU (SIM) model. Soil moisture data derived from ASCAT backscatter observations are assimilated into SIM using a Simplified Extended Kalman Filter (SEKF) over 3.5 years. The benefit of the assimilation is tested by comparison to a delayed cut-off version of SIM, in which the land surface is forced with more accurate atmospheric analyses, due to the availability of additional atmospheric observations after the near-real time data cut-off. However, comparing the near-real time and delayed cut-off SIM models revealed that the main difference between them is a dry bias in the near-real time precipitation forcing, which resulted in a dry bias in the root-zone soil moisture and associated surface moisture flux forecasts. While assimilating the ASCAT data did reduce the root-zone soil moisture dry bias (by nearly 50%), this was more likely due to a bias within the SEKF, than due to the assimilation having accurately responded to the precipitation errors. Several improvements to the assimilation are identified to address this, and a bias-aware strategy is suggested for explicitly correcting the model bias. However, in this experiment the moisture added by the SEKF was quickly lost from the model surface due to the enhanced surface fluxes (particularly drainage) induced by the wetter soil moisture states. Consequently, by the end of each winter, during which frozen conditions prevent the ASCAT data from being assimilated, the model land surface had returned to its original (dry-biased) climate. This highlights that it would be more effective to address the precipitation bias directly, than to correct it by constraining the model soil moisture through data assimilation.

scholarly journals Verification of operational Quantitative Discharge Forecast (QDF) for a regional warning system – the AMPHORE case studies in the upper Po River

2008 ◽  
Vol 8 (1) ◽  
pp. 161-173 ◽  
Author(s):  
D. Rabuffetti ◽  
G. Ravazzani ◽  
C. Corbari ◽  
M. Mancini
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Hydrological Model ◽  
Lag Time ◽  
Flood Forecasting ◽  
Flood Routing ◽  
Distributed Hydrological Model ◽  
Po River ◽  
Prediction And Prevention ◽  
Small Catchments

Abstract. In recent years, the interest in the prediction and prevention of natural hazards related to hydrometeorological events has grown due to the increased frequency of extreme rainstorms. Several research projects have been developed to test hydrometeorological models for real-time flood forecasting. However, flood forecasting systems are still not widespread in operational context. Real-world examples are mainly dedicated to the use of flood routing model, best suited for large river basins. For small basins, it is necessary to take advantage of the lag time between the onset of a rainstorm and the beginning of the hydrograph rise, with the use of rainfall-runoff transformation models. Nevertheless, when the lag time is very short, a rainfall predictor is required, as a result, meteorological models are often coupled with hydrological simulation. While this chaining allows floods to be forecasted on small catchments with response times ranging from 6 to 12 h it, however, causes new problems for the reliability of Quantitative Precipitation Forecasts (QPF) and also creates additional accuracy problems for space and time scales. The aim of this work is to evaluate the degree to which uncertain QPF affects the reliability of the whole hydro-meteorological alert system for small catchments. For this purpose, a distributed hydrological model (FEST-WB) was developed and analysed in operational setting experiments, i.e. the hydrological model was forced with rain observation until the time of forecast and with the QPF for the successive period, as is usual in real-time procedures. Analysis focuses on the AMPHORE case studies in Piemonte in November 2002.

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