scholarly journals River flow prediction in data scarce regions: soil moisture integrated satellite rainfall products outperform rain gauge observations in West Africa

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Luca Brocca ◽  
Christian Massari ◽  
Thierry Pellarin ◽  
Paolo Filippucci ◽  
Luca Ciabatta ◽  
...  
Keyword(s):  
Soil Moisture ◽  
West Africa ◽  
River Flow ◽  
Rain Gauge ◽  
Flow Prediction ◽  
Satellite Rainfall

Satellite soil moisture improves rainfall just where needed

2020 ◽  
Author(s):  
Luca Brocca ◽  
Stefania Camici ◽  
Christian Massari ◽  
Luca Ciabatta ◽  
Paolo Filippucci ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Discharge ◽  
Large Scale ◽  
River Flow ◽  
Global Scale ◽  
Rain Gauge ◽  
Rainfall Runoff ◽  
Global Precipitation Climatology Centre ◽  
Satellite Rainfall ◽  
Global Precipitation

<p>Soil moisture is a fundamental variable in the water and energy cycle and its knowledge in many applications is crucial. In the last decade, some authors have proposed the use of satellite soil moisture for estimating and improving rainfall, doing hydrology backward. From this research idea, several studies have been published and currently preoperational satellite rainfall products exploiting satellite soil moisture products have been made available.</p><p>The assessment of such products on a global scale has revealed an important result, i.e., the soil moisture based products perform better than state of the art products exactly over regions in which the data are needed: Africa and South America. However, over these areas the assessment against rain gauge observations is problematic and independent approaches are needed to assess the quality of such products and their potential benefit in hydrological applications. On this basis, the use of the satellite rainfall products as input into rainfall-runoff models, and their indirect assessment through river discharge observations is an alternative and valuable approach for evaluating their quality.</p><p>For this study, a newly developed large scale dataset of river discharge observations over 500+ basins throughout Africa has been exploited. Based on such unique dataset, a large scale assessment of multiple near real time satellite rainfall products has been performed: (1) the Early Run version of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement), IMERG Early Run, (2) SM2RAIN-ASCAT (https://doi.org/10.5281/zenodo.3405563), and (3) GPM+SM2RAIN (http://doi.org/10.5281/zenodo.3345323). Additionally, gauge-based and reanalysis rainfall products have been considered, i.e., (4) the Global Precipitation Climatology Centre (GPCC), and (5) the latest European Centre for Medium-Range Weather Forecasts reanalysis, ERA5. As rainfall-runoff model, the semi-distributed MISDc (Modello Idrologico Semi-Distribuito in continuo) model has been employed in the period 2007-2018 at daily temporal scale.</p><p>First results over a part of the dataset reveal the great value of satellite soil moisture products in improving satellite rainfall estimates for river flow prediction in Africa. Such results highlight the need to exploit such products for operational systems in Africa addressed to the mitigation of the flood risk and water resources management.</p>

scholarly journals A neural network model for short term river flow prediction

2006 ◽  
Vol 6 (4) ◽  
pp. 629-635 ◽  
Author(s):  
R. Teschl ◽  
W. L. Randeu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
River Flow ◽  
Radar Data ◽  
Rain Gauge ◽  
Huge Amount ◽  
Short Term ◽  
Flow Prediction ◽  
Spatial Coverage ◽  
Radar Measurements

Abstract. This paper presents a model using rain gauge and weather radar data to predict the runoff of a small alpine catchment in Austria. The gapless spatial coverage of the radar is important to detect small convective shower cells, but managing such a huge amount of data is a demanding task for an artificial neural network. The method described here uses statistical analysis to reduce the amount of data and find an appropriate input vector. Based on this analysis, radar measurements (pixels) representing areas requiring approximately the same time to dewater are grouped.

scholarly journals Soil moisture modelling of a SMOS pixel: interest of using the PERSIANN database over the Valencia Anchor Station

2010 ◽  
Vol 14 (8) ◽  
pp. 1509-1525 ◽  
Author(s):  
S. Juglea ◽  
Y. Kerr ◽  
A. Mialon ◽  
E. Lopez-Baeza ◽  
D. Braithwaite ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Spatial Scales ◽  
Simulated Data ◽  
Rain Gauge ◽  
Moisture Distribution ◽  
Accurate Estimation ◽  
Surface Soil Moisture ◽  
Anchor Station ◽  
Satellite Rainfall

Abstract. In the framework of Soil Moisture and Ocean Salinity (SMOS) Calibration/Validation (Cal/Val) activities, this study addresses the use of the PERSIANN-CCS1database in hydrological applications to accurately simulate a whole SMOS pixel by representing the spatial and temporal heterogeneity of the soil moisture fields over a wide area (50×50 km2). The study focuses on the Valencia Anchor Station (VAS) experimental site, in Spain, which is one of the main SMOS Cal/Val sites in Europe. A faithful representation of the soil moisture distribution at SMOS pixel scale (50×50 km2) requires an accurate estimation of the amount and temporal/spatial distribution of precipitation. To quantify the gain of using the comprehensive PERSIANN database instead of sparsely distributed rain gauge measurements, comparisons between in situ observations and satellite rainfall data are done both at point and areal scale. An overestimation of the satellite rainfall amounts is observed in most of the cases (about 66%) but the precipitation occurrences are in general retrieved (about 67%). To simulate the high variability in space and time of surface soil moisture, a Soil Vegetation Atmosphere Transfer (SVAT) model – ISBA (Interactions between Soil Biosphere Atmosphere) is used. The interest of using satellite rainfall estimates as well as the influence that the precipitation events can induce on the modelling of the water content in the soil is depicted by a comparison between different soil moisture data. Point-like and spatialized simulated data using rain gauge observations or PERSIANN – CCS database as well as ground measurements are used. It is shown that a good adequacy is reached in most part of the year, the precipitation differences having less impact upon the simulated soil moisture. The behaviour of simulated surface soil moisture at SMOS scale is verified by the use of remote sensing data from the Advanced Microwave Scanning Radiometer on Earth observing System (AMSR-E). We show that the PERSIANN database provides useful information at temporal and spatial scales in the context of soil moisture retrieval. 1Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System – http://chrs.web.uci.edu/persiann

The Potential Utilization of Satellite Derived Rainfall in a Data-Scarce Basin

2013 ◽  
Vol 726-731 ◽  
pp. 3531-3537
Author(s):  
Kwaku Amaning Adjei ◽  
Li Liang Ren ◽  
Emmanuel Kwame Appiah-Adjei ◽  
Samuel Nii Odai
Keyword(s):  
West Africa ◽  
Time Scales ◽  
Rain Gauge ◽  
Great Benefit ◽  
The Other ◽  
Its Analysis ◽  
Satellite Rainfall ◽  
Annual Time ◽  
Rainfall Estimates

This study assessed the potential for the use of satellite derived rainfall in a data-scarce basin like the Black Volta in West Africa. Using a point to pixel approach, accumulations of ground measurements on daily, monthly and annual time scales were compared with accumulations derived from 0.1° resolution daily gridded satellite Rainfall Estimates (RFE). The results from the analysis of the RFE data showed monthly correlations ranged from 0.76 to 0.92 when compared with the rain gauge measurements. These results obtained, indicate “good” to “very good” in terms of correlation (r) for the monthly and annual datasets. The study found that the use of satellite derived rainfall, like the RFE from FEWSNET, in the basin would be of great benefit considering the difficulties in accessing data both locally and from the other riparian countries; but its analysis and use should be on the monthly and annual scales.

scholarly journals Role of Runoff–Infiltration Partitioning and Resolved Overland Flow on Land–Atmosphere Feedbacks: A Case Study with the WRF-Hydro Coupled Modeling System for West Africa

2016 ◽  
Vol 17 (5) ◽  
pp. 1489-1516 ◽  
Author(s):  
Joel Arnault ◽  
Sven Wagner ◽  
Thomas Rummler ◽  
Benjamin Fersch ◽  
Jan Bliefernicht ◽  
...  
Keyword(s):  
West Africa ◽  
Overland Flow ◽  
River Flow ◽  
Efficiency Coefficient ◽  
Wet Season ◽  
Coupled Modeling ◽  
Study Region ◽  
Outer Domain ◽  
Terrestrial Water

Abstract The analysis of land–atmosphere feedbacks requires detailed representation of land processes in atmospheric models. The focus here is on runoff–infiltration partitioning and resolved overland flow. In the standard version of WRF, runoff–infiltration partitioning is described as a purely vertical process. In WRF-Hydro, runoff is enhanced with lateral water flows. The study region is the Sissili catchment (12 800 km2) in West Africa, and the study period is from March 2003 to February 2004. The WRF setup here includes an outer and inner domain at 10- and 2-km resolution covering the West Africa and Sissili regions, respectively. In this WRF-Hydro setup, the inner domain is coupled with a subgrid at 500-m resolution to compute overland and river flow. Model results are compared with TRMM precipitation, model tree ensemble (MTE) evapotranspiration, Climate Change Initiative (CCI) soil moisture, CRU temperature, and streamflow observation. The role of runoff–infiltration partitioning and resolved overland flow on land–atmosphere feedbacks is addressed with a sensitivity analysis of WRF results to the runoff–infiltration partitioning parameter and a comparison between WRF and WRF-Hydro results, respectively. In the outer domain, precipitation is sensitive to runoff–infiltration partitioning at the scale of the Sissili area (~100 × 100 km2), but not of area A (500 × 2500 km2). In the inner domain, where precipitation patterns are mainly prescribed by lateral boundary conditions, sensitivity is small, but additionally resolved overland flow here clearly increases infiltration and evapotranspiration at the beginning of the wet season when soils are still dry. The WRF-Hydro setup presented here shows potential for joint atmospheric and terrestrial water balance studies and reproduces observed daily discharge with a Nash–Sutcliffe model efficiency coefficient of 0.43.

scholarly journals Experiences in using the TMPA-3B42R satellite data to complement rain gauge measurements in the Ecuadorian coastal foothills

2013 ◽  
Vol 17 (7) ◽  
pp. 2905-2915 ◽  
Author(s):  
M. Arias-Hidalgo ◽  
B. Bhattacharya ◽  
A. E. Mynett ◽  
A. van Griensven
Keyword(s):  
Satellite Data ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
New Technologies ◽  
Simple Procedure ◽  
Rain Gauge ◽  
Experimental Methodology ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Satellite Rainfall

Abstract. At present, new technologies are becoming available to extend the coverage of conventional meteorological datasets. An example is the TMPA-3B42R dataset (research – v6). The usefulness of this satellite rainfall product has been investigated in the hydrological modeling of the Vinces River catchment (Ecuadorian lowlands). The initial TMPA-3B42R information exhibited some features of the precipitation spatial pattern (e.g., decreasing southwards and westwards). It showed a remarkable bias compared to the ground-based rainfall values. Several time scales (annual, seasonal, monthly, etc.) were considered for bias correction. High correlations between the TMPA-3B42R and the rain gauge data were still found for the monthly resolution, and accordingly a bias correction at that level was performed. Bias correction factors were calculated, and, adopting a simple procedure, they were spatially distributed to enhance the satellite data. By means of rain gauge hyetographs, the bias-corrected monthly TMPA-3B42R data were disaggregated to daily resolution. These synthetic time series were inserted in a hydrological model to complement the available rain gauge data to assess the model performance. The results were quite comparable with those using only the rain gauge data. Although the model outcomes did not improve remarkably, the contribution of this experimental methodology was that, despite a high bias, the satellite rainfall data could still be corrected for use in rainfall-runoff modeling at catchment and daily level. In absence of rain gauge data, the approach may have the potential to provide useful data at scales larger than the present modeling resolution (e.g., monthly/basin).

scholarly journals Sequential GMDH Algorithm and Its Application to River Flow Prediction

1976 ◽  
Vol 12 (2) ◽  
pp. 209-214
Author(s):  
Saburo IKEDA ◽  
Mikiko OCHIAI ◽  
Yoshikazu SAWARAGI
Keyword(s):  
River Flow ◽  
Flow Prediction

scholarly journals Impact of ASAR soil moisture data on the MM5 precipitation forecast for the Tanaro flood event of April 2009

2011 ◽  
Vol 11 (12) ◽  
pp. 3135-3149 ◽  
Author(s):  
G. Panegrossi ◽  
R. Ferretti ◽  
L. Pulvirenti ◽  
N. Pierdicca
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Ground Truth ◽  
Rain Gauge ◽  
Precipitation Forecast ◽  
Flood Event ◽  
Strong Impact ◽  
Moisture Field ◽  
The Impact

Abstract. The representation of land-atmosphere interactions in weather forecast models has a strong impact on the Planetary Boundary Layer (PBL) and, in turn, on the forecast. Soil moisture is one of the key variables in land surface modelling, and an inadequate initial soil moisture field can introduce major biases in the surface heat and moisture fluxes and have a long-lasting effect on the model behaviour. Detecting the variability of soil characteristics at small scales is particularly important in mesoscale models because of the continued increase of their spatial resolution. In this paper, the high resolution soil moisture field derived from ENVISAT/ASAR observations is used to derive the soil moisture initial condition for the MM5 simulation of the Tanaro flood event of April 2009. The ASAR-derived soil moisture field shows significantly drier conditions compared to the ECMWF analysis. The impact of soil moisture on the forecast has been evaluated in terms of predicted precipitation and rain gauge data available for this event have been used as ground truth. The use of the drier, highly resolved soil moisture content (SMC) shows a significant impact on the precipitation forecast, particularly evident during the early phase of the event. The timing of the onset of the precipitation, as well as the intensity of rainfall and the location of rain/no rain areas, are better predicted. The overall accuracy of the forecast using ASAR SMC data is significantly increased during the first 30 h of simulation. The impact of initial SMC on the precipitation has been related to the change in the water vapour field in the PBL prior to the onset of the precipitation, due to surface evaporation. This study represents a first attempt to establish whether high resolution SAR-based SMC data might be useful for operational use, in anticipation of the launch of the Sentinel-1 satellite.

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