scholarly journals Impact of rainfall spatial distribution on rainfall-runoff modelling efficiency and initial soil moisture conditions estimation

2011 ◽  
Vol 11 (1) ◽  
pp. 157-170 ◽  
Author(s):  
Y. Tramblay ◽  
C. Bouvier ◽  
P.-A. Ayral ◽  
A. Marchandise
Keyword(s):  
Soil Moisture ◽  
Initial Conditions ◽  
Radar Data ◽  
Rain Gauge ◽  
Production Model ◽  
Initial Condition ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Moisture Index ◽  
Event Based

Abstract. A good knowledge of rainfall is essential for hydrological operational purposes such as flood forecasting. The objective of this paper was to analyze, on a relatively large sample of flood events, how rainfall-runoff modeling using an event-based model can be sensitive to the use of spatial rainfall compared to mean areal rainfall over the watershed. This comparison was based not only on the model's efficiency in reproducing the flood events but also through the estimation of the initial conditions by the model, using different rainfall inputs. The initial conditions of soil moisture are indeed a key factor for flood modeling in the Mediterranean region. In order to provide a soil moisture index that could be related to the initial condition of the model, the soil moisture output of the Safran-Isba-Modcou (SIM) model developed by Météo-France was used. This study was done in the Gardon catchment (545 km2) in South France, using uniform or spatial rainfall data derived from rain gauge and radar for 16 flood events. The event-based model considered combines the SCS runoff production model and the Lag and Route routing model. Results show that spatial rainfall increases the efficiency of the model. The advantage of using spatial rainfall is marked for some of the largest flood events. In addition, the relationship between the model's initial condition and the external predictor of soil moisture provided by the SIM model is better when using spatial rainfall, in particular when using spatial radar data with R2 values increasing from 0.61 to 0.72.

scholarly journals Estimation of antecedent wetness conditions for flood modelling in northern Morocco

2012 ◽  
Vol 16 (11) ◽  
pp. 4375-4386 ◽  
Author(s):  
Y. Tramblay ◽  
R. Bouaicha ◽  
L. Brocca ◽  
W. Dorigo ◽  
C. Bouvier ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Initial Conditions ◽  
Remote Sensing Data ◽  
Data Availability ◽  
Flood Events ◽  
Catchment Scale ◽  
Ungauged Catchments ◽  
Sensing Data ◽  
Event Based

Abstract. In northern Morocco are located most of the dams and reservoirs of the country, while this region is affected by severe rainfall events causing floods. To improve the management of the water regulation structures, there is a need to develop rainfall–runoff models to both maximize the storage capacity and reduce the risks caused by floods. In this study, a model is developed to reproduce the flood events for a 655 km2 catchment located upstream of the 6th largest dam in Morocco. Constrained by data availability, a standard event-based model combining a SCS-CN (Soil Conservation Service Curve Number) loss model and a Clark unit hydrograph was developed for hourly discharge simulation using 16 flood events that occurred between 1984 and 2008. The model was found satisfactory to reproduce the runoff and the temporal evolution of floods, even with limited rainfall data. Several antecedent wetness conditions estimators for the catchment were compared with the initial condition of the model. Theses estimators include an antecedent discharge index, an antecedent precipitation index and a continuous daily soil moisture accounting model (SMA), based on precipitation and evapotranspiration. The SMA model performed the best to estimate the initial conditions of the event-based hydrological model (R2 = 0.9). Its daily output has been compared with ASCAT and AMSR-E remote sensing data products, which were both able to reproduce with accuracy the daily simulated soil moisture dynamics at the catchment scale. This same approach could be implemented in other catchments of this region for operational purposes. The results of this study suggest that remote sensing data are potentially useful to estimate the soil moisture conditions in the case of ungauged catchments in Northern Africa.

Role of initial condition and parametric uncertainty in a severe hailstorm forecast

2021 ◽  
Author(s):  
Patrick Kuntze ◽  
Annette Miltenberger ◽  
Corinna Hoose ◽  
Michael Kunz
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Parametric Uncertainty ◽  
Radar Data ◽  
Cloud Microphysics ◽  
Initial Condition ◽  
Relative Contribution ◽  
Cloud Properties ◽  
Set Up ◽  
The Impact

<p>Forecasting high impact weather events is a major challenge for numerical weather prediction. Initial condition uncertainty plays a major role but so potentially do uncertainties arising from the representation of physical processes, e.g. cloud microphysics. In this project, we investigate the impact of these uncertainties for the forecast of cloud properties, precipitation and hail of a selected severe convective storm over South-Eastern Germany.<br>To investigate the joint impact of initial condition and parametric uncertainty a large ensemble including perturbed initial conditions and systematic variations in several cloud microphysical parameters is conducted with the ICON model (at 1 km grid-spacing). The comparison of the baseline, unperturbed simulation to satellite, radiosonde, and radar data shows that the model reproduces the key features of the storm and its evolution. In particular also substantial hail precipitation at the surface is predicted. Here, we will present first results including the simulation set-up, the evaluation of the baseline simulation, and the variability of hail forecasts from the ensemble simulation.<br>In a later stage of the project we aim to assess the relative contribution of the introduced model variations to changes in the microphysical evolution of the storm and to the fore- cast uncertainty in larger-scale meteorological conditions.</p>

scholarly journals Impacts of Antecedent Soil moisture on the Rainfall–Runoff Transformation Process Based on High-Resolution Observations in Soil Tank Experiments

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 296 ◽  
Author(s):  
Shuang Song ◽  
Wen Wang
Keyword(s):  
Soil Moisture ◽  
Overland Flow ◽  
Response Times ◽  
Hysteretic Behavior ◽  
Rainfall Runoff ◽  
Moisture Index ◽  
Antecedent Soil Moisture ◽  
Non Linear ◽  
Subsurface Runoff ◽  
The Impact

An experimental soil tank (12 m long × 1.5 m wide × 1.5m deep) equipped with a spatially distributed instrument network was designed to conduct the artificial rainfall–runoff experiments. Soil moisture (SM), precipitation, surface runoff (SR) and subsurface runoff (SSR) were continuously monitored. A total of 32 rainfall–runoff events were analyzed to investigate the non-linear patterns of rainfall–runoff response and estimate the impact of antecedent soil moisture (ASM) on runoff formation. Results suggested that ASM had a significant impact on runoff at this plot scale, and a moisture threshold-like value which was close to field capacity existed in the relationship between soil water content and event-based runoff coefficient (φe), SSR and SSR/SR. A non-linear relationship between antecedent soil moisture index (ASI) that represented the initial storage capacity of the soil tank and total runoff was also observed. Response times of SR and SM to rainfall showed a marked variability under different conditions. Under wet conditions, SM at 10 cm started to increase prior to SR on average, whereas it responds slower than SR under dry conditions due to the effect of water repellency. The predominant contributor to SR generation for all events is the Hortonian overland flow (HOF). There is a hysteretic behavior between subsurface runoff flow and soil moisture with a switch in the hysteretic loop direction based on the wetness conditions prior to the event.

Ingredients of German flood events

2020 ◽  
Author(s):  
Ralf Merz ◽  
Larisa Tarasova ◽  
Stefano Basso
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Rainfall Intensity ◽  
Machine Learning Techniques ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Runoff Generation ◽  
Regional Variability ◽  
Large Variability ◽  
Runoff Events

<p>Floods can be caused by a large variety of different processes, such as short, but intense rainfall bursts, long rainfall events, which are wetting up substantial parts of the catchment, or rain on snow cover or frozen soils. Although there is a plethora on studies analysing or modelling rainfall-runoff processes, it is still not well understood, what rainfall and runoff generation conditions are needed to generate flood runoff and how these characteristics vary between catchments. In this databased approach we decipher the ingredients of flood events occurred in 161 catchments across Germany. For each catchment rainfall-runoff events are separated from observed time series for the period 1950-2013, resulting in about 170,000 single events. A peak-over-threshold approach is used to select flood events out of these runoff events. For each event, spatially and temporally distributed rainfall and runoff generation characteristics, such as snow cover and soil moisture, as well as their interaction are derived. Then we decipher those event characteristics controlling flood event occurrence by using machine learning techniques.</p><p>On average, the most important event characteristic controlling flood occurrence in Germany is, as expected, event rainfall volume, followed by the overlap of rainfall and soil moisture and the extent of wet areas in the catchment (area with high soil moisture content). Rainfall intensity is another important characteristic. However, a large variability in its importance is noticeable between dryer catchments where short rainfall floods occur regularly and wetter catchments, where rainfall intensity might be less important for flood generation. To analyse the regional variability of flood ingredients, we cluster the catchments according to similarity in their flood controlling event characteristics and test how good the flood occurrence can be predicted from regionalised event characteristics. Finally, we analyse the regional variability of the flood ingredients in the light of climate and landscape catchment characteristics.</p>

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 Rainfall-runoff modeling using Doppler weather radar data for Adyar watershed, India

MAUSAM ◽  
2021 ◽  
Vol 65 (1) ◽  
pp. 49-56
Author(s):  
S.JOSEPHINE VANAJA ◽  
B.V. MUDGAL ◽  
S.B. THAMPI
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Doppler Weather Radar ◽  
Rainfall Runoff ◽  
Cyclonic Storm ◽  
Rain Gauges ◽  
Runoff Modeling

Precipitation is a significant input for hydrologic models; so, it needs to be quantified precisely. The measurement with rain gauges gives the rainfall at a particular location, whereas the radar obtains instantaneous snapshots of electromagnetic backscatter from rain volumes that are then converted into rainfall via algorithms. It has been proved that the radar measurement of areal rainfall can outperform rain gauge network measurements, especially in remote areas where rain gauges are sparse, and remotely sensed satellite rainfall data are too inaccurate. The research focuses on a technique to improve rainfall-runoff modeling based on radar derived rainfall data for Adyar watershed, Chennai, India. A hydrologic model called ‘Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS)’ is used for simulating rainfall-runoff processes. CARTOSAT 30 m DEM is used for watershed delineation using HEC-GeoHMS. The Adyar watershed is within 100 km radius circle from the Doppler Weather Radar station, hence it has been chosen as the study area. The cyclonic storm Jal event from 4-8 November, 2010 period is selected for the study. The data for this period are collected from the Statistical Department, and the Cyclone Detection Radar Centre, Chennai, India. The results show that the runoff is over predicted using calibrated Doppler radar data in comparison with the point rainfall from rain gauge stations.

scholarly journals How crucial is it to account for the Antecedent Moisture Conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments

2009 ◽  
Vol 6 (2) ◽  
pp. 1707-1736 ◽  
Author(s):  
L. Berthet ◽  
V. Andréassian ◽  
C. Perrin ◽  
P. Javelle
Keyword(s):  
Soil Moisture ◽  
Initial Conditions ◽  
Flood Forecasting ◽  
Hydrologic Model ◽  
Data Availability ◽  
Data Set ◽  
Soil Moisture Accounting ◽  
Antecedent Moisture ◽  
Rainfall Runoff Model ◽  
Event Based

Abstract. This paper compares event-based and continuous hydrological modelling approaches for real-time forecasting of river flows. Both approaches are compared using a lumped hydrologic model (whose structure includes a soil moisture accounting (SMA) store and a routing store) on a data set of 178 French catchments. The main focus of this study was to investigate the actual impact of soil moisture initial conditions on the performance of flood forecasting models and the possible compensations with updating techniques. The rainfall runoff model assimilation technique we used does not impact the SMA component of the model but only its routing part. Tests were made by running the SMA store continuously or on event basis, everything else being equal. The results show that the continuous approach remains the reference to ensure good forecasting performances. We show, however, that the possibility to assimilate the last observed flow considerably reduces the differences in performance. Last, we present a robust alternative to initialize the SMA store where continuous approaches are impossible because of data availability problems.

scholarly journals Estimation of antecedent wetness conditions for flood modelling in Northern Morocco

2012 ◽  
Vol 9 (8) ◽  
pp. 9361-9390 ◽  
Author(s):  
Y. Tramblay ◽  
R. Bouaicha ◽  
L. Brocca ◽  
W. Dorigo ◽  
C. Bouvier ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Initial Conditions ◽  
Remote Sensing Data ◽  
Data Availability ◽  
Flood Events ◽  
Catchment Scale ◽  
Ungauged Catchments ◽  
Sensing Data ◽  
Antecedent Precipitation

Abstract. In Northern Morocco are located most of the dams and reservoirs of the country, while this region is affected by severe rainfall events causing floods. To improve the management of the water regulation structures, there is a need to develop rainfall-runoff models to both maximize the storage capacity and reduce the risks caused by floods. In this study, a model is developed to reproduce the flood events for a 655 km2 catchment located upstream of the 6th largest dam of the Morocco. Constrained by data availability, a standard event-based model was developed for hourly discharge using 16 flood events that occurred between 1984 and 2008. The model was found satisfactory to reproduce the runoff and the temporal evolution of floods, even with limited rainfall data. Several antecedent wetness conditions estimators for the catchment were compared with the initial condition of the model. These estimators include the discharge of the previous days, the antecedent precipitation index and a continuous daily soil moisture accounting model (SMA). The SMA model performed the best to estimate the initial conditions of the model, with R2=0.9. Its daily output has been compared with ASCAT and AMSR-E remote sensing data products, both were able to reproduce with accuracy the daily soil moisture dynamics at the catchment scale. This same approach could be implemented in other catchments of this region for operational purposes. The results of this study indicate the potential usefulness of remote sensing data to estimate the soil moisture conditions in the case of ungauged catchments in Northern Africa.

scholarly journals Rainfall-runoff simulations in the Lukovska River Basin with the HEC-HMS model

2019 ◽  
pp. 33-60
Author(s):  
Ranka Eric ◽  
Andrijana Todorovic ◽  
Jasna Plavsic ◽  
Vesna Djukic
Keyword(s):  
River Basin ◽  
Optimal Parameter ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Runoff Generation ◽  
Hydrologic Models ◽  
Generation Mechanisms ◽  
Event Based

Hydrologic models are important for effective water resources management at a basin level. This paper describes an application of the HEC-HMS hydrologic model for simulations of flood hydrographs in the Lukovska River basin. Five flood events observed at the Mercez stream gauge were available for modelling purposes. These events are from two distinct periods and two seasons with different prevailing runoff generation mechanisms. Hence the events are assigned to either ?present? or ?past?, and ?spring? or ?summer? group. The optimal parameter sets of each group are obtained by averaging the optimal parameters for individual events within the group. To assess model transferability, its applicability for simulation of flood events which are not considered in the model calibration, a cross-validation is performed. The results indicate that model parameters vary across the events, and that parameter transfer generally leads to considerable errors in hydrograph peaks and volumes, with the exception of simulation of summer events with ?spring? parameters. Based on these results, recommendations for event-based modeling are given.

scholarly journals Effects of antecedent soil moisture on runoff modeling in small semiarid watersheds of southeastern Arizona

2011 ◽  
Vol 8 (3) ◽  
pp. 6227-6256 ◽  
Author(s):  
Y. Zhang ◽  
H. Wei ◽  
M. A. Nearing
Keyword(s):  
Soil Moisture ◽  
Initial Conditions ◽  
Rain Event ◽  
Rainfall Runoff ◽  
Antecedent Soil Moisture ◽  
Runoff Modeling ◽  
Semiarid Watersheds ◽  
Walnut Gulch ◽  
Semiarid Rangeland

Abstract. Antecedent soil moisture prior to a rain event influences the rainfall-runoff relationship. Very few studies have looked at the effects of antecedent soil moisture on runoff modeling sensitivities in arid and semi-arid areas. This study examines the influence of initial soil moisture on model runoff prediction capability in small semiarid watersheds using model sensitivity and by comparing the use of antecedent vs. average long term soil water content for defining the model initial conditions for the modified Green-Ampt Mein-Larson model within the Rangeland Hydrology and Erosion Model (RHEM). Measured rainfall, runoff, and soil moisture data from four semiarid rangeland watersheds ranging in size from 0.34 to 4.53 ha on the Walnut Gulch Experimental Watershed in southeastern Arizona, USA, were used. Results showed that: (a) there were no significant correlations between measured runoff ratio and antecedent soil moisture in any of the four watersheds; (b) average sensitivities of simulated runoff amounts and peaks to antecedent soil moisture were 0.05 mm and 0.18 mm h−1, respectively, with each 1 % change in antecedent soil moisture; (c) runoff amounts and peaks simulated with long term average soil moisture were statistically equivalent to those simulated with measured antecedent soil moisture. The relative lack of sensitivity of modeled runoff to antecedent soil moisture in this case is contrary to results reported in other studies, and is largely due to the fact that the surface soil is nearly always very dry in this environment.

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