scholarly journals Benefits and limitations of data assimilation for discharge forecasting using an event-based rainfall–runoff model

2013 ◽  
Vol 13 (3) ◽  
pp. 583-596 ◽  
Author(s):  
M. Coustau ◽  
S. Ricci ◽  
V. Borrell-Estupina ◽  
C. Bouvier ◽  
O. Thual
Keyword(s):  
Data Assimilation ◽  
Hydrological Model ◽  
Peak Discharge ◽  
State Variables ◽  
Rainfall Runoff ◽  
Discharge Data ◽  
Southern France ◽  
Flood Peak ◽  
Event Based ◽  
The Impact

Abstract. Mediterranean catchments in southern France are threatened by potentially devastating fast floods which are difficult to anticipate. In order to improve the skill of rainfall-runoff models in predicting such flash floods, hydrologists use data assimilation techniques to provide real-time updates of the model using observational data. This approach seeks to reduce the uncertainties present in different components of the hydrological model (forcing, parameters or state variables) in order to minimize the error in simulated discharges. This article presents a data assimilation procedure, the best linear unbiased estimator (BLUE), used with the goal of improving the peak discharge predictions generated by an event-based hydrological model Soil Conservation Service lag and route (SCS-LR). For a given prediction date, selected model inputs are corrected by assimilating discharge data observed at the basin outlet. This study is conducted on the Lez Mediterranean basin in southern France. The key objectives of this article are (i) to select the parameter(s) which allow for the most efficient and reliable correction of the simulated discharges, (ii) to demonstrate the impact of the correction of the initial condition upon simulated discharges, and (iii) to identify and understand conditions in which this technique fails to improve the forecast skill. The correction of the initial moisture deficit of the soil reservoir proves to be the most efficient control parameter for adjusting the peak discharge. Using data assimilation, this correction leads to an average of 12% improvement in the flood peak magnitude forecast in 75% of cases. The investigation of the other 25% of cases points out a number of precautions for the appropriate use of this data assimilation procedure.

scholarly journals Erratum: Kabeja, C., et al. The Impact of Reforestation Induced Land Cover Change (1990–2017) on Flood Peak Discharge Using HEC-HMS Hydrological Model and Satellite Observations: A Study in Two Mountain Basins, China. Water 2020, 12, 1347

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 676
Author(s):  
Crispin Kabeja ◽  
Rui Li ◽  
Jianping Guo ◽  
Digne Edmond Rwabuhungu Rwatangabo ◽  
Marc Manyifika ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Hydrological Model ◽  
Satellite Observations ◽  
Peak Discharge ◽  
Flood Peak ◽  
Mountain Basins ◽  
The Impact

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scholarly journals On the asymptotic behavior of flood peak distributions

2006 ◽  
Vol 10 (2) ◽  
pp. 233-243 ◽  
Author(s):  
E. Gaume
Keyword(s):  
Frequency Distribution ◽  
Return Period ◽  
Asymptotic Properties ◽  
Flood Frequency ◽  
Point Of View ◽  
Rainfall Runoff ◽  
New Approach ◽  
Flood Peak ◽  
Flood Quantiles ◽  
The Impact

Abstract. This paper presents some analytical results and numerical illustrations on the asymptotic properties of flood peak distributions obtained through derived flood frequency approaches. It confirms and extends the results of previous works: i.e. the shape of the flood peak distributions are asymptotically controlled by the rainfall statistical properties, given limited and reasonable assumptions concerning the rainfall-runoff process. This result is partial so far: the impact of the rainfall spatial heterogeneity has not been studied for instance. From a practical point of view, it provides a general framework for analysis of the outcomes of previous works based on derived flood frequency approaches and leads to some proposals for the estimation of very large return-period flood quantiles. This paper, focussed on asymptotic distribution properties, does not propose any new approach for the extrapolation of flood frequency distribution to estimate intermediate return period flood quantiles. Nevertheless, the large distance between frequent flood peak values and the asymptotic values as well as the simulations conducted in this paper help quantifying the ill condition of the problem of flood frequency distribution extrapolation: it illustrates how large the range of possibilities for the shapes of flood peak distributions is.

scholarly journals Water discharge estimates from large radar altimetry datasets in the Amazon basin

2012 ◽  
Vol 9 (6) ◽  
pp. 7591-7611 ◽  
Author(s):  
A. C. V. Getirana ◽  
C. Peters-Lidard
Keyword(s):  
Data Assimilation ◽  
Amazon Basin ◽  
Water Discharge ◽  
Rating Curve ◽  
Radar Altimetry ◽  
Discharge Data ◽  
Modeling Uncertainties ◽  
The Mean ◽  
Relative Errors ◽  
The Impact

Abstract. In this study, we evaluate the use of a large radar altimetry dataset as a complementary gauging network capable of providing water discharge in ungauged regions within the Amazon basin. A rating-curve-based methodology is adopted to derive water discharge from altimetric data provided by Envisat at 444 virtual stations (VS). The stage-discharge relations at VS are built based on radar altimetry and outputs from a global flow routing scheme. In order to quantify the impact of modeling uncertainties on rating-curve based discharges, another experiment is performed using simulated discharges derived from a simplified data assimilation procedure. Discharge estimates at 90 VS are evaluated against observations during the curve fitting calibration (2002–2005) and evaluation (2006–2008) periods, resulting in mean relative RMS errors as high as 52% and 12% for experiments without and with assimilation, respectively. Without data assimilation, uncertainty of discharge estimates can be mostly attributed to forcing errors at smaller scales, generating a positive correlation between performance and drainage area. Mean relative errors (RE) of altimetry-based discharges varied from 15% to 92% for large and small drainage areas, respectively. Rating curves produced a mean RE of 54% versus 68% from model outputs. Assimilating discharge data decreases the mean RE from 68% to 12%. These results demonstrate the feasibility of applying the proposed methodology to the regional or global scales. Also, it is shown the potential of satellite altimetry for predicting water discharge in poorly-gauged and ungauged river basins.

Effects of channel engineering on flood dynamics along the middle and lower Rhône over the last two centuries.

2021 ◽  
Author(s):  
Mathieu Lucas ◽  
Michel Lang ◽  
Jérôme Le Coz ◽  
Benjamin Renard ◽  
Hervé Piegay
Keyword(s):  
Peak Discharge ◽  
Anthropogenic Pressures ◽  
Flood Peak ◽  
Area Of Interest ◽  
Channel Engineering ◽  
Indirect Consequence ◽  
Channel Patterns ◽  
Flood Dynamics ◽  
The Impact ◽  
Flood Waves

<p>The Rhône River has undergone many anthropogenic transformations to improve his navigability and produce hydroelectricity since the mid-19th century. From the longitudinal dikes of the 1850’s to the hydroelectric diversion schemes of the 1950’s and 1960’s, these structures had a direct impact on the channel geometry along the 300km of river course between Lyon (France) and the Mediterranean Sea. An indirect consequence could be a change in the flood dynamics along the channel course, caused by the simplification of the channel patterns and the floodplain accretion. This communication aims to assess the potential changes in the flood propagation along the middle and lower Rhône valley throughout a century of anthropogenic reconfigurations of the channel. The possible impact of these human pressures on the inundation risk and the attenuation of the flood peak discharge is also discussed. Through the use of digitized hydrometric data recorded since 1840 on multiple stream gauges of the Rhône river, a variety of floods of the same type and magnitude are selected. The oceanic flood types (as described by Pardé, 1925) that take their origin from heavy rainfalls upstream of the area of interest are preferred. Thus, complex flood waves due to floods from the lower Rhône valley tributaries are avoided, to keep the analysis as simple as possible. The flood travel time and the peak discharge attenuation of the selected events are compared over the years of channel transformations, permitting us to estimate the impact of anthropogenic pressures on the flood dynamics.</p>

Hydrological modelling and anthropogenic water use

2021 ◽  
Author(s):  
Ponnambalam Rameshwaran ◽  
Ali Rudd ◽  
Vicky Bell ◽  
Matt Brown ◽  
Helen Davies ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Use ◽  
Hydrological Model ◽  
Model Performance ◽  
River Flows ◽  
Discharge Data ◽  
New Approach ◽  
Water Demands ◽  
The Impact ◽  
Grid Based

<p>Despite Britain’s often-rainy maritime climate, anthropogenic water demands have a significant impact on river flows, particularly during dry summers. In future years, projected population growth and climate change are likely to increase the demand for water and lead to greater pressures on available freshwater resources.</p><p>Across England, abstraction (from groundwater, surface water or tidal sources) and discharge data along with ‘Hands off Flow’ conditions are available for thousands of individual locations; each with a licence for use, an amount, an indication of when abstraction can take place, and the actual amount of water abstracted (generally less than the licence amount). Here we demonstrate how these data can be used in combination to incorporate anthropogenic artificial influences into a grid-based hydrological model. Model simulations of both high and low river flows are generally improved when abstractions and discharges are included, though for some catchments model performance decreases. The new approach provides a methodological baseline for further work investigating the impact of anthropogenic water use and projected climate change on future river flows.</p>

scholarly journals Effects of Roughness Coefficients and Complex Hillslope Morphology on Runoff Variables under Laboratory Conditions

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2550 ◽  
Author(s):  
Masoud Meshkat ◽  
Nosratollah Amanian ◽  
Ali Talebi ◽  
Mahboobeh Kiani-Harchegani ◽  
Jesús Rodrigo-Comino
Keyword(s):  
Soil Erosion ◽  
Peak Discharge ◽  
Control Measures ◽  
P Value ◽  
Rainfall Runoff ◽  
Runoff Generation ◽  
Mean Values ◽  
Time Of Concentration ◽  
Significant Difference ◽  
The Impact

The geometry of hillslopes (plan and profile) affects soil erosion under rainfall-runoff processes. This issue comprises of several factors, which must be identified and assessed if efficient control measures are to be designed. The main aim of the current research was to investigate the impact of surface Roughness Coefficients (RCs) and Complex Hillslopes (CHs) on runoff variables viz. time of generation, time of concentration, and peak discharge value. A total of 81 experiments were conducted with a rainfall intensity of 7 L min−1 on three types of soils with different RCs (i.e., low = 0.015, medium = 0.016, and high = 0.018) and CHs (i.e., profile curvature and plan shape). An inclination of 20% was used for three replications. The results indicate a significant difference (p-value ≤ 0.001) in the above-mentioned runoff variables under different RCs and CHs. Our investigation of the combined effects of RCs and CHs on the runoff variables shows that the plan and profile impacts are consistent with a variation in RC. This can implicate that at low RC, the effect of the plan shape (i.e., convergent) on runoff variables increases but at high RC, the impact of the profile curvature overcomes the plan shapes and the profile curvature’s changes become the criteria for changing the behavior of the runoff variables. The lowest mean values of runoff generation and time of concentration were obtained in the convex-convergent and the convex-divergent at 1.15 min and 2.68 min, respectively, for the soil with an RC of 0.015. The highest mean of peak discharge was obtained in the concave-divergent CH in the soil with an RC of 0.018. We conclude that these results can be useful in order to design planned soil erosion control measures where the soil roughness and slope morphology play a key role in activating runoff generation.

A 20-year reforecast study combining high-resolution hydrological modelling, ensemble forecasting and data assimilation for the 12 largest tributaries of the Rhine

2020 ◽  
Author(s):  
Bart van Osnabrugge ◽  
Maarten Smoorenburg ◽  
Remko Uijlenhoet ◽  
Albrecht Weerts
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Hydrological Model ◽  
Forecast Skill ◽  
Ensemble Forecasting ◽  
Added Value ◽  
River Rhine ◽  
Medium Term ◽  
Initial State ◽  
The Impact

<p>There is an ongoing trend in hydrological forecasting towards both spatially distributed (gridded) models, ensemble forecasting and data assimilation techniques to improve forecasts’ initial states. While in the last years those different aspects have been investigated separately, there are only few studies where the three techniques are combined: ensemble forecasts with state updating of a gridded hydrological model. Additionally, the studies that have addressed this combination of techniques either focus on a small area, a short study period, or both. We here aim to fill this knowledge gap with a 20-year data assimilation and ensemble reforecast experiment with a high resolution gridded hydrological model (wflow_hbv, 1200x1200m) of the full Rhine basin (160 000 km<sup>2</sup>). To put the impact of state updating in an operational forecasting context, the data assimilation results were compared with AR post-processing as used by the Dutch Forecasting Centre (WMCN).</p><p>This data assimilation and reforecast experiment was conducted for the twelve main tributaries of the river Rhine. The effect on forecast skill of state updating with the Asynchronous Ensemble Kalman Filter (AEnKF) and AR error correction are compared for medium-term (15-day) forecasts over a period of 20 years (1996 to 2016). State updating improved the initial state for all subbasins and resulted in lasting skill score increase. AR also improved the forecast skill, but the forecast skill with AR did not always converge towards the uncorrected model skill, and instead can deteriorate for longer lead times. AR correction outperformed the AEnKF state updating for the first two days, after which state updating became more effective and outperformed AR. We conclude that state updating has more potential for medium-term hydrological forecasts than the operational AR procedure.</p><p>Further research is underway to investigate the importance, or added value, of long-term reforecasts as opposed to studies covering a short time span which are often more feasible and therefore more often found in literature.</p>

scholarly journals An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation

2017 ◽  
Vol 44 ◽  
pp. 89-100 ◽  
Author(s):  
Luca Cenci ◽  
Luca Pulvirenti ◽  
Giorgio Boni ◽  
Marco Chini ◽  
Patrick Matgen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Hydrological Model ◽  
High Spatial Resolution ◽  
Flash Flood ◽  
Soil Moisture Data ◽  
Time Period ◽  
The Impact

Abstract. The assimilation of satellite-derived soil moisture estimates (soil moisture–data assimilation, SM–DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to monitor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM–DA in recent years (e.g. the Advanced SCATterometer – ASCAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportunity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM–DA for enhancing flash flood predictions of a hydrological model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was carried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014–February 2015. It provided some important findings: (i) revealing the potential provided by S1-based SM–DA for improving discharge predictions, especially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spatial resolution does provide an important contribution in a SM–DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time period, and thus should be supported by further research activities, we believe this research is timely in order to enhance our understanding of the potential contribution of the S1 data within the SM–DA framework for flash flood risk mitigation.

scholarly journals A coupled stochastic rainfall-evapotranspiration model for hydrological impact analysis

2017 ◽  
Author(s):  
Minh Tu Pham ◽  
Hilde Vernieuwe ◽  
Bernard De Baets ◽  
Niko E. C. Verhoest
Keyword(s):  
Time Series ◽  
Impact Analysis ◽  
Hydrological Model ◽  
Hydrological Cycle ◽  
Rainfall Runoff ◽  
Hydrological Impact ◽  
Long Time ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

Abstract. A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such exercise, discharge is often considered, as especially extreme high discharges often cause damage due to the coinciding floods. Investigating extreme discharges generally requires long time series of precipitation and evapotranspiration that are used to force a rainfall-runoff model. However, such kind of data may not be available and one should resort to stochastically-generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events is not well studied. In this paper, stochastically-generated rainfall and coinciding evapotranspiration time series are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically-generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall-evapotranspiration model has a large potential for hydrological impact analysis.

scholarly journals Assessing the Impact of LAI Data Assimilation on Simulations of the Soil Water Balance and Maize Development Using MOHID-Land

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1367 ◽  
Author(s):  
Tiago Ramos ◽  
Lucian Simionesei ◽  
Ana Oliveira ◽  
Hanaa Darouich ◽  
Ramiro Neves
Keyword(s):  
Data Assimilation ◽  
Water Balance ◽  
Soil Water ◽  
Hydrological Modeling ◽  
Soil Water Balance ◽  
Landsat 8 ◽  
State Variables ◽  
Area Index ◽  
Final Model ◽  
The Impact

Hydrological modeling at the catchment scale requires the upscaling of many input parameters for better characterizing landscape heterogeneity, including soil, land use and climate variability. In this sense, remote sensing is often considered as a practical solution. This study aimed to access the impact of assimilation of leaf area index (LAI) data derived from Landsat 8 imagery on MOHID-Land’s simulations of the soil water balance and maize state variables (LAI, canopy height, aboveground dry biomass and yield). Data assimilation impacts on final model results were first assessed by comparing distinct modeling approaches to measured data. Then, the uncertainty related to assimilated LAI values was quantified on final model results using a Monte Carlo method. While LAI assimilation improved MOHID-Land’s estimates of the soil water balance and simulations of crop state variables during early stages, it was never sufficient to overcome the absence of a local calibrated crop dataset. Final model estimates further showed great uncertainty for LAI assimilated values during earlier crop stages, decreasing then with season reaching its end. Thus, while model simulations can be improved using LAI data assimilation, additional data sources should be considered for complementing crop parameterization.

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