Increasing river flood preparedness by real-time warning based on wetness state conditions

doi:10.1016/j.jhydrol.2013.03.015

Journal of Hydrology

Volume 489, 10 May 2013, Pages 227-237

https://doi.org/10.1016/j.jhydrol.2013.03.015 Get rights and content

Highlights

•
Soil moisture monitoring for two purposes.
•
General overview of soil moisture state by hydrological modelling and indirect observation techniques.
•
Combining soil moisture and rainfall to calculate discharge exceedance probability, by logistic regression.
•
Exceedance probability is mapped and can be used for flood warning purposes.

Summary

High wetness state levels can be considered as a primary indicator for potential river flooding. Therefore it is advisable to visualise real-time soil moisture information in flood forecasting or warning systems. Monitoring of soil moisture, however, is not an easy task due to its variable nature in time, space and depth. This paper presents and compares methods to assess the severity of the soil moisture state of hydrological catchments considered in a typical operational flood forecasting system. The severity of the relative soil moisture state is obtained and mapped by comparing the actual simulation result with the historical simulation results of a lumped conceptual hydrological model, directly by making use of the soil moisture component of the model or indirectly considering the baseflow component. Another approach uses rainfall, evapotranspiration and river flow observations. By applying a baseflow filter to the river flow observations and an advanced method for empirical catchment water balance computation, two indirect soil moisture indicators were defined, namely the filtered baseflow and the water balance based relative soil moisture content. It is shown that each of the methods allows to obtain useful estimates of the soil moisture state of a catchment in real time. The severity level of the soil moisture state is computed after comparison with long term statistics derived from a long term simulation. The severity level moreover is used to calculate the probability of exceedance of a predefined riverflow threshold, e.g. flood threshold, at the outlet or a given location in the catchment. This is done by means of a logit relation of the river flow probability of exceedance with the soil moisture indicator. The different soil moisture indicators are compared in their predicting capabilities by calculating and comparing the Brier score. Interestingly, the application of the logit relation or the use of a simple water balance computation for the catchment, based on real-time rainfall, evapotranspiration and river flow observations, leads to more reliable probability of exceedance estimates than the common direct use of total runoff results from a state-of-the art rainfall–runoff model. Mapping the probability of exceedance for the different hydrological catchments together with the width of the confidence interval on this probability is proposed as a useful tool to increase the preparedness for potential floods.

Introduction

The overall soil moisture state of a catchment, including soil moisture and ground water levels, is an important factor in the initiation of river floods. This is because the soil moisture has an important role in the hydrological cycle, governing the evaporation, runoff, infiltration and percolation processes (Entekhabi et al., 1994). One therefore could consider real time information on soil moisture conditions in the river catchment as an indicator for potential future floods, hence as input for awareness raising and increasing the preparedness of flood crisis management bodies for potential future floods. This requires the soil moisture data to be continuously assessed/quantified, mapped and communicated (Schaedel and Becker, 2002). However, the soil moisture is highly variable in time, space and depth, depending on the evaporation, the vegetation water demand, the groundwater level, rainfall (intensity and quantity) and soil type. To obtain a sufficient spatial soil moisture coverage, in situ measurements are inadequate. Therefore, indirect techniques are commonly used to assess the soil moisture conditions. These techniques can be divided into three categories: remote sensing based, modelling based and indirect observation techniques. The latter two techniques are further discussed and tested in this paper.

The soil moisture indicators (SMIs) obtained by these techniques are evaluated in their performance to produce accurate flood warnings. A combination of the SMI and the antecedent rainfall is used to calculate the probability of exceedance of a predefined discharge threshold, by means of a logit relation. Not only is a combination of SMI and rainfall tested in its forecasting performance, it is also compared with the prediction performance of the total runoff generated by a rainfall runoff model. The different methods are tested in their predicting capabilities by making use of the Brier score (Brier, 1950).

Hydrological models predict runoff making use of rainfall and evapotranspiration data. Most hydrological models, in operational use for flood modelling and forecasting, are conceptual models, which contain reservoirs, representing the soil moisture content. Depending on the relative storage content of these reservoirs and rainfall and evapotranspiration inputs, runoff is generated. The water storage predicted by these models can be considered as a measure of the catchment soil wetness and saturation level. This saturation level is spatially averaged if a lumped model is considered. Lacava et al. (2010) mentioned that modelled soil moisture data can reliably represent the real soil moisture; they used it to validate remotely sensed soil moisture data. Together with rainfall the initial soil moisture state plays a key role in the runoff response of a basin as predicted by a model (Zehe et al., 2005). Also the baseflow can be used as soil wetness and saturation indicator. Although baseflow is the ground water contribution to the streamflow and soil moisture is related to the upper zone in the soil, both are highly correlated (Van Steenbergen and Willems, 2012), which is also confirmed later on in this paper. Also Tramblay et al. (2010) tested modelled soil moisture and baseflow as SMIs and found that both were valid predictors. Therefore both the water content in the storage reservoir of conceptual hydrological models and the baseflow are in this paper considered as SMIs.

Closely related to the hydrological modelling techniques, indirect observations offer a second approach to estimate the relative soil moisture state of a catchment. By means of filter methods (Chapman, 1999, Arnold and Allen, 1999, Eckhardt, 2005), observed river flow series can be separated in different runoff subflows (e.g. overland flow, interflow, baseflow). Combining these subflow separation results with rainfall and evapotranspiration time series data, empirical water balance computations allow calculation of the relative soil moisture state. In this paper the subflow separation technique proposed by Willems (2009), based on the recursive digital filter proposed by Chapman (1991) is applied. For the soil moisture state estimation based on water balance computation different types of methods could be applied, such as the stochastic approach (Rodriguez-Iturbe et al., 1999, Porporato et al., 2004, Verma et al., 2011) or the quasi-terrestrial water balance method (Moiwo et al., 2011). An alternative empirical method is applied in this study. The advantage of these methods is that only a limited number of model assumptions are needed to make estimates on baseflow or soil moisture content.

Section snippets

Study area

The focus of the study is on the Flanders region in the north of Belgium. In total 154 catchments are considered, covering most of Flanders, including some upstream parts in the north of France and the Walloon region. The total area covered by the models in this study is approximately 22,250 km². The lumped subcatchment areas vary from 1 to 5227 km² per modelled catchment. The climatic conditions do not vary a lot between the different catchments. The average annual rainfall varies from 750 mm to

NAM model

NAM (Nedbør-Afstrømnings Model) is a lumped conceptual rainfall–runoff model, implemented in the Mike11 software of DHI Water and Environment. NAM generates three components of the catchment runoff (overland flow, interflow, baseflow) by means of reservoir-based routing and storage components. The storage components describe the storage of catchment water at the surface, in the soil (subsurface) and in the groundwater. The model also contains snow storage, but this is not considered in this

Soil moisture indicators

From Fig. 3, Fig. 5 the seasonal variations of the different SMI are clearly visible. To show that next to the seasonal variability also the time variability among the different SMIs is closely related, Fig. 7 shows a time series plot of the different SMIs for the same example Gete catchment. When the absolute values of the modelled and empirical SMIs are compared (L/L_max with u/u_max and BF with BF_fil) large differences exist, but this does not pose problem since the relative values are used as

Conclusions

In this research soil moisture has been used for two purposes. Firstly it is shown that soil moisture monitoring by means of hydrological modelling techniques or indirect observation techniques can be used to provide a general overview of the soil moisture state of different hydrological catchments. Secondly, combining the soil moisture indicator with daily rainfall is very efficient in assessing the probability of exceedance of a predefined discharge threshold for the next two days. This

Acknowledgements

The results presented in this paper were obtained by a research project on flood forecasting for Flanders Hydraulics Research, a division of the Flemish governmental authorities in Belgium.

References (35)

R. Barniv et al.
Confidence intervals for controlling the probability of bankruptcy
Omega
(2000)
P. Cabus
River flow prediction through rainfall–runoff modeling with a probability-distributed model (PDM) in Flanders, Belgium
Agric. Water Manage.
(2008)
K.P. Georgakakos
Analytical results for operational flash flood guidance
J. Hydrol.
(2006)
S.-C. Kao et al.
A copula-based joint deficit index for droughts
J. Hydrol.
(2010)
T. Lacava et al.
Soil moisture variations monitoring by AMSU-based soil wetness indices: a long-term inter-comparison with ground measurements
Remote Sens. Environ.
(2010)
H. Madsen
Automatic calibration of a conceptual rainfall–runoff model using multiple objectives
J. Hydrol.
(2000)
J.P. Moiwo et al.
Estimating soil moisture storage change using quasi-terrestrial water balance method
Agric. Water Manage.
(2011)
S.B. Shaw et al.
Examining individual recession events instead of a data cloud: Using a modified interpretation of dQ/dt-Q streamflow recession in glaciated watersheds to better inform models of low flow
J. Hydrol.
(2012)
Y. Tramblay et al.
Assessment of initial soil moisture conditions for event-based rainfall–runoff modeling
J. Hydrol.
(2010)
N. Van Steenbergen et al.
A non-parametric approach for probabilistic flood forecasting in support of uncertainty communication
Environ. Modell. Softw.
(2012)

P. Verma et al.

A stochastic model describing the impact of daily rainfall depth distribution on the soil water balance

Adv. Water Resour.

(2011)

G. Villarini et al.

Towards probabilistic forecasting of flash floods: the combined effects of uncertainty in radar-rainfall and flash flood guidance

J. Hydrol.

(2010)

P. Willems

A time series tool to support the multi-criteria performance evaluation of rainfall–runoff models

Environ. Modell. Softw.

(2009)

E. Zehe et al.

Uncertainty of simulated catchment runoff response in the presence of threshold processes: role of initial soil moisture and precipitation

J. Hydrol.

(2005)

J.G. Arnold et al.

Automated methods for estimating base flow and groundwater recharge from stream flow records

J. Am. Water Resour. Assoc.

(1999)

B. Biswal et al.

Geomorphological origin of recession curves

Geophys. Res. Lett.

(2010)

G.W. Brier

Verification of forecasts expressed in terms of probability

Mon. Weather Rev.

(1950)

Cited by (27)

Maximising runoff retention by vegetated landscape elements positioned through spatial optimisation
2024, Landscape and Urban Planning
Ecosystem services provided by vegetated landscape elements (vLEs) are increasingly recognised. One of the services provided is the mitigation of downstream flood risk. Obviously, the type and spatial configuration of vLEs impact the magnitude and timing of the runoff retention. Hence policy programs focused on the conservation and restoration of vLEs would benefit from a capability to determine the optimal spatial configuration of vLEs leading to maximum impact for minimal cost.
We integrated the Landlab rainfall-runoff model in an iterative spatial optimisation framework to deal with rasterised linear parcel boundaries whereby the cumulative capability to reduce discharge through the installation of vLEs is the ranking criterion. We applied the procedure to a 191 ha agricultural watershed situated in the Belgian Loess belt encompassing 34 km parcel boundaries.
Our results demonstrated that discharge volume can be more effectively reduced when vLEs are implemented based on the priority ranking obtained through our approach compared to both a random positioning of vLEs of the same length and type and the existing vLE configuration. The priority parcel boundaries are mainly located along preferential flow paths, highlighting the importance of the upslope area associated with vLEs and the infiltration enhancement they provoke. The application potential of the optimisation approach is not limited to the topic of finding priority locations for vLEs to reduce discharge but can be applied in a variety of disciplines that require answering questions about the optimal spatial configuration of interventions.
Linking hydrological, hydraulic and water quality models for river water environmental capacity assessment
2023, Science of the Total Environment
Citation Excerpt :
The RR module can be applied independently or used to represent one or more catchments that join a river network (Yadav et al., 2019; Sajadi Bami et al., 2020; Aredo et al., 2021). The NAM model has been successfully applied in many parts of the world with different hydrological and climatic regimes such as South Africa (Makungo et al., 2010; Odiyo et al., 2012), India (Nayak et al., 2013; Kar et al., 2015), Denmark (Madsen, 2000), United Kingdom (Liu and Sun, 2010), Australia (Bennett et al., 2016; Shao et al., 2018), Ireland (Bedri et al., 2014), Belgium (Van Steenbergen and Willems, 2013). In this study, the NAM model was used to generate a time-varying flow data series as the input for the hydraulic model.
The water environment of a river network can self-clean to a certain extent; however, when the wastewater discharge load exceeds a certain threshold, the balance of nature is disrupted, leading to water pollution. This emphasises the urgent need to evaluate river water environmental capacity (RWEC) as a necessary parameter for sustainable development. However, to quantify the RWEC, it is important to estimate the hydrological and hydrodynamic factors in the basin, leading to the linking of these models. The present investigation aims to propose an integrated framework, named RWEC, consisting of hydrological and hydrodynamic models, a database system, and GIS to evaluate the water environmental carrying capacity of the selected river network. The rain – runoff (RR), hydrodynamic (HD), ecological (ECO), and RWEC models were used. Groups of data, including meteorology, hydrology, and the environment, combined with topographic data and waste sources, were applied. Groups of models and data were integrated into a seven-step framework to calculate the RWEC. The case study is a basin in Binh Duong Province, Vietnam and four pollutants were selected: NH₄ ⁺, BOD₅, NO₃⁻, PO₄ ³⁻. The flow and water quality factors in the river basin in the study area were measured based on hydraulic models, and the water quality was calibrated. The role of hydrological, hydraulic, and water quality models in the RWEC calculation was clarified. According to the baseline and forecast scenarios, the calculation of the RWEC for the scenarios was performed. In the baseline scenario, RWEC_NH4+ is in the range (−283, −22) kg/day, RWEC_BOD5 ranges from (143, 3126) kg/day, RWEC_NO3- is in the range (−778, 2166) kg/day, and RWEC_PO43- is in the range (−31, 46) kg/day. The dependence of RWEC on environmental factors, self-cleaning factors, and the difference between the baseline and forecast scenarios were clarified.
Classifying floods by quantifying driver contributions in the Eastern Monsoon Region of China
2020, Journal of Hydrology
Understanding the generation mechanisms of floods is important to estimating future flood hazards by improving flood frequency analysis and flood trend interpretation. From the hydrological perspective of flood classification, flood types are determined by the seasonality or magnitudes of hydrological drivers (e.g., rainfall, snowmelt, catchment wetness). Few studies attribute floods to the causative drivers by quantifying the contributions of each driver to the flood peaks. Therefore, we propose the Quantification of Driver Contributions for Flood Classification (QDC-FC) method, which combines a data-driven hydrological model, the Shapley value, which quantifies driver contributions to flood peaks, and a classification tree for dividing floods into five types: long-rain floods (>1 day), rain-on-snow floods, snowmelt floods, short-rain floods ( $\leq$ 1 day), and antecedent-wet floods. In the Eastern Monsoon Region (EMR) of China, the method is applied in 225 catchments where the data-driven model has high Nash–Sutcliffe efficiency (NSE > 0.6) in daily runoff simulation and a high R-squared value ( $R^{2} > 0.6$ ) in flood peak simulation. Under the impact of the eastern monsoon, short-rain floods and long-rain floods are the most frequent types in 143 and 69 catchments, respectively. In 213 catchments, all extreme floods (with return periods larger than 10 years) are caused by short rain or long rain. Antecedent wetness causes more than 20% of floods in 26 out of 66 catchments in northeastern China and northern China with cumulative catchment wetness in summer. Although snowmelt-induced floods occur in April in some catchments of northeastern China, only one catchment has more than 20% of floods caused by snowmelt. As a new method for flood classification, the QDC-FC sheds light on the mixed generation mechanisms of floods and possible drivers of flood trends in the EMR of China.
Development and testing of a rainfall-runoff model for flood simulation in dry mountain catchments: A case study for the Dez River Basin
2019, Physics and Chemistry of the Earth
Citation Excerpt :
Since the event-based rainfall–runoff models require less parameterization and more simple than continuous simulation approaches (Berthet et al., 2009; Coustau et al., 2012; Massari et al., 2014), they are more widely applied in hydrological simulations. Despite all these benefits, the major limitation of these models is the difficulty in assessing the antecedent soil moisture conditions, which can be very different from one storm event to the next (Hino et al., 1988; Tramblay et al., 2010, 2012; Coustau et al., 2012; Van Steenbergen and Willems, 2013; Hoseini et al., 2017). Therefore, there is a clear need to reduce the uncertainties associated with the initial moisture condition in event-based flood forecasting and design flood estimation techniques.
Studying the rainfall-runoff response of the catchments has several important reasons in hydrology. The main reason is limitation of hydrologic parameters that can be measured in hydrological system. Therefore, the model that can estimate the transformation of rainfall to runoff with available range of measurements, particularly to limited gauged catchments can provide a means of prediction that will be useful in decision-making. Now days, various models have been developed around the world to model individual hydrological processes. In this study, the Watershed Modelling System (WMS) software was applied for rainfall-runoff modelling on Dez catchment located in Khuzestan province, IRAN since it faces serious flooding caused by heavy rainfall. The use of WMS software which interconnects the terrain models and the Geographic Information System (GIS) with commercial standard hydrological model including HEC-1 results in higher accuracy and saving time through modelling process. In this study, two historical storms (7 days in March 1993 and November–December 2001) used by applying two different modelling methods to determine the hyetographs which are one part of hydrologic process performing runoff response. Data collected from 29 November to 5 December 2001 were used for the calibration and the model verification were done for the period of 2–7 March 1993. Watershed parameters are calibrated manually to perform a successful simulation of discharge at three sub-basins in DEZ catchments. The outputs of hydrologic process using each method compared with observed data and the model performance were tested using statistical analyses. Considering the importance of parameters uncertainty and its effects on the response of the model, the sensitivity analysis was performed and the parameter which imposed the most impact on the model was identified. It was found that CN and STRTL parameters are the most important parameter in defining the peak flow, runoff volume and time to peak for all investigated methods in the studied basin. Sensitivity analysis reduces the complexity and uncertainty of hydrological simulations and provides a better understanding of the consistency between output and input in hydrologic modelling studies. The results of this research will benefit future modelling efforts by providing validated hydrological software to forecast hydrograph flooding on a regional scale. This model was designed for the Dez catchments, and this regional scale model could be used as a prototype for the model applications in other areas where lack of data exist.
Initial soil moisture effects on flash flood generation – A comparison between basins of contrasting hydro-climatic conditions
2016, Journal of Hydrology
Citation Excerpt :
Soil moisture can, in fact, control whether a given rainstorm produces a major flash flood or not, due to the non-linear nature of runoff response to rainfall (Hlavcova et al., 2005; Komma et al., 2007; Zehe and Blöschl, 2004). In the framework of flood warning systems, the knowledge of soil moisture is crucial (Georgakakos, 2006; Javelle et al., 2010; Lacava et al., 2005; Raynaud et al., 2015; Van Steenbergen and Willems, 2013). Hence, it is essential to capture antecedent soil moisture well for flood forecasting applications (Berthet et al., 2009; Yatheendradas et al., 2008).
The purpose of this paper is to contribute to the understanding of the importance of the initial soil moisture state for flash flood magnitudes. Four extreme events that occurred in different case study regions were analysed, one winter and one autumn flash flood in the Giofiros and Almirida catchments in Crete, and two summer floods in the Rastenberg catchment in Austria. The hydrological processes were simulated by the spatially distributed flash flood model Kampus. For the Crete cases Kampus model was calibrated against remotely sensed soil moisture while for the Austrian case the model was calibrated against observed runoff. Kampus model was then used to estimate the sensitivity of the stream flow peak to initial soil moisture. The largest of the events analysed (in terms of specific peak discharge) was found to have a sensitivity of less than 0.2% flood peak change per % soil moisture change while the smallest event had a sensitivity of more than 3% flood peak change per % soil moisture change. This suggests that initial soil moisture effects on the flash flood response probably depend on event magnitude rather than on the climate or region. Moreover, the Austrian catchment was found to exhibit a more nonlinear relationship between antecedent soil moisture and the peak discharge than the Cretan catchments which was explained by differences in the soil type.
Modelling hydrological losses for varying rainfall and moisture conditions in South Australian catchments
2015, Journal of Hydrology: Regional Studies
Citation Excerpt :
Moreover, event-based rainfall–runoff models require less parameterisation (Berthet et al., 2009; Coustau et al., 2012; Massari, 2014). However, the major limitation of the event-based method is the difficulty in assessing the antecedent soil moisture conditions, which can be very different from one storm event to the next (Coustau et al., 2012; Hino et al., 1988; Tramblay et al., 2010, 2012; Van Steenbergen and Willems, 2013). Therefore, there is a clear need to reduce the uncertainties associated with the initial moisture condition in event-based flood forecasting and design flood estimation techniques.
The study is based on unregulated catchments located in Mt. Lofty, Northern and Yorke regions of South Australia (SA).
Hydrological losses, which are frequently used in design flood estimation, have a wide range of spatial and temporal variability. However, the current practice for many design applications is to use a single loss value. Adopting a single representative value for loss is likely to introduce a high degree of uncertainty and potential bias. This paper identifies the relationships between losses and other parameters that can be incorporated in hydrological models to make reasonably accurate estimates of the losses. This paper assesses the variability of losses and identifies a method that can model initial loss (IL) using the parameters total rainfall (TR), rainfall duration (D) and antecedent wetness (AW). This study is based on 1162 rainfall events from the selected catchments.
This paper introduces two nomographs, TR–D and TR–AW, which are implemented using k-colour maps and a central tendency method. The developed methods are then validated using the rainfall runoff model, Water Bound Network Model (WBNM). This study will yield improvements to existing loss models by utilising rainfall and antecedent data, instead of using representative values to generalise real situations.

View all citing articles on Scopus

View full text

Increasing river flood preparedness by real-time warning based on wetness state conditions

Highlights

Summary

Introduction

Section snippets

Study area

NAM model

Soil moisture indicators

Conclusions

Acknowledgements

Omega

Agric. Water Manage.

J. Hydrol.

J. Hydrol.

Remote Sens. Environ.

J. Hydrol.

Agric. Water Manage.

J. Hydrol.

J. Hydrol.

Environ. Modell. Softw.

Adv. Water Resour.

J. Hydrol.

Environ. Modell. Softw.

J. Hydrol.

Automated methods for estimating base flow and groundwater recharge from stream flow records

J. Am. Water Resour. Assoc.

Geomorphological origin of recession curves

Geophys. Res. Lett.

Verification of forecasts expressed in terms of probability

Mon. Weather Rev.