Hydrological Forecasting System Research Articles

To improve model soil moisture estimation in arid/semi-arid region using in situ and remote sensing information

Soil moisture plays a key role in water and energy exchange in the land hydrologic process. Effective soil moisture information can be used for many applications in weather and hydrological forecasting, water resources, and irrigation system management and planning. However, to accurate modeling of soil moisture variation in the soil layer is still very challenging. In this study, in situ and remote sensing information of near-surface soil moisture is assimilated into the Noah land surface model (LSM) to estimate deep-layer soil moisture variation. The sequential Monte Carlo-Particle Filter technique, being well known for capability of modeling high nonlinear and non-Gaussian processes, is applied to assimilate surface soil moisture measurement to the deep layers. The experiments were carried out over several locations over the semi-arid region of the US. Comparing with in situ observations, the assimilation runs show much improved from the control (non-assimilation) runs for estimating both soil moisture and temperature at 5-, 20-, and 50-cm soil depths in the Noah LSM.

Hydro-economic assessment of hydrological forecasting systems

An increasing number of publications show that ensemble hydrological forecasts exhibit good performance when compared to observed streamflow. Many studies also conclude that ensemble forecasts lead to a better performance than deterministic ones. This investigation takes one step further by not only comparing ensemble and deterministic forecasts to observed values, but by employing the forecasts in a stochastic decision-making assistance tool for hydroelectricity production, during a flood event on the Gatineau River in Canada. This allows the comparison between different types of forecasts according to their value in terms of energy, spillage and storage in a reservoir. The motivation for this is to adopt the point of view of an end-user, here a hydroelectricity production society. We show that ensemble forecasts exhibit excellent performances when compared to observations and are also satisfying when involved in operation management for electricity production. Further improvement in terms of productivity can be reached through the use of a simple post-processing method.

Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria

Abstract. Hydrological Ensemble Prediction Systems (HEPS), obtained by forcing rainfall-runoff models with Meteorological Ensemble Prediction Systems (MEPS), have been recognized as useful approaches to quantify uncertainties of hydrological forecasting systems. This task is complex both in terms of the coupling of information and computational time, which may create an operational barrier. The main objective of the current work is to assess the degree of simplification (reduction of the number of hydrological members) that can be achieved with a HEPS configured using 16 lumped hydrological models driven by the 50 weather ensemble forecasts from the European Centre for Medium-range Weather Forecasts (ECMWF). Here, Backward Greedy Selection (BGS) is proposed to assess the weight that each model must represent within a subset that offers similar or better performance than a reference set of 800 hydrological members. These hydrological models' weights represent the participation of each hydrological model within a simplified HEPS which would issue real-time forecasts in a relatively short computational time. The methodology uses a variation of the k-fold cross-validation, allowing an optimal use of the information, and employs a multi-criterion framework that represents the combination of resolution, reliability, consistency, and diversity. Results show that the degree of reduction of members can be established in terms of maximum number of members required (complexity of the HEPS) or the maximization of the relationship between the different scores (performance).

Can a multi-model approach improve hydrological ensemble forecasting? A study on 29 French catchments using 16 hydrological model structures

Abstract. An operational hydrological ensemble forecasting system based on a meteorological ensemble prediction system (M-EPS) coupled with a hydrological model searches to capture the uncertainties associated with the meteorological prediction to better predict river flows. However, the structure of the hydrological model is also an important source of uncertainty that has to be taken into account. This study aims at evaluating and comparing the performance and the reliability of different types of hydrological ensemble prediction systems (H-EPS), when ensemble weather forecasts are combined with a multi-model approach. The study is based on 29 catchments in France and 16 lumped hydrological model structures, driven by the weather forecasts from the European centre for medium-range weather forecasts (ECMWF). Results show that the ensemble predictions produced by a combination of several hydrological model structures and meteorological ensembles have higher skill and reliability than ensemble predictions given either by one single hydrological model fed by weather ensemble predictions or by several hydrological models and a deterministic meteorological forecast.

The feasibility of daily, weekly and ten-day water-level forecasting in Colombia

This paper analyses the feasibility of forecasting daily, weekly and ten-day water-levels at 20 hydrological stations forming part of the monitoring network supporting the Institute of Hydrology, Meteorology and Environmental Studies' (IDEAM) Alert Service in Colombia (http://www.ideam.gov.co). Such viability was determined by a set of orthogonal performance criteria and implementing optimally adaptive linear combinations (OALC) was recommended for this study as a viable operator for configuring a real-time hydrological forecast system. It is shown that the forecast for daily, weekly and ten-day levels had satisfactory viability for 70% of the cases studied.

Hydrological seasonal forecast over France: feasibility and prospects

AbstractThis article presents a first evaluation of a hydrological forecasting suite at seasonal time scales over France. The hydrometeorological model SAFRAN‐ISBA‐MODCOU is forced by seasonal forecasts from the DEMETER project for the March–April–May period. Despite a simple downscaling method, the atmospheric forcings are reasonably well represented at the finest scale. The computed soil moisture shows some predictability with large regions of correlation above 0.3. Probabilistic scores for soil moisture and river flows for four different catchments are higher than that for atmospheric variables. These results suggest to go further for building an operational hydrological seasonal forecast system. Copyright © 2010 Royal Meteorological Society

Online updating procedures for a real-time hydrological forecasting system

Rainfall-runoff-models can explain major parts of the natural runoff pattern but never simulate the observed hydrograph exactly. Reasons for errors are various sources of uncertainties embedded in the model forecasting system. Errors are due to measurement errors, the selected time period for calibration and validation, the parametric uncertainty and the model imprecision. In on-line forecasting systems forecasted input data is used which additionally generates a major uncertainty for the hydrological forecasting system. Techniques for partially compensating these uncertainties are investigated in the recent study in a medium sized catchment in the Austrian part of the Danube basin. The catchment area is about 1000 km2. The forecasting system consists of a semi-distributed continuous rainfall-runoff model that uses quantitative precipitation and temperature forecasts. To provide adequate system states at the beginning of the forecasting period continuous simulation is required, especially in winter. In this study two online updating methods are used and combined for enhancing the runoff forecasts. The first method is used for updating the system states at the beginning of the forecasting period by changing the precipitation input. The second method is an autoregressive error model, which is used to eliminate systematic errors in the model output. In combination those two methods work together well as each method is more effective in different runoff situations.

Error‐correction methods and evaluation of an ensemble based hydrological forecasting system for the Upper Danube catchment

AbstractWithin the EU Project PREVention, Information and Early Warning (PREVIEW), ensembles of discharge series have been generated for the Danube catchment by the use of various weather forecast products. Hydrological models applied for streamflow prediction often have simulation errors that degrade forecast quality and limit the operational usefulness of the forecasts. Therefore, error‐correction methods have been tested for adjusting the ensemble traces using a transformation derived with simulated and observed flows. This article presents first results of the combination of state‐space models and wavelet transformations in order to update errors between the simulated (forecasted) and the observed discharge. Copyright © 2008 Royal Meteorological Society

An empirical method for estimating future flood risks for flood warnings

Abstract. Since medium and long-term precipitation forecasts are still not reliable enough, rough estimates of the degree of the extremity of forthcoming flood events that might occur in the course of dangerous meteorological situations approaching a basin could be useful to decision-makers as additional information for flood warnings. One approach to answering such a problem is to use real-time data on the soil moisture conditions in a catchment in conjunction with estimates of the extremity of the future rainfall and experience with the basin's behaviour during historical floods. A scenario-based method is proposed for such a future flood risk estimation, based on an a priori evaluation of the extremity of hypothetical floods generated by combinations of synthetic extreme precipitation and previously observed antecedent pre-flood basin saturations. The Hron river basin, located in central Slovakia, was chosen as the pilot basin in the case study. A time series of the basin's average daily precipitation was derived using spatial interpolation techniques. A lumped HBV-type daily conceptual rainfall-runoff model was adopted for modelling runoff. Analysis of the relationship of the modelled historical pre-flood soil moisture and flood causing-precipitation revealed the independence of both quantities for rainfall durations lasting 1 to 5 days. The basin's average annual maximum 1 to 5 day precipitation depths were analysed statistically and synthetic extreme precipitation scenarios associated with rainfall depths with return periods of 5, 20, 50 and 100 years, durations of 1 to 5 days and temporal distribution of extreme rainfall observed in the past were set up for runoff simulation. Using event-based flood simulations, synthetic flood waves were generated for random combinations of the rainfall scenarios and historical pre-flood soil moisture conditions. The effect of any antecedent basin saturation on the extremity of floods was quantified empirically and critical values of the basin saturation leading to floods with a higher return period than the return period of precipitation were identified. A method for implementing such critical values into flood risk warnings in a hydrological forecasting and warning system in the basin was suggested.

A feasible method for fractional snow cover mapping in boreal zone based on a reflectance model

A feasible method for mapping the fraction of Snow Covered Area (SCA) in the boreal zone is presented. The method ( SCAmod) is based on a semi-empirical model, where three reflectance contributors (wet snow, snow-free ground and forest canopy), interconnected by an effective canopy transmissivity and SCA, constitute the observed reflectance from the target area. Given the reflectance observation, SCA is solved from the model. The predetermined values for the reflectance contributors can be adjusted to an optional wavelength region, which makes SCAmod adaptable to various optical sensors. The effective forest canopy transmissivity specifies the effect of forests on the local reflectance observation; it is estimated using Earth observation data similar to that employed in the actual SCA estimation. This approach enables operational snow mapping for extensive areas, as auxiliary forest data are not needed. Our study area covers 404 000 km 2, comprising all drainage basins of Finland (with an average area of 60 km 2) and some transboundary drainage basins common with Russia, Norway and Sweden. Applying SCAmod to NOAA/AVHRR cloud-free data acquired during melting periods 2001–2003, we estimated the areal fraction of snow cover for all the 5845 basins. The validation against in situ SCA from the Finnish snow course network indicates that with SCAmod, 15% (absolute SCA-units) accuracy for SCA is gained. Good results were also obtained from the validation against snow cover information provided by the Finnish weather station network, for example, 94% of snow-free and fully snow-covered basins were recognized. A general formula for deriving the statistical accuracy of SCA estimates provided by SCAmod is presented, complemented by the results when the AVHRR data are employed. Snow melting in Finland has been operatively monitored with SCAmod in Finnish Environment Institute (SYKE) since year 2001. The SCA estimates have been assimilated to the Finnish national hydrological modelling and forecasting system since 2003, showing a substantial improvement in forecasts.

A real time hydrological forecasting system using a fuzzy clustering approach

A new technique to predict extreme and rare situations of hydrometric levels in hydrological basins is presented in this paper. A fuzzy logic approach has been exploited for the adaptive clustering of input data and for the forecasting model. The methodology has been developed, in collaboration with an Italian manufacturer of meteorological and environmental sensing equipment, for the design of a system prototype to be installed in the “Padule di Fucecchio” basin in Middle-North of Italy. All the presented data come from monitoring equipments installed in this basin. The effectiveness of the method has been evaluated by comparing the performance to that obtained with a neural network forecasting approach.

The case for probabilistic forecasting in hydrology

That forecasts should be stated in probabilistic, rather than deterministic, terms has been argued from common sense and decision-theoretic perspectives for almost a century. Yet most operational hydrological forecasting systems produce deterministic forecasts and most research in operational hydrology has been devoted to finding the ‘best’ estimates rather than quantifying the predictive uncertainty. This essay presents a compendium of reasons for probabilistic forecasting of hydrological variates. Probabilistic forecasts are scientifically more honest, enable risk-based warnings of floods, enable rational decision making, and offer additional economic benefits. The growing demand for information about risk and the rising capability to quantify predictive uncertainties create an unparalleled opportunity for the hydrological profession to dramatically enhance the forecasting paradigm.

Determination of snow water equivalent using RADARSAT SAR data in eastern Canada

In the 1998-1999 winter, the operational feasibility of using RADARSAT SAR data to estimate the spatial distribution of snow water equivalent (SWE) in a large hydroelectric complex managed by Hydro-Québec (La Grande River watershed) has been successfully demonstrated. This watershed is located in the subarctic climatic region in the north-west of the Québec province. The vegetation consists of moderately dense to open Black Spruce forests, open lands, burned lands and peat bogs. In the last few years, an original approach well adapted for this region has been developed to estimate the SWE from SAR data (ERS-1, RADARSAT). This approach is based on the fact that the snow cover characteristics influence the underlying soil temperature which influences the dielectric properties of the soil and then the recorded backscattering signal. Then, a linear relationship between the backscattering ratios of a winter image and a snow-free (fall) image, and the snowpack thermal resistance (thermal insulation properties) has been established. Consequently, the algorithm infers the SWE from the estimated thermal resistance and the measured mean density of the snowpack. This algorithm has been implemented within a MapInfo™ application that has been named EQeau. It allows mapping of the spatial distribution of the estimated SWE at the desired level (pixel, square grid, sub-watershed). During the 1998-1999 winter, EQeau has been used successfully in a pre-operational mode using calibrated Wide beam images (W1) from RADARSAT. The algorithm has given mean estimated SWE values similar to the SWE values derived from Hydro-Quebec snow transects (relative difference between 1% and 13%). Also, the SWE increase measured from January to March 1999 is clearly detected on the maps covering almost 77 000 km2. The next steps will be the evaluation of the ScanSAR images and the demonstration of the economical advantages of using RADARSAT data in a hydrological forecasting system. Copyright © 1999 John Wiley & Sons, Ltd.

Parameterization of hydrological model using NOAA/AVHRR data

The National Oceanic and Atmospheric Administration (NOAA) is currently developing a hydrological forecast system for the Nile river. Soil moisture capacity (SMC) is one of the parameters required to run this system. Lack of ground data permit us to simulate this parameter for only a few basins. Therefore, we attempted to use vegetation index as a tool for parameterization of the SMC.

Development and application of a real-time flood forecasting system in the Veneto region of Italy

The paper describes three flood forecasting models used in the framework of the integrated hydrological forecasting system of the Veneto Region. The three models are based on the unit hydrograph theory, even though the computation of the “effective” rainfall and the real-time correction procedure are carried out in different ways. A comparison of the model performances is also made.

Role of hydrological forecasts and river flow modelling in rational agricultural water management in the perspective of a climate change — A case study of the Rivers Upper Nile and Niger

An international scientific conference under the auspices of WMO, UNEP and ICSU on the role of CO 2 and other greenhouse gases in climate variation made a statement that, if the concentration of CO 2 in the atmosphere doubles in the near future as present trends indicate, the global mean surface temperature would increase by 1.5–4.5°C. The paper discusses the impact such a climate change may have on water resources and on their management in agriculture. It stresses the importance of the possible increase in variance and frequency of extreme climatic events and subsequent water yield. It further outlines the ways of exploring the consequences of such development by using hydrological and water management models. It also proposes that real-time hydrological forecasting systems, able to produce forecasts with long, medium and short lead times, are one of the most effective measures to offset possible negative effects of the climate change, by increasing the effectiveness of storage management. These propositions are documented by means of case studies from developing countries using models on the River Nile, in particular in the basin of Lake Victoria, and of a hydrological forecasting system on the River Niger. It is proposed that it is important to develop a simulation model of the whole River Nile, together with a real-time forecasting system on the river in order to operate efficiently existing and future storages. Modelling of climate change impact on the water yields of River Niger is also discussed.

On the Use of Recursive Algorithms in Real-time River Flow Forecasting — Hungarian Experiences

At operational hydrological forecasting system for the River Danube is discussed. Two recursive algorithms are run in parallel as decision aids for the forecaster. The first one is a discrete linear cascade model that is based upon the linearized unsteady flow equations and is supplemented by an ARMAX residual model. Parameters are estimated off-line. Forecasts are updated by a linear Kalman filter. The second model acts as an operational back-up and is an entirely stochastic model that uses the self-tuning principle. Numerical results are presented for the whole Hungarian Danube reach.