Improving operational flood forecasting using data assimilation

Hoogwatervoorspellingssystemen die betrouwbaar en nauwkeurig overstromingen kunnen voorspellen zijn erg belangrijk, omdat dit het aantal slachtoffers en de economische schade van overstromingen kan beperken. Het begrijpen van het gedrag van extreme hydrologische gebeurtenissen en het vermogen van de modelleur om betere en nauwkeurigere prognoses te krijgen, zijn uitdagingen binnen de toegepaste hydrologie. Omdat modellen slechts een versimpelde weergave van de complexe werkelijkheid geven, kleven er aan hydrologische voorspellingen veel onzekerheden. Dit proefschrift draagt bij aan een verbeterd begrip en kwantificatie van hydrologische modelonzekerheid, vooral gekoppeld aan de initi¨ele condities van het model, en in mindere mate aan de modelstructuur en de parameters

On the curious case of the recent decade, mid-spring precipitation deficit in central Europe

Central Europe has experienced a severe drought almost every April for the last 14 years consecutively, driven by record high temperatures, low flows, high evapotranspiration, and high soil moisture deficit. The dynamic of this recent and recurrent mid-spring dryness is not yet understood. Here we show that the period 2007â€``2020 was characterized by a reduction of ~50% of the usual April rainfall amount over large areas in central Europe. The precipitation deficit and the record high temperatures were triggered by a multiyear recurrent high-pressure system centered over the North Sea and northern Germany and a decline in the temperature gradient between the Arctic region and the mid-latitudes, which diverted the Atlantic storm tracks northward. From a long-term perspective, the precipitation, temperature, and soil moisture anomalies observed over the last 14 years have reached the highest amplitudes over the observational record. Our study provides an in-depth analysis of the hydroclimate extremes in central Europe over the last 140 years and their atmospheric drivers, enabling us to increase our dynamical understating of long-term dry periods, which is vital to enhance forecasting and mitigation of such events

Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models

This is the published version. Copyright 2014 American Geophysical UnionThis paper presents a hybrid local-global sensitivity analysis method termed the Distributed Evaluation of Local Sensitivity Analysis (DELSA), which is used here to identify important and unimportant parameters and evaluate how model parameter importance changes as parameter values change. DELSA uses derivative-based “local” methods to obtain the distribution of parameter sensitivity across the parameter space, which promotes consideration of sensitivity analysis results in the context of simulated dynamics. This work presents DELSA, discusses how it relates to existing methods, and uses two hydrologic test cases to compare its performance with the popular global, variance-based Sobol' method. The first test case is a simple nonlinear reservoir model with two parameters. The second test case involves five alternative “bucket-style” hydrologic models with up to 14 parameters applied to a medium-sized catchment (200 km2) in the Belgian Ardennes. Results show that in both examples, Sobol' and DELSA identify similar important and unimportant parameters, with DELSA enabling more detailed insight at much lower computational cost. For example, in the real-world problem the time delay in runoff is the most important parameter in all models, but DELSA shows that for about 20% of parameter sets it is not important at all and alternative mechanisms and parameters dominate. Moreover, the time delay was identified as important in regions producing poor model fits, whereas other parameters were identified as more important in regions of the parameter space producing better model fits. The ability to understand how parameter importance varies through parameter space is critical to inform decisions about, for example, additional data collection and model development. The ability to perform such analyses with modest computational requirements provides exciting opportunities to evaluate complicated models as well as many alternative models

State updating of a distributed hydrological model with Ensemble Kalman Filtering: Effects of updating frequency and observation network density on forecast accuracy

This paper presents a study on the optimal setup for discharge assimilation within a spatially distributed hydrological model. The Ensemble Kalman filter (EnKF) is employed to update the grid-based distributed states of such an hourly spatially distributed version of the HBV-96 model. By using a physically based model for the routing, the time delay and attenuation are modelled more realistically. The discharge and states at a given time step are assumed to be dependent on the previous time step only (Markov property). <br><br> Synthetic and real world experiments are carried out for the Upper Ourthe (1600 km<sup>2</sup>), a relatively quickly responding catchment in the Belgian Ardennes. We assess the impact on the forecasted discharge of (1) various sets of the spatially distributed discharge gauges and (2) the filtering frequency. The results show that the hydrological forecast at the catchment outlet is improved by assimilating interior gauges. This augmentation of the observation vector improves the forecast more than increasing the updating frequency. In terms of the model states, the EnKF procedure is found to mainly change the pdfs of the two routing model storages, even when the uncertainty in the discharge simulations is smaller than the defined observation uncertainty

Evaluating the skill of the mesoscale Hydrologic Model (mHM) for discharge simulation in sparsely-gauged basins in Nigeria

Predictive hydrologic modelling to understand and support agricultural water resources management and food security policies in Nigeria is a demanding task due to the paucity of hydro-meteorological measurements. This study assessed the skill of using different remotely sensed rainfall products in a multi-calibration framework for evaluating the performance of the mesoscale hydrologic Model (mHM) across four different data-scarce basins in Nigeria. Grid-based rainfall estimates obtained from several sources were used to drive the mHM in different basins in Nigeria. Model calibration was first performed using only discharge records, and also by using a combination of discharge and actual evapotranspiration, forced with different rainfall products. The mHM forced with CHIRPS produced reasonable Kling-Gupta efficiency KGE) results (0.5&gt; KGE &lt;0.85) under both calibration frameworks. However, constraining model parameters under a multi-calibration arrangement showed no significant discharge simulation improvement in this study. Results show the utility of the mHM for discharge simulation in data-sparse basins in Nigeria.</p

On noice in data assimilation schemes for improved flood forecasting using distributed hydrological models

We investigate the effects of noise specification on the quality of hydrological forecasts via an advanced data assimilation (DA) procedure using a distributed hydrological model driven by numerical weather predictions. The sequential DA procedure is based on (1) a multivariate rainfall ensemble generator, which provides spatial and temporal correlation error structures of input forcing, and (2) lagged particle filtering to update past and current state variables simultaneously in a lag-time window to consider the response times of internal hydrologic processes. The procedure is evaluated for streamflow forecasting of three flood events in two fast-responding catchments in Japan (Maruyama and Katsura). The rainfall ensembles are derived from ground-based rain gauge observations for the analysis step and numerical weather predictions for the forecast step. The ensemble simulation performs multi-site updating using information from the streamflow gauging network and considers the artificial effects of reservoir release. Sensitivity analysis is performed to assess the impacts of noise specification in DA, comparing a different setup of random state noise and input forcing with/without multivariate conditional simulation (MCS) of rainfall ensembles. The results show that lagged particle filtering (LPF) forced with MCS provides good performance with small and consistent random state noise, whereas LPF forced with Thiessen rainfall interpolation requires larger random state noise to yield performance comparable to that of LPF + MCS for short lead times

Operational aspects of asynchronous filtering for flood forecasting

This study investigates the suitability of the asynchronous ensemble Kalman filter (AEnKF) and a partitioned updating scheme for hydrological forecasting. The AEnKF requires forward integration of the model for the analysis and enables assimilation of current and past observations simultaneously at a single analysis step. The results of discharge assimilation into a grid-based hydrological model (using a soil moisture error model) for the Upper Ourthe catchment in the Belgian Ardennes show that including past predictions and observations in the data assimilation method improves the model forecasts. Additionally, we show that elimination of the strongly non-linear relation between the soil moisture storage and assimilated discharge observations from the model update becomes beneficial for improved operational forecasting, which is evaluated using several validation measures