The hydrological response of the Ourthe catchment to climate change as modelled by the HBV model

The Meuse is an important river in Western Europe, which is almost exclusively rain-fed. Projected changes in precipitation characteristics due to climate change, therefore, are expected to have a considerable effect on the hydrological regime of the river Meuse. We focus on an important tributary of the Meuse, the Ourthe, measuring about 1600 km2. The well-known hydrological model HBV is forced with three high-resolution (0.088°) regional climate scenarios, each based on one of three different IPCC CO2 emission scenarios: A1B, A2 and B1. To represent the current climate, a reference model run at the same resolution is used. Prior to running the hydrological model, the biases in the climate model output are investigated and corrected for. Different approaches to correct the distributed climate model output using single-site observations are compared. Correcting the spatially averaged temperature and precipitation is found to give the best results, but still large differences exist between observations and simulations. The bias corrected data are then used to force HBV. Results indicate a small increase in overall discharge, especially for the B1 scenario during the beginning of the 21st century. Towards the end of the century, all scenarios show a decrease in summer discharge, partially because of the diminished buffering effect by the snow pack, and an increased discharge in winter. It should be stressed, however, that we used results from only one GCM (the only one available at such a high resolution). It would be interesting to repeat the analysis with multiple model

Climatology of daily rainfall semi-variance in The Netherlands

Rain gauges can offer high quality rainfall measurements at their locations. Networks of rain gauges can offer better insight into the space-time variability of rainfall, but they tend to be too widely spaced for accurate estimates between points. While remote sensing systems, such as radars and networks of microwave links, can offer good insight in the spatial variability of rainfall they tend to have more problems in identifying the correct rain amounts at the ground. A way to estimate the variability of rainfall between gauge points is to interpolate between them using fitted variograms. If a dense rain gauge network is lacking it is difficult to estimate variograms accurately. In this paper a 30-year dataset of daily rain accumulations gathered at 29 automatic weather stations operated by KNMI (Royal Netherlands Meteorological Institute) and a one-year dataset of 10 gauges in a network with a radius of 5 km around CESAR (Cabauw Experimental Site for Atmospheric Research) are employed to estimate variograms. Fitted variogram parameters are shown to vary according to season, following simple cosine functions. Semi-variances at short ranges during winter and spring tend to be underestimated, but semi-variances during summer and autumn are well predicted

Multi-level optical signal generation using a segmented-electrode InP IQ-MZM with integrated CMOS binary drivers

We present a segmented-electrode InP IQ-MZM, capable of multi-level optical signal generation (5-bit per I/Q arm) by employing direct digital drive from integrated, low-power (1W) CMOS binary drivers. Programmable, multi-level operation is demonstrated experimentally on one MZM of the device

Spatial division multiplexing for optical data center networks

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De Wageningen Lowland Runoff Simulator (WALRUS): een snel neerslag-afvoermodel speciaal voor laaglandstroomgebieden

De Wageningen Lowland Runoff Simulator (WALRUS) is een nieuw neerslag-afvoermodel dat het gat moet vullen tussen complexe, ruimtelijk gedistribueerde modellen die vaak gebruikt worden in laaglandstroomgebieden en simpele, ruimtelijk geïntegreerde, parametrische modellen die voornamelijk zijn ontwikkeld voor hellende stroomgebieden. WALRUS houdt expliciet rekening met hydrologische processen die belangrijk zijn in laaglandgebieden, in het bijzonder (1) de koppeling tussen grondwater en onverzadigde zone, (2) vochttoestandafhankelijke stroomroutes, (3) grondwater-oppervlaktewaterterugkoppeling en (4) kwel, wegzijging en het inlaten of wegpompen van oppervlaktewater. WALRUS bestaat uit een gekoppeld reservoir voor grondwater en onverzadigde zone, een reservoir voor snelle stroomroutes en een oppervlaktewaterreservoir. Het is geschikt voor operationele toepassingen omdat het efficiënt rekent en numeriek stabiel is. In de vrij toegankelijke modelcode zijn standaardrelaties geïmplementeerd, zodat er slechts vier parameters overblijven die gekalibreerd hoeven te worden. Het model is geschikt voor het operationeel simuleren van hoogwater en droogte ten behoeve van risico-analyses en scenario-analyses, voor het ontwerpen van infrastructuur en voor het aanvullen van ontbrekende gegevens in afvoermeetreekse

Mixtures of Gaussians for uncertainty description in bivariate latent heat flux proxies

This paper proposes a new probabilistic approach for describing uncertainty in the ensembles of latent heat flux proxies. The proxies are obtained from hourly Bowen ratio and satellite-derived measurements, respectively, at several locations in the southern Great Plains region in the United States. The novelty of the presented approach is that the proxies are not considered separately, but as bivariate samples from an underlying probability density function. To describe the latter, the use of Gaussian mixture density models¿a class of nonparametric, data-adaptive probability density functions¿is proposed. In this way any subjective assumptions (e.g., Gaussianity) on the form of bivariate latent heat flux ensembles are avoided. This makes the estimated mixtures potentially useful in nonlinear interpolation and nonlinear probabilistic data assimilation of noisy latent heat flux measurements. The results in this study show that both of these applications are feasible through regionalization of estimated mixture densities. The regionalization scheme investigated here utilizes land cover and vegetation fraction as discriminatory variables

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