Catchment-scale non-linear groundwater-surface water interactions in densely drained lowland catchments

Freely discharging lowland catchments are characterized by a strongly seasonal contracting and expanding system of discharging streams and ditches. Due to this rapidly changing active channel network, discharge and solute transport cannot be modeled by a single characteristic travel path, travel time distribution, unit hydrograph, or linear reservoir. We propose a systematic spatial averaging approach to derive catchment-scale storage and discharge from point-scale water balances. The effects of spatial heterogeneity in soil properties, vegetation, and drainage network are lumped and described by a relation between groundwater storage and the spatial probability distribution of groundwater depths with measurable parameters. The model describes how, in lowland catchments, the catchment-scale flux from groundwater to surface water via various flow routes is affected by a changing active channel network, the unsaturated zone and surface ponding. We used observations of groundwater levels and catchment discharge of a 6.6 km2 Dutch watershed in combination with a high-resolution spatially distributed hydrological model to test the model approach. Good results were obtained when modeling hourly discharges for a period of eight years. The validity of the underlying assumptions still needs to be tested under different conditions and for catchments of various sizes. Nevertheless, at this stage the model can already improve monitoring efficiency of groundwater-surface water interaction

Representing uncertainty in continental-scale gridded precipitation fields for agrometeorological modeling

This work proposes a relatively simple methodology for creating ensembles of precipitation inputs that are consistent with the spatial and temporal scale necessary for regional crop modeling. A high-quality reference precipitation dataset [the European Land Data Assimilation System (ELDAS)] was used as a basis to define the uncertainty in an operational precipitation database [the Crop Growth Monitoring System (CGMS)]. The distributions of precipitation residuals (CGMS ¿ ELDAS) were determined for classes of CGMS precipitation and transformed to a Gaussian distribution using normal score transformations. In cases of zero CGMS precipitation, the occurrence of rainfall was controlled by an indicator variable. The resulting normal-score-transformed precipitation residuals appeared to be approximately multivariate Gaussian and exhibited strong spatial correlation; however, temporal correlation was very weak. An ensemble of 100 precipitation realizations was created based on back-transformed spatially correlated Gaussian residuals and indicator realizations. Quantile¿quantile plots of 100 realizations against the ELDAS reference data for selected sites revealed similar distributions (except for the 100th percentile, owing to some large residuals in the realizations). The semivariograms of realizations for sampled days showed considerable variability in the overall variance; the range of the spatial correlation was similar to that of the ELDAS reference dataset. The intermittency characteristics of wet and dry periods were reproduced well for most of the selected sites, but the method failed to reproduce the dry period statistics in semiarid areas (e.g., southern Spain). Finally, a case study demonstrates how rainfall ensembles can be used in operational crop modeling and crop yield forecasting

Discharge estimation in a backwater affected meandering river

Variable effects of backwaters complicate the development of rating curves at hydrometric measurement stations. In areas influenced by backwater, single-parameter rating curve techniques are often inapplicable. To overcome this, several authors have advocated the use of an additional downstream level gauge to estimate the longitudinal surface level gradient, but this is cumbersome in a lowland meandering river with considerable transverse surface level gradients. Recent developments allow river flow to be continuously monitored through velocity measurements with an acoustic Doppler current profiler (H-ADCP), deployed horizontally at a river bank. This approach was adopted to obtain continuous discharge estimates at a cross-section in the River Mahakam at a station located about 300 km upstream of the river mouth in the Mahakam delta. The discharge station represents an area influenced by variable backwater effects from lakes, tributaries and floodplain ponds, and by tides. We applied both the standard index velocity method and a recently developed methodology to obtain a continuous time-series of discharge from the H-ADCP data. Measurements with a boat-mounted ADCP were used for calibration and validation of the model to translate H-ADCP velocity to discharge. As a comparison with conventional discharge estimation techniques, a stage-discharge relation using Jones formula was developed. The discharge rate at the station exceeded 3300 m3 s-1. Discharge series from a traditional stage-discharge relation did not capture the overall discharge dynamics, as inferred from H-ADCP data. For a specific river stage, the discharge range could be as high as 2000 m3 s-1, which is far beyond what could be explained from kinematic wave dynamics. Backwater effects from lakes were shown to be significant, whereas the river-tide interaction may impact discharge variation in the fortnightly frequency band. Fortnightly tides cannot easily be isolated from river discharge variation, which features similar periodicitie

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

Rainfall Generator for the Meuse Basin

A new methodology has been proposed to provide a better physical basis for the estimation of design discharges of the Dutch rivers. This new methodology is known as "rainfall generator". The hydrological part of the rainfall generator, a rainfall-runoff simulation in the Meuse basin, is described in this report. Therefore, ten generated records containing 3,000 year of precipitation and temperature data have been used as input for the HBV Meuse model. The main part of the actual work consists of the construction of a program which automatically executes the calculation sequenc

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