Małgorzata Kleniewska

Uncertainty of Deardorff’s soil moisture model based on continuous TDR measurements for sandy loam soil

Abstract Knowledge on soil moisture is indispensable for a range of hydrological models, since it exerts a considerable influence on runoff conditions. Proper tools are nowadays applied in order to gain in-sight into soil moisture status, especially of uppermost soil layers, which are prone to weather changes and land use practices. In order to establish relationships between meteorological conditions and topsoil moisture, a simple model would be required, characterized by low computational effort, simple structure and low number of identified and calibrated parameters. We demonstrated, that existing model for shallow soils, considering mass exchange between two layers (the upper and the lower), as well as with the atmosphere and subsoil, worked well for sandy loam with deep ground water table in Warsaw conurbation. GLUE (Generalized Likelihood Uncertainty Estimation) linked with GSA (Global Sensitivity Analysis) provided for final determination of parameter values and model confidence ranges. Including the uncertainty in a model structure, caused that the median soil moisture solution of the GLUE was shifted from the one optimal in deterministic sense. From the point of view of practical model application, the main shortcoming were the underestimated water exchange rates between the lower soil layer (ranging from the depth of 0.1 to 0.2 m below ground level) and subsoil. General model quality was found to be satisfactory and promising for its utilization for establishing measures to regain retention in urbanized conditions.

Towards rainfall interception capacity estimation using ALS LiDAR data

In this study we develop a spatial model for interception capacity of vegetation based on LiDAR data. The study is conducted in the natural wetland river valley dominated meadows, reeds and small bushes. The multiple regression model was chosen to relate the field measurements of interception capacity and LiDAR statistics at 2m grid. The optimal model was chosen by stepwise selection and further manual variables selection resulting in the r2 of 0.52 and the residual standard error of 0.27 mm. The model preserved the vegetation pattern spatially and showed reasonable estimates for both vegetation covered and not covered by field sampling. The model was, however, affected by LiDAR measurements corrupted by river inundation. The results show good perspective for using LiDAR data for interception capacity estimation.

Derivation from the Landsat 7 NDVI and ground truth validation of LAI and interception storage capacity for wetland ecosystems in Biebrza Valley, Poland

Wetlands are very valuable areas because they provide a wide range of ecosystems services therefore modeling of wetland areas is very relevant, however, the most widely used hydrological models were developed in the 90s and usually are not adjusted to simulate wetland conditions. In case of wetlands including interception storage into the model’s calculation is even more challenging, because literature data hardly exists. This study includes the computation of interception storage capacity based on Landsat 7 image and ground truthing measurements conducted in the Biebrza Valley, Poland. The method was based on collecting and weighing dry, wet and fully saturated samples of sedges. During the experiments measurements of fresh/dry biomass and leaf area index (LAI) were performed. The research was repeated three times during the same season (May, June and July 2013) to observe temporal variability of parameters. Ground truthing measurements were used for the validating estimation of parameters derived from images acquired in a similar period as the measurements campaigns. The use of remote sensing has as major advantage of being able to obtain an area covering spatially and temporally distributed estimate of the interception storage capacity. Results from this study proved that interception capacity of wetlands vegetation is changing considerably during the vegetation season (temporal variability) and reaches its maximum value when plants are fully developed. Different areas depending on existing plants species are characterized with different values of interception capacity (spatial variability). This research frames within the INTREV and HiWET projects, funded respectively by National Science Centre (NCN) in Poland and BELSPO STEREO III.