Application of Sentinel-1 soil moisture information for improving groundwater simulations

To support robust water management, water managers should have access to up-to-date information about their water system. For example, Dutch regional water authorities are interested in temporally and spatially distributed groundwater level information. The Netherlands Hydrological Model LHM is often used for retrieving such information on several spatial scales in the Netherlands (De Lange et al., 2014). LHM is a physically-based distributed integrated hydrological model for simulating surface water, unsaturated zone and saturated zone dynamics. However, a validation of saturated zone simulations shows that, on a local to regional scale, deviations occur between observations and simulations of groundwater levels. The availability of high-resolution remotely sensed hydrological information has led to new possibilities for hydrological model improvements. Assimilating soil moisture information can improve both unsaturated and saturated zone simulations (Camporese et al., 2009; Zhang et al., 2016). Recently, a fine-resolution surface soil moisture product based on the freely available Sentinel-1 imagery has been developed. We use this new soil moisture information in combination with an Ensemble Kalman Filter to improve groundwater simulations of the LHM and to develop an accurate system for real-time groundwater simulations and forecasts. The open-source data assimilation framework OpenDA is used to implement the filter technique. The Twente region in the Netherlands serves as a case study. The availability of in-situ soil moisture and groundwater level measurement networks enables validation of the results. The results of this study show the potential of using high-resolution Sentinel-1 satellite imagery for water management. Water managers can use this knowledge to improve forecasts of groundwater levels and to estimate effects of control measures. Furthermore, water managers can use the results to explore the use of soil moisture information for water management. References Camporese, M., Paniconi, C., Putti, M., & Salandin, P. (2009). Ensemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow. Water Resources Research, 45(10). doi:10.1029/2008wr007031. De Lange, W. J., Prinsen, G. F., Hoogewoud, J. C., Veldhuizen, A. A., Verkaik, J., Oude Essink, G. H. P., van Walsum,P.E.V.,Delsman,J.R.,Hunink,J.C.,Massop,H.T.L.,&Kroon,T.(2014).Anoperational,multi-scale, multi-model system for consensus-based, integrated water management and policy analysis: The Netherlands Hydrological Instrument. Environmental Modelling & Software, 59, 98-108. doi:10.1016/j.envsoft.2014.05.009. Zhang, D., Madsen, H., Ridler, M. E., Kidmose, J., Jensen, K. H., & Refsgaard, J. C. (2016). Multivariate hydrological data assimilation of soil moisture and groundwater head. Hydrology and Earth System Sciences

Bodemvocht uit satellietdata:wat kan de Nederlandse waterbeheerder ermee?

Het onderzoeksproject ‘Optimizing Water Availability with Sentinel-1 Satellites’ heeft als doel te onderzoeken hoe satellietdata gebruikt kan worden in het Nederlandse waterbeheer. Het onderzoek laat zien dat de satelliet Sentinel-1 buiten het groeiseizoen om al een vrij goed beeld geeft van het bodemvochtgehalte. Hiermee kan bijvoorbeeld de berijdbaarheid van landbouwpercelen in kaart gebracht kan worden. Ook is met Deltares en HKV een data-assimilatietool ontwikkeld die ingezet kan worden om simulaties met het Landelijk Hydrologisch Model te verbeteren

Climate control of terrestrial carbon exchange across biomes and continents

Peer reviewe

Integraal natuurherstel in beekdalen : Ontwikkeling van diffuse afvoersystemen, gedempte afvoerdynamiek en beekprofielherstel

De drie belangrijkste hydrologische maatregelen aan beeksystemen die in deze studie centraal staan zijn: 1. Het herstellen van heterogene en diffuse afvoersystemen. 2. Het dempen van de afvoerdynamiek door de afvoerhydrologie aan te pakken. 3. Het genuanceerd verondiepen en versmallen van beken voor integraal beekdalherstel. Daarnaast blijven aanvullende morfologische maatregelen (passend bij de toekomstige afvoer) en eutrofiëring-reducerende maatregelen (zoals oppervlakkige afstroming van slib bufferen en zuiveren in de haarvaten) veel aandacht vragen

The role of evidence-based information in regional operational water management in the Netherlands

The integration of evidence-based information in operational water management is essential for robust decision-making. We investigated the current use of experiential and evidence-based information in Dutch regional operational water management. Interviews with operational water managers at regional water authorities in the Netherlands reveal that they use both evidence-based and experiential information for decision-making. While operational water management is shifting towards an evidence-based approach, experiential information is still important for decision-making. To fulfil the current information need, the operational water managers indicate they would like to have access to high-resolution spatial data, value-added products and tools for communication to stakeholders. We argue that hydrological models are suitable tools to support these needs. However, while several evidence-based information types are used by operational water managers, hydrological models are limitedly applied. Hydrological models are regarded as inaccurate for operational water management at desired spatial scales. Also, operational water managers often struggle to correctly interpret hydrological model output. We propose several means to overcome these problems, including educating operational water managers to interpret hydrological model output and selecting suitable indicators for evidence-based decision-making

Applying transfer function-noise modelling to characterize soil moisture dynamics: a data-driven approach using remote sensing data

The increasing availability of remotely sensed soil moisture data offers new opportunities for data-driven modelling approaches as alternatives for process-based modelling. This study presents the applicability of transfer function-noise (TFN) modelling for predicting unsaturated zone conditions. The TFN models are calibrated using SMAP L3 Enhanced surface soil moisture data. We found that soil moisture conditions are accurately represented by TFN models when exponential functions are used to define impulse-response functions. A sensitivity analysis showed the importance of using a calibrated period which is representative of the hydrological conditions for which the TFN model will be applied. The IR function parameters provide valuable information on water system characteristics, such as the total response and the response times of soil moisture to precipitation and evapotranspiration. Finally, we encourage exploring the possibilities of TFN soil moisture modelling, as predicting soil moisture conditions is promising for operational settings

Data underlying the publication: Applying transfer function-noise modelling to characterize soil moisture dynamics: a data-driven approach using remote sensing data

This dataset includes the input data, Python scripts, and Pastas model output for the scientific manuscript "Applying transfer function-noise modelling to characterize soil moisture dynamics: a data-driven approach using remote sensing data". The manuscript is currently under review. The data covers the years 2016, 2017, and 2018. We refer to the readme file included in the dataset for further details