DHI GRAS

Hørsholm, Denmark
Hørsholm, Denmark
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Rasmussen M.O.,DHI GRAS | Sorensen M.K.,DHI GRAS | Wu B.,CAS Institute of Remote Sensing | Yan N.,Peking University | And 2 more authors.
International Journal of Applied Earth Observation and Geoinformation | Year: 2014

A method for the estimation of daily evapotranspiration is tested for the North China Plain. The method is designed to be simple to implement and with very limited requirements for ground data (air temperature and humidity). The method uses MODIS NDVI and Land Surface Temperature (LST) data to derive evaporative fraction, using an adaptation of the "triangle method". The energy available for evapotranspiration is estimated using a combination of satellite data from MODIS and the (geostationary) Fengyun 2-series of sensors and station-based air temperature data. A gapfilling routine is applied to the time series of evaporative fraction to create complete daily maps for the region, allowing for use of the ET-estimates for applications requiring complete daily coverage (e.g. hydrological models). Results show that ET estimation on a daily scale is feasible with the proposed method, and that seasonal patterns are in accordance with other independent ET-estimates. There are some indications that our ET-estimates are somewhat overestimated when comparing to other RS-based methods and model simulations. It is demonstrated that the proposed method provides a relatively simple way of obtaining spatially distributed daily estimates of ET, making the method suitable for applications in studies where ground data availability is limited. © 2014 Elsevier B.V. All rights reserved.


Bauer-Gottwein P.,Technical University of Denmark | Jensen I.H.,Technical University of Denmark | Guzinski R.,DHI GRAS | Bredtoft G.K.T.,Technical University of Denmark | And 3 more authors.
Hydrology and Earth System Sciences | Year: 2015

Operational probabilistic forecasts of river discharge are essential for effective water resources management. Many studies have addressed this topic using different approaches ranging from purely statistical black-box approaches to physically based and distributed modeling schemes employing data assimilation techniques. However, few studies have attempted to develop operational probabilistic forecasting approaches for large and poorly gauged river basins. The objective of this study is to develop open-source software tools to support hydrologic forecasting and integrated water resources management in Africa. We present an operational probabilistic forecasting approach which uses public-domain climate forcing data and a hydrologic-hydrodynamic model which is entirely based on open-source software. Data assimilation techniques are used to inform the forecasts with the latest available observations. Forecasts are produced in real time for lead times of 0-7 days. The operational probabilistic forecasts are evaluated using a selection of performance statistics and indicators and the performance is compared to persistence and climatology benchmarks. The forecasting system delivers useful forecasts for the Kavango River, which are reliable and sharp. Results indicate that the value of the forecasts is greatest for intermediate lead times between 4 and 7 days. © 2015 Author(s).


Huber S.,DHI GRAS | Hansen L.B.,DHI GRAS | Ramussen M.O.,DHI GRAS | Kaas H.,DHI GRAS
European Space Agency, (Special Publication) ESA SP | Year: 2016

The process of selecting a site for aquaculture is complex and many factors are feeding into it. Spatial information from satellites and models is highly valuable in this process. For instance, water temperature is crucial for the fish's health and feed management and chlorophyll-A concentration (chl-a) is used as a water quality indicator. Near-real time satellite information can be used for monitoring purposes and historic patterns of these variables can be included into the process of choosing a suitable site. Modelled data can be used as complementary source, for predictive purposes and during cloudy periods, when optical satellite data is unavailable. In this paper we present a concept of how information from satellites and models can feed into siting. Moreover, we compare temperature and chl-A both from satellites and models, to evaluate the quality as well as difference between these products in the Danish waters.


Oliver J.,DHI Water - Environment - Health | Larsen O.,DHI Water - Environment - Health | Rasmussen M.O.,DHI GRAS | Lanuza E.,DHI Water - Environment - Health | Chakravarthy A.,DHI Water - Environment - Health
Journal of Disaster Research | Year: 2015

Throughout history, human beings have been attracted to waterfront living. Today, most residents live in cities, most of which, in turn, are built on flood plains and in coastal areas – areas often threatened by floods. Physical changes to the environment have changed the response of catchments and rivers to heavy rainfall. Despite attempts to control the size of floods, economic growth – especially as experienced in Asia – has led to an explosion in exposure to floods. The most integrated, cost-effective method for disaster reduction and prevention requires that risk be assessed purposefully and adequately. Disaster risk is captured in two major components: occurrence probability and event intensity and reach, and its consequences. Understanding the risks associated with floods in Asia has been hindered by the complexity of flood dynamics in large river basins and in existing or unreliable datasets. With calculation power increasingly available, the development of flexible modeling systems and the appearance of new datasets, socalled probabilistic floodmodels can now be developed for large areas to quantify risks. A flexible modeling framework has been developed at DHI to better characterize flood plains and complex hydraulic systems in datapoor and highly exposed areas in Asia. The model relies on automated processes merging freely available datasets such as HydroSHEDS, WorldPop, crowd-sourced data available in OpenStreet Map and Landsat 7 and 8 satellite imagery. The combination of spatial data sources provides opportunities to optimize the hydrodynamic model domain and to improve the lowresolution digital elevation model. Such methods enhance flood hazard information conventionally derived from deterministic models by taking a full probabilistic approach considering source loading conditions, e.g., weather events and sea level rise, and the performance of existing and planned mitigation measures and failures of control structures such as dykes. With risks better quantified, new opportunities arise for cost-effective mitigation and resilience measures and for the development of novel risk transfer schemes through the use of insurance and capital markets. © 2015, Fuji Technology Press. All rights reserved.

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