This demonstrator portal is one of the outcomes of the Hydrological Earth Observation modelling exploration (HydEOmex) project. HydEOmex was a short-term pilot project running from January to May 2016 designed to demonstrate the potential of Earth Observations in hydrological applications for a range of stakeholders. HydEOmex was funded by Natural Environment Research Council and more information about the project as well as other outputs from the project can be found on the HydEOmex project website hosted by Centre for Ecology & Hydrology.

## Partners

The application was developed to demonstrate capabilities for data sharing, analysis, and presentation. Several partner institutions were involved:

## Datasets

### Standardized Streamflow Index

The Standardised Streamflow Index (SSI) has been calculated for 99 National Hydrological Monitoring Programme (NHMP) gauging stations using streamflow data held by the National River Flow Archive (NRFA).

SSI is an index typically used to characterise hydrological droughts (Vicente-Serrano et al., 2012) using methods originally developed for precipitation (McKee et al., 1993). SSI is calculated for user-defined accumulation periods (in this case, 1, 3, 6 and 12 months) using monthly mean streamflow data which is transformed to the normal distribution (with a standard deviation of one and mean of zero). This transformation allows SSI to be compared over space and time. The SSI indicates the severity and probability of hydrological drought occurrence with more negative values indicating more severe, but less likely droughts. Furthermore, the SSI can also denote periods of persistent wetness, through positive values of the index.

Standardised Streamflow Index (SSI)
Severity categories (Lloyd-Hughes and Saunders, 2002)
SSI Value Severity category
<-2.00Extreme drought
-1.50 to -1.99Severe drought
-1.00 to -1.49Moderate drought
0 to -0.99Mild drought
0 to 0.99Mildly wet
1.00 to 1.49Moderately wet
1.50 to 1.99Severely wet
>2.00Extremely wet

• Barker, L. J., Hannaford, J., Chiverton, A., and Svensson, C. (2015). From meteorological to hydrological drought using standardised indicators. Hydrology and Earth System Sciences Discussions 10.5194/hessd-12-12827-2015: 12827-12875.
• Lloyd‐Hughes, B., and Saunders, M. A. (2002). A drought climatology for Europe. International Journal of Climatology 22: 1571-1592, 10.1002/joc.846.
• McKee, T. B., Doesken, N. J., and Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology: 179-183.
• Vicente-Serrano, S. M., López-Moreno, J. I., Beguería, S., Lorenzo-Lacruz, J., Azorin-Molina, C., and Morán-Tejeda, E. (2012). Accurate computation of a streamflow drought index. Journal of Hydrologic Engineering 17: 318-332, http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000433.

### Soil Moisture

Soil Moisture data were obtained from the Climate Change Initiative program of the European Space Agency and reprocessed to 1 km square cell size.

Note that while this dataset covers the whole of Great Britain, diffreent areas have no data at different days because of cloud cover.

• http://www.esa-soilmoisture-cci.org/
• Liu, Y. Y., W. A. Dorigo, et al. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sensing of Environment 123: 280-297 10.1016/j.rse.2012.03.014.
• Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, and M. Ertl (2012). Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-7, 2012. XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia
• Terms and Conditions of Data Usage for the ESA CCI SM product

### Enhanced Vegetation Index

Gridded data of Enhanced Vegetation Index (EVI) with 500 m cell size for Great Britain was based on remote sensing imagery from MODIS land product (MOD09A1) 8-day surface reflectance composites and provided by NASA's Reverb system. MODIS imagery were preprocessed (mosaicked and reprojected) and EVI was calculated based on the best possible observation during an 8 days time step from 2007 to 2015, as follows:

$$EVI = G \cdot {NIR - Red \over NIR + C_1 \cdot Red - C_2 \cdot Blue + L}$$

Where Near-Infrared (NIR), Red, and Blue are atmospherically corrected (or partially atmospherically corrected) surface reflectance, and C1, C2, and L are coefficients to correct for atmospheric condition. For the standard MODIS EVI product, L=1, C1=6, C2=7.5 and gain factor G=2.5.

Ranging between 0 (no vegetation) and 1 (abundant vegetation), EVI is an 'optimized' vegetation index designed to enhance the vegetation signal correcting for some distortions in the reflected light caused by the particles in the air as well as the ground cover below the vegetation (Huete et al., 2002).

The data sets presented in the portal were smoothed using an inverse Fourier transformation to reduce any sensor and non-sensor noises (Dash et al., 2010).

### Rainfall

Rainfall produced by Centre for Ecology and Hydrology from MetOffice data as gridded estimates of daily areal rainfall.

This part of the development version of CEH-GEAR dataset.