See the earth2observe website and my poster presented at GEWEX in Canmore, Canada, in May 2018 here. Latest update is my presentation at the JULES meeting 5th September here.
The impact of uncertainty in rainfall on water resource prediction.
Toby Marthews, Eleanor Blyth
Water resources estimates depend on two things: the accuracy of the meteorological forcing data and the ability of the model to interpret that forcing into appropriate quantities. The eartH2Observe project has been concerned with quantifying improvements that can be made to global and local water resource estimates if satellite products are used. They can be used to either improve the model or improve the meteorological forcing data.
However, there is an additional dimension to the problem. Accuracy of the meteorological data will improve the further back in time you look where more data can be used, such as raingauges. If a near-real time estimate is needed, then the accuracy will depend on products that can be produced near-real time. That is weather-forecast model output and/or satellite products that are not rainguage corrected.
In this study we quantify the impact of using satellite rainfall products that have not been raingauge corrected on the water-resources in extreme rainfall conditions (above or below the 10% threshold set for each month for each 25km grid square across the world).
We have 4 global satellite products to compare to a rainguage-corrected product; all of them available globally for 30 years and combined with the non-rainfall (i.e. temperature, wind speed, radiation etc) meteorological forcing to force the land-based models.
A series of land surface and large-scale hydrology models are then run with these 4 new driving datasets and the outputs compared to the model run using the original. The errors associated with the satellite products are considered to be typical of the errors or uncertainty that would be encountered as the water resource prediction approaches near real time.
The assessment of uncertainty relates to soil moisture, surface runoff, evapotranspiration and groundwater recharge in the extreme rainfall conditions – both at the high rainfall and low rainfall end. The uncertainty in high-rainfall affects the water resources in a different way to the uncertainties in the low rainfall. This study explores those differences.
The impact of uncertainty in rainfall on water resource prediction.
Toby Marthews, Eleanor Blyth
Water resources estimates depend on two things: the accuracy of the meteorological forcing data and the ability of the model to interpret that forcing into appropriate quantities. The eartH2Observe project has been concerned with quantifying improvements that can be made to global and local water resource estimates if satellite products are used. They can be used to either improve the model or improve the meteorological forcing data.
However, there is an additional dimension to the problem. Accuracy of the meteorological data will improve the further back in time you look where more data can be used, such as raingauges. If a near-real time estimate is needed, then the accuracy will depend on products that can be produced near-real time. That is weather-forecast model output and/or satellite products that are not rainguage corrected.
In this study we quantify the impact of using satellite rainfall products that have not been raingauge corrected on the water-resources in extreme rainfall conditions (above or below the 10% threshold set for each month for each 25km grid square across the world).
We have 4 global satellite products to compare to a rainguage-corrected product; all of them available globally for 30 years and combined with the non-rainfall (i.e. temperature, wind speed, radiation etc) meteorological forcing to force the land-based models.
A series of land surface and large-scale hydrology models are then run with these 4 new driving datasets and the outputs compared to the model run using the original. The errors associated with the satellite products are considered to be typical of the errors or uncertainty that would be encountered as the water resource prediction approaches near real time.
The assessment of uncertainty relates to soil moisture, surface runoff, evapotranspiration and groundwater recharge in the extreme rainfall conditions – both at the high rainfall and low rainfall end. The uncertainty in high-rainfall affects the water resources in a different way to the uncertainties in the low rainfall. This study explores those differences.