Showing posts with label Blue Nile. Show all posts
Showing posts with label Blue Nile. Show all posts

Monday, July 10, 2017

A look back to go forward with JGrass-NewAGE

Let's give a look to the last two papers to delineate what is missing. They are all about the use of the JGrass-NewAGE system.


The key aspect of the whole treatment of the water budget is the closure strategy based on the Budyko hypothesis. This has been obtained by a suitable adaptation of the previous Adige-Hymod component that has to be futher cleaned: closing the budget in this way should become more easy and "normal”.  Notably, the method implies that we cannot easily account for cc, unless we let TB increase or decrease with time. However, there is no clear way to obtain this sliding TB from cc simulations.
Another aspect that distinguishes Pp is the use of MODIS for assessing the snow cover. That method would require a better definition, and a standardization of procedures which, again, is missing.
A weak point of this procedure is that ET does not depends on soil cover  characteristics. These characteristics have to be introduced appropriately, for instance giving plants' properties, which include foliage and some plants and roots dimensions which  affect water flows.

Other questions  involve the amount of simplification made when considering an HRU as the basic unit, and how a forcing attributed to a single point (inside a HRU) is representative of the variability of the whole area. Esemplificative is the case of the ET response, obtained by a single point or a small group of points inside the HRU and not by processing all the points. 
With respect to this, the original (version 0) of JGrass-NewAGE was more farsighted by introducing the  energy index calculator components. This OMS component was set to estimate the ratio of radiation received a single pixel inside the basin in a prescribed amount of time (i.e. a month, a year) with respect to a reference pixels. Then, when working in “real time” this factor was used to estimate (approximately) the radiation of the whole HRU on the basis of the single estimate of the reference point and saving, therefore, a lot of computational time. This type of simplifications should/could be reintroduced back again and used when relevant (for instance in Pp this should have been irrelevant for temperature, since its small variation inside each HRU). 


Besides what already said for the Pp that is valid also for BNp, there is to talk, for this paper, about satellite data. All the procedures to obtain those data were performed by using R scripts which are not reproducible and/or not well designed and/or not designed to be available to third parties. To make that research really sparkling, it would be necessary that those scripts and procedures would become real OMS components to be connected with the other ones that constitutesthe core of the paper.

The second point, obviously, is the use of very large HRU that was made: more than 400 square kilometers each on average (not so big, just as large as a square of 20 times 20 km). Therefore the arguments used in Pp, where HRU are of a few kilometers estension, are much more important here: how the sub grid variability of forcings like radiation affects the final results ?
Another aspect not well investigated is how the routing scheme used  affects the results. Clearly in a so large basin, this has to be investigated more thoroughly. Our tools have several routing schemes, including an integrator of a 1d de Saint-Venant equation. Using them could have some importance for the final assessment of our work.

Beyond this, another question arises: can we use those data and those results (in Pp and BNp) as benchmarks for future develpments of our system ? If not, this would be a big wound in our claim to do replicable research.


So, Let's get our sleeves back. 

Wednesday, February 1, 2017

Modelling the water budget of the Upper Blue Nile basin using the JGrass-NewAge model system and satellite data

This paper must be read after its companion on rainfall published in Atmospheric Research. There we were concerned with rainfall estimates over the large areas of Upper Blue Nile (UBN). Here we move on to estimate all the other components of the water budget. A similar goal was searched at small scales and with different tools, in this other paper about Posina catchment. So the paper can be considered sort of complimentary and covering a range of possibilities allowed by the JGrass-NewAGE system.

The paper abstracts reads: 
"The Upper Blue Nile basin is one of the most data-scarce regions in developing countries, hence, the hydrological information required for informed decision making in water resources management is limited. The hydrological complexity of the basin, tied with the lack of hydrometerological data, means that most hydrological studies in the region are either restricted to small subbasins where there are relatively better hydrometeorological data available, or at the whole basin scale but at very coarse time scales and spatial resolutions. In this study we develop a methodology that can improve the state-of-art by using the available, but sparse, hydrometerological data and satellite products to obtain the estimates of all the components of the hydrological cycle (precipitation, evapotranspiration, discharge, and storage). To this scope, we use the JGrass-NewAge system and various remote sensing products. The satellite products SM2R-CCI is used for obtaining the rainfall inputs; SAF EUMETSAT for cloud cover fraction for proper net radiation estimation; GLEAM for comparison with estimated ET; and GRACE gravimetry data for comparison of the total water storage amounts available. Results are obtained at daily time-steps for the period 1994-2009 (16 years), and they can be used as a reference for any water resource development activities in the region. The overall long term mean budget analysis shows that precipitation of the basin is 1360 ±230 mm per year. Evapotranspiration covers 56% of the yearly budget, runoff is 33%. Storage varies from minus 10% to plus 17% of the budget. "
The manuscript was submitted to HESS and went trough  a first round of revision (see the Discussion page). A revised manuscript was submitted. Please find below (on Zenodo):
The paper is now accepted and available at the HESS site.
   

Thursday, November 26, 2015

Rainfall on the Blue Nile

Assume you want to forecast the discharge and the hydrological cycle of a large African river.  Meteorological stations are just a few in a wide area, and gauge stations too, indeed. So what to do,while attending that more measurements are made ?  Simply you have to use satellite data. This paper, by Abera et al., deals with the analysis of five satellite data and analyse their reliability in the Blue Nile region.
Among the five there is the innovative product by Luca Brocca SM2RAIN which infers precipitations from the soil moisture analysis.
Please find the preprint of the paper under the Figure.  The abstract of the paper reads: 

"In a region where ground-based gauge data are scarce, satellite rainfall estimation (SREs) products are a viable option for proper space-time rainfall characterization. However, their accuracy and performances vary from region to region, and must be assessed. In this study, five high resolution satellite products (TRMM, CMORPH, TAMSAT, SM2R-CCI, and CFSR) are compared and analysed using the available rain gauge data in one of the most topographically and climatologically complex regions, Upper Blue Nile basin. The basin rainfall is investigated systematically, and it is found that, at some locations, the difference in mean annual rainfall estimates between these SREs could be as much up to 2700 mm. Considering three goodness-of-fit indexes, correlation, bias and root mean square error (RMSE) between the SREs and ground-based gauge rainfall, CMORPH, TAMSAT and SM2R-CCI outperform the other two. TAMSAT has the highest (91%) detection skill for dry days, followed by the CFSR (77%). For lower rainfall values, CMORPH, TAMSAT and CFSR show a higher accuracy index than SM2R-CCI and TRMM. On the contrary, the SM2R-CCI has the highest accuracy index for medium rainfall ranges (10-20 mm). In addition to the identification of the best performing products, the study tried to determine the bias correction of the estimates. A confusion matrix is used to investigate the detection ability of satellite rainfall products for different rainfall intensities. The empirical cumulative distribution (ecd f ) mapping technique is used to correct the SREs intensities distribution. This method provides a means to improve the rainfall estimation of all SREs, and the highest improvement obtained is for CMORPH (from -70% to -4%). "

A presentation (here) on the same topics of the paper was given at the 10th Alexander von Humboldt EGU Topical Conference held in Addis Ababa,  this 18-20 November.

The page where the complimentary material for the paper can be found is here.