Gabriel Senay in his office (source: SMITHSONIAN MAGAZINE ) |

This blog aims to use as an academic and professional diary. I will comment on what I researched, read and any issues that interest me in the subject of hydrology, geomorphology, GIS-RS and any quantitative analysis (Envirometrics).

## Apr 24, 2015

### The innovative use of satellites to predict famine

## Apr 10, 2015

### Very interesting paper on basin water yields

From the simplified water balance equation \[ P=E+R+ \Delta S \] where P is rainfall, E is evapotranspiration, and R runoff and \( \Delta S \) change in storage differences for a given hydrological year. Assuming the \( \Delta S = 0 \) for a given hydrological year, or even better for longer hydrological years, after re-arranging the equation will be:

\[ \frac{R}{P} = 1-\frac{E}{P} \]

From this point, there are some studies on how to estimate the \( E \) from the \( P \) and \( PET \) (e.g. Zeng and Cai, 2015\( ^{3} \) and papers cited therein), defines \( E \) as function of aridity index (\( PET/P \)), \[ E= P(1+ \frac{PET}{P} - (1+(\frac{PET}{P})w)^{\frac{1}{w}})) \]

This \( E \) formulation is substituted, and the reciprocal of the aridity index (\( P/PET \)), wetnes index, is used for analytical simplicity , i.e:

\[ \frac{R}{P}=(1+ (\frac{P}{PET})^{-m})^{\frac{1}{m}}-(\frac{P}{PET})^{-1} \]

which relates annual water yield ( R ) to a wetness index (precipitation/ potential evapotranspiration; P/PET) and watershed characteristics (m). m could be connected to many watershed characterstics, such as soil, vegetation, basin area, geomorphometry.

There is also an intersting efforts by Voepel et'al\( ^{2} \) on the controls of hydrologic partitioning at the catchment scale. One important thing in thier approach is the way they concptualize basin wetting and aridity in relation to the Normalized Difference Vegetation Index (NDVI). This would make the job easy, because it is easy to use NDVI from satellite.

Anyways, what interest me is \( m \) can be assessed with wide ranges of basin characterstics, and, further refine the equation with the dominante physical paramater(s).

**References**

- \( ^{1} \) Guoyi Zhou, Xiaohua Wei, Xiuzhi Chen1, Ping Zhou, Xiaodong Liu, Yin Xiao, Ge Sun, David F. Scott, Shuyidan Zhou, Liusheng Han & Yongxian Su: Global pattern for the effect of climate and land cover on water yield.“ Nature Communications 6, Article number: 5918 DOI: 10.1038/ncomms6918
- \( ^{2} \)Voepel, H., B. Ruddell, R. Schumer, P. A. Troch, P. D. Brooks, A. Neal, M. Durcik, and M. Sivapalan (2011), Quantifying the role of climate and landscape characteristics on hydrologic partitioning and vegetation response, Water Resour. Res., 47, W00J09, doi:10.1029/2010WR009944.
- \( ^{3} \)Ruijie Zeng, Ximing Cai. (2015) Assessing the temporal variance of evapotranspiration considering climate and catchment storage factors. Advances in Water Resources 79, 51-60.
- Gerrits, A. M. J., H. H. G. Savenije, E. J. M. Veling, and L. Pfister (2009), Analytical derivation of the Budyko curve based on rainfall characteristics and a simple evaporation model, Water Resour. Res., 45, W04403, doi:10.1029/2008WR007308

## Apr 4, 2015

### consultant and business idea

The ultimate objective of hydrology as a subject and hydrologist as a profession is to understand the hydrological processes to estimate the available water at different scales. Hence, it feels for me that organising ones work for the sake of this objective will always have practical and professional career.

### Remote sensing rainfall products: part 4:TAMSAT

This is the fourth rainfall products i am looking at for hydrological research. TAMSAT stands for Tropical Applications of Meteorology using SATellite data and ground-based observations. TAMSAT is rainfall products designed for africa, calibrated with ground measurments. Detal description about it can be found here. While the dekadal and monthly estimation is avaliable for the whole of africa with 1km resolution, since 1983, the daily estimation is only avalaible 2013 onwards. Here I would like to use this products, and compare with other estimates.As usual, i downloaded the data in my harddisk, and process using R.

loading packages

```
library(raster)
## Loading required package: sp
library(ncdf4)
library(rgdal)
## rgdal: version: 0.9-1, (SVN revision 518)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 1.9.2, released 2012/10/08
## Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/3.1/Resources/library/rgdal/gdal
## Loaded PROJ.4 runtime: Rel. 4.8.0, 6 March 2012, [PJ_VERSION: 480]
## Path to PROJ.4 shared files: /Library/Frameworks/R.framework/Versions/3.1/Resources/library/rgdal/proj
library(lattice)
library(rasterVis)
## Loading required package: latticeExtra
## Loading required package: RColorBrewer
## Loading required package: hexbin
library(ncdf)
```

Next, let’s load the raster into R.

```
setwd("/Users/administrator/Documents/PHDResearch/UBN_rainfall/TAMSAT/Project/")
tamsatlist<-list.files(pattern ='.nc', full.names = TRUE)
```

Next, let’s create a raster stack all two years of daily raster file.

```
#create raster stack
tamsat.stack <- stack(tamsatlist)
tamsat.stack
## class : RasterStack
## dimensions : 1974, 1894, 3738756, 730 (nrow, ncol, ncell, nlayers)
## resolution : 0.0375, 0.0375 (x, y)
## extent : -19.03125, 51.99375, -35.98125, 38.04375 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
## names : Rain.Fall.Estimate.1, Rain.Fall.Estimate.2, Rain.Fall.Estimate.3, Rain.Fall.Estimate.4, Rain.Fall.Estimate.5, Rain.Fall.Estimate.6, Rain.Fall.Estimate.7, Rain.Fall.Estimate.8, Rain.Fall.Estimate.9, Rain.Fall.Estimate.10, Rain.Fall.Estimate.11, Rain.Fall.Estimate.12, Rain.Fall.Estimate.13, Rain.Fall.Estimate.14, Rain.Fall.Estimate.15, ...
```

These are time series raster data for the whole africa region, however, i would like to extract at some stations in my study area. let me do just for a single station. First I need to create spatialpoint object, then extract the time series layer at this location.

```
ADET<-cbind(37.47,11.27); ADET<-SpatialPoints(ADET)
TAMSAT_station<-data.frame(
time=seq(as.Date('2013-01-01'), as.Date('2014-12-31'), 'day'),
ADET=as.vector(extract(tamsat.stack, ADET))
)
```

the time series rainfall at ADET

```
xyplot(ADET~time|equal.count(as.numeric(time), 1, overlap = 0.1),
TAMSAT_station,
type = c("l","g"), lw=2, strip = FALSE,
xlab="time", ylab="TAMSAT rainfall[mm]",
scales = list(x = list(relation = "sliced", axs = "i"),
y = list(alternating = FALSE)))
```