RESTORATION OF VEGETATION COVER INKUKU, KENYA

Effect of semi-circular bunds on retaining rainwater and thestimulation vegetation recovery

Femke HilhorstAug 2016

AuthorFemke Hilhorst

SupervisorsMartine van de PloegRoel Dijksma

CommissionerNaga Foundation

Hydrology and Quantitative Water Management GroupWageningen UniversityThe Netherlands

Feb 2016

Abstract

Naga foundation works to restore vegetation cover and want to know the effect of their re-greeningprojects on the hydraulic cycle. The goal of this research is to do an exploratory study on that subjectin Kuku (Kenya) and to make recommendations for long-term research. The goal of Naga foundation(affiliated with the better known "Justdiggit" organization) is to restore vegetation in the sub-arid regionof Kenya. The concept of Naga Foundation is that more vegetation increases evapotranspiration (ET),which leads to higher humidity and ultimately into local cloud formation and rainfall. However, thistheory is never proved. The aim of this study is to do an exploratory research linked to a Naga projectin Kuku (Kenya). Naga created half mooned bunds there to harvest rainwater in a gently slopingarea of 7 km2. This rainwater is then available for ET by the new grown vegetation. Water balancecomponents were measured, e.g. precipitation, evapotranspiration, storage change, surface runoff andhydraulic conductivity. Storage change was predominantly focused on the quantitative water retentionby the bunds. A weather station and a soil station were installed in order to set a long term monitoringnetwork for precipitation, evapotranspiration and storage change. The study was executed during rainyseason, with a total precipitation of 112 mm in 35 days. The effect of the bunds on the surroundings isin particular visible in vegetation cover and soil moisture content. After the rainy season, the vegetationcover in the reference plot (without bunds) increased with 33% compared to a 369% increase in thestudy area (with bunds). During the rainy season, soil moisture content was averagely 15% higher inthe study area as well. On 24 April 2016 a surface runon event was monitored of 43 mm while the localprecipitation was 8 mm, so water entered the project area superficially (runon). The bunds retainedduring that runon event averagely 2.1 m3 of water. To estimate evapotranspiration on a larger scale,S-SEBI was used. This is a remote sensing algorithm, that solves the surface energy balance with asinput Landsat 8 and field data, e.g. wind speed, relative humidity and atmospheric pressure (Roerinket al, 2000). This model can be used to analyse the scale on which the Naga projects have impact onevapotranspiration, therefore it is advised to use it for long term monitoring.

Acknowledgements

I would first like to thank my internship supervisor’s Martine van der Ploeg of the Soil Physics and LandManagement Group and Roel Dijksma of the Hydrology and Quantitative Water Management Group atWageningen University. They always made time for my questions and allowed this study to be my ownwork. Thereby I want to say that I appreciated the feedback of Liesbeth Wilschut and Michel Riksen onmy research proposal. Also a thanks to Fons Jaspers to involve me enthusiastic with the project.

I also want to thank Naga foundation to allow me to go to Kuku. Sander de Haas, thank you to giveme just enough guidance. I liked the fact that you indicated what you wanted, but also left me completelyfree about the content of my research. Also a thanks to Roos Willard to act properly on finance and Nadiade Waal for making it possible that the blogs were posted on the Naga site (https://justdiggit.org/blog-femke-olivier-week-2, https://justdiggit.org/blog-femke-week-8, https://justdiggit.org/final-blog-femke).

Thereby, I would like to thank Maasai Wilderness and Conservation Trust (MWCT) for advice andpractical affairs around the fieldwork. I would especially like to thank Lana Muller, Dirk van der Goes,George King’ola and Roy Omanje. You have helped me if I needed a car for transporting instruments, oradvice on possible stealing of equipment. You knew the area and locals like no other, and this informationwas valuable for me. I could count on you guys.

I also want to thank Ryan Teuling, Philip Wenting, Hennie Gertsen, Martine van der Ploeg and RoelDijksma for borrowing field equipment. Also a thanks to Bert Heusinkveld for suggestions about theweather station brand and your advice regarding the self made lysimeters. Lastly, I would like to thankOscar Hartogensis for guidance regarding transpiration measurements.

ContentsAbstract v

Acknowledgements vii

1 Problem description 1

2 Study area 3

3 Method 53.1 Hydrological cycle 5

3.1.1 Precipitation 63.1.2 Soil moisture storage 73.1.3 Evapotranspiration 83.1.4 Hydraulic conductivity 83.1.5 Surface runoff 9

3.2 Effect bunds 103.2.1 Quantitative retention bunds 103.2.2 Impact of bunds on hydraulic cycle and vegetation 12

3.3 Further Monitoring 123.3.1 Install weather & soil station 123.3.2 Evapotranspiration by remote sensing 12

4 Materials for field observations 17

5 Results 195.1 Hydrological cycle 19

5.1.1 Precipitation 195.1.2 Soil moisture, hydrological conductivity & evaporation 205.1.3 Surface Runoff 21

5.2 Effect bunds 225.2.1 Quantitative retention bunds 225.2.2 Impact of bunds on hydraulic cycle and vegetation 23

5.3 Future monitoring 245.3.1 Install weather & soil station 245.3.2 Evapotranspiration by remote sensing 25

6 Summary of findings 316.1 Field problems 316.2 Field data analysis 316.3 Limit and constrains S-SEBI 326.4 Suggestion for future research 326.5 Conclusions 32

Bibliography 35

1 | Problem description

Due to climate change, overexploitation and unsustainable land use, the vegetation cover decreased inthe sub-arid region in Kenya. This can result to a loss of biodiversity, food scarcity, water stress, floodsand migration (Naga, 2015). Naga started several re-greening projects in the south of Kenya, to create ahydrological corridor. This theory is that more vegetation leads to more evapotranspiration and therebymore air moisture. More water in the air then leads to faster cloud formation and eventually more rain.However, this concept is never proved. One of their re-greening projects is constructing semi-circularbunds as shown in Figure 1.1. The goal of these bunds is to retain rainwater before it drains away,so vegetation undergoes less water stress. However, the quantitative effect of semi-circular bunds onretaining rainwater is not examined yet. The water balance elements in the Kuku catchment are also notstudied. This is however crucial to understand of the effect of water supply on vegetation cover.

Figure 1.1: Semi-circular bunds that experienced one runon event.

ObjectiveThis study aims to measure the water balance elements in the project area of Naga in Kuku, to understandthe rainfall runoff relation better. The quantitative effect of semi-circular bunds to retain rainwater needsto be analysed as well. Naga intends to monitor the effect of their re-greening projects long term. Becausethis study is the first step for long term monitoring, it is important to investigate methods for futureresearch. So the goals of this study are (i) exploration research on hydrological cycle, (ii) the effect ofsemi-circular bunds on the hydrological cycle & vegetation cover and (iii) give handles for long termresearch.

2 | Study area

Naga has several re-greening projects in southern Kenya (Kuku) to create a hydrological corridor. Thisstudy focusses on one particular re-greening project over there: the semi-circular bunds project. Figure 2.1demonstrates the location of this re-greening project. The project area is located between the Kilimanjaroand the Chyulu hills.

Figure 2.1: The semi-circular bunds re-greening project of Naga is located south of Kenya (Kuku).

Construction of semi-circular bunds in Kuku started February 2016. The plan is to make bunds in thewhite bordered plots represented in Figure 2.2. The background of Figure 2.2 is a true colour Landsat 8image. To analyse the effect of semi-circular bunds on the water balance, one representative area is chosenwith bunds (red dot, Figure 2.2) and one without bunds (red dot, Figure 2.2) to execute fieldwork. Thesetwo area’s are both 375 m2, have a similar vegetation cover, a slope of 0.9o and are 400 m apart fromeach other to minimize the spatial environmental effect on the water balance. The study and referencearea are chosen to be located in the upper plot (Figure 2.2), because semi-circular bund constructionstarted there.

2 km

Figure 2.2: Project area of Naga with study area (with bunds, red dot) and reference area (without bunds, bluedot).

3 | Method

There are three goals of this study, as explained in the problem description (Chapter 1). This Chapterwill explain the method to achieve these three goals.

3.1 Hydrological cycle

A central principle in hydrology is that of the conservation of mass. The laws and equations are based on itin this study. Equation 3.1 assumes that there is subsurface flow, only surface outflow at catchment outlet,no groundwater storage and no vegetation cover. With these assumptions, the simplified catchment waterbalance reads as:

P = ET +Qsurface + I +∆S

∆t. (3.1)

With P precipitation, ET evapotranspiration, ∆S∆t soil moisture storage change over time, Qsurface surface

discharge, I infiltration. In the Figure below, a simplified water balance is shown that is applied in thisstudy.

I

∆S

Qsurface

ET

P

Figure 3.1: Principle of the catchment water balance.

There is almost no historical data available of these fluxes in the Kuku area, therefore an exploration studyis performed to analyse the hydrological cycle. Fieldwork was carried out during the March-May rainyseason (Camberlin and Philippon, 2002). Because the water balance terms are measured individually,and because of mentioned assumptions, the water balance never closes. That is why this study does notfocus on closing the water balance, but only on an exploration study regarding the hydrological cycle.Different methods to measure water fluxes results in a higher accuracy of the obtained data, thereforethe water balance elements were measured several ways if possible. The hydraulic cycle is studied in thereference area and study area which is represented in Figure 3.2.

6 | Chapter 3. Method

Figure 3.2: Measured fluxes in study (left) and reference area (right).

3.1.1 Precipitation

Precipitation data is obtained following:• Ten manual rain gauges (totalizers);

• one tipping bucket of the brand Hobo;

• one tipping bucket of the brand Davis.Historical precipitation data (from 2010) is available from the Maasai wildlife conservation trust (MWCT)in Iltala, Kenya. Because the spatial and temporal variability of precipitation is high in Kenya, it isimportant to measure precipitation particular at the Kuku site. The intensity of the precipitation eventsare important to study, because it is known that precipitation events show sharp peaks (Bowen, 1956).To measure temporal distribution of precipitation, a tipping bucket (automatic rain gauge) is installedat the study site of the brand Hobo. A weather station including a tipping bucket is also placed inthe study area, this is explained more extensively in Section 3.3. Totalisers (manual rain gauges) areinstalled as well. Five totalizers are placed in the study area and five totalizers in the reference plot.Quantitative precipitation estimation from satellites is considered to be less accurate than from raingauges. It is particular valuable over oceans and remote areas (Dijksma et al., 2013). Therefore, nosatellite precipitation data is obtained in this study. The historical precipitation data from MWCT iscompared with the observations.

Figure 3.3: Totalizers (left) and tipping bucket (right).

3.1. Hydrological cycle | 7

3.1.2 Soil moisture storage

Soil moisture storage is measured by:• Thetaprobe• Soil moisture station

An other water balance term is water storage in the soil. Soil moisture content is measured with aThetaprobe (see Figure 3.4). This soil moisture sensor measures the VMC by applying the FrequencyDomain technique. The sensor is able to measure within a measurement range of 5-55% VMC and anaccuracy of 5% with standard calibration. Soil moisture is measured daily using the Thetaprobe at 15places in the study area and at 15 places in the reference area (so 30 points in total). The measurementpoints are spatially distributed random and shown in Figure 3.2. Soil moisture is measured using a soilmoisture station of the brand Davis (see Section 3.3). This station includes 4 soil moisture sensors, so thespatial coverage is low. However, the soil moisture station logs data every 15 minutes, so the temporalvariability is captured accurately.

Figure 3.4: Thetaprobe to measure soil moisture content in percentages. Measured at 15 locations in study areaand 15 points in reference area.

8 | Chapter 3. Method

3.1.3 Evapotranspiration

Evapotranspiration (ET) is the sum of bare soil evaporation (E) and plant transpiration (T). Evaporationis measured following:• Lake evaporation Epan by a evaporation pan;• Soil evaporation Esoil by use of self made lysimeters;

To measure the lake evaporation, an evaporation pan is installed in the study area. The pan is fencedto prevent animals from drinking the water. Evaporation pans prove to be one of the most inexpensive,simplest and widely used methods to measure evaporative losses (Fekih et al., 2013). The pan measureshow much water can be evaporated into the atmosphere given ample supply of water.

Figure 3.5: Evaporation pan that is installed in Kuku, Kenya. The evaporation pan is filled up daily until thewater level touches the iron wire (right picture).

Hand made lysimeters are placed at the Kuku site to measure bare soil evaporation (see Figure 3.6).Rings are hit into the ground and weighed daily by using a Soehnle Page Profi kitchen scale. This scalehas an accuracy of grams and a weight limit of 15 kg. The weight difference of the soil samples givesinformation about the quantity of water in the soil that evaporates. This measuring technique is basedon the study of Heusinkveld et al. (2004). In this study, dew was measured by use of a hand madelysimeter. The difference is that in this study the weight of the soil sample is measured manually insteadof a sensor that is dug in the soil.

Figure 3.6: Hand made lysimeters. The left pictures represents an empty lysimeter. The right picture shows alysimeter that is placed in a bund.

3.1.4 Hydraulic conductivity

The groundwater depth at Kuku is so deep that there is limited interaction between the groundwater andsubsurface. When precipitation hits the ground, it can infiltrate, evaporate or generate overland flow.The infiltration capacity is measured by a mini disk infiltrometer which is shown in Figure 3.7.

3.1. Hydrological cycle | 9

Mini DiskInfiltrometer

User’s Manual

Version 3

Figure 3.7: Sketch of the mini disk infiltrometer (left) and the infiltrometer in action during fieldwork in Kuku(right).

The hydraulic conductivity is measured by this device. The infiltrometer has two chambers that are filledwith water. The upper chamber controls the suction rate and the lower chamber is a water reservoir thatreleases water into the soil (see sketch, Figure 3.7). The mini disk infiltrometer is sealed on the bottomwith a porous disk. When the infiltrometer is placed on the soil, water leaves the bottom chamber and ata certain rate. The soil type together with this certain rate are the input values to calculate the hydraulicconductivity of the soil (Zhang, 1997):

k =C1

A, (3.2)

with C1 is the slope of the curve of the cumulative infiltration against the square root of time in ms−1

and A is a value that is calculated by Genuchten parameters based on the soil type (Carsel and Parrish,1988).

3.1.5 Surface runoff

Because the Kuku site has a sub-humid climate with thin vegetation, Horton overland flow generallydominates. The contribution of subsurface storm flow is then less important. Horton overland flow(Qsurface) describes horizontal flow as the infiltration capacity exceeds (Beven, 2011; Horton, 1939). Tomeasure the surface flow, two surface runoff plots are installed (Figure 3.8).

Figure 3.8: Surface runoff set-up.

10 | Chapter 3. Method

Jansen (2007) has set up a similar runoff plot in the Kitui area, which is located 200 kilometres fromKuku. The maximum runoff rate in the Kitui catchment is about 2.5 mm min−1 (= 150 mm hour−1).To capture all this precipitation, a large enough container must be placed. The most common way tomeasure the quantity of the surface flow is with pressure transducers. These sensors are hung at certainwater level in the barrel and measure the pressure. When another sensor is installed that measures airpressure, the difference between the sensors gives the pressure of the water column above the sensor.With density of water (ρ = 1000 kg m3), the gravity constant g, the measured pressure of the watercolumn above the sensor pcolumn in Pa and

hcolumn =pcolumn

ρ g, (3.3)

the height of the water column above the sensor can be calculated (Dijksma et al., 2013). In this study,a diver is used as pressure transducer because they can be borrowed from the Wageningen University. Adiver in the barrel measures the change in water level which gives information about surface runoff rate.A sketch of the runoff plot is represented in Figure 3.9.

gutterbarrel

agricultural plastic

2 m2

Figure 3.9: Surface runoff plot sketch.

The walls of the plot are covered with agricultural plastic and at the downstream part of the plot, a gutteris be installed. A cement slab is attached under the gutter to prevent leakage. Water flows through thegutter and finally into the barrel. It is also necessary to measure the slope of the runoff plots. Thisis measured with a trypod. It is not common to measure surface runoff by use of satellites, becausethe temporal resolution of surface runoff is much shorter than that of satellites. An elevation map withstreamlines is made in ArcMap to analyse if surface runon is plausible.

3.2 Effect bunds

The effect of the bunds on hydrological cycle and vegetation cover is measured during this study.

3.2.1 Quantitative retention bunds

The quantitative retention of rainwater is measured by:• automatically, by use of a diver;• manually, by measuring the water level in the bunds after a runon event and correcting it forinfiltration.

The quantitative effect of inner semi-circular bunds to retain water is analysed by the measurement set-upshown in Figure 3.10.

3.2. Effect bunds | 11

diver

catchment area

agricultural plastic

Figure 3.10: The measurement technique to analyse the quantitative effect of inner semi-circular bunds. A topview of four bunds are represented in this Figure. The measurement set-up is conducted on the lower bund.

After thorough observation of the site, several representative bunds are chosen and completely drapedwith agricultural plastic. A diver in the inner semi-circular bunds measures the water level. When thecatchment area that a bund captures is know (the green area), the volume of water that is retained canbe calculated.

Figure 3.11: Measurement set-up of water retaining experiment. The right picture represents one bund that isdraped in agricultural plastic. A diver (right picture) is placed on the bund to measure the water level.

As is shown in Figure 3.12, the reached water level during a surface runon event in a semi-circular bundcan be visually observed.

Figure 3.12: As is shown, a clear boundary is visually observable from where the water reached during a surfacerunon event.

The water level that was reached in the bunds during a runon event in the study area is measured.Together with the catchment area (Figure 3.10) and the correction for infiltration, the quantitative waterretention of the bunds is calculated. The infiltrated water I in cm is estimated by:

12 | Chapter 3. Method

I =Sbefore − Safter

φ· dmeasure . (3.4)

With Sbefore & Safter the average soil moisture content of all bunds in the study area before and afterthe runon event. The porosity φ is estimated by assuming that saturated soil moisture content is equalto porosity. So porosity = soil moisture content during saturated state. The soil moisture contentmeasuring depth is dmeasure in cm. In this study, soil moisture content is measured at 15 cm depth (bya Thetaprobe).

3.2.2 Impact of bunds on hydraulic cycle and vegetation

The effect of the bunds on the surroundings is determined by comparing the hydraulic cycle and vegetationcover in the study and reference area. The method of obtaining the water balance elements in studyand reference area is already explained in detail previously. The vegetation cover is estimated before andafter the rainy season by making a grid in the studied areas and sketching the vegetation cover in eachgrid cell before and after the rainy season.

3.3 Further Monitoring

3.3.1 Install weather & soil station

This study is the basis of further monitoring. The measuring time of this study is only a few weeks.To see the long term effect of vegetation on weather and soil characteristics, it is crucial to set up along term measurement installation. The Dutch web shop "weerspecialist.nl" sells weather stations withleaf & soil station accessories of the brand Davis. Davis proves to be a robust and cheap brand. Davissells weather stations for private use, but the quality seems to be good. A weather station (the VantagePro2) is set up at the Kuku site to measure temperature, humidity, barometric pressure, wind speed anddirection, dew point and rainfall. A accessory that is linked to the Vantage Pro2 is the Leaf & Soil Stationwith four soil temperature and soil moisture sensors (weerspecialist, 2016). This is a sufficient first stepfor future monitoring. The weather and soil measurement are also used in this study to interpret themeasured water balance elements. Thereby, data that is obtained from this weather station is requiredto calculate evapotranspiration by use of the S-SEBI model (see next Section).

Figure 3.13: Weather station (left) with soil moisture/leaf wetness accessory (right). Both of the brand Davis.

3.3.2 Evapotranspiration by remote sensing

Evaporation is not only dependent on the water cycle, but also on the energy balance. Estimating ET

by using remote sensing is based on this energy balance principle. The main advantage to estimate ET

by using satellites is that the spatial distribution is high, down to a resolution of tens of meters. Satelliteimages have however a poor temporal coverage, from ones a day to once every two weeks. The energy

3.3. Further Monitoring | 13

balance is calculated by using remote sensing measurements and the Simplified Surface Energy BalanceIndex (S-SEBI). This model determines the albedo dependant maximum temperature for dry conditionsand an albedo dependant minimum temperature for wet conditions. The S-SEBI has been developed tosolve the surface energy balance by use of remote sensing. It is given by:

Rn = G0 +H + λE, (3.5)

with Rn net radiation, G0 soil heat flux, H sensible heat flux and λE latent heat flux (all in Wm−2).Evapotranspiration can direct and convenient be derived from the latent heat flux. To estimate thelatent heat flux based on the energy balance, the other energy terms must be calculated as well. Satelliteimages of Landsat 8 and measured weather data in Kuku is required to calculate the energy terms usingthe S-SEBI model. The LANDSAT OLI sensor passed by on 8 May 2016, 07:30 GMT.

Input parameters

First the input parameters of the S-SEBI model are discussed. Four input parameters are derived byLandsat images in combination with field measurements. The fifth input parameter (longwave radiation)is calculated by using empirical equation.

1. Surface AlbedoTo calculate surface albedo, the following Equation is used (Zhong and Yinhai, 1988; Bastiaanssen et al.,1998):

α =αtoa − αatm

τ2oc

. (3.6)

With τoc atmospheric transmittance, αtoa albedo without atmospheric correction and αatm averageportion of incoming solar radiations across all bands that is back-scattered to the satellite before itreaches earth’s surface. The value of the atmospheric albedo αatm is situated between 0.025 and 0.04(Allen et al., 2002). In this study, αatm = 0.03 is adopted that is based on Bastiaanssen et al. (1998). Tocalculate albedo without atmospheric correction αtoa, the following Equation is used (Silva et al., 2016):

αtoa = 0.300r2 + 0.277r3 + 0.233r4 + 0.143r5 + 0.036r6 + 0.012r7. (3.7)

With rN is the reflectance of LANDSAT OLI bands N 2 to 7, which is calculated by (Chander andMarkham, 2003):

rN =MρQcal +Aρ

cos θsdr, (3.8)

with θs solar zenith angle, rN top of atmosphere spectral reflectance for each band, Mρ band-specificmultiplicative rescaling factor, Aρ band-specific additive rescaling factor and Qcal quantized, calibratedstandard product pixel values (DN). dr corresponds to the correction of the eccentricity of the terrestrialorbit, given by (Silva et al., 2016):

dr =

(1

dES

)2

, (3.9)

with dES earth to sun distance. The atmospheric transmittance is calculated following (Allen et al.,2007), assuming clear sky:

τoc = 0.35 + 0.627exp

[− 0.000146Pair

cos θs− 0.075

(W

cos θs

)0.4], (3.10)

with Pair local atmospheric pressure in mb and W atmospheric precipitable water (mm) which is givenby the Equation (Garrison and Adler, 1990):

W = 0.0014ePair + 2.1, (3.11)

14 | Chapter 3. Method

with e atmospheric vapour pressure (mb) that is the product of saturated vapour pressure es and relativehumidity RH (−):

e = RHes. (3.12)

To determine the saturated atmospheric vapour pressure in mb, the Tabata (1973) relation is used:

log es = 8.42926609− 1827.17843

Ta− 71208.27

T 2a

. (3.13)

with Ta atmospheric temperature in K.Relative humidity RH, local atmospheric pressure Pair and atmospheric temperature Ta is obtained

from field measurements. The rest of the data is calculated from the LANDSAT OLI images and thecorresponding meta datafile.

2. NDVIThe Normalized Difference Vegetation Index (NDVI) is a graphical indicator that can determine if a pixelcontains live green vegetation or not. The NDVI is stated as (Carlson and Ripley, 1997; Carlson et al.,1990):

NDV I =anir − avisanir + avis

, (3.14)

with avis and anir surface reflectance averaged over ranges of wavelengths in visible and near infra-redregions of the spectrum, respectively. The NDVI is calculated using LANDSAT OLI band 4 (visible) and5 (near infra-red) of 8 May 2016. This method is sufficient for estimating the fractional green vegetationcover for landscapes in (semi)-arid regions (Xiao and Moody, 2005).

3. Land surface emissivityAn operative and easy to use method to obtain the land surface emissivity (LSE) is by use of the NDVI(see previous Section). Different approaches are applied in the literature to estimate the LSE from NDVI(Sobrino and Caselles, 1993; Van de Griend and Owe, 1993; Valor and Caselles, 1996; Sobrino et al.,2004). The method that is applied by Sobrino et al. (2004) is used in this study, because it shows thatit works sufficient when comparing it to a reference method. First the proportion of vegetation Pv isformulated following (Sobrino et al., 2004; Suresh et al., 2016):

Pv =

(NDV I −NDV Imin

NDV Imax −NDV Imin

)2

. (3.15)

The land surface emissivity ε is then calculated by this empirical relation:

ε = 0.004Pv + 0.986. (3.16)

4. Surface TemperatureTo estimate land surface temperature, band 10 of LANDSAT OLI and the emissivity are required. Firstthe digital numbers of band 10 are converted to top of atmosphere radiance rλ by (Mishra et al., 2014):

rλ = MLQcal +AL, (3.17)

withML band-specific multiplicative rescaling factor, AL band-specific additive rescaling factor and Qcal

quantized, calibrated standard product pixel values (DN), which is actually the LANDSAT OLI image ofband 10. To convert the rλ to satellite brightness temperature Tb in ◦C, the following Equation is used(Mishra et al., 2014):

Tb =K2

ln(K1

Lλ+ 1) − 273.15, (3.18)

3.3. Further Monitoring | 15

where K1 and K2 are band-specific thermal conversion constants. Lastly, the land surface temperatureTsurface is derived by (Suresh et al., 2016):

Tsurface = Tb +Qcal

(Tb

14380

)ln(ε), (3.19)

with ε land surface emissivity that is calculated in the previous Section.

5. Incoming Longwave RadiationThe incoming longwave radiation L↓ is defined as the downward thermal radiation from the atmospherein Wm−2:

L↓ = εaσT4a , (3.20)

with Ta atmospheric temperature (K), σ the Stefan-Bolzmann constant and εa atmospheric emissivitythat can be calculated by the empirical Equation (Bastiaanssen, 1995; Allen et al., 2011):

εa = 0.85(− ln τoc).09. (3.21)

Evaporative Fraction

The sensible and latent heat flux are not calculated separately, but as a evaporative fraction (4). Tocalculate the evaporative fraction of each pixel, surface albedo is plot against the surface temperature ina program called ERDAS (Geosystems, 2004). This plot is called a feature space plot. This feature spaceplot is constructed to assess the input values for the calculation of the evaporative fraction (Roerinket al., 2000):

4 =TH − Tsurface

TH − TλE, (3.22)

where TλE the temperature that is valid when λEmax(r0) = Rn−G0 and H = 0. TH is the temperaturewhich is valid when Hmax(r0) = Rn−Gn and λE = 0. The study of Roerink et al. (2000) gives a moredetailed explanation regarding the S-SEBI algorithm theory.

Calculate Energy Balance Terms

The last step of deriving the evapotranspiration with S-SEBI is by determining the other radiation terms.

Net RadiationThe net radiation is the amount of radiation that reaches the land surface (Rn). The energy balancetakes shortwave (S) and longwave (L) radiation into account:

Rn = S↓ − S↑ + L↓ − L↑,= (1− α)τS↓sun + L↓ − σεT 4

surface,

S↓sun = Ssun cos(θs).

(3.23)

With Ssun sun constant (assumed 1367 Wm−2 in this study), ε emissivity, τ atmospheric transmittanceand σ Plank constant.

Soil Heat FluxThe soil heat flux G0 is an other flux of the energy balance. It is derived by:

G0 =(0.05 + 0.25Pv

)Rn, (3.24)

with Pv the proportion of vegetation that calculated in Equation 3.15.

16 | Chapter 3. Method

Sensible heat fluxThe heat transfer between the ground and atmosphere is defined as the sensible heat flux H. It iscalculated as:

H = (1−4)(Rn −G0). (3.25)

Latent Heat FluxThe last energy balance term is the latent heat flux λE and is calculated following:

4 =λE

Rn −G0,

λE = 4(Rn −G0).

(3.26)

The evapotranspiration can no convenient be calculated using the latent heat flux. Evapotranspirationis the end product of S-SEBI.

Evapotranspiration

The latent heat flux is based on the energy balance and has the unit: Wm2. To convert the latent heatflux to ET (in mm day−1), the following Equation is used:

ET =λE

ρw · lv. (3.27)

With ρw density of water (kg m−3) and lv latent heat of vaporization (J kg−1). The latent heat ofvaporization is calculated by (Gebrekiros, 2016):

ρw(kJ kg−1) = 2500− 2.36 · Ta(oC), (3.28)

with Ta air temperature that is measured by the Davis weather station in Kuku.

4 | Materials for field observations

The appropriate material for field observations is elaborated in this Chapter.

Precipitation

• Tipping bucket with Hobo-datalogger,• HoboWare Lite 3.0 Software (pce instruments, 2015),• totalizers (10x).

Evaporation

• Aluminium rings, several depths,• light reflectance pan.

Surface runoff

• Agricultural plastic foil,• 135 litre barrel (2x),• gutter (2x),• cement,• diver (3x; 2x for runoff plots, 1x for atmospheric pressure),• trypod.

Soil moisture content

• Thetaprobe,• aluminium rings (5x).

Hydrological conductivity

• Mini disk infiltrometer.

Semi-circular bunds

• Agricultural plastic foil,• diver (3x),• tape measure.

Further monitoring

• Vantage Pro2,• leaf & soil station,• 6510USB data logger.

5 | Results

The outcome of the field measurements and remote sensing analysis are explained in this Chapter.

5.1 Hydrological cycle

At first, an exploration study is performed on the hydraulic cycle at the Naga project area in Kuku, Kenya.The results of the field measurements are explained in this Section.

5.1.1 Precipitation

The cumulative precipitation during the March-May rainy season in 2016 was 112 mm. The measurementset-up was not finished during the first precipitation event, only the manual rain gauges in the study areawere placed. So the first precipitation peak (of 57.2 mm) has only been monitored by five totalizers inthe study area.

11 apr 17 apr 23 apr 29 apr 5 may 11 may

Pre

cipi

tatio

n [m

m]

02

46

810

14 study arearef area

Figure 5.1: Precipitation measured by totalizers in study and reference area.

Manual rain gauges were installed to analyse if precipitation spatial varies between the study and referencearea and to validate the tipping bucket data. There were five totalizers placed in the study area andfive in the reference area (see Figure 3.2 for the exact locations). Figure 5.1 shows the mean measuredprecipitation by the totalizers in the study area (orange) and the reference area (purple). The study andreference area are only 400 meters apart from each other, but the daily cumulative precipitation stillvaries as shown. The cumulative precipitation of the rainy season without the first precipitation peak is54.8 mm in the study area compared to 59.6 mm in the reference area. This is a difference of 4.9 mm.

5 apr 12 apr 19 apr 26 apr 3 may 10 may

Pre

cipi

tatio

n [m

m]

010

2030

4050

tipping buckettotalizers

Figure 5.2: Precipitation results of the mean totalizer measurements and tipping bucket output.

The tipping bucket is placed in the study area. Therefore, the tipping bucket data is validated by the fivetotalizers that were placed in the study area. Figure 5.2 represents the measured cumulative precipitation

20 | Chapter 5. Results

by the tipping bucket (green) and the average measured precipitation by the totalizers in the study area(blue). As is shown, the first precipitation peak is only measured by the totalizers, because the tippingbucket was not placed yet. What is noticeable, is that there is little deviation between the totalizers andthe tipping bucket data. So tipping bucket data is reliable.

5.1.2 Soil moisture, hydrological conductivity & evaporation

Figure 5.3 shows the hydraulic cycle elements that were measured manually. The precipitation in thisFigure is the mean cumulative precipitation of all ten totalizers (Pman). The soil moisture content inFigure 5.3 is the mean daily value of 15 measurement points in the study area (Sstudy) and in the referencearea (Sref). As is shown, soil moisture content generally increases when a precipitation event occurs.The soil moisture content in the study area is averagely 15% higher than in the reference area.

The average hydraulic conductivity that is measured in the study area (kstudy) and reference area(kref) is shown in middle plot of Figure 5.3. The hydraulic conductivity is often higher in the referencearea, compared to the study area and is often inversely proportional to the soil moisture content. Thiscan be explained by the fact that more water particles in the pores has as result that there is less spacefor (rain)water to infiltrate (Horton, 1941).

The lowest plot of Figure 5.3 shows the result the evaporation field measurements. The mean soilevaporation measured in the study and reference area (respectively Estudy and Eref) have a similar valueand trend. So soil evaporation does not vary that much between the study and reference area. Thepan evaporation Epan trend is similar to the measured soil evaporation Esoil. However, the quantity ofEpan is greater, because soil resistance is not taken into account. Epan is measured to validate only theEsoil trend. A negative Esoil is observed on the fourth of May, this is however impossible (a negativeevaporation). Lake evaporation deviates from Esoil, so this negative value probably due a measurementerror.

1015

2025

S [%

]

Sstudy

Sref

Pman

130

P

[mm

]

0.00

50.

015

k [m

m s

−1] kstudy

kref

apr 09 apr 19 apr 29 mei 09 mei 19

05

10E

[mm

d−1

] Estudy

Eref

Epan

Figure 5.3: Results of precipitation, soil moisture content, hydraulic conductivity and evaporation measurements.

5.1. Hydrological cycle | 21

5.1.3 Surface Runoff

Figure 5.4 show the measured surface runoff (Qsurface, green) and the precipitation that is measured bythe Davis tipping bucket (Paut, blue) both placed in the study area. The first precipitation peak on 6April is not measured by the tipping bucket, so it is also not represented in Figure 5.4. There were twosignificant surface runoff event during the March-May 2016 rainy season at the Naga project area in Kuku.It is observed in the field that Qsurface occurred on 6 April, but it is not measured quantitatively. On 24April, the diver in the surface runoff set-up measured a Qsurface of 43 mm. However, the precipitationthat day was only 8 mm, which means that water enters the Naga project from upstream. This input ofwater in the study area is called runon. So the surface runoff set-up actually mostly measured surfacerunon Qrunon instead of surface runoff Qsurface.

apr 09 apr 19 apr 29 mei 09

010

2030

40

Date

Q/P

[mm

]

QsurfacePaut

Figure 5.4: Result of the monitored surface runoff (green) and precipitation (blue).

Figure 5.5 represents an elevation map of the area between the Kilimanjaro and Chyulu hills. As is shown,the Naga project area (red dot) is located downhill of the catchment. Spatial variation of precipitationis high in this sub-arid region (Mul et al., 2009). This means that if it rain upstream, surface runon canoccur at the Naga project area. So local precipitation and surface runoff is not necessarily correlated.

Elevation [m] 3806

690

0 2010 Km

Figure 5.5: Elevation map with streamlines. Bottom left is the Kilimanjaro and upper right represents the Chyuluhills. The red dot shows the Naga project area.

22 | Chapter 5. Results

5.2 Effect bunds

The semi-circular bunds are constructed to harvest surface runoff. How much Qsurface is retained by thebunds is discussed in this Section. The environmental effect of the retained surface runoff is elaboratedas well.

5.2.1 Quantitative retention bunds

As explained in the previous Section, on 24 April a surface runon event is monitored. This day, the bundsretained rainwater. Figure 5.6 shows the seven bunds in the study area. After the surface runon event,the water level in the bunds is measured and corrected for infiltration. The catchment area (pink) andbund retention area (mint green) is calculated in ArcMap and represented in the first two columns ofTable 5.1.

B5

B7

B4

B2

B6

B3

B1

LegendBunds

Retention area bund

Catchment

Figure 5.6: In ArcMap, the catchment area of each bund is calculated.

Together with the retention area of the bunds and the measured water level corrected for infiltration, theretained water of the bunds in calculated and represented in the last two columns of Table 5.1. So inthe study area, the bunds averagely retained 2.1 m3 of water, which is equal to 52 mm. Note that thisis more than the surface runoff set-up monitored (which was 43 mm, see previous Section).

Table 5.1: Retention of the bunds that are sketched in Figure 5.6.

Catchment area (m2) Bund area (m2) Retained water (m3) Retained water (mm)B1 42.0 5.8 1.6 33.1B2 37.2 8.9 1.6 35.2B3 23.6 7.5 1.1 34.4B4 22.2 9.1 3.2 102.6B5 38.0 10.6 3.2 65.7B6 25.1 8.5 1.7 51.1B7 47.4 9.8 2.3 39.8mean 33.6 8.6 2.1 51.7

5.2. Effect bunds | 23

5.2.2 Impact of bunds on hydraulic cycle and vegetation

As previously explained, the measurement period was during the March-May rainy season. Figure 5.7shows the NDVI before and in end the rainy season. The NDVI value ranges between -1 and 1, withpositive values from about 0.2 indicating alive vegetation. Quantities of alive vegetation has grown duringrainy season as shown in Figure 5.7, in all probability because of the increase in soil moisture contentdue to rainfall.

0,54

0

0,54

0

Figure 5.7: The NDVI before and after the rainy season.

So the vegetation cover increased during the rainy season. But how much more does vegetation coverincrease in an area with bunds compared to an area without bunds? That question is answered by Figure5.8, which represents the sketched vegetation cover in the study area (upper plot) and reference area(lower plot) before and after the rainy season. Note that the vegetation increase is less in the referencearea compared to the study area. Vegetation particularly increased nearby the gullies in the referencearea, probably because water is transported there during runon events (so higher soil moisture). Strikingis that the vegetation cover mainly increased at the edges of the semi-circular bunds in the study area.

Figure 5.8: Sketched vegetation cover before (left) and after (right) the rainy season. Upper plots representsthe study area, lower plots are the reference area.

After the rainy season, vegetation cover increased with 369% in the study area, compared to 33% in thereference area (see Table 5.2). It should be mentioned that the vegetation cover was less in the study

24 | Chapter 5. Results

area (11.5 m2) compared to the reference area (25.3 m2) before the rainy season.

Table 5.2: Vegetation cover analysis in study and reference area.

Study Area Reference Areavegetation before (m2) 11.5 25.3vegetation after (m2) 53.8 33.7

increase of vegetation (m2) 42.33 8.4increase of vegetation (%) 369.0 33.2

Also soil moisture content and hydraulic conductivity are effected by the bunds. Soil moisture content is15% higher in the study area compared to the reference area during the rainy season. Also, the hydraulicconductivity is 17% higher in the reference area during rainy season, presumably because the pores are lessfiled with water (lower soil moisture content). Therefore, the soil has less capacity to infiltrate rainwater.See Figure 5.3 for the plots of soil moisture content (S) hydraulic conductivity (k).

5.3 Future monitoring

An important part of this study is to investigate opportunities for future studies. The development of theinstalled weather and soil station is discussed. The output of the S-SEBI model is deliberated as well.

5.3.1 Install weather & soil station

The first step is placing a weather and soil station, because the long term effect of rainwater retentionon soil moisture content and eventuality the weather can then be monitored. The weather station hasa data logger which transfers soil and weather data every 15 minutes to the internet. So here in theNetherlands, the current weather and soil characteristics at the Naga project in Kuku can be viewed. Themeasured data of the four soil moisture sensors is shown in Figure 5.9.

apr 09 apr 19 apr 29 mei 09

050

100

150

S [c

entib

ar]

Range Explanation0-10 cb Saturated soil10-30 cb Soil adequately wet30-100 cb Usual range100-200 cb Soil too dry

Figure 5.9 & Table 5.3: The Figure represents the soil moisture content output of the Davis soil station. Theblack and red are lines are the sensors outside the bunds, the blue and green lines are sensors in a bund. TheTable explains the soil moisture content scale.

The soil station measures soil moisture content with centibar as unit. This is in contrast to theThetaprobe, that measures soil moisture content in percentage. To transform centibar into percent-age, the clay, loam and sand ratio is required. However, this data is not available. Therefore it is hardcompare the Thetaprobe soil moisture content with the output of the Davis soil station. The soil stationis especially valuable to analyse if vegetation experiences water stress. Because two sensors are placedinside and two outside the bunds, the effect of semi-circular bunds on vegetation water stress can beanalysed as well.

As is shown in Figure 5.9, water stress is lower in the bunds (green & blue line) compared to outsideof the bunds (black & red line). What is noticeable as well, is that the trend of the soil moisture content

5.3. Future monitoring | 25

abruptly varies from direction. This is because soil moisture content had a first order temperaturecorrection T (four temperature sensors are also placed next to the soil moisture sensors). If soil moisturecontent is corrected by a second order temperature variable dT

dt , the slope of temperature is taken intoaccount as well, and this probably leads to less abrupt changes in soil moisture content. For a morecomprehensive explanation of soil moisture content temperature corrections see Ploeg (2008).

5.3.2 Evapotranspiration by remote sensing

The Simplified Surface Energy Balance Index (S-SEBI) model solves the surface energy balance withsatellite images of Landsat 8 and field data measured by the Davis weather station that is positioned inKuku. First the S-SEBI input results are discussed where after the energy balance is solved.

Input S-SEBI

With satellite images and the field data, four input variables are calculated (see Figure 5.10). Incom-ing longwave radiation is calculated by a empirical relation. This Section presents the results of theintermediate steps that were required in order to make Figure 5.10.

4. Surface temperature

0,54

0

28,1

15,4

2. NDVI

3. Emissivity

1. Albedo

0,37

0,10

0,99

0,986

Figure 5.10: Input of the S-SEBI algorithm. Albedo (-), NDVI (-), emissivity (-) and surface temperature (oC).

26 | Chapter 5. Results

1. Surface albedoData that is represented in Table 5.4 is required to calculate the surface albedo. These variables aremeasured by the weather station in Kuku and obtained by the meta data file of Landsat 8.

Table 5.4: Input to calculate the surface albedo.

Variable Symbol Value UnitAir temperature Ta 18.8 oCRelative humidity RH 0.64 −Air pressure P 1022.4 mbarSolar zenith angle θs 33 o

Earth to sun distance dES 1.01 −

With the variables of Table 5.4 as input, the next elements are calculated as explained in the method(Section 3.3.2):

Table 5.5: Calculated variables needed to estimate the surface albedo.

Variable Symbol Value UnitSaturated atmospheric vapour pressure es 21.6 mbarAtmospheric vapour pressure e 13.9 mbarAtmospheric precipitable water W 21.9 mmEccentricity correction dr 0.981 −Atmospheric transmittance τ 0.748 −

Using the input (Table 5.4), the calculated variables (Table 5.5) and Landsat OLI bands 2 to 7, thesurface albedo is calculated and represented in the top left image of Figure 5.10. The surface albedo canalso be called surface reflectivity, which is defined as the ratio between incoming and reflected (electro-magnetic) radiation. So a high albedo means that the surface reflects a lot of radiation. In the Nagaproject area, surface albedo (=surface reflectivity) is relatively low.

2. NDVITo obtain the NDVI, the fourth and fifth band of Landsat 8 is required. Naga wants to increase thevegetation cover in the two black outlines plots (Figure 5.10). The NDVI is relatively low in the Nagaproject area as is shown in the top right image of Figure 5.10, which means that the vegetation covercan be much improved. Especially the southern plot has relatively little living vegetation.

3. Land surface emissivityOnly NDVI data is required to calculate the land surface emissivity. The emissivity is shown in the bottomleft image of Figure 5.10 and is relatively low in the Naga project area.

4. Surface temperatureThe surface temperature is represented in the bottom right image of Figure 5.10. Note that surfacetemperature is higher in the Naga projects. So in the Naga project areas the (i) surface albedo, (ii) vege-tation cover and (iii) land surface emissivity are relative low and the (v) surface temperature is relativelyhigh.

5. Incoming Longwave RadiationThe Stefan-Boltzmann constant (= 5.67 · 10−8), air temperature (Table 5.4) and atmospheric transmit-tance (τ) are needed to estimate the incoming longwave radiation. The incoming longwave radiation hasa value of 324.4 Wm−2 and is assumed to be equal for the entire modelled area.

5.3. Future monitoring | 27

Evaporative Fraction

To calculate the evaporative fraction, the surface temperature (Tsurface) is plotted against the surfacealbedo for each raster cell. This is called a feature space plot which is represented in Figure 5.11. S-SEBIis based on the principle that the surface temperature of a raster cell is evapotranspiration (ET) controlledor radiation controlled. Close to the bottom white drawn line, Tsurface raster cells are ET controlled:Tsurface increases when ET decreases. The raster cells are more radiation controlled when the cells liecloser to the top white. At one point there is no more water available in the raster cell to evaporate andall the incoming radiation is used to heat up the surface. However, the reflectance also increases at thispoint, which results in a lower total radiation. Therefore, the gradient of the upper white line is negative.

Figure 5.11: Feature space plot made in Erdas Imagine (Geosystems, 2004). Surface temperature plotted againstsurface albedo (= surface reflectance).

Using the slope of the two drawn white lines of Figure 5.11, the evaporative fraction is calculated andshown in Figure 5.12.

1,05

0,16

Figure 5.12: The evaporative fraction (−).

28 | Chapter 5. Results

Calculate Energy Balance Terms

Now the energy balance terms are calculated using the evaporative fraction. The S-SEBI results areshown in Figure 5.13. The net radiation is a product the sensible heat flux, the soil heat flux and thelatent heat flux.

300

50

900

5

A. Net radiation B. Soil heat flux

1105

870

C. Sensible heat flux

900

110

D. Latent heat flux

Figure 5.13: The energy balance terms. With the nett radiation (Rn)that is a product of soil heat flux (G0), sensible heat flux (H) and latent heat flux (λE).

Net radiationThe needed variables to calculate the net radiation Rn are represented in Table 5.6.

Table 5.6: Required variables to calculate the net radiation.

Variable Symbol Value UnitSun constant Ssun 18.8 Wm−2

Solar zenith angle θs 33 o

Atmospheric transmittance τ 0.748 −Plank constant σ 6.62606 · 1034 J s

Also the downward long wave radiation L↓ (= 324.4 Wm−2), surface temperature Tsurface and emissivityε are required to calculate Rn which is elaborated in the results section: Input S-SEBI. The top left plotof Figure 5.13 shows the net radiation. Note that this is the total radiation and the other tree plots ofFigure 5.13 are a product of this plot.

5.3. Future monitoring | 29

Soil heat fluxTo calculate soil heat flux, the proportion of vegetation Pv and total radiation Rn is needed. Note thescale of the soil heat flux, shown in the upper right plot of Figure 5.13. The soil heat flux proportion ofthe total radiation is lower than the other two heat fluxes (sensible and latent).

Sensible heat fluxThe soil heat flux and total radiation are required to calculate sensible heat flux. The sensible heat fluxis represented in the bottom left image of Figure 5.13. Notice that in the two Naga plots, the sensibleheat flux is relatively high. The proportion of H is the largest of the radiation balance in the Naga plots.So most of the energy goes to heating up the air there.

Latent heat fluxThe evaporative fraction, total radiation and soil heat flux are required to calculate the latent heat flux.Especially in the bottom plot of Naga (plot D), the latent heat flux is low. This means that if Nagawants to increase evapotranspiration by use of their projects, they can make the most profit in the lowerplot.

Evapotranspiration

The evapotranspiration in mm day−1 is calculated using the latent heat flux and represented in Figure5.14. This Figure is of interest for this particular study.

31,7 mm/d

3,9 mm/d

Figure 5.14: Evapotranspiration calculated by the S-SEBI model. Black dot is study area, red dot represents thereference area.

To validate the output of the S-SEBI model, the measured soil evaporation in the study area Esoil iscompared with the S-SEBI output ET. When the Landsat 8 passed over Naga project on 8 April 2016,the measured soil evaporation at the study area was 3.5 mm day−1. However, the output of the S-SEBImodel in the study area is 10 mm day−1 on 8 April. Transpiration was not measured in the field, onlysoil evaporation. So the exclusion of measured transpiration can be an explanation for the difference of6.5 mm day−1. So in this study, the S-SEBI model can not be validated. But the order of magnitude ofthe S-SEBI model outcome is reasonable.

6 | Summary of findings

In this Chapter the field problems are reviewed. The limits and constrains of the S-SEBI model are alsodiscussed. Suggestion for future research is considered as well.

6.1 Field problems

There were some unexpected turns and equipment fails in the field. At first, the data logger of a tippingbucket lost data in the field. On month of data was erased when reading the data logger. Thereby, thebund retention experiment (Section 3.2.1) failed. Two bund retention experiments were set up. But ondiver in the bund retention set up did not monitor any surface water increase during the surface runonevent on 28 April. The other diver did not connect to the computer after the surface runon event. Also,two surface runoff experiments were set up, one in the study area and one in the reference area. But thesurface runoff set-up in the reference area was demolished by wildlife or livestock just before the surfacerunon event. Therefore surface runon was only monitored in the study area.

6.2 Field data analysis

Surface runon has probable been underestimated because water has flown over the hills (see Figure 6.1).So the surface runon had an extra obstacle before the water ended up in the container.

Figure 6.1: Surface runoff set-up.

Thereby, infiltration was changeling to estimate because the water content profile is not known. Thisincludes rooting depth, pore fraction and infiltration depth. Therefore, the rainwater fraction that infil-trates is hard to estimate. So during this study, only the hydraulic conductivity of the upper soil layeris measured. That is why the water balance (P = ET +Qsurface + I + ∆S

∆t ) can not be closed becauseone water balance element is missing (I = infiltration). Therefore, the first part of this study is mainlyan exploration research to analyse the hydraulic cycle.

Lastly, the bund experiment should be interpreted as an rough estimate of the actual retention. Waterlevel is measured only at one location in a bund, but the water level in the bund can vary. Also, infiltrationis estimated by a (over)simplified method using soil moisture content data (Equation 3.4). Infiltration isprobable underestimated because the soil moisture content is measured one day after the surface runonevent. The soil moisture content was probable higher right after this event. Therefore, the 2.1 m2 waterretention per bund is probable underestimated and can only be interpreted as a rough estimate. Becausethe bund experiment failed, there is no bund retention data were the infiltration can be neglected.

32 | Chapter 6. Summary of findings

6.3 Limit and constrains S-SEBI

The Landsat 8 mission has a temporal resolution of 16 days. The model requires a cloudless sky (cloudcover is 0 %), which declines the temporal resolution even more. In this study, a weather station is set upbegin April and the S-SEBI model requires this particular field data. So there was only one opportunityto calculate the evapotranspiration by the model. When the S-SEBI model is run several times, thereliability of S-SEBI increases. It will be valuable to run the S-SEBI for different days to analyse not onlythe spatial differences in time, but also the temporal variation of evapotranspiration.

Thereby, the gradient lines in the feature space plot (white lines, Figure 5.11) are subjective. A otherperson draws the gradient lines differently. The evaporative fraction is dependent on these gradient lines,so the evaporative fraction subjective as well.

S-SEBI assumes measured field data to be equal for the whole modelled area. However, weathervariables can have strong local variations (Hubbard, 1994). The reliability of the model increases whenmore weather stations are placed throughout the modelled area.

Lastly, there was no evapotranspiration measured in the field. Therefore it is impossible to validatethe S-SEBI data with field data. Soil evaporation was measured, but transpiration is not obtained in thefield.

6.4 Suggestion for future research

The concept of the hydrological corridor devised by Naga is:

vegetation ET air moisture clouds rain

Figure 6.2: Hypothesis Naga’s hydrological corridor.

So the more vegetation cover leads to an increase of evapotranspiration (ET). This leads to more airmoisture, more local cloud formation and finally an increase in rainfall over the hydrological corridor. Thechronology of studying the impact of the Naga projects on eventually rainfall should be in order of thesuccession describes in the diagram above. If the Naga projects do not even have any effect on vegetationor ET, the hydrological corridor hypothesis is then already rejected. Therefore, this study focusses onthe first two aspects of the sequence (vegetation, ET). For near future research, the suggestion is to(i) study the effect of the Naga projects on vegetation cover on a larger spatial scale and (ii) executingS-SEBI for the whole hydrological corridor (larger spatial scale) and running the model more often intime (larger temporal scale). If it is proved that the projects have an impact on evapotranspiration andvegetation, the effect of ET on air moisture and eventually cloud formation and rainfall can be analysed.But this should only be considered for distance future research.

To analyse the effect of the Naga projects on vegetation cover in the future, the NDVI should bemonitored on a larger temporal scale. Field observations are required to validate the NDVI data.

To run S-SEBI on the whole hydrological corridor, the suggestion is to place several weather stations(+ evapotranspiration accessory) in the hydrological corridor to (i) use weather variables as input of themodel and (ii) validate S-SEBI with evapotranspiration data.

6.5 Conclusions

What is striking about the hydrological cycle in the Naga project, is that the local precipitation is notnecessarily the input of water cycle. Most water balances use precipitation as input, but surface runon(Qrunon) is just as an important input element in the Naga semi-circular bund project: on 24 April Qrunon

is 43 mm compared to P is 8 mm. So the water balance that initially is drafted (Equation 3.1), needsto be modified to:

P +Qrunon = ET +Qsurface + I +∆S

∆t.

6.5. Conclusions | 33

The semi-circular bunds have greatest effect on soil moisture content and hydrological conductivity,respectively 15 % higher and 17 % lower in the area with bunds. The effect of the semi-circular bundson vegetation cover is also present: vegetation cover increased after the rainy season with 369 % in thearea with bunds compared to an increase of 33 % in the area without bunds. So for this pilot study, therecan be concluded that the semi-circular bunds increase the vegetation cover (so the first box of Figure6.2 is proven to be true). However, this outcome should only be interpreted as rough estimate, becausethe temporal and spatial scale of this study is poor. To investigate the effect of the Naga projects onthe second box of Figure 6.2 (ET), it is advised to use the S-SEBI model to estimate ET on a largetemporal and spatial scale. This study clearly explains how to implement this S-SEBI model. The outputof the S-SEBI model (Figure 5.14) is reasonable. However, the output should be validated and the modelshould be executed over time (temporal) and on the whole hydrological corridor (spatial).

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