Assessment of Sand Dam Potential for Ephemeral Rivers

in Yatta and Mwala Constituencies, Eastern Kenya

Alex R. Oduor, Kipruto Cherogony, Douglas Nyolei and Maimbo Mabanga Malesu

A Tribute to The Nobel Peace LaureateProfessor Wangari Maathai

Who commissioned this study via the GBM Organization

Definition of a sand damA masonry barrier across an ephemeral river that

conserves water beneath a throwback of sand

Upstream view of a sand dam

Rationale for dependency on sandy rivers

• Rainfall unreliability: Range, seasonality,

• High evaporation and/or siltation of surface water bodies

• Inadequate runoff capturing on the hillsides

• Saline/brackish river water

• Sand conserving water for long periods thus the major dependent source

Fetching water from sandy rivers

Challenges for inhabitants

• Undulating slopes: water carried either by women or ferried on donkey carts. Labourintensive/time consuming

• Despite its reliability in terms of availability, quantity is insufficient for inhabitants

• During dry seasons, the waters salt content increase in some rivers

Objective of the study

• Develop criteria for identifying sandy rivers.

• Use GIS to generate watersheds for sandy rivers.

• Use GIS to locate ideal sand dam sites.

• Compute runoff generated in each watershed.

• Determine potential sand dam and water storage capacity.

• Discuss implications of sand dam water on livelihood enhancement to the inhabitants.

Identifying sand dam rivers

• A size threshold for stream delineation was set using GIS modeling technique on DEM,.

• With this approach, streams with <5m or > 20m widths were annexed.

• Streams <5m widths:– Slightly higher slopes with fairly rugged beds. – Bank erosion evident in some sections, – Conservation using check dams.

• Streams >20m widths:– Too large – Good for sub-surface dams or Sand Galleries.

• The sand rivers were confirmed through observation of the area in google map:

Identification of sandy rivers using Google image

Identification of sandy rivers using Google image

Incorporation of hydrologic and engineering principles in GIS to model, compute and assess the runoff potential of watersheds

• Landuse, slope and soil basemaps generated to determine RWH Potentials.

• The land use/land cover generated from most recent Aster imagery.

• Slope map generated from a 20m DEM.

• The layers pre-processed to ensure compliance with required data format, resolution and projection.

• Proportional loss model used to determine runoff potential for the sand dams (See Equations 1 and 2).

Proportional loss model to determine watershed potential

Where = the net rainfall rate at time t (mm/h);= the rainfall rate at time t (mm/h);= the runoff coefficient (-);= the runoff volume of the storm (mm);= the total rainfall volume of the storm (mm);

)()()( tiTCtin

)(tin

)(ti

)(TC

R

I

NB: DEM is then used to generate areal maps of watershed whose product

with runoff coefficient and rainfall produces the watershed potential

Determination of sand dam & water storage potentials

Where Vsd = Sand dam capacity (m3);Vw = Water abstraction potential (m3);Ai = Area of the lower elevation layer (m2);Ai+1 = Area of the upper elevation layer (m2);h = thickness of elevation (m);

Results & Discussions

Watersheds and river courses with sand dam potential

• Water courses are denser in Mwala than in Yatta constituency, attributed to the plateau phenomenon

• Watersheds in Mwala have undulating slopes and higher sand generation capacity than those of Yatta

• Mwala has 17 watersheds with potential for sand dams.

• There are a total of 263 sand dams (125 existing, 138 proposed).

• The BWR per HH during a 6 months dry period is 28800 m3.

• Total number of families that can be potentially served is 998.

Or an average of 56 families per watershed.

River network for Mwala and Yatta constituencies

Spatial depiction of watersheds with sand dam Potential

6

18

5

9

5

2

2

3

3

9

3

9

6

2

4

4

3

5

5

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Embui

Ikombe

Imetoni

Isivuni

Kaitha

Kaliambeu

Kathiori

Kiliva

Kyaimu

Kyawango

Masaani

Mitini (Wai)

Mivukoni

Muani

Musoka

Mutanyu

Muvwana

Wamunyu

Wethanga

Vol (Million Ltrs/yr)

0

2

4

6

8

10

12

14

16

18

Emb

ui

Iko

mb

e

Imet

on

i

Isiv

un

i

Kait

ha

Kaly

amb

eu

Kath

iori

Kili

va

Kyai

mu

Kyaw

ango

Mas

aan

i

Mit

ini (

Wai

)

Miv

uko

ni

Mu

ani

Mu

soka

Mu

tan

yu

Mu

vwan

a

Wam

un

yu

Existing Proposed

SN Watershed

Area Ave rainfall

Potential/ (m3)

Number of sand dams Dam Potential Proposed Dam cost

(km2) (mm) Existing Proposed Total Estimated in (m3) US$/Watershed

1 Embui 26.6 853 5637 10 9 19 296.7 46375

2 Ikombe 88.0 828 17974 6 6 12 1497.8 28607

3 Isivuni 43.7 841 9109 2 3 5 1821.7 14053

4 Kaitha 24.7 779 4790 3 4 7 684.3 16515

5 Kalyambeu 7.3 854 1550 5 4 9 172.2 21524

6 Kathiori 9.2 877 1948 11 6 17 114.6 25288

7 Kiliva 17.1 855 3423 6 3 9 380.4 10983

8 Kyaimu 17.0 799 3327 13 7 20 166.4 27823

9 Kyawango 47.8 796 9492 8 13 21 452.0 56468

10 Masaani 13.7 802 2735 4 2 6 455.9 11588

11 Mitini (Wai) 42.5 862 9087 16 8 24 378.6 58499

12 Mivukoni 26.6 851 5652 5 2 7 807.4 9725

13 Muani 11.6 795 2290 7 3 10 229.0 18431

14 Musoka 18.0 838 3776 10 3 13 290.5 14179

15 Mutanyu 16.7 900 3569 2 4 6 594.9 13828

16 Muvwana 14.9 844 3113 1 3 4 778.2 12770

17 Wamunyu 24.3 819 4961 11 7 18 275.6 42209

18 Wethanga 22.0 882 4766 1 1 2 2382.8 3836

TOTAL 471.7 837.5 97198 121 88 209 11778.8 432703

Mapped depiction of river courses with potential for sand dam technology

Conditions for sand dam water storage capacities

Topography Sand particle/stone sizes Litres/m3 Percentage

Excellent potential Hilly & stony Course sand 350 35%

High potential Gullies from stony hills Medium course sand 250 25%

Medium potential Flat farmland Fine textured sand 100 10%

Low potential Stony riverbeds Boulders / fractured rocks <100 <10%

storage capacityCatchment conditions

Potentiality of storage

Mwala has a topography of gullies developing from stony hills with 25% water storage capacity as cf’dto Yatta with flat rocky topography and a sand dam water storage capacity of 10%.

Only 5% of

runoff generated from watersheds is potentially available for use in the sand dams

Watershed

Watershed

potential (m^3)

Sand dam

potential (m^3)

Water storage

potential (m^3)

Embui 14,181,962 2,974,805 743,701

Ikombe 94,608,417 19,397,042 4,849,261

Isivuni 43,719,162 9,124,574 2,281,143

Kaitha 24,718,689 4,795,739 1,198,935

Kalyambeu 7,286,510 1,548,463 387,116

Kathiori 15,056,056 3,035,785 758,946

Kiliva 17,169,100 3,425,871 856,468

Kyaimu 17,643,320 3,463,361 865,840

Kyawango 47,944,044 9,522,691 2,380,673

Masaani 37,412,440 8,164,833 2,041,208

Mitini (Wai) 44,304,177 9,449,628 2,362,407

Mivukoni 26,622,938 5,648,835 1,412,209

Muani 10,870,052 2,143,824 535,956

Musoka 18,038,737 3,781,905 945,476

Mutanyu 21,775,705 4,546,540 1,136,635

Muvwana 18,027,132 3,757,468 939,367

Wamunyu 24,436,479 4,977,917 1,244,479

Wethanga 68,099,723 15,212,238 3,803,060

Grand Total 551,914,641 114,971,519 28,742,880

20.83 5.21 % of watershed potential

There is a good potential for sub-surface dams and sand galleries which are yet untapped

• Although sand dams can’t be ignored, a higher potential for harnessing runoff water exists in the hillsides

• Per-capita storage can be increased by rehabilitating existing sand dams

More accurate runoff estimation can be achieved using SCS-CN method

Where:

Q = runoff (mm)

P = rainfall (mm)

S = maximum recharge capacity (Water storage parameter of

rainfall after 5 days antecedent moisture retention

NB:

The area different land uses is obtained using command tools

of Arc/Info or ArcView within GIS environment.

254

25400

CNS

)7.0(

)3.0( 2

SP

SPQ

A B C D98 98 98 98

Paved with curbs and storm sewers 98 98 98 98

Gravel 76 85 89 91

Dirt 72 82 87 89

Without conservation treatment (no terraces) 72 81 88 91

With conservation treatment (terraces, contours) 62 71 78 81

Poor (<50% ground cover or heavily grazed) 68 79 86 89

Good (50-75% ground cover, not heavily grazed) 39 61 74 80

Meadow (grass, no grazing, mowed for hay) 30 58 71 78

Brush (good, >75% ground cover) 30 48 65 73

Poor (small trees/brush destroyed by overgrazing / burning 45 66 77 83

Fair (grazing but not burned; some brush) 36 60 73 79

Good (no grazing, brush covers > 75% impervious) 30 55 70 77

Grass covers 50% of ground 49 69 79 84

Good (Grass covers >75% of area) 39 61 74 80

Commercial & business districts (85% impervious) 89 92 94 95

Industcrial districts (72% impervious) 81 88 91 93

0.05 ha plots, about 65% impervious 77 85 90 92

0.1 ha plots, about 38% impervious 61 75 83 87

0.2 ha plots, about 25% impervious 54 70 80 85

0.4 ha plots, about 20% impervious 51 68 79 84

Wood and Forests

Open spaces (lawns, parks, golf courses, cemetries

Residential areas

Description of land use

Hydrologic soil groups

Paved parking lots, roofs, driveways

Streets and roads

Cultivated (agricultural crop) land*

Pasture or range land

Runoff Curve Numbers

• Surface water technologies e.g. Earth dams, Ponds & sub-surface dams are cheaper than sand dams. Their potentials should be explored.

• River catchments with potentials of > 6M ltrs/yr to be given priority attention during implementation. These include: Mitini, Ikombe and Isivuni.

Ero kamanofor your understanding