1 L Fumagalli 1 T Bonomi 1 F Fava 1 B Di Mauro 1 CH Kane 2 M Faye 3 S Wade 3 G Faye 3 B Hamidou 4 R Colombo 1 University Milano Bicocca ID: 143451
Download Presentation The PPT/PDF document "F. Fussi" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
F. Fussi
(1), L. Fumagalli(1), T. Bonomi(1), F. Fava(1), B. Di Mauro(1), C.H. Kane(2), M. Faye(3), S. Wade(3), G. Faye(3), B. Hamidou (4) , R. Colombo(1)University Milano Bicocca, (2) University of Thies, (3)University Cheik Anta Diop – Dakar, (4) SNAPE - Conakry contact: fabio.fussi@usa.net
Use of remote sensing and terrain modeling to identify suitable zones for manual drilling in Africa and support low cost water supply
Groundwater, poverty and
development, Overseas Development Institute, London 28/11/2014Slide2
STRUCTURE OF THE PRESENTATION
Introduction to manual drillingPrevious studies on suitable zones for manual drillingThe research projectGeneral research approachHydrogeological data processingRemote SensingResults achieved and next stepsGroundwater, poverty and development, Overseas Development Institute, London 28/11/2014Slide3
Introduction to manual drilling
MANUAL DRILLING techniques of drilling boreholes for groundwater exploitation using human or animal power (not mechanized equipment). These techniques are well known in countries with large alluvial deposits (India, Nepal, Bangladesh, etc)High quality hand drilled wells can provide sustainable and clean water supplyGroundwater, poverty and development, Overseas Development Institute, London 28/11/2014
Introduction to Manual DrillingSlide4
Advantages and limitations
Advantages of manual drillingCheaper than mechanized boreholesEasy to implement with locally made equipment“manual work intensive” and not “capital intensive” ; source of income for local groupsLimitations of manual drillingManual drilling is feasible only under hydrogeological suitable conditionsSoft unconsolidated shallow geological layers Water level not too deep Good hydraulic conductivity of shallow porous aquifersIT IS IMPORTANT TO IDENTIFY THOSE ZONES WHERE HYDROGEOLOGICAL CONDITIONS ARE SUITABLE FOR MANUAL DRILLING
Introduction to Manual Drilling
Groundwater, poverty and
development, Overseas Development Institute, London 28/11/2014Slide5
Previous maps of suitable
zones for manual drillingBENIN BURUNDICENTRAL AFRICAN REPUBLICCHADGUINEA IVORY COAST LIBERIA MADAGASCAR MALI MAURITANIA NIGER SENEGAL SIERRA LEONE
TOGO
ZAMBIA
Since 2008 UNICEF has produced maps of suitable zones for manual drilling in 15 countries in Africa:
Previous studies on suitable zones
Groundwater, poverty and development, Overseas Development Institute, London 28/11/2014
http://www.unicef.org/wash/index_54332.htmlSlide6
Previous method to identify suitable
conditions for manual drilling (UNICEF)Previous studies on suitable zonesData sources:EXISTING MAPSNATIONAL DATABASE OF WATER POINTSQUALITATIVE EXPERIENCE OF WATER EXPERTS IN THE COUNTRYLimitations:Input data are limited and often at very broad scale,inadequate for reliable interpretation (where data are limited) and more detailed studyNot systematic and structured procedure for data analysis. Difficult to compare results from different countriesGroundwater, poverty and development, Overseas Development Institute, London 28/11/2014
COMBINED ANALYSIS OF 3 PARAMETERS:Slide7
THE UPGRO RESEARCH PROJECT
University Milano Bicocca (Italy)University Cheik
Anta Diop (Senegal)
SNAPE (Guinea)
UNICEF (Guinea and Senegal)
PARTNERSFUNDED BY:
Groundwater, poverty and
development,
Overseas Development
Institute, London 28/11/2014
Use of remote sensing and terrain modeling to identify suitable zones for manual drilling in Africa and support low cost water supplySlide8
Main goal of the research
OBJECTIVE OF THE RESEARCHIntegration of direct hydrogeological information from existing database with indirect parameters from Remote Sensing and terrain modeling to characterize shallow aquifers and identify suitable zones for manual drillingDURATIONNovember 2013 – April 2015Groundwater, poverty and development, Overseas Development Institute, London 28/11/2014
The research projectSlide9
Research study area
REGION OF KANKAN AND FARANAH (EAST GUINEA)REGION OF LOUGA – KEBEMER (NORTH WEST SENEGAL
Groundwater, poverty and development, Overseas Development Institute, London 28/11/2014
The research projectSlide10
Scientific approach
Geo-Environmental indicators (Geology, Soil, Morphometry, Vegetation dynamics, Soil moisture, Thermal Inertia)Thematic maps, Remote Sensing (optical, radar), Digital terrain model
(
Geo)Statistical model
Borehole logs
interpretation, pump test, geophysics
Map of suitable zones for manual drilling
Hydrogeological
features at observation points
Groundwater, poverty and
development,
Overseas Development
Institute, London 28/11/2014
General research approachSlide11
Methodological
approachGeology, Sols, etcTopography &GeomorphologyVegetation Type & PersistenceThermal InertiaSoil MoistureBase maps
STRM/ASTER DEM
MODIS Veg. Ind.
MODIS LST & ALBEDO
RADAR ASARP, K, T, suitability class(estimated)
Classification
(e.g. CART)
Borelogs
- TANGAFRIC
Training set (P, K, T, class)
Testing
set (P, K, T, class)
P, K, T,
suitability
class
(
observed
)
Accuracy
assessment
(
e.g
. Q2, RMSE)
cal
val
Spatial
maps
Groundwater, poverty and
development,
Overseas Development
Institute, London 28/11/2014
General research approachSlide12
Definition of a 2 steps procedure to
estimate suitability for manual drillingGroundwater, poverty and development, Overseas Development Institute, London 28/11/2014Feasibility: It depends ondepth of hard rock depth of water level thickness of hard lateritic layersPotential for exploitation-It is obtained from assigning class of potential based on hydraulic t
ransmissivity of porous unconsolidated aquifer from 0 to 50 m (considered the maximum epth for manual drilling)
Estimation potential
General research approachSlide13
Development of a specific software and
codification of hydrogeological dataCALIBRATION OF HYDRAULIC PARAMETERS THROUGH FIELD MEASUREMENTSPump and recovery test in hand dug wellsK = 4*10-5 m/sDATABASE OF WATER
POINTS SENEGAL AND GUINEA
STANDARDIZATION OF DATABASE STRUCTURE
IDENTIFICATION OF MOST FREQUENT CATEGORIES
ASSIGNING STRATIGRAPHIC CODES
Groundwater, poverty and
development,
Overseas Development
Institute, London 28/11/2014
Hydrogeological
data processingSlide14
Processing of
stratigraphic dataCALIBRATION OF HYDRAULIC PARAMETERS THROUGH FIELD MEASUREMENTSPump and recovery test in hand dug wellsK = 4*10-5 m/sGroundwater, poverty and development, Overseas Development Institute, London 28/11/2014
DATABASE OF WATER POINTS AND CODIFIED STRATIGRAPHIC LOGS FOR SENEGAL AND GUINEA
PROCESSING OF STRATIGRAPHIC AND PIEZOMETRIC DATA
Hydrogeological
data processing
Bonomi
, T., (2009): Database development and 3D modeling of textural variations in heterogeneous, unconsolidated aquifer media: Application to the Milan plain.
Computers &
Geosciences
35 (2009) 134–145
Processing of
hydrogeological
data have been based on previous experience in Italy (
Bonomi
, 2009) and adapted to the context of Senegal and GuineaSlide15
Processing of
stratigraphic dataGroundwater, poverty and development, Overseas Development Institute, London 28/11/2014EXTRACTION OF:Each 2 m intervals:TEXTURE
HYDRAULIC CONDUCTIVITYFor the whole log:
DEPTH OF HARD ROCK,
DEPTH OF WATER TABLETHICKNESS
OF HARD LATERITIC LAYERSHYDRAULIC CONDUCTIVITY (K) OF UNCONSOLIDATED LAYERTRANSMISSIVITY
Processing of textural data at intervals of 2 m (
Tangram
software)
Hydrogeological
data processingSlide16
Extraction of
hydrogeologicalparameters at borehole logs positionGroundwater, poverty and development, Overseas Development Institute, London 28/11/2014
Depth of water
Depth of hard rock
Thickness of
lateriteAverage K (in exploitable layers)
Hydrogeological
data processingSlide17
Estimating feasibility and potential
for manual drilling at logs positionGroundwater, poverty and development, Overseas Development Institute, London 28/11/2014Hydrogeological data processingSENEGAL STUDY AREASlide18
Multitemporal
analysis of ATI Thermal Inertia (TI) is related to the characteristics of the surface materials as well as surface water content. Apparent Thermal Inertia (ATI) [K-1]can be estimated using solely remote sensing optical and thermal data (Van Doning et al., 2012 IJAEG). α0 is the albedo estimated from MODIS satellite optical data (MCD43B3 – 16 day – 1km)A is the amplitude of the diurnal cycle of the land surface
temperature estimated from day/night MODIS thermal
observations (MOD11C1/MYD11C1 – daily
(4 overpass/day) – 1km)
.C is a solar correction factor.
Van
Doninck
J., Peters J., De
Baets
B., De
Clercq
E.M.,
Ducheynec
E.,
Verhoesta
N.E.C. (2011).
The potential of
multitemporal
Aqua and Terra MODIS apparent thermal inertia as a soil moisture indicator. Int. J. App. Earth Obs.
Geoinf
. 13, 934–941
Remote Sensing
Groundwater, poverty and
development,
Overseas Development
Institute, London 28/11/2014Slide19
Multitemporal
analysis of ATI Daily time series of ATI (2012)MODIS DATA (1km)MOD11C1/MYD11C1 + MCD43B3Data selection: no precipitation/cloudsDry season ATI map
LOUGA
0
.2
0.4 [K-1]
Dry ATI
map
2012 (1km) -
Northern
Senegal
N
50 km
Groundwater, poverty and development,
28/11/2014, Overseas Development
Ini
Remote SensingSlide20
Vegetation Dynamics
The abundance, type and seasonal variations of vegetation are dependent from nutrient and water availability. Especially in drylands, vegetation can thus be related to water availability across the landscape.Vegetation persistence or poor sensitivity from precipitation patterns may reflect the presence of shallow water and/or substrates suitable for deep plant roots development. Normalized Difference Vegetation Index (NDVI) time series were analysed to assess phenological indicators of vegetation cover and persistence during the dry season. 20072009
20102011
2012
2008
YEAR
Remote Sensing
Groundwater, poverty and
development,
Overseas Development
Institute, London 28/11/2014Slide21
Vegetation Dynamics
MOD13Q1 (TERRA/AQUA MODIS - 16 day - Vegetation Indices, 250 m) 2008-2012Filtering and smoothing procedureExtraction of phenological metrics. Start of season, End of season, maximum, minimun, seasonal mean, etcVegetation persistence indicators (5 years average)Mean dry season NDVIRate of NDVI decreasDry season lenght
LOUGA
DRY SEASON LENGHT
(Number of days)
200-220
220-240
240-260
260-280
280-300
LOUGA
DRY SEASON MEAN NDVI
0.1
0.25
Remote Sensing
Groundwater, poverty and
development,
Overseas Development
Institute, London 28/11/2014Slide22
SOIL MOISTURE DYNAMICS
Soil moisture dynamics may indicate areas with high water holding capacity or water accumulation. ENVISAT-ASAR radar data (Global mode -1km) between 2008 and 2012 in C-band are used to estimate soil moisture by inversion of the radiative transfer model proposed by Karam et al., 1992.Biomass maps derived from SPOT vegetation were used to separate the contribution of vegetation and soil moisture on the radar signal. Karam M.A., Fung A.K., Lang R.H. and Chauhan N.S., 1992 : Microwave scattering model for layered vegetation, IEEE Trans. Geosci. Remote Sens., 30:767-784.
LOUGA
SOIL MOISTURE (%)
0
100
June
2010
Remote Sensing
Groundwater, poverty and
development,
Overseas Development
Institute, London 28/11/2014Slide23
Results achieved and next steps
MAIN RESULTS ACHIEVEDElaboration of a specific software to organize and process hydrogeological data based on the inventory of Senegal and GuineaDefinition of a structured method to estimate suitability for manual drillingProduction of maps of hydrogeological parameters and suitability for manual drilling at borehole logs positions in SenegalDefinition of procedures and generation of maps of soil moisture, phenology and ATI from optical and radar dataFirst set of direct field data (geophysics, pump test) collected and intepreted in SenegalNEXT STEPSCompleting field data collection in Senegal. And (depending from Ebola outbreak) in Guinea.Validation of hydrogeological interpretation with field informationDefinition of geostatistical model and integrated analysis of data
Spatialisation of hydrogeological interpretation from borehole logs position to the whole area
Generating maps of suitability for manual drilling
Groundwater, poverty and
development, Overseas Development Institute, London 28/11/2014Slide24
Groundwater, poverty and
development, Overseas Development Institute, London 28/11/2014Thanks very much for the attention