ABSTRACT:

A common challenge in interpreting and validating remote sensing data is in comparing these data to direct observations on the ground. Often remotely sensed data will cover large regions and have different temporal and spatial sampling frequency than point observations derived in the field. This kind of analysis requires geospatial tools to enable resampling, assessment of spatial statistics and extrapolation of point data to broader regions. The integration of satellite missions (GRACE) with hydrology models for determining drought indicators and water levels has been done in the United States (Houborg, et al., 2012; Zaitchick et al., 2008) using data assimilation from sophisticated observatory networks that are not available, for example, in sub-saharan Africa. However, there are studies that have analyzed operational, technical, institutional, financial, and environmental predictors of functionality for groundwater access (well) data collected from over 25,000 community-managed handpumps in Liberia, Sierra Leone, and Uganda (Foster, 2013). Lahren and Cook (2016) code and analyze the reasons for failure in 250,000 water points in 25 countries and found that 30% of boreholes are not functioning, either for "technical or mechanical” reasons, or for "low quantity". According to the World Bank, water supply failure in Africa is estimated to “represent a lost investment in excess of $1.2 billion” (Bonsor et. al. 2015). Women and girls continue to be the world’s water collectors, spending a significant fraction of their time and energy on the task (Sorenson et al 2011, Graham et al 2016, Cook et al 2016). Can a planetary scale observational tool be used to understand groundwater access and vulnerability for domestic use in rural households? If so, we can further investigate and develop the GRACE for Girls project.

Research Questions

How much is hydrological scarcity contributing to handpump failure in Africa?

In areas where domestic water access is primarily through wells, are areas with non-functional wells because of low quantity observable with remote sensing data?
What spatial statistics can be used to understand the reasons for well failure using geolocated water points and falling groundwater levels?

Sample data

Point data: The Water Point Data Exchange (WPDx) is a global platfrom for sharing water point data to understand water services with 240,000 + water points in the dataset with the quantity of data varying between government support for complete datasets (all 101,000 water points in Uganda) as well as data in other countries with known GRACE observable groundwater levels (India).

The WPDx data downloaded in February 2016, and coded for well failure due to water resources issues (Lahren and Cook (2016)), is provided on Hydroshare. Go to Collaborate. Ask to Join Freshwater Group. Click on link for Freshwaterhack of UWGeohackweek. Go to Collection Contents. Click on Freshwaterhack Project: Groundwater Resources and GRACE

Remote sensing data: The Gravity Recovery and Climate Experiment (GRACE, a joint mission of NASA and the German Aerospace Center) measures the Earth's gravity anomalies to study how mass is distributed around the planet and used for studying Earth's eceans, geology, and climate.

GRACE land are available at http://grace.jpl.nasa.gov, supported by the NASA MEaSUREs Program.
D.N. Wiese. 2015. GRACE monthly global water mass grids NETCDF RELEASE 5.0. Ver. 5.0. PO.DAAC, CA, USA. Dataset accessed [YYYY-MM-DD] at http://dx.doi.org/10.5067/TEMSC-OCL05.

Watkins, M. M., D. N. Wiese, D.-N. Yuan, C. Boening, and F. W. Landerer (2015), Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons, J. Geophys. Res. Solid Earth, 120, doi:10.1002/2014JB011547.