Data Collection through Local Involvement
RELEVANCE OF NON-TRADITIONAL DATA
PROJECT RATIONALE
The iMoMo Project was incubated by the Global Programme Water of the Swiss Agency for Development and Cooperation from 2012 through 2017. The project had two main goals:
Fostering innovation in low-cost, high-tech, non-traditional, people-centered observations and monitoring.
Modernizing pathways from observation to decision-support for effective and sustainable water resources management.
The projects focus was in the global context of irrigation agriculture.
Nowadays, 25 % of the global crop area is irrigated, producing 40 % of all crops and 60 % of all cereals while irrigation accounts for 70 % of water withdrawals and 90 % of consumptive water use globally. Rising population numbers and changing nutritional demand will push agriculture towards increasing irrigation as projected food production will double in the 21st century. Together with climate uncertainty, these developments will exacerbate water stress, especially in the global drylands. As a result, stakeholders at all levels will be impacted as traditional water monitoring and management approaches have failed to answer most of the multi-scale water challenges.
Knowledge about runoff formation, water distribution as well as effective management is often hampered by the lack of sufficient data. One key reason is that traditional monitoring does not scale because of prohibitive investment and operation/maintenance costs as well as due to vandalism. Even where data are available, stakeholders often lack knowledge and technology on how to make best use of data in tactical and strategic terms. It often leaves them with the practice of informed guessing, e.g. by gambling on rains for agricultural production. Suboptimal and unsustainable outcomes result. They translate into water insecurity with adverse impacts on communities, their livelihoods and ecosystems.
NON-TRADITIONAL DATA
The ultimate goal of data collection in water resources, is to provide a set of sufficient good quality data that can be used in decision-making in all aspects of planning and management, in the wide range of possible applications, including for research. Decisions may be made directly from raw data measurements or based on derived statistics or on the results of many stages of modelling beyond the raw data stage. But in all circumstances, it is the collected data that form the basis for these decisions.
The paucity of good water-related supply and use data is an expression of decades of stakeholders neglect to invest adequately in data acquisition but also often caused by weak enforceability of national water legislation or the absence thereof. This often also translates into lack of awareness by local communities about the challenges associated with proper water resources management. Despite significant focus from donors and the acknowledgment that the modernization of monitoring and measurement networks could bring large benefits to countries and their populations, the situation remains dire even today. On a local level, water users often perceive monitoring as a step towards increasing water use tariffs or constraining use rather than a mean to improve efficiency and secure reliable access. It is therefore crucial that measurement is supported by robust institutions to effectively engage vested interests, monitor and control water use and resolve disputes.
However, with the increasingly wide-spread diffusion of mobile digital technology at global scales, decentralized approaches and initiatives are emerging that provide opportunity to address these challenges in new and innovative ways using information and communication technology (ICT) in this context. The mobile phone-based, crowd-sourced data collection (also referred to as crowd-sensing) is one of these opportunities which is monitoring conducted, in whole or in part, by amateurs and/or non-professionals using a smartphone.
Apart from smartphones being communication and computation devices, the rich set of sensors embedded in them enable a whole range of new application in MCS in domains like health care, social networks, safety, environmental monitoring and modeling, eCommerce and transportation. In hydrology and water resources management domains, the acquisition of non-traditional data and incorporation of these in modeling and decision-making is showing promising results, especially for flood prediction and modeling and operational application for accounting and accountability.
The challenges of crowd-sourced data collection differ from one context to another. This is especially true when comparing developed (information-rich) versus developing information-poor nations where in the latter as compared to the former opportunities to involve citizens are fewer for various reasons, including levels of education, limited mobility and access to GSM/internet (esp. in the remote rural context) and the daily livelihood struggle.
Generally, little experience has been collected globally on how sustained crowd-sensing campaigns can be successfully setup and implemented in the long-run in the context of developing economies and emerging markets. Recent global initiatives (e.g. https://wow.metoffice.gov.uk) show that if the approach relies exclusively on voluntary participation of citizens the temporal and spatial resolution of the collected data is positively correlated with a) the population density and b) the development status (GDP) of a certain region/country.
Exactly because of the above-mentioned fundamental contextual differences, a recent United Nations Report stresses the importance enabling a revolution using non-traditional data collected through local involvement in the Global South. This includes, among other things, the development of global principles and standards, the sharing and making accessible of innovation technologies and the mobilization of adequate resources. As an example, the recently established WMO HydroHub (The WMO Global Hydrometry Support Facility) by the World Meteorological Organization (WMO) is aiming exactly in this direction.
Normally, citizens are mere consumers of relevant information (e.g. knowledge about and during extreme events such as e.g. flooding, meteorological and hydrological forecasts, water quality indicators, including access to save drinking water points and ecosystems health, etc.) that is derived from various types of traditional hydrological and meteorological data. The participation in the data collection process by citizens replaces this unidirectional flow of information from the agency to citizens and establishes a bidirectional communication channel between data producers and consumers by assigning both roles to citizens and agencies alike.
In this regard, traditional observations from official stations can get augmented by non-traditional observations from citizens. The former is normally characterized by an automatic and autonomous data collection process that delivers high frequency and accuracy data, high costs are associated with the installation, operation and maintenance of such stations, they require expert knowledge and are prone to vandalism. Compared to that, non-traditional citizens' data are normally less accurate and intermittent due to technological and human factors that both influence the data quality. This can impact the acceptance of non-traditional data and normally requires thorough quality control and assurance measures (QA/QC) to be put in place. At the same time, data collection is less costly, at least when viewed from the perspective of required equipment costs, requires little expert knowledge and vandalism is less of an issue.
Another recurrent issue is that non-traditional monitoring technologies, which puts people and their mobile portable sensors in charge, is a dramatic change in the philosophy on how to monitor water flows, in on-farm and off-farm settings alike. Agencies such as the World Meteorological Organization (WMO) which sets monitoring standards are only just now starting to begin endorsing the deployment and operational use of these non-traditional technologies at global levels.
