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Measuring the Themi River downstream river profile.
Pangani River large intake to sugar cane plantation
Instrumenting gauging sites in Nhamandeme Irrigation Scheme.
Tito Kitomari taking a discharge measurement with the smart phone discharge.ch app.
Upper Ewaso N'giro River Basin stakeholders taking a discharge.ch measurement at an irrigation furro

NON-TRADITIONAL WATER MONITORING THROUGH LOCAL INVOLVEMENT

Generate, manage and exchange key water data and knowledge through local involvement for sustainable and effective water outcomes
in the developing context 

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Learnings from the Global iMoMo Initiative

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CRITICAL PAUCITY OF KEY WATER DATA

In developing and transition countries and despite significant global investments in hydro-meteorology, data on water remain scarce/fragmented. One key reason is that traditional monitoring does not scale because of high investment costs and difficult operation and maintenance of traditional technology, esp. in remote and/or poor regions and because vandalism is an issue in many places. And even where there are sufficient data, these are often difficult to access and interpret for local stakeholders due to outdated data transmission and the lack of access to modern tools for data management, analysis, synthesis and exchange.

The collection of non-traditional data through local involvement using low-cost, high-tech devices (also referred to as crowd-sensing or crowd-based data collection) has emerged as an interesting pathway for obtaining more data in cases where needed. These non-traditional data can then complement data from in-situ stations as well as from remote sensing.

The Swiss Agency for Development and Cooperation and the Global Programme Water there has recognised this early on and supported the Global iMoMo Initiative with the goal to foster innovation in this domain. Selected results and learnings from the multi-year, global effort are reported here.

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NEW TECHNOLOGIES & OPPORTUNITIES

In recent years, there have been substantial technology developments in environmental sensing and mobile communication technologies as well as web-based accounting. These enable the application and deployment of affordable and scalable high-tech solutions for better water management at different scales, from local up to transboundary levels. 

Especially on the side of data acquisition, low-cost, high-tech data collection through local involvement is increasingly recognised to be a powerful approach for increasing the availability of water data in domains and regions not covered so far by monitoring. As an example, the mobile Android-based app discharge.ch measures discharge in small to medium-sized rivers and channels using the smartphone camera and on the phone processing. Measurements are automatically synchronised with the web where they can be managed, analysed and shared/exported, e.g. in a web-based scheme irrigation management system such as the one available here: wua.imomohub.kg for irrigation collectives such as water user associations.

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NEW CHALLENGES

Data collection through local involvement requires careful planning, including the definition of collection protocols and establishment of contractual relationships with local people involved and constant quality control and assurance.

Otherwise, stakeholders wishing to deploy these technologies run the risk of launching initiatives that neither generate desired outcomes in terms of the quality, frequency and reliability of data collected nor have a lasting, long-term impact beyond any projects lifetime.


Here, we synthesize key learnings and findings regarding these issues from the multi-year global iMoMo Initiative. Click below to learn more about limits of altruistic data collection, the importance of institutional contexts, the proper accounting of campaign costs and the limits scalability and about the importance of valorization of non-traditional data.

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CASE STUDIES

Over the period of 6 years, the iMoMo project and its multi-national team has worked in Africa and Central Asia to deploy crowd-sensing technologies and implement data collection campaigns for the acquisition of non-traditional data. 


Case studies from Mozambique, Tanzania, Uganda, Kenya and Kyrgyzstan are presented and discussed in detail. 

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