The CZO Multiscale TROPIcal CatchmentS (M-TROPICS) provides the international scientific community with unique decennial time series of meteorological, hydrological, geochemical, and ecological variables in tropical environments. The CZO M-TROPICS involves academic and governmental partners in tropical countries (Cameroun, India, Lao PDR, and Vietnam) and is included in the Research Infrastructure OZCAR, the French contribution to the international CZO initiative.
Long-term monitoring of the variables needed for establishing water, biogeochemical (including particulate matter), and energy budgets: water and inorganic and organic matter in solution (major anions and cations, carbon), in suspension (suspended particulate matter, including organic carbon), and bed particulate matter
Impact assessment of global change (land-use, climate) on water fluxes, chemical weathering, and physical erosion
Data and information dissemination to the scientific and stakeholder communities
Capacity building in the field of catchment hydrology and soil erosion, through on-the-job training, teaching, and student internships, and basic geochemistry through analytical platforms
Recommendations on land use policy to the national authorities
Multiscaleapproach, both spatially (from microplot to catchment and larger river basins scales) and temporally (from sub-hourly to multi-decennial time-series)
Multidisciplinaryapproach, currently involving hydrology, biogeochemistry, soil science, agronomy, ecology, remote sensing, experimentation, and modelling
Besides data collection and dissemination, the achievements of M-TROPICS on March 2022 are:
252 scientific publications in international journals
33 scientific publications in national journals
1 special issue in the Lao Journal of Agriculture and Forestry (2008): Management of soil erosion and water resources in the uplands of Lao P.D.R., by Ribolzi O. (Ed.), Pierret A. (Ed.), Gebbie L. (Ed.), Sengtaheuanghoung O. (Ed.), and Chanphengxay M. (Pref.)
57 PhDs, 7 post-docs, 6 HDR, and 281 MSc, BSc and Agric. Eng. degrees
The Earth Critical Zone (CZ) is defined as the thin layer between the top of the canopy and the bottom of groundwater aquifer in which complex interactions involving rock, soil water, air and living organisms regulate the natural habitat and determine the availability of life sustaining resources. This concept brings together scientific disciplines in the aim to tackle crucial environmental issues regarding how the various components of the CZ react to global changes, including land use and climate changes:
What are the water, solute, and particulate fluxes exported from tropical catchments?
What is the impact of rapid land use changes on hydrology, water quality, soil resources?
The strategies adopted to answer these questions are often integrated approaches on experimental catchments, where hydrological, sedimentary, biogeochemical and ecological studies can be coupled. Acquiring simultaneous time series of meteorological, hydrological, geochemical, and ecological data over decades on river systems (both small experimental watersheds and larger basins) representative of the diversity of ecosystems is pivotal for the understanding of these processes, building integrated modelling and for proposing predictive scenarios.
Among the Critical Zone Observatories (CZOs) that have been implemented by the Earth Science community in the past 30 years, very few were set up in the Tropics despite the huge importance of these regions in terms of population density, fast-changing land use, biodiversity hotspots, biomass stock on continents (humid forests), size of river systems. In addition, rainfall in the Tropics is mostly governed by monsoon systems, which are particularly sensitive to climate change.
M-TROPICS at JFFoS 2022 in Kyoto
Laurie Boithias presented M-TROPICS datasets collected in Mekong river basin, Lao PDR, at the interdisciplinary Japanese-French Frontiers of Science (JFFoS) Symposium 2022 in Kyoto, Japan: small and large catchment-scale climate, hydrology, and water quality long-term monitoring data including E. coli.
Escherichia coli concentration, multiscale monitoring over the decade 2011–2021 in the Mekong River basin, Lao PDR
Bacterial pathogens in surface waters may threaten human health, especially in developing countries, where untreated surface water is often used for domestic needs. The objective of the long-term multiscale monitoring of Escherichia coli concentration in stream water, and that of associated variables (temperature, electrical conductance, dissolved oxygen concentration and saturation, pH, oxidation-reduction potential, turbidity, and […]
Groundwater irrigation reduces overall poverty but increases socioeconomic vulnerability in a semiarid region of southern India
The development of irrigation is generally considered an efficient way to reduce poverty in rural areas, although its impact on the inequality between farmers is more debated. In fact, assessing the impact of water management on different categories of farmers requires resituating it within the different dimensions of the local socio-technical context. This study, led […]
Distribution of Burkholderia pseudomallei within a 300‑cm deep soil profile: implications for environmental sampling
The environmental distribution of Burkholderia pseudomallei, the causative agent of melioidosis, remains poorly understood. This study performed in Lao PDR and supervised by Alain Pierret (IRD-iEES Paris) and Olivier Ribolzi (IRD-GET), in the frame of the PhD of Khemngeun Pongmala, provides novel information about a putative association of soil biogeochemical heterogeneity and the vertical distribution […]
AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods
Machine learning has shown great promise for simulating hydrological phenomena. However, the development of machine-learning-based hydrological models requires advanced skills from diverse fields, such as programming and hydrological modeling. Additionally, data pre-processing and post-processing when training and testing machine-learning models are a time-intensive process. In this study led by Ather Abbas, PhD student at UNIST, […]
Impact of Rainfall Variability and Land Use Change on River Discharge in South Cameroon
Climate change, climate variability, and anthropogenic forcings such as land use change are the main drivers of river discharge variability. However, an understanding of their simultaneous impacts on river discharge remained limited in some parts of the world. The objective of this article led by Valentin Brice Ebodé from the University of Youndé, Cameroon, was […]