Citizen scientists' engagement in flood risk-related data collection: a case study in Bui River Basin, Vietnam.

Time constraints, financial limitations, and inadequate tools restrict the flood data collection in undeveloped countries, especially in the Asian and African regions. Engaging citizens in data collection and contribution has the potential to overcome these challenges. This research demonstrates the applicability of citizen science for gathering flood risk-related data on residential flooding, land use information, and flood damage to paddy fields for the Bui River Basin in Vietnam. Locals living in or around flood-affected areas participated in data collection campaigns as citizen scientists using self-investigation or investigation with a data collection app, a web form, and paper forms. We developed a community-based rainfall monitoring network in the study area using low-cost rain gauges to draw locals' attention to the citizen science program. Fifty-nine participants contributed 594 completed questionnaires and measurements for four investigated subjects in the first year of implementation. Five citizen scientists were active participants and contributed more than 50 completed questionnaires or measurements, while nearly 50% of citizen scientists participated only one time. We compared the flood risk-related data obtained from citizen scientists with other independent data sources and found that the agreement between the two datasets on flooding points, land use classification, and the flood damage rate to paddy fields was acceptable (overall agreement above 73%). Rainfall monitoring activities encouraged the participants to proactively update data on flood events and land use situations during the data collection campaign. The study's outcomes demonstrate that citizen science can help to fill the gap in flood data in data-scarce areas.

Measuring the unseen: mobilizing citizen scientists to monitor groundwater in Nepal.

Groundwater-level monitoring provides crucial information on the nature and status of aquifers and their response to stressors like climate change, groundwater extraction, and land use changes. Therefore, the development of a spatially distributed long-term monitoring network is indispensable for sustainable groundwater resource management. Despite being one of our greatest unseen resources, groundwater systems are too often poorly understood, ineffectively managed, and unsustainably used. This study investigates the feasibility of establishing a groundwater monitoring network mobilizing citizen scientists. We established a network of 45 shallow monitoring wells in the Kathmandu Valley using existing wells. We recruited 75% of the citizen scientists through personal connections and the rest through outreach programs at academic institutes and site visits. We used various methods to encourage citizen scientists to complete regular measurements and solicited feedback from them based on their experiences. Citizen scientists were more consistent during the monsoon season (June through September) than non-monsoon seasons. The depth-to-water below the ground surface varied from - 0.11 m (negative sign represents a groundwater level higher than the ground surface) to 11.5 m, with a mean of 4.07 m and standard deviation of 2.63 m. Groundwater levels began to rise abruptly with the onset of monsoon season and the shallowest and the deepest groundwater levels were recorded in peak rainfall months and dry months respectively. Citizen science-based groundwater monitoring using existing wells would be an economic and sustainable approach for groundwater monitoring. Improved groundwater-level data will provide essential information for understanding the shallow groundwater system of the valley, which will assist concerned authorities in planning and formulating evidence-based policy on sustainable groundwater management.

Assessment of rainfall erosivity (R-factor) during 1986-2015 across Nepal: a step towards soil loss estimation.

Rainfall is a main cause of soil erosion which varies spatially and temporarily. R-factor is an erosive power of the rainfall that is responsible for soil detachment and subsequent displacement. Mathematically, it is expressed as a sum of the product of kinetic energy and maximum 30-min rain intensity. A precise assessment of R-factor needs higher temporal resolution rainfall data (sub-hourly) for a period of several years, which is rarely available. Many empirical approaches are used to predict R-factor as a function of mean monthly and annual rainfall amount. In this study, we used Loureiro and Countinho (Journal of Hydrology 250:12-18, 2001) approximation approach to estimate R-factor and explore its intra-annual variability using 30 years (1986-2015) of daily rainfall data from 280 stations distributed across Nepal. This study employs different intra-annual variability indices and calculates erosivity density (ED) and weighted erosivity density (WED). The country average mean annual R-factor (MAR), annual ED, and WED are found to be 9434.8 MJ mm ha-1 h-1 year-1, 4.39 MJ ha-1 h-1,and 1.61 MJ ha-1 h-1, respectively. On a monthly scale, July is the highest erosive month followed by August (> 2000 MJ mm ha-1 h-1 month-1). Likewise, November is the lowest erosive month followed by December (~ 50 MJ mm ha-1 h-1 month-1). Spatial distributions of these indices show clear delineations of areas with different erosivity patterns at different time of the year. In addition, this study explores inter-annual variation, temporal evolution, and trend estimation of R-factors over the country (for the first time). Significant rising trends are observed in the western region of the country. We found that the mean soil erosion for Nepal is estimated at 21.01 ton ha-1 year-1. The smallest R-factors are observed in the north-western region of the country and the maximum values are observed at mid hills and southern plains of the country. Our study could be an initial but important step for effective soil conservation, land use planning, and agricultural production.