Abstract:Changes in land use and land cover are major drivers of hydrological alteration in the tropical Andes. However, quantifying their impacts is fraught with difficulties because of the extreme diversity in meteorological boundary conditions, which contrasts strongly with the lack of knowledge about local hydrological processes. Although local studies have reduced data scarcity in certain regions, the complexity of the tropical Andes poses a big challenge to regional hydrological prediction. This study analyses data generated from a participatory monitoring network of 25 headwater catchments covering three of the major Andean biomes (páramo, jalca and puna) and links their hydrological responses to main types of human interventions (cultivation, afforestation and grazing). A paired catchment setup was implemented to evaluate the impacts of change using a 'trading spacefor-time' approach. Catchments were selected based on regional representativeness and contrasting land use types. Precipitation and discharge have been monitored and analysed at high temporal resolution for a time period between 1 and 5 years. The observed catchment responses clearly reflect the extraordinarily wide spectrum of hydrological processes of the tropical Andes. They range from perennially humid páramos in Ecuador and northern Peru with extremely large specific discharge and baseflows, to highly seasonal, flashy catchments in the drier punas of southern Peru and Bolivia. The impacts of land use are similarly diverse and their magnitudes are a function of catchment properties, original and replacement vegetation and management type. Cultivation and afforestation consistently affect the entire range of discharges, particularly low flows. The impacts of grazing are more variable but have the largest effect on the catchment hydrological regulation. Overall, anthropogenic interventions result in increased streamflow variability and significant reductions in catchment regulation capacity and water yield, irrespective of the hydrological properties of the original biome.

This article presents a hydrometeorological dataset from a network of paired instrumented catchments, obtained by participatory monitoring through a partnership of academic and non-governmental institutions. The network consists of 28 headwater catchments (<20 km2) covering three major biomes in 9 locations of the tropical Andes. The data consist of precipitation event records at 0.254 mm resolution or finer, water level and streamflow time series at 5 min intervals, data aggregations at hourly and daily scale, a set of hydrological indices derived from the daily time series, and catchment physiographic descriptors. The catchment network is designed to characterise the impacts of land-use and watershed interventions on the catchment hydrological response, with each catchment representing a typical land use and land cover practice within its location. As such, it aims to support evidence-based decision making on land management, in particular evaluating the effectiveness of catchment interventions, for which hydrometeorological data scarcity is a major bottleneck. The data will also be useful for broader research on Andean ecosystems, and their hydrology and meteorology.

Applications such as real-time control, uncertainty analysis and optimization require an extensive number of model iterations. Full hydrodynamic sewer models are not sufficient for these applications due to the excessive computation time. Simplifications are consequently required. A lumped conceptual modelling approach results in a much faster calculation. The process of identifying and calibrating the conceptual model structure could, however, be time-consuming. Moreover, many conceptual models lack accuracy, or do not account for backwater effects. To overcome these problems, a modelling methodology was developed which is suited for semi-automatic calibration. The methodology is tested for the sewer system of the city of Geel in the Grote Nete river basin in Belgium, using both synthetic design storm events and long time series of rainfall input. A MATLAB/Simulink(®) tool was developed to guide the modeller through the step-wise model construction, reducing significantly the time required for the conceptual modelling process.