Environmental changes in biodiversity hotspot ecosystems of
South Ecuador: RESPonse and feedback effECTs (FOR2730)

Abstract:

As a consequence of the remote location of the Andean páramo is knowledge on their hydrologic functioning limited, notwithstanding this alpine tundra ecosystem act as water towers for a large fraction of the society. Given the harsh environmental conditions in this region is year-round monitoring cumbersome, and it would be beneficially if the monitoring needed for the understanding of the rainfall-runoff response could be limited in time. To identify the hydrological response and the effect of temporal monitoring a nested (n?=?7) hydrological monitoring network was set up in the Zhurucay catchment (7.53?km2), south Ecuador. The research questions were: (1) can event sampling provide similar information in comparison to continuous monitoring, and (2) if so, how many events are needed to achieve a similar degree of information? A subset of 34 rainfall runoff events was compared to monthly values derived from a continuous monitoring scheme from December 2010 to November 2013. Land cover and physiographic characteristics were correlated with eleven hydrological indices. Results show that despite some distinct differences between event and continuous sampling, both datasets reveal similar information; more in particular the monitoring of a single event in the rainy season provides the same information as continuous monitoring, while during the dry season 10 events ought to be monitored.

Abstract:

With respect to sampling for regression-based digital soil mapping (DSM), the above all aim is to ensure that the spatial variability of the soil is well-captured without introducing any bias, while the design remains feasible according to operational constraints such as accessibility, man power and cost. Representativeness of the sample concerning the population to be sampled needs to be guaranteed in any regression-based modelling approach. Four selected sampling designs were adapted to show that basically any design may be optimised to represent the n-dimensional predictor space of a particular area, while selecting points is only permitted from a small accessible sub-area or from outside the area. Sampling efficiency may be evaluated based on the representation of the predictor space. However, not only each predictor’s probability function but also the interaction between predictors may have to be considered, to select a representative sample. Instead of sampling a previously un-sampled area with limited accessibility, the four sampling designs may also be used to subsample an existing dataset and, thereby, optimise a suboptimal dataset based on the predictor space of the area which shall be mapped by DSM.