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

Abstract:

A new satellite-based algorithm for rainfall retrieval in high spatio-temporal resolution fo Ecuador is presented. The algorithm relies on the precipitation information from the Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) and infrared (IR) data from the Geostationary Operational Environmental Satellite-16 (GOES-16). It wa developed to (i) classify the rainfall area (ii) assign the rainfall rate. In each step, we selected the most important predictors and hyperparameter tuning parameters monthly. Between 19 April 2017 and 30 November 2017, brightness temperature derived from the GOES-16 IR channels and ancillary geo-information were trained with microwave-only IMERG-V06 using random forest (RF). Validation was done against independent microwave-only IMERG-V06 information not used for training. The validation results showed the new rainfall retrieva technique (multispectral) outperforms the IR-only IMERG rainfall product. This offers using the multispectral IR data can improve the retrieval performance compared to single-spectrum IR approaches. The standard verification scored a median Heidke skill score of ~0.6 for the rain area delineation and R between ~0.5 and ~0.62 for the rainfall rate assignment, indicating uncertainties for Andes’s high elevation. Comparison of RF rainfall rates in 2 km2 resolution with daily rain gauge measurements reveals the correlation of R = ~0.33.

Abstract:

Weather radar networks are an excellent tool for quantitative precipitation estimation (QPE), due to their high resolution in space and time, particularly in remote mountain areas such as the Tropical Andes. Nevertheless, reduction of the temporal and spatial resolution might severely reduce the quality of QPE. Thus, the main objective of this study was to analyze the impact of spatial and temporal resolutions of radar data on the cumulative QPE. For this, data from the world’s highest X-band weather radar (4450 m a.s.l.), located in the Andes of Ecuador (Paute River basin), and from a rain gauge network were used. Dierent time resolutions (1, 5, 10, 15, 20, 30, and 60 min) and spatial resolutions (0.5, 0.25, and 0.1 km) were evaluated. An optical flow method was validated for 11 rainfall events (with dierent features) and applied to enhance the temporal resolution of radar data to 1-min intervals. The results show that 1-min temporal resolution images are able to capture rain event features in detail. The radar–rain gauge correlation decreases considerably when the time resolution increases (r from 0.69 to 0.31, time resolution from 1 to 60 min). No significant dierence was found in the rain total volume (3%) calculated with the three spatial resolution data. A spatial resolution of 0.5 km on radar imagery is suitable to quantify rainfall in the AndesMountains. This study improves knowledge on rainfall spatial distribution in the Ecuadorian Andes, and it will be the basis for future hydrometeorological studies

Abstract:

Chemical element concentrations and fluxes in the hydrological cycle of a mountain rain forest in southern Ecuador are influenced by annual seasonality, long term trends, inter-annual ENSO cycles and other environmental factors. Some knowledge about this processes was collected in previous studies. But little is known about how much of the variation in the data can be explained by those processes. Goal of this thesis is therefore to build linear models that explain the variation in element concentrations and fluxes in the incident precipitation and in the troughfall of an small forested catchment in southern Ecuador. The linear models contain seasonal terms, trend terms, ENSO temerature anomalie terms and environmental variable terms. For each analysed element four linear model were build to explain variation in concentrations and fluxes in incident precipitation and troughfall. The models contained all terms at the beginning and were then optimized to a model with only the significant terms for each element in each flux and concentration. By analysing a time series from 1998 to 2010 with monthly means of element concentrations of weekly measurements of troughfall and incident precipitation, and their resulting fluxes, the following hypothesis are tested. Namely seasonal terms are significantly explaining the variation in the concentrations and fluxes, longterm trends are explaining the variation, ENSO related temperature anomalies are explaining the variation and other environmental factors are explaining the variation. The findings showed that in 45 % of the models seasonality is significantly contributing to the explaining of the variation. A significant trend terms is part of 30% of the models and a significant ENSO term in 18%. The range of percentage of significant environmental variables starts with 16% for wind direction and 18% for flower or fruiting phenology. goes to 25 % and 31 % for fire activity and heavy rain activity respectively and finally goes to 57% for conductivity. To mention is that in this case conductivity is present in 90% of the conductivity models. The resulting R squares showed that the best models are the troughfall models. The best model here explains almost 80% of the variation, the median is around 50% of explained variation and the worst model explains 27 % of the variation. In the incident precipitation concentration and in the troughfall and incident precipitation fluxes the best models are between 30 and 40 % of explained variation, the median is about 20% for incident precipitation and between 10 and 15 % for the fluxes and the lowest values are about 5 %. The model quality test shows that the not crucial criteria of normal distribution ot the model residuals is violated in some models. The crucial temporal independence criteria is most likely violated in few models and in one model it is clearly violated. All in all the thesis could show that seasonality, trend, ENSO related temperature anomalies and the environmental variables fire activity, conductivity, wind direction, heavy rain, and flower and fruiting phenology are in various combinations contributing significantly to the explaining of the variation in concentration and fluxes of incident precipitation and troughfall. The models are strong in explaining the variation in cases like potassium troughfall concentration, where rainfall seasonality leads to big concentration variation, while in other cases, like magnesium incident precipitation concentrations, where little variation occurs and factors that are not included as model terms lead to clear patterns in the concentrations, the model can explain almost no variation.