Browsing by Author "Crespo, P."

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Evaluation of TRMM 3B42 precipitation estimates and WRF retrospective precipitation simulation over the Pacific–Andean region of Ecuador and Peru
(2014) Ochoa, A.; Pineda, L.; Crespo, P.; Willems, Patrick
The Pacific–Andean region in western South America suffers from rainfall data scarcity, as is the case for many regions in the South. An important research question is whether the latest satellite-based and numerical weather prediction (NWP) model outputs capture well the temporal and spatial patterns of rainfall over the region, and hence have the potential to compensate for the data scarcity. Based on an interpolated gauge-based rainfall data set, the performance of the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and its predecessor V6, and the North Western South America Retrospective Simulation (OA-NOSA30) are evaluated over 21 sub-catchments in the Pacific–Andean region of Ecuador and Peru (PAEP). In general, precipitation estimates from TRMM and OANOSA30 capture the seasonal features of precipitation in the study area. Quantitatively, only the southern sub-catchments of Ecuador and northern Peru (3.6–6◦ S) are relatively well estimated by both products. The accuracy is considerably less in the northern and central basins of Ecuador (0–3.6◦ S). It is shown that the probability of detection (POD) is better for light precipitation (POD decreases from 0.6 for rates less than 5 mm day−1 to 0.2 for rates higher than 20 mm day−1 ). Compared to its predecessor, 3B42 V7 shows modest regionwide improvements in reducing biases. The improvement is specific to the coastal and open ocean sub-catchments. In view of hydrological applications, the correlation of TRMM and OA-NOSA30 estimates with observations increases wit
Model intercomparison to explore catchment functioning: results from a remote montane tropical rainforest
(2012-07-24) Plesca, I.; Timbe, Edison; Exbrayat, J. F.; Windhorst, David; Kraft, P.; Crespo, P.; Vaché, Kellie B.; Frede, Hans Georg; Breuer, Lutz
Catchment-scale runoff generation involves a complex interaction of physical and chemical processes operating over a wide distribution of spatial and temporal scales. Understanding runoff generation is challenged by this inherent complexity – the more uncertain step of predicting the hydrologic response of catchments is that much more challenging. Many different hypotheses have been implemented in hydrological models to capture runoff generation processes and provide hydrologic predictions. These concepts have been developed based on extended field observations. Here we propose inferring water flux understanding and catchment exploring through the application of a variety of available hydrological models as a mechanism to build upon and extend models that have been developed to capture particular hydrological processes. We view this ensemble modeling strategy as particularly appropriate in ungauged catchments. The study is carried out in a tropical montane rainforest catchment in Southern Ecuador. The catchment is 75 km2 and is covered by forest in the south, while the northern slopes have been partly deforested for grazing. Annual rainfall is highly variable, reaching up to 5700 mm per year in the upper parts of the catchment. To explore the dominating runoff processes, an ensemble of 6 hydrological models with different structures applied over different levels of both spatial and temporal detail was developed. The ensemble includes spatially lumped (HBV-light), semi-distributed (HEC-HMS, CHIMP, SWAT, LASCAM) and a fully distributed model (HBV-N-D). The hydro-statistical toolkit WETSPRO was used to characterize simulated and observed hydrographs. Estimated baseflow indices, flow minima and maxima, flow duration curves and cumulative errors were generated and compared among the ensemble of models. This process facilitated the exploration of processes controlling runoff generation, enabled an evaluation of the applicability of the screened models to tropical montane rainforests, and provided the capacity to evaluate and explain where different models failed.