Hydrological modelling with TOPMODEL of Chingaza páramo, Colombia

Paramo ecosystems are located on the upper parts of the tropical mountains, below the snow line areas or in isolated areas where no glacier ecosystems occur. These ecosystems are considered important for their biodiversity, but mainly because they are permanent source of water for populations located at the upper and middle parts of the Andes. Recent studies indicate that ecosystems located at high altitudes, are more vulnerable to climate change and to changes in land use, which threatens the ecosystem services derived from them. There are very few studies in these ecosystems, within which, studies on the hydrological functioning are even scarcer, which seems to be related to their position in the top of the mountains and difficulties associated with the access to them. This implies that there is a need to create tools that allow us to study these ecosystems, overcoming the current difficulties. Hydrological TOPMODEL is used to investigate the hydrological functioning of the paramo of Chingaza, through a case study in La Chucua basin. For this, we calibrate and validate the model using two data sets of climate and the hydrology of the basin (climate and discharge from 2008 and 2009, respectively). Through the calibration procedure we obtain a high efficiency value of 0.76 model (coefficient Nash - Sutcliffe), which adequately represents the hydrological behavior of the paramo. Simulations with better adjustment between measured and predicted values of discharge, have low values of surface infiltration excess runoff, indicating high water storage capacity on the soils. This agrees with the predominance of subsurface flow in studied ecosystem, given the special characteristics of soils. Results also show the large influence of factors represented in the model (topography and soils), on water basin response to rainfall events. This is significant evidence of exceptional hydrological behavior of the paramos, mainly related to the presence of soil with high organic matter content. These results imply that TOPMODEL is a robust tool, able to represent in a precise manner the hydrological functioning of the basin La Chucua, and consequently it is expected that TOPMODEL can also represent hydrological conditions of Chingaza paramo.

[1]  Dominik E. Reusser,et al.  Why can't we do better than Topmodel? , 2008 .

[2]  Bernard Francou,et al.  Climate change and tropical Andean glaciers: Past, present and future , 2008 .

[3]  Anthony J. Jakeman,et al.  A dynamic model for predicting hydrologic response to land cover Changes in gauged and ungauged catchments , 2004 .

[4]  C. Tobon-Marin,et al.  Monitoring and modelling hydrological fluxes in support of nutrient cycling studies in Amazonian rain forest ecosystems , 1999 .

[5]  R. Silva,et al.  Estudo Comparativo de Três Formulações do TOPMODEL na Bacia do Rio Pequeno, São José dos Pinhais, PR , 2007 .

[6]  Wouter Buytaert,et al.  Human impact on the hydrology of the Andean páramos , 2006 .

[7]  Patrick Willems,et al.  Spatial and temporal rainfall variability in mountainous areas: A case study from the south Ecuadorian Andes , 2006 .

[8]  P. Foster,et al.  The potential negative impacts of global climate change on tropical montane cloud forests , 2001 .

[9]  M. Kobiyama,et al.  TOPMODEL: TEORIA INTEGRADA E REVISÃO TOPMODEL: integrated theory and review , 2007 .

[10]  E. Albek,et al.  Hydrological modeling of Seydi Suyu watershed (Turkey) with HSPF , 2004 .

[11]  Keith Beven,et al.  Models as multiple working hypotheses: hydrological simulation of tropical alpine wetlands , 2011 .

[12]  Jan Seibert,et al.  Multi‐criterial validation of TOPMODEL in a mountainous catchment , 1999 .

[13]  Keith Beven,et al.  Rainfall‐runoff modelling of a humid tropical catchment: the TOPMODEL approach , 2002 .

[14]  E. S. Corbett,et al.  A CALIBRATION PROCEDURE USING TOPMODEL TO DETERMINE SUITABILITY FOR EVALUATING POTENTIAL CLIMATE CHANGE EFFECTS ON WATER YIELD 1 , 1999 .

[15]  Keith Beven,et al.  Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology , 2001 .

[16]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[17]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[18]  K. Beven,et al.  Testing a physically-based flood forecasting model (TOPMODEL) for three U.K. catchments , 1984 .

[19]  Keith Beven,et al.  Using internal catchment information to reduce the uncertainty of discharge and baseflow predictions , 2007 .

[20]  Wouter Buytaert,et al.  Water for cities: The impact of climate change and demographic growth in the tropical Andes , 2012 .

[21]  Henrik Madsen,et al.  Uncertainty assessment of integrated distributed hydrological models using GLUE with Markov chain Monte Carlo sampling , 2006 .

[22]  Keith Beven,et al.  TOPMODEL : a critique. , 1997 .

[23]  W. Buytaert,et al.  Potential impacts of climate change on the environmental services of humid tropical alpine regions , 2011 .

[24]  Jorge Luis Ceballos Liévano,et al.  Páramos y ecosistemas alto andinos de Colombia en condición HotSpot y Global Climatic Tensor. Anexo :descripción geomorfológica de la alta montaña por zonas geográficas , 2002 .

[25]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[26]  Rolf Weingartner,et al.  An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools , 2009, Environ. Model. Softw..

[27]  L. Breuer,et al.  Water resources in South America: sources and supply, pollutants and perspectives , 2013 .

[28]  P. E. O'connell,et al.  River flow forecasting through conceptual models part III - The Ray catchment at Grendon Underwood , 1970 .

[29]  J. Monteith Evaporation and environment. , 1965, Symposia of the Society for Experimental Biology.

[30]  Michael J. Sale,et al.  A comparison of geographical information systems-based algorithms for computing the TOPMODEL topographic index , 2004 .

[31]  R. Célleri,et al.  Predicting climate change impacts on water resources in the tropical Andes: Effects of GCM uncertainty , 2009 .

[32]  Wouter Buytaert,et al.  Improving parameter priors for data-scarce estimation problems , 2013 .

[33]  Marco Franchini,et al.  Physical interpretation and sensitivity analysis of the TOPMODEL , 1996 .