This study introduces a method to quantify the conditional predictive uncertainty in hydrological post-processing contexts when it is cumbersome to calculate the likelihood (intractable likelihood). Sometimes, it can be difficult to calculate the likelihood itself in hydrological modelling, specially working with complex models or with ungauged catchments. Therefore, we propose the ABC post-processor that exchanges the requirement of calculating the likelihood function by the use of some sufficient summary statistics and synthetic datasets. The aim is to show that the conditional predictive distribution is qualitatively similar produced by the exact predictive (MCMC post-processor) or the approximate predictive (ABC post-processor). We also use MCMC post-processor as a benchmark to make results more comparable with the proposed method. We test the ABC post-processor in two scenarios: i) the Aipe catchment with tropical climate and a spatially-lumped hydrological model (Colombia) and ii) the Oria catchment with oceanic climate and a spatially-distributed hydrological model (Spain). The main finding of the study is that the approximate (ABC post-processor) conditional predictive uncertainty is almost equivalent to the exact predictive (MCMC post-processor) in both scenarios.

La dinámica de un mundo global y el hombre como nueva fuerza geológica plantean la necesidad de tomar decisiones robustas, diseñar estrategias de manejo y de adaptarse al cambio climático. Este estudio investiga la respuesta hidrológica de la cuenca hidrográfica del río Aipe (688.9 km 2), en Huila, Colombia, en acorde con los escenarios de cambio climático desde 2011 a 2040. El modelo hidrológico abcd de Thomas (4 parámetros) fue calibrado y validado comparando el caudal simulado y lo observado en el punto de cierre de la cuenca (en la estación Puente Carretera), usando series históricas mensuales (1992-2012). Realizamos la evaluación de la sensibilidad e identificabilidad de los parámetros con la herramienta 'Monte Carlo Analysis Toolbox' (MCAT). Los resultados muestran que el modelo es capaz de representar adecuadamente los caudales mensuales observados en el punto de desagüe de la cuenca, al encontrarse un índice de eficiencia de Nash-Sutcliffe (NSE) de 0,89. Los parámetros más influyentes son a (almacenamiento del agua en el suelo) y c (aporte al acuífero). Con respecto a la simulación de los escenarios, la línea base (1992-2012) estimó un caudal medio de 15,44 m 3 s-1 ; el escenario de extrapolación de tendencias estimó un caudal de 13,79 m 3 s-1 (-10,64%); el escenario de ensamble multi-modelo de 9,34 m 3 s-1 (-39,47%) y el escenario A2 de 5,74 m 3 s-1 (-62,60%). Proponemos una batería de medidas de adaptación al cambio climático que buscan la gestión integral del recurso hídrico. Modelling the potential impacts of climate change on the hydrology of the Aipe river basin in Huila, Colombia

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This research develops an extension of the Model Conditional Processor (MCP), which merges clusters with Gaussian mixture models to offer an alternative solution to manage heteroscedastic errors. The new method is called the Gaussian mixture clustering post-processor (GMCP). The results of the proposed post-processor were compared to the traditional MCP and MCP using a truncated Normal distribution (MCPt) by applying multiple deterministic and probabilistic verification indices. This research also assesses the GMCP’s capacity to estimate the predictive uncertainty of the monthly streamflow under different climate conditions in the “Second Workshop on Model Parameter Estimation Experiment” (MOPEX) catchments distributed in the SE part of the USA. The results indicate that all three post-processors showed promising results. However, the GMCP post-processor has shown significant potential in generating more reliable, sharp, and accurate monthly streamflow predictions than the MCP and MCPt methods, especially in dry catchments. Moreover, the MCP and MCPt provided similar performances for monthly streamflow and better performances in wet catchments than in dry catchments. The GMCP constitutes a promising solution to handle heteroscedastic errors in monthly streamflow, therefore moving towards a more realistic monthly hydrological prediction to support effective decision-making in planning and managing water resources.