Results indicate that in the two study basins, no single model performed best in all cases. In addition, no distributed model was able to consistently outperform the lumped model benchmark. However, one or more distributed models were able to outperform the lumped model benchmark in many of the analyses. Several calibrated distributed models achieved higher correlation and lower bias than the calibrated lumped benchmark in the calibration, validation, and combined periods. Evaluating a number of specific precipitation-runoff events, one calibrated distributed model was able to perform at a level equal to or better than the calibrated lumped model benchmark in terms of event-averaged peak and runoff volume error. However, three distributed models were able to provide improved peak timing compared to the lumped benchmark. Taken together, calibrated distributed models provided specific improvements over the lumped benchmark in 24% of the model-basin pairs for peak flow, 12% of the model-basin pairs for event runoff volume, and 41% of the model-basin pairs for peak timing. Model calibration improved the performance statistics of nearly all models (lumped and distributed). Analysis of several precipitation/runoff events indicates that distributed models may more accurately model the dynamics of the rain/snow line (and resulting hydrologic conditions) compared to the lumped benchmark model. Analysis of SWE simulations shows that better results were achieved at higher elevation observation sites. Although the performance of distributed models was mixed compared to the lumped benchmark, all calibrated models performed well compared to results in the DMIP 2 Oklahoma basins in terms of run period correlation and %Bias, and event-averaged peak and runoff error. This finding is noteworthy considering that these Sierra Nevada basins have complications such as orographicallyenhanced precipitation, snow accumulation and melt, rain on snow events, and highly variable topography. Looking at these findings and those from the previous DMIP experiments, it is clear that at this point in their evolution, distributed models have the potential to provide valuable information on specific flood events that could complement lumped model simulations.

In semi-arid regions, where hydrological resources are very vulnerable and where there are water shortages in many regions of the world, it is of great importance to assess the vulnerability that a system is facing or will face to the potential impacts of climatic changes and changes on the use of land. For that reason, this research focuses on evaluating the global vulnerability of a hydrological basin, taking into consideration these changes. Being different from the existing methodologies that assess the vulnerability, our methodology interconnects through a new interface a distributed hydrological model, global climate models, climate change scenarios, land use change scenarios and the largest number of system variables calculated with information from official sources. Another important point of our methodology is that it quantifies the global vulnerability of the system, taking into consideration hydrological, environmental, economic and social vulnerabilities. The results obtained show that the proposed methodology may provide a new approach to analyze vulnerability in semi-arid regions. Moreover, it made it possible to diagnose and establish that the greatest current and future vulnerabilities of the system are the result of activities in agricultural areas and urban centers.

No abstract

<p class="Resumen">La evaluación de los impactos del Cambio Climático en un sistema de alta montaña es un objetivo primordial en la planificación y prevención de situaciones de riesgo como son las crecidas y las inundaciones. Sin embargo, evaluar con exactitud los impactos en los principales flujos y almacenamientos que intervienen en dicho sistema no es una tarea sencilla. Por lo cual, el objetivo de este estudio ha sido implementar el modelo hidrológico TETIS como herramienta de análisis en la evaluación de los impactos del Cambio Climático a escala de celda en una cuenca. Este modelo se ha calibrado automáticamente empleando el algoritmo de optimización Shuffled Complex Evolution. En las proyecciones futuras de las variables de precipitación y temperatura usadas por el modelo TETIS, se han usado los multimodelos climáticos del Coupled Model Intercomparison Project y los escenarios del Panel Intergubernamental del Cambio Climático. Los resultados obtenidos han mostrado que existe una modificación en la dinámica del sistema presentando un mayor riesgo por avenidas máximas extraordinarias e inundaciones.</p>

The success of hydrological modeling of a high mountain basin depends in most case on the accurate quantification of the snowmelt. However, mathematically modeling snowmelt is not a simple task due to, on one hand, the high number of variables that can be relevant and can change significantly in space and, in the other hand, the low availability of most of them in practical engineering. Therefore, this research proposes to modify the original equation of the classical degree-day model to introduce the spatial and temporal variability of the degree-day factor. To evaluate the effects of the variability in the hydrological modeling and the snowmelt modeling at the cell and hillslope scale. We propose to introduce the spatial and temporal variability of the degree-day factor using maps of radiation indices. These maps consider the position of the sun according to the time of year, solar radiation, insolation, topography and shaded-relief topography. Our priority has been to keep the parsimony of the snowmelt model that can be implemented in high mountain basins with limited observed input. The snowmelt model was included as a new module in the TETIS distributed hydrological model. The results show significant improvements in hydrological modeling in the spring period when the snowmelt is more important. At cell and hillslope scale errors are diminished in the snowpack, improving the representation of the flows and storages that intervene in high mountain basins.

No abstract

There is great uncertainty about the future effects of climate change on the global economic, social, environmental, and water sectors. This paper focuses on watershed vulnerabilities to climate change by coupling a distributed hydrological model with artificial neural networks and spatially distributed indicators for the use of a predictive model of such vulnerability. The analyses are complemented by a Monte Carlo evaluation of the uncertainty associated with the projections of the global circulation models, including how such uncertainty impacts the vulnerability forecast. To test the proposal, the paper uses current and future vulnerabilities of the Turbio River watershed, located in the semi-arid zone of Guanajuato (Mexico). The results show that nearly 50% of the watershed currently has medium and high vulnerabilities, and only the natural areas in the watershed show low vulnerabilities. In the future, an increase from medium to high vulnerability is expected to occur in urban and agricultural areas of the basin, with an associated uncertainty of ±15 mm in the projected precipitation.

La calibración de un modelo lluvia-escorrentía es un proceso complicado que puede llevar mucho tiempo y esfuerzo. Su éxito está condicionado a la experiencia del modelador y al conocimiento que se tenga del sistema físico que se desea modelar. Por ello, en este trabajo, se propone una metodología que combina la calibración manual y automática para obtener los parámetros efectivos de un modelo hidrológico aplicado a una cuenca de alta montaña. Lo anterior se debe a que calibrar simultáneamente los parámetros hidrológicos y los parámetros de fusión de nieve puede no representar adecuadamente las condiciones reales de los sistemas de alta montaña. La metodología propuesta ha sido probada usando el modelo hidrológico distribuido TETIS desarrollado en la Universitat Politècnica de València. Los resultados obtenidos son buenos al alcanzarse índices de eficiencia de Nash-Sutcliffe cercanos a 0.9 en calibración y a 0.8 en validación, con parámetros dentro de los rangos establecidos en la literatura científica.