Global climate models (GCMs) are generally used to forecast weather, understand the present climate, and project climate change. Their reliability usually rests on their capability to represent climatic processes, and most evaluations directly measure the spatiotemporal agreement of scalar climate variables. However, climate naturally involves complex interactions that are hard to infer and, therefore, difficult to evaluate. Climate networks (CNs) have been used to infer flows of mass and energy in the complex climate system. Here, an Evaluation of Models by Causal Flows (EMCaF) is proposed. EMCaF focuses on the assessment of properties about mass and energy flows in the CNs derived from GCMs. First, causal CNs are inferred from GCMs, and then the capabilities to reproduce characteristic transfer flows are assessed with reference models. A more in‐depth feature is the possibility to assess how climate change disturbs CNs properties. In addition to the quantitative difference between modelled and observed values taken into account in standard evaluations, the EMCaF approach aims to assess the weaknesses and strengths of GCMs to represent climate mechanisms and processes that couple different components of the climate system. The comparison of models through this approach allows having complimentary feedback on model evaluations to understand possible causes of errors and enable a judgement based on processes. The approach is illustrated by evaluating one GCM and subsequently assessing changes of its CNs under future climate projections. Results show that known climatic patterns are assimilated and that causal strength patterns are likely to agree with the wind magnitude as a transfer factor. Significative issues are then explored, showing the capabilities of the approach and allowing understand fundamental structures in transport flows, compare their properties, and assess changes in the future. Different alternatives and considerations in each step of the approach are discussed to expand its applicability.

Precipitation is the most relevant element in the hydrological cycle and vital for the biosphere. However, when extreme precipitation events occur, the consequences could be devastating for humans (droughts or floods). An accurate prediction of precipitation helps decision-makers to develop adequate mitigation plans. In this study, linear and nonlinear models with lagged predictors and the implementation of a nonlinear autoregressive model with exogenous variables (NARX) network were used to predict monthly rainfall in Labrado and Chirimachay meteorological stations. To define a suitable model, ridge regression, lasso, random forest (RF), support vector machine (SVM), and NARX network were used. Although the results were “unsatisfactory” with the linear models, the specific direct influences of variables such as Niño 1 + 2, Sahel rainfall, hurricane activity, North Pacific Oscillation, and the same delayed rainfall signal were identified. RF and SVM also demonstrated poor performance. However, RF had a better fit than linear models, and SVM has a better fit than RF models. Instead, the NARX model was trained using several architectures to identify an optimal one for the best prediction twelve months ahead. As an overall evaluation, the NARX model showed “good” results for Labrado and “satisfactory” results for Chirimachay. The predictions yielded by NARX models, for the first six months ahead, were entirely accurate. This study highlighted the strengths of NARX networks in the prediction of chaotic and nonlinear signals such as rainfall in regions that obey complex processes. The results would serve to make short-term plans and give support to decision-makers in the management of water resources.

The reliability of climate models depends ultimately on their adequacy in relevant real situations. However, climate in mountains, a very sensitive system, is scarcely monitored, making the assessment of global climate models (GCMs) projections problematic. This is even more critical for tropical mountain regions, where complex atmospheric processes acting across scales are specially challenging for GCMs. To help bridge this gap, we evaluated the representation of extreme climate indices by GCMs and reanalysis data in the Andes of Ecuador. This work presents an intercomparison of 11 climate precipitation indices (Climate Change Detection and Indices, ETCCDIs) reconstructed for the period 1 January 1981–31 December 2000 using the data of six climate stations situated in a medium‐sized Andean catchment in southern Ecuador, reanalysis data sets (RAD) ERA40, ERA‐Interim, NCEP/NCAR Reanalysis 1 (NCEP/NCAR‐R1) and NCEP/DOE Reanalysis 2 (NCEP/DOE‐R2), and the data sets of 19 and 29 models of the Coupled Model Intercomparison Project, Phases 3 and 5 (CMIP3&5). Temporal and spatial analysis highlights that the values and the variability of ETCCDIs based on reanalysis and CMIP3&5 data overestimate observations, especially in ENSO years. However, frequency‐type indices are in general better captured than amount‐related indices in RAD. In general, reanalysis data displayed a similar uncertainty as the CMIP model data sets and some indices present lower uncertainty. The uncertainty of ETCCDIs based on CMIP5 remains similar to CMIP3 GCMs, with small variations. The indices using NCEP/NCAR‐R1, NCEP/DOE‐R2, and ERA‐Interim data performed better than those obtained with the ERA40 data sets, with no discernible improvement between both NCEP products. It can be concluded that for the given study region CMIP3&5 models and reanalysis products with respectively good and poor performance, exist, however data should be carefully screened before use. Furthermore, these results confirm that the specificity of the studied region is a key to identify limiting aspects on the GCMs and reanalysis extreme climate representation.

Unraveling the relationship between humidity, wind, and rainfall is vitally important to understand the dynamics of water vapor transport. In recent years, the use of causal networks to identify causal flows has gained much ground in the field of climatology to provide new insights about physical processes and hypothesize previously unknown ones. In this paper, the concept of a virtual control volume is proposed, which resembles the Eulerian description of a vector field, but is based on causal flows instead. A virtual control surface is used to identify the influence of surrounding climatic processes on the control volume (i.e., the study region). Such an influence is characterized by using a causal inference method that gives information about its direction and strength. The proposed approach was evaluated by inferring and spatially delineating areas of influence of humidity and wind on the rainfall of Ecuador. It was possible to confirm known patterns of influence, such as the influence of the Pacific Ocean on the coast and the influence of the Atlantic Ocean on the Amazon. Moreover, the approach was able to identify plausible new hypotheses, such as the influence of humidity on rainfall in the northern part of the boundary between the Andes and the Amazon, as well as the origin (the Amazon or the tropical Atlantic) and the altitude at which surrounding humidity and wind influence rainfall within the control volume. These hypotheses highlight the ability of the approach to exploit a large amount of scalar data and identify pathways of influence between climatic variables.

RESUMENLas inundaciones son una causa severa de muertes y pérdidas económicas. Para prevenir, mitigar y reducir los riesgos por inundaciones y sus consecuencias, los modelos hidráulicos permiten el análisis y mapeo de dichas inundaciones. Los resultados de un modelo apropiado, que trabaje en base a condiciones locales, son herramientas valiosas para los gobiernos locales, conduciendo a un manejo sustentable de las llanuras de inundación. Alrededor del mundo, muy pocos ríos de alta montaña han sido modelados; y, debido a su orografía, la escasez de datos presenta una dificultad adicional en su investigación. Tomando en cuenta que todos los modelos unidimensionales asumen que el fondo del río tiene una pendiente pequeña, este estudio evalúa dos modelos unidimensionales ampliamente usados: Mike11 y HEC-RAS, para modelar un río de alta montaña. La mejor configuración del modelo, bajo condiciones topográficamente complejas, y su potencial uso fueron valorados mediante su calibración y validación. Al contrario de los resultados obtenidos para el modelo Mike11, tanto en calibración como en validación, hemos encontrado que el modelo HEC-RAS no es capaz de encontrar una solución estable durante el modelamiento hidrodinámico del río. Este estudio sienta un precedente en cuanto a modelación unidimensional en ríos de alta montaña con escasez de datos. Palabras clave: modelación de ríos 1D, HEC-RAS, Mike11, ríos de alta montaña, Ecuador. ABSTRACTFloods represent a severe cause of deaths and economic loss. In order to prevent, mitigate, and reduce flood risks and their consequences, hydraulic models allow analysing and mapping floods. The results of an appropriate model that works under local conditions are a valuable tool for local governments leading to sustainable management of floodplains. Around the world, high-mountain rivers have been poorly modelled; their orography and data scarcity present an extra research difficulty. Considering that all one-dimensional models assume that the river bed slope is small, this study evaluated two widely applied one-dimensional models: Mike11 and HEC-RAS, for modelling a high mountain river. Their best configuration under complex topographical conditions and their potential use was assessed by calibration and validation of the models. We found that the HEC-RAS model was not able to define a stable solution of the hydrodynamic modelling of the river, while Mike11 yielded stable results. Furthermore, the validation of the Mike11 model showed good performance. This study sets a precedent in the 1D modelling of high-mountain rivers with data scarcity.