Département Analyse des Systèmes, Evaluation Intégrée et Modélisation

Adaptive radiations are known for rapid niche diversification in response to ecological opportunity. While most resources usually exist prior to adaptive radiation, novel niches associated with novel resources can be created as a clade diversifies. For example, in African lake cichlid radiations some species prey upon other species of the clade (intraclade consumers). Using a trait-based eco-evolutionary model, we investigate the evolution of intraclade consumers in adaptive radiations and the effect of this novel trophic interaction on the diversification process of the radiating clade. We find that the evolutionary emergence of intraclade consumers halts the diversification processes of other ecomorphs as a result of increased top-down control of density. Because high productivity enables earlier evolution of intraclade consumers, highly productive environments come to harbour less species-rich radiations than comparable radiations in less productive environments. Our results reveal how macroevolutionary and community patterns can emerge from ecological and microevolutionary processes.

Daily streamflow dynamics can be accurately simulated by conceptual models as simple as a single bucket in some catchments, while they require more complex configurations in other catchments. However, without resorting to calibration, anticipating where and why a given model structure may be appropriate remains difficult. In this work, we explored the feasibility of relating suitable model structures to the climate and streamflow characteristics of 508 catchments in Brazil. Specifically, we tested four model structures using up to three reservoirs, where each reservoir is intended to represent a catchment function: the rainfall-runoff threshold, the fast, and the slow hydrograph response. We hypothesized a relationship between suitable model structures and hydrological signatures of aridity (I_A) and baseflow index (I_B). Our results show that different classes of signatures resulted in distinct patterns of model performance. Wet catchments (I_A < 0.9) with low baseflow (I_B < 0.4) were the easiest to model, with a single-reservoir model presenting a relatively good performance. In the case of low baseflow, adding a rainfall-runoff threshold reservoir resulted in better performance than adding a slow response reservoir, whereas in the case of high baseflow (I_B < 0.6) the opposite occurred. In the case of low baseflow, the inclusion of a slow response reservoir helped the simulation of dry catchments (I_A < 1.1), but not of wet ones, which we attributed to the impact of permeability in dry catchments. These results indicate a path toward model structure identification from streamflow signatures and potentially from landscape features.

Excessive groundwater pumping exacerbates aquifer depletion and poses a major threat in regions all around the world that already suffer from overuse or climate change. In this situation, accurate and reliable predictions of long-term aquifer water balances are key prerequisites to manage groundwater sustainably. Compared to spatially explicit numerical models, lumped hydrological models are computationally fast, lean on data requirement and more accessable for quantifying uncertainty. However, lumped hydrological models are mainly designed to simulate river discharge only, not aquifer storage. Consequently, calibration only includes stream flow data. In this study, we hypothesize that we can extend a lumped hydrological models (here HBV) towards a lumped geohydrological model (LGhM) by additional, designated terms for water budget and groundwater storage. The model building is inspired by the geometry and hydrogeological large-scale properties of the catchment's aquifers. Underground flow routing resembles major groundwater flow paths. The model is calibrated and evaluated on both groundwater storage data and surface discharge data. We apply our LGhM to a MODFLOW-based virtual reality describing the unconfined Wairau Plain Aquifer, New Zealand. We consider and discuss specifically river-groundwater exchange processes, long-term forecast of aquifer storage dynamics, and groundwater depletion in a hypothetical, persistent drought. Our model evaluation shows very plausible predictive capabilities in 40-year forecasts with synthetic weather time series and several years of groundwater depletion in the extreme drought case.

Perceptual catchment models are a key first step towards hydrologic synthesis and provide the process foundation of predictive models. However, their development is usually limited to the headwater catchment scale where field work can be done. Here we develop a perceptual model approach for a regional scale catchment. We use a systematic "top-down" approach based on an expert-driven interpretation of the streamflow responses of nested subcatchments. This approach complements the traditional "bottom-up" perceptual model development based on fieldwork observations, which is impractical at the regional scale due to measurement limitations. As an example to illustrate our approach and demonstrate its feasibility, we use the 27,100 km² Moselle catchment, with 26 gauged subcatchments. Our perceptual model of the Moselle catchment presents a description of dominant processes that we consider a-posteriori reasonable, but not a-priori obvious: where precipitation, much more than evaporation or groundwater exchange controls the spatial variability of average streamflow, lithology influences the partitioning between baseflow and quickflow, and topography and land use control hydrograph lag times. A striking feature of our analysis is the rather minor role that vegetation and soil appear to play in streamflow spatial variability at the scale of our inquiry. We use our perceptual model to build a distributed yet parsimonious hydrological model in Part 2 of this paper series. Although the perceptual model is specific for the Moselle, we hope that the perceptual model approach used here can be helpful to others working in large catchments in different hydroclimates, where other factors will dominate.

Regional scale distributed conceptual models are typically developed with a bottom-up approach, which is process-inclusive but prone to over-parameterization. Here we demonstrate a proof of concept top-down approach for distributed conceptual model development, intended to emphasize dominant streamflow generating processes and to fulfill the principle of model parsimony. A key challenge in applying the top-down approach to distributed model development is devising a model comparison experiment that is both informative and limited to a few model alternatives. Here, we show how such model comparisons can be informed by a perceptual model of key processes that control streamflow response variability at the regional scale. We demonstrate our approach for the 27,100 km² Moselle catchment, using the perceptual model developed in Part 1 of this two-part paper. We develop 5 distributed model structures for simulating daily streamflow at 26 subcatchments, and validate them on subcatchments that are not used during the calibration process. Our model comparisons illustrate how the spatial distribution of precipitation, lithology and topography affect the simulation of key signatures of streamflow response variability in the Moselle catchment, providing a basis to justify model decisions. Our analyses show how a minimally parameterized distributed model, with 12 calibration parameters, matches signatures of streamflow average (r = 0.96), baseflow index (r = 0.86), and hydrograph lag time (correct at 22 out of 26 subcatchments). Our proposed top-down approach contributes to improving distributed model development strategies, and can be used to develop parsimonious process based regional models elsewhere.