The notion that landscape features have coevolved over time is well known in the Earth sciences. Hydrologists have recently called for a more rigorous connection between emerging spatial patterns of landscape features and the hydrological response of catchments, and have termed this concept catchment coevolution. In this paper we review recent literature on this subject and attempt to synthesize what we have learned into a general framework that would improve predictions of hydrologic change. We first present empirical evidence of the interaction and feedback of landscape evolution and changes in hydrological response. From this review it is clear that the independent drivers of catchment coevolution are climate, geology, and tectonics. We identify common currency that allows comparing the levels of activity of these independent drivers, such that, at least conceptually, we can quantify the rate of evolution or aging. Knowing the hydrologic age of a catchment by itself is not very meaningful without linking age to hydrologic response. Two avenues of investigation have been used to understand the relationship between (differences in) age and hydrological response: (i) one that is based on relating present landscape features to runoff processes that are hypothesized to be responsible for the current fingerprints in the landscape; and (ii) one that takes advantage of an experimental design known as space‐for‐time substitution. Both methods have yielded significant insights in the hydrologic response of landscapes with different histories. If we want to make accurate predictions of hydrologic change, we will also need to be able to predict how the catchment will further coevolve in association with changes in the activity levels of the drivers (e.g., climate). There is ample evidence in the literature that suggests that whole‐system prediction of catchment coevolution is, at least in principle, plausible. With this imperative we outline a research agenda that implements the concepts of catchment coevolution for building a holistic framework toward improving predictions of hydrologic change.

Distributions of water transit times (TTDs), and related storage‐selection (SAS) distributions, are spatially integrated metrics of hydrological transport within landscapes. Recent works confirm that the form of TTDs and SAS distributions should be considered time variant—possibly depending, in predictable ways, on the dynamic storage of water within the landscape. We report on a 28 day periodic‐steady‐state‐tracer experiment performed on a model hillslope contained within a 1 m³ sloping lysimeter. Using experimental data, we calibrate physically based, spatially distributed flow and transport models, and use the calibrated models to generate time‐variable SAS distributions, which are subsequently compared to those directly observed from the actual experiment. The objective is to use the spatially distributed estimates of storage and flux from the model to characterize how temporal variation in water storage influences temporal variation in flow path configurations, and resulting SAS distributions. The simulated SAS distributions mimicked well the shape of observed distributions, once the model domain reflected the spatial heterogeneity of the lysimeter soil. The spatially distributed flux vectors illustrate how the magnitude and directionality of water flux changes as the water table surface rises and falls, yielding greater contributions of younger water when the water table surface rises nearer to the soil surface. The illustrated mechanism is compliant with conclusions drawn from other recent studies and supports the notion of an inverse‐storage effect, whereby the probability of younger water exiting the system increases with storage. This mechanism may be prevalent in hillslopes and headwater catchments where discharge dynamics are controlled by vertical fluctuations in the water table surface of an unconfined aquifer.

Many remote sensing‐based evapotranspiration (RSBET) algorithms have been proposed in the past decades and evaluated using flux tower data, mainly over North America and Europe. Model evaluation across South America has been done locally or using only a single algorithm at a time. Here, we provide the first evaluation of multiple RSBET models, at a daily scale, across a wide variety of biomes, climate zones, and land uses in South America. We used meteorological data from 25 flux towers to force four RSBET models: Priestley–Taylor Jet Propulsion Laboratory (PT‐JPL), Global Land Evaporation Amsterdam Model (GLEAM), Penman–Monteith Mu model (PM‐MOD), and Penman–Monteith Nagler model (PM‐VI). was predicted satisfactorily by all four models, with correlations consistently higher () for GLEAM and PT‐JPL, and PM‐MOD and PM‐VI presenting overall better responses in terms of percent bias (%). As for PM‐VI, this outcome is expected, given that the model requires calibration with local data. Model skill seems to be unrelated to land‐use but instead presented some dependency on biome and climate, with the models producing the best results for wet to moderately wet environments. Our findings show the suitability of individual models for a number of combinations of land cover types, biomes, and climates. At the same time, no model outperformed the others for all conditions, which emphasizes the need for adapting individual algorithms to take into account intrinsic characteristics of climates and ecosystems in South America.

Water availability in the dry western United States (US) under climate change and increasing water use demand has become a serious concern. Previous studies have projected future runoff changes across the western US but ignored the impacts of ecosystem response to elevated CO₂ concentration. Here, we aim to understand the impacts of elevated CO₂ on future runoff changes through ecosystem responses to both rising CO₂ and associated warming using the Noah‐MP model with representations of vegetation dynamics and plant hydraulics. We first validated Noah‐MP against observed runoff, leaf area index (LAI), and terrestrial water storage anomaly from 1980 to 2015. We then projected future runoff with Noah‐MP under downscaled climates from three climate models under Representative Concentration Pathway 8.5. The projected runoff declines variably from the Pacific Northwest by −11% to the Lower Colorado River basin by −92% from 2016 to 2099. To discern the exact causes, we conducted an attribution analysis of the modeled evapotranspiration from two additional sensitivity experiments: one with constant CO₂ and another one with static monthly LAI climatology. Results show that surface “greening” (due to the CO₂ fertilization effect) and the stomatal closure effect are the second largest contributors to future runoff change, following the warming effect. These two counteracting CO₂ effects are roughly compensatory, leaving the warming effect to remain the dominant contributor to the projected runoff declines at large river basin scales. This study suggests that both surface “greening” and stomatal closure effects are important factors and should be considered together in water resource projections.

Despite hosting ∼16% of the global freshwater and almost 50% of water resources in South America, Brazilian catchment‐scale relationships between drivers and streamflow are still poorly understood. Here, we used streamflow signatures and attributes of 735 catchments from the Catchment Attributes for Brazil data set to investigate the dominant hydrological processes for the catchments. We also assess how catchments group based on hydrologic behavior similarities and analyze which climatic/landscape attributes control the streamflow variability. To classify and group the catchments, we used the k‐means method optimized by the Elbow approach, along with a Principal Component Analysis. Uncertainty on catchment grouping was checked by k‐fold cross‐validation. Then, we used a recursive feature elimination using the random forest technique to assess the most influential catchment attributes to the hydrological signatures. Our results revealed six similarity groups, which followed mainly an aridity gradient ranging from the wettest to the driest, but also seasonality. The climate is the primary driver of hydrological behavior for the water‐limited groups, highlighting the influence and importance of the atmospheric demand in several Brazilian catchments. High soil storage capacity in energy‐limited catchments associated with high precipitation led to high discharge all year due to the subsurface fluxes' contribution. Our findings may be useful to improve streamflow predictability and hydrological behavior identification by further understanding hydrological similarities and their signatures due to catchment landscape characteristics. Further, by employing an easily reproducible methodology and clear metrics to weigh uncertainty, our study provides a significant step toward establishing a catchment‐scale common classification system.