This study discusses the role of the aridity index in controlling the long-term fluxes of direct (Qd) and baseflow (Qd). Here we present an analytical derivation starting from similar assumptions as the one presented in the original work of Budyko that leads to two simple expressions for the control of the aridity index on Qb and Qd. These two analytical equations can be combined into a single expression with the same properties as the original Budyko equation. Our study provides a method for predicting long term baseflow and direct runoff for poorly gaged basins as well as permit the derivation of sensitivity equations for the assessment of the impact of these fluxes with respect to changes in aridity index.

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 remote sensing based ET 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). ET was predicted satisfactorily by all four models, with correlations consistently higher (R²>0.6) for GLEAM and PT-JPL, and PM-MOD and PM-VI presenting overall better responses in terms of PBIAS (-10

Water availability in the dry Western United States (US) under a warming climate 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, LAI, and terrestrial water storage anomaly from 1980–2015. We then projected future runoff with Noah-MP under downscaled climates from three climate models under RCP8.5. The projected runoff declines variably from the Pacific Northwest by –11% to the Lower Colorado River basin by –92% from 2016–2099. To discern the exact causes, we conducted an attribution analysis of two additional sensitivity experiments: one with constant CO2 and another with monthly LAI climatology based on the Penman-Monteith equation. 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.

Uncovering the hillslope scale flow and transport dynamics in an experimental hydrologic systemMinseok Kim1, Till H. M. Volkmann1,2, Aaron Bugaj1, Yadi Wang3, Antônio A. Meira Neto4, Katarena Matos4, Ciaran J. Harman5,6, Peter A. Troch1,41Biosphere 2, University of Arizona, Tucson, AZ, USA,2Applied Intelligence, Accenture, Kronberg im Taunus, Germany, 3Department of Environmental Science, University of Arizona, Tucson, AZ, USA, 4Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA, 5Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD, USA,6Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD, USAHillslope scale water flow and transport dynamics have been extensively studied (Burt & McDonnell, 2015; Hewlett & Hibbert, 1963), but observing those internal dynamics in high spatial and temporal resolutions remains challenging. In this study, we uncover internal water flow and transport dynamics in an artificial hillslope in the Landscape Evolution Observatory (LEO), Biosphere 2, University of Arizona, Tucson, USA, using the experimental dataset collected in December 2016. Complete information about the hillslope and experiment can be found elsewhere (Pangle et al., 2015; Till H. M. Volkmann et al., 2018); Here, we only summarize some relevant information.The first part of the animation describes the experimental system and setup (time 00:12 – 04:14 in Animation S1). The LEO hillslope is 330 m3 (30 m long, 11 m wide, and 1 m deep) sloping soil lysimeter. The hillslope is primarily made up of loamy-sand textured basaltic tephra, and the most downslope 5.5 m3 is filled with gravel-textured basaltic tephra. A custom irrigation system supplies reverse osmosis filtered water onto the LEO surface. The downslope boundary is exposed to atmospheric pressure, creating the seepage face boundary condition. The sensor networks (including pressure transducers and volumetric water content sensors) and the water isotope sampling locations and intervals (7 hrs to 101 hrs) are illustrated in Animation S1 (time 02:09 – 03:01). The isotope composition of subsurface water is obtained from laser-based online measurements of vapor that is extracted via custom gas probes through equilibrium calculation (T. H.M. Volkmann & Weiler, 2014). The irrigation sequence of this experiment was designed to generate a periodic steady state, which allows the application of the PERidoic Tracer Hierarchy method (Harman & Kim, 2014) for the observation of the time-variable transit time distributions and the StorAge Selection functions. Deuterium-labeled water was irrigated during the first two irrigation events.The second part of the animation shows the dynamics of the perched water table and soil water content (time 04:15 – 06:53). The extent of the saturated zone was estimated using the pressure transducer data and Delaunay triangulation (Delaunay, 1934). The experimental data show the saturation from below mechanisms—wetting up from the bedrock surface into the soil profile (McDonnell, 1997)—and the saturation from downslope to upslope. The water table profile forms a wedge-like shape, which is a characteristic of hillslope with a high hillslope (Peclet) number (Berne et al., 2005; Brutsaert, 1994). The hillslope Peclet number of the LEO hillslope during the experiment is high (> 10) (Kim et al., 2020). Significant time delays in the water table dynamics are observed at some upslope locations (e.g., at 13 m upslope), which is mostly due to the delayed water supply from the convergent upslope area. The water content data indicates that the convergent upslope water content began to decrease around the timing of the water table peak at 13 m upslope.The third part of the animation shows the tracer dynamics (from time 06:43). The animated experimental data reveal two notable water transport dynamics. First, the vertical tracer movement is faster at the upslope. This faster movement at the upslope is, in a sense, counter-intuitive because the upslope region is drier than the downslope. This is due to the lateral flow in the saturated zone and the tension saturated zone, that are thicker at the downslope. While water velocity is higher at the downslope, the direction of velocity is not vertical but rotated towards the downslope in those zones.Second, the animated data illustrate that old water is present only at the downslope. This observation is a characteristic of hillslope with a high hillslope number, in which old water is preferentially discharged (Kim et al., 2020). Indeed, the observed SAS function in this hillslope is concave (Kim et al., 2020), indicating that the hillslope preferentially discharges old water that is stored at the downslope.