Abstract. The Budyko framework posits that a catchment's long-term mean evapotranspiration (ET) is primarily governed by the availabilities of water and energy, represented by long-term mean precipitation (P) and potential evapotranspiration (PET), respectively. This assertion is supported by the distinctive clustering pattern that catchments take in Budyko space. Several semi-empirical, nonparametric curves have been shown to generally represent this clustering pattern but cannot explain deviations from the central tendency. Parametric Budyko equations attempt to generalize the nonparametric framework, through the introduction of a catchment-specific parameter (n or w). Prevailing interpretations of Budyko curves suggest that the explicit functional forms represent trajectories through Budyko space for individual catchments undergoing changes in the aridity index, PETP, while the n and w values represent catchment biophysical features; however, neither of these interpretations arise from the derivation of the Budyko equations. In this study, we reexamine, reinterpret, and test these two key assumptions of the current Budyko framework both theoretically and empirically. In our theoretical test, we use a biophysical model for ET to demonstrate that n and w values can change without invoking changes in landscape biophysical features and that catchments are not required to follow Budyko curve trajectories. Our empirical test uses data from 728 reference catchments in the United Kingdom (UK) and United States (US) to illustrate that catchments rarely follow Budyko curve trajectories and that n and w are not transferable between catchments or across time for individual catchments. This nontransferability implies that n and w are proxy variables for ETP, rendering the parametric Budyko equations underdetermined and lacking predictive ability. Finally, we show that the parametric Budyko equations are nonunique, suggesting their physical interpretations are unfounded. Overall, we conclude that, while the shape of Budyko curves generally captures the global behavior of multiple catchments, their specific functional forms are arbitrary and not reflective of the dynamic behavior of individual catchments.

No abstract

Aptamers are oligonucleotides that bind with high affinity to target molecules of interest. One such target is glycated hemoglobin (gHb), a biomarker for assessing glycemic control and diabetes diagnosis. By the coupling of aptamers with surface plasmon resonance (SPR) sensing surfaces, a fast, reliable and inexpensive assay for gHb can be developed. In this study, we tested the affinity of SPR-sensing surfaces, composed of aptamers and antifouling self-assembled monolayers (SAMs), to hemoglobin (Hb) and gHb. First, we developed a gHb-targeted aptamer (GHA) through a modified Systematic Evolution of Ligands by EXponential (SELEX) enrichment process and tested its affinity to gHb using the Nano-Affi protocol. GHA was used to produce three distinct SAM-SPR-sensing surfaces: (Type-1) a SAM of GHA directly attached to a sensor surface; (Type-2) GHA attached to a SAM of 11-mercaptoundecanoic acid (11MUA) on a sensor surface; (Type-3) GHA attached to a binary SAM of 11MUA and 3,6-dioxa-8-mercaptooctan-1-ol (DMOL) on a sensor surface. Type-2 and Type-3 surfaces were characterized by cyclic voltammetry and electrochemical impedance spectroscopy to confirm that GHA bound to the underlying SAMs. The adsorption kinetics for Hb and gHb interacting with each SPR sensing surface were used to quantify their respective affinities. The Type-1 surface without antifouling modification had a dissociation constant ratio (K D,Hb / K D,gHb ) of 9.7, as compared to 809.3 for the Type-3 surface, demonstrating a higher association of GHA to gHb for sensor surfaces with antifouling modifications than those without. The enhanced selectivity of GHA to gHb can likely be attributed to the inclusion of DMOL in the SAM-modified surface, which reduced interference from nonspecific adsorption of proteins. Results suggest that pairing aptamers with antifouling SAMs can significantly improve their target affinity, potentially allowing for the development of novel, low cost, and fast assays.

Abstract. The non-parametric Budyko framework provides empirical relationships between a catchment's long-term mean evapotranspiration (E) and the aridity index, defined as the ratio of mean rainfall depth (P) to mean potential evapotranspiration (E0). The parametric Budyko equations attempt to generalize this framework by introducing a catchment-specific parameter (n or w), intended to represent differences in catchment climate and landscape features. Many studies have developed complex regression relationships for the catchment-specific parameter in terms of biophysical features, all of which use known values of P, E0, and E to numerically invert the parametric Budyko equations to obtain values of n or w. In this study, we analytically invert both forms of the parametric Budyko equations, producing expressions for n and w only in terms of P, E0, and E. These expressions allow for n and w to be explicitly expressed in terms of biophysical features through the dependence of P, E0, and E on those same features.

In many spring‐fed rivers, benthic macroalgae and periphytic algae are increasing and, in some cases, replacing rooted vascular plants, which are critical to ecosystem function. While most research has focused on the role of nutrients in driving this change, in‐channel hydrodynamics also control vascular plant and algal abundances and their interactions. Understanding relationships between hydrology and primary producers is essential for developing ecologically relevant flow regulations. We investigated the relationship between flow velocity and primary producer abundance in spring‐fed rivers using observational data from 16 springs to determine critical velocity thresholds for periphyton, macroalgae, and vascular plants. We also used flow suppression experiments to quantify periphyton growth rates and test for hysteretic behavior. Results suggest a critical velocity of 0.22 m/s (95% CI: 0.13–0.28 m/s) for periphyton but no specific thresholds for macroalgae or vascular plants. Experimental and theoretical results supported these findings and suggest periphyton establishment is not hysteretic.

The concentration ratio of glycated to non-glycated forms of various blood proteins can be used as a diagnostic measure in diabetes to determine a history of glycemic compliance. Depending on a protein's half-life in blood, compliance can be assessed from a few days to several months in the past, which can then be used to provide additional therapeutic guidance. Current glycated protein detection methods are limited in their ability to measure multiple proteins, and are susceptible to interference from other blood pathologies. In this study, we developed and characterized DNA aptamers for use in Surface Plasmon Resonance (SPR) sensors to assess the blood protein hemoglobin. The aptamers were developed by way of a modified Systematic Evolution of Ligands by Exponential Enrichment (SELEX) process which selects DNA sequences that have a high binding affinity to a specific protein. DNA products resulting from this process are sequenced and identified aptamers are then synthesized. The SELEX process was performed to produce aptamers for a glycated form of hemoglobin. Equilibrium dissociation constants for the binding of the identified aptamer to glycated hemoglobin, hemoglobin, and fibrinogen were calculated from fitted Langmuir isotherms obtained through SPR. These constants were determined to be 94 nM, 147 nM, and 244 nM respectively. This aptamer can potentially be used to create a SPR aptamer based biosensor for detection of glycated hemoglobin, a technology that has the potential to deliver low-cost and immediate glycemic compliance assessment in either a clinical or home setting.

Abstract. The Budyko framework posits that a catchment's long-term mean evapotranspiration (E) is primarily governed by the availabilities of water and energy, represented by long-term mean precipitation (P) and potential evapotranspiration (E0), respectively. This assertion is supported by the distinctive clustering pattern that catchments take in Budyko space. Several semi-empirical, non-parametric curves have been shown to generally represent this clustering pattern but cannot explain deviations from the central tendency. Parametric Budyko equations attempt to generalize the non-parametric framework, through the introduction of a catchment-specific parameter (n or w). Prevailing interpretations of Budyko curves suggest that the explicit functional forms represent trajectories through Budyko space for individual catchments undergoing changes in aridity index, (E0/P), while n and w values represent catchment biophysical features; however, neither of these interpretations arise from the derivation of the Budyko equations. In this study, we re-examine, reinterpret, and test these two key components of the current Budyko framework both theoretically and empirically. In our theoretical test, we use a biophysical model for E to demonstrate that n and w values can change without invoking changes in landscape biophysical features and that catchments are not required to follow Budyko curve trajectories. Our empirical test uses data from 728 reference catchments in the United Kingdom and United States to illustrate that catchments rarely follow Budyko curve trajectories and that n and w are not transferable between catchments or across time for individual catchments. This non-transferability implies n and w are proxy variables for E/P, rendering the parametric Budyko equations under-determined and lacking of predictive ability. Finally, we show that the parametric Budyko equations are non-unique, suggesting their physical interpretations are unfounded. Overall, we conclude that, while the shape of Budyko curves generally captures the global behavior of multiple catchments, their specific functional forms are arbitrary and not reflective of the dynamic behavior of individual catchments.

Terrestrial water resource availability is fundamentally governed by the partitioning of inputs (precipitation, 𝐴𝐴 𝐴𝐴 ) to outputs (evapotranspiration, 𝐴𝐴 𝐴𝐴 , and runoff, 𝐴𝐴 𝐴𝐴 ) based on atmospheric demand (potential evapotranspiration, 𝐴𝐴 𝐴𝐴0 )and changes in storage (Milly, 1994). Given the critical nature of 𝐴𝐴 𝐴𝐴 in determining the water available for humans and ecosystems (Best, 2019;Rodell et al., 2018), the importance of a mechanistic understanding of landscape water budget partitioning and associated uncertainties is paramount. The largest source of errors in closing water budgets is uncertainty in 𝐴𝐴 𝐴𝐴 (Koppa et al., 2021), and thus a wide array of approaches has been developed for the measurement and estimation of 𝐴𝐴 𝐴𝐴 (see reviews in Wang and Dickinson (2012) and McMahon et al. (2013)). The determinants of 𝐴𝐴 𝐴𝐴 are hydroclimatic (water supply, 𝐴𝐴 𝐴𝐴 , and energy demand, 𝐴𝐴 𝐴𝐴0 ) and ecological (transpiration from plants comprises between 50% and 70% of 𝐴𝐴 𝐴𝐴 ; Lian et al., 2018;Stoy et al., 2019). In idealized ecohydrologically efficient landscapes, where the natural vegetation has adapted to local climate variability (Hunt et al., 2021;Troch et al., 2009), 𝐴𝐴 𝐴𝐴 will be maximized to the limit of the minimum of supply or output capacity. However, in real landscapes 𝐴𝐴 𝐸𝐸 (where overbar indicates long-term temporal mean) is also generally below the input and capacity limits of 𝐴𝐴 𝑃𝑃 and 𝐴𝐴 𝐸𝐸0 (Budyko, 1974;Gentine et al., 2012;Reaver et al., 2022), indicating hydrologic inefficiency. Much work has sought to explain and predict the observed natural variability in hydrologic inefficiency. An especially influential approach, strongly supported by observational evidence, has been the framework popularized by Budyko (1974) in which 𝐴𝐴 𝐸𝐸 is described by semi-empirical functions of aridity index 𝐴𝐴 𝐴𝐴= 𝐸𝐸0∕𝑃𝑃 that are generally known as Budyko curves. For example, observations of 𝐴𝐴 𝐸𝐸 from large collections of catchments (Gentine Abstract The mechanisms underlying observed global patterns of partitioning precipitation ( 𝐴𝐴 𝐴𝐴 ) to evapotranspiration ( 𝐴𝐴 𝐴𝐴 ) and runoff ( 𝐴𝐴 𝐴𝐴 ) are controversially debated. We test the hypothesis that asynchrony between climatic water supply and demand is sufficient to explain spatio-temporal variability of water availability. We developed a simple analytical model for 𝐴𝐴 𝐴𝐴 that is determined by four dimensionless characteristics of intra-annual water supply and demand asynchrony. The analytical model, populated with gridded climate data, accurately predicted global runoff patterns within 2%-4% of independent estimates from global climate models, with spatial patterns closely correlated to observations ( 𝐴𝐴 𝐴𝐴 2 = 0.93). The supply-demand asynchrony hypothesis provides a physically based explanation for variability of water availability using easily measurable characteristics of climate. The model revealed widespread responsiveness of water budgets to changes in climate asynchrony in almost every global region. Furthermore, the analytical model using global averages ...