Southwest Watershed Research Center

Long-term research is essential for guiding the development of agroecosystems to meet escalating production demands in a manner that is environmentally sound and socially acceptable. Research must integrate biophysical and socioeconomic factors to provide geographically scalable knowledge that involves stakeholders across the research-education-extension-policy spectrum. In response to this need, the Long-Term Agroecosystem Research (LTAR) network developed a "Common Experiment," which seeks to develop and disseminate multi-region, science-based information to enable implementation of visionary agricultural innovations while simultaneously promoting food security, well-being, environmental quality, and climate adaptation and mitigation. The core design of the Common Experiment contrasts prevailing and alternative/aspirational production systems, with the latter including novel innovations hypothesized to advance sustainable intensification in locally appropriate ways. Treatments in the Common Experiment represent a diversity of production systems under cropland, grazing land, and integrated crop/grazing land management. Where possible, treatments are evaluated at multiple spatial scales (e.g., from plot to enterprise) and are designed to evolve over the course of the experiment with stakeholder input. A common assessment framework guides data collection for the experiment and is complemented by metric-specific protocols and an emerging data management infrastructure. Currently, there are large differences among sites in the application of the experimental framework and degree of stakeholder engagement; differences largely grounded in pragmatic issues related to land access, site expertise, and resource availability. The full potential of the LTAR Common Experiment may be realized with strategic investments in network capacity.

Schenk (2008) proposed a model for the depth distribution of plant roots based on a simple hydrological scheme and the assumptions that plants will take up the shallowest water available first, and will distribute their roots in proportion to uptake at each depth. Here, we derive an analytical solution to the Schenk model under an idealized climate (in which infiltration events are treated as a marked Poisson process), explore properties of the result, and compare with data. The solution suggests that in very humid and arid climates the soil wetting and drying cycles induced by root water uptake are generally confined to a characteristic depth below the surface. This depth depends on the typical magnitude of rainfall events (most strongly so in arid climates), the typical total transpiration demand between rainfall events (most strongly in humid climates), and the plant-available water holding capacity of the soil. Root water uptake (and thus predicted root density) in very humid and arid landscapes decreases exponentially with depth at a rate determined by this characteristic depth. However in a mesic climate soils may be wet or dry to greater depths below the near-surface, and the duration spent in each state increases with depth. Consequently, root water uptake and root density in mesic climates more closely resembles a power law distribution. When the aridity index is exactly 1 the characteristic depth diverges and the mean rooting depth approaches infinity. This suggests that the deepest rooting depths will occur in mesic environments. We compared this model to another analytical solution and a compiled database of root distributions (159 combined locations). For a larger comparison dataset, we also compared 99th percentile rooting depth to rooting depths modeled by two other frameworks and a database of observed rooting depths (1271 combined locations). Results demonstrate that the analytical formulation of the Schenk model performs well as a shallow bound on rooting depths, captures something of the non-exponential form of root distributions, and its error is similar to or less than that of other modeling frameworks. Errors may be partly explained by the deviation of real climate from the idealizations used to obtain an analytical solution (exponentially-distributed infiltration events and no seasonality).

Abstract Biological soil crusts (biocrusts) are widespread soil photosynthetic communities covering about 12% of Earth's land surface, and play crucial roles in terrestrial carbon (C) and nitrogen (N) cycles, yet scalable quantifications of biocrusts and their biogeochemical contributions are notably lacking. While remote sensing has enormous potential to assess, scale, and contextualize biocrusts and their functions, the applicability of hyperspectral data in predicting C‐ and N‐related biocrust traits remains largely unexplored. We address this issue by evaluating the potential of in situ hyperspectral data to predict C and N across a range of biocrust species and different environmental conditions. We found that in situ hyperspectral reflectance measurements can be used to predict biocrust tissue C/N ratios and N concentrations with relatively high accuracy but to a lesser extent for potential biocrust N2 fixation rates. Critical wavelength domains included the visible region of the spectrum from roughly 490–600 nm, which most effectively captured variations in biocrust tissue C, and the shortwave infrared region from 1,150 to 1,350 nm and 1,550–1,650 nm, which most effectively captured biocrust tissue N and N2 fixation potential. Finally, we provide evidence that multi‐ and hyperspectral missions with targeted band placement, such as the proposed 26‐band Landsat Next, could be effective in predicting biocrust traits. This work provides a critical step in understanding how to apply data from new and upcoming satellite missions to the monitoring of biocrusts.

Abstract Reductions in streamflow caused by groundwater pumping, known as “streamflow depletion,” link the hydrologic process of stream‐aquifer interactions to human modifications of the water cycle. Isolating the impacts of groundwater pumping on streamflow is challenging because other climate and human activities concurrently impact streamflow, making it difficult to separate individual drivers of hydrologic change. In addition, there can be lags between when pumping occurs and when streamflow is affected. However, accurate quantification of streamflow depletion is critical to integrated groundwater and surface water management decision making. Here, we highlight research priorities to help advance fundamental hydrologic science and better serve the decision‐making process. Key priorities include (a) linking streamflow depletion to decision‐relevant outcomes such as ecosystem function and water users to align with partner needs; (b) enhancing partner trust and applicability of streamflow depletion methods through benchmarking and coupled model development; and (c) improving links between streamflow depletion quantification and decision‐making processes. Catalyzing research efforts around the common goal of enhancing our streamflow depletion decision‐support capabilities will require disciplinary advances within the water science community and a commitment to transdisciplinary collaboration with diverse water‐connected disciplines, professions, governments, organizations, and communities.

Allometric models provide a rapid, nondestructive means for estimating aboveground biomass (AGB) of perennial grass species. In the absence of site-specific models, allometric relationships developed at other sites at other times are often used. This implicitly assumes that size-biomass relationships are highly robust. In this study, we assess the comparability of allometric relationships developed at two points in time (2005 and 2015) on different soils on a Sonoran Desert savanna in southern Arizona. We used peak growing season field measurements to develop single-species and multispecies regression models using basal diameter and height to predict the current year's AGB for seven perennial grass species. Basal diameter exhibited the strongest relationship with AGB among single-species (adjusted R2 = 0.54 to 0.87) and multispecies models (adjusted R2 = 0.73). Inclusion of height did little to improve biomass predictions. Our models generally underestimated observed 2015 AGB on the loamy site, whereas models developed in 2005 on a sandier site overestimated the 2015 AGB. Results suggest site-specific allometric models should be used when possible. However, in lieu of such models, relationships developed at other sites or at other times may be appropriate depending on the level of precision needed to address a specific research question.

ABSTRACT Stochastic weather generators create time series that reproduce key weather dynamics present in long-term observations. The dataset detailed herein is a large-scale gridded parameterization for CLImate GENerator (CLIGEN) that fills spatial gaps in the coverage of existing regional CLIGEN parameterizations, thereby obtaining near-global availability of combined coverages. This dataset primarily covers countries north of 40° latitude with 0.25° spatial resolution. Various CLIGEN parameters were estimated based on 20-year records from four popular global climate products. Precipitation parameters were statistically downscaled to estimate point-scale values, while point-scale temperature and solar radiation parameters were approximated by direct calculation from high-resolution datasets. Surrogate parameter values were used in some cases, such as with wind parameters. Cross-validation was done to assess the downscaling approach for six precipitation parameters using known point-scale values from ground-based CLIGEN parameterizations. These parameter values were derived from daily accumulation records at 7,281 stations and high temporal resolution records at 609 stations. Two sensitive parameters, monthly average storm accumulation and maximum 30-minute intensity, were shown have RMSE values of 1.48 mm and 4.67 mm hr−1, respectively. Cumulative precipitation and the annual number of days with precipitation occurrence were both within 5% of ground-based parameterizations, effectively improving climate data availability.