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Integrated hydrological modeling is an effective method for understanding interactions between parts of the hydrologic cycle, quantifying water resources, and furthering knowledge of hydrologic processes. However, these models are dependent on robust and accurate datasets that physically represent spatial characteristics as model inputs. This study evaluates multiple data‐driven approaches for estimating hydraulic conductivity and subsurface properties at the continental‐scale, constructed from existing subsurface dataset components. Each subsurface configuration represents upper (unconfined) hydrogeology, lower (confined) hydrogeology, and the presence of a vertical flow barrier. Configurations are tested in two large‐scale U.S. watersheds using an integrated model. Model results are compared to observed streamflow and steady state water table depth (WTD). We provide model results for a range of configurations and show that both WTD and surface water partitioning are important indicators of performance. We also show that geology data source, total subsurface depth, anisotropy, and inclusion of a vertical flow barrier are the most important considerations for subsurface configurations. While a range of configurations proved viable, we provide a recommended Selected National Configuration 1 km resolution subsurface dataset for use in distributed large‐and continental‐scale hydrologic modeling.

This study synthesizes two different methods for estimating hydraulic conductivity (K) at large scales. We derive analytical approaches that estimate K and apply them to the contiguous United States. We then compare these analytical approaches to three‐dimensional, national gridded K data products and three transmissivity (T) data products developed from publicly available sources. We evaluate these data products using multiple approaches: comparing their statistics qualitatively and quantitatively and with hydrologic model simulations. Some of these datasets were used as inputs for an integrated hydrologic model of the Upper Colorado River Basin and the comparison of the results with observations was used to further evaluate the K data products. Simulated average daily streamflow was compared to daily flow data from 10 USGS stream gages in the domain, and annually averaged simulated groundwater depths are compared to observations from nearly 2000 monitoring wells. We find streamflow predictions from analytically informed simulations to be similar in relative bias and Spearman's rho to the geologically informed simulations.R‐squared values for groundwater depth predictions are close between the best performing analytically and geologically informed simulations at 0.68 and 0.70 respectively, with RMSE values under 10 m. We also show that the analytical approach derived by this study produces estimates of K that are similar in spatial distribution, standard deviation, mean value, and modeling performance to geologically‐informed estimates. The results of this work are used to inform a follow‐on study that tests additional data‐driven approaches in multiple basins within the contiguous United States.

Zea mays(maize) makes phytoalexins such as sesquiterpenoid zealexins, to combat invading pathogens. Zealexins are produced from farnesyl diphosphate in microgram per gram fresh weight quantities. As farnesyl diphosphate is also a precursor for many compounds essential for plant growth, the question arises as to howZ. maysproduces high levels of zealexins without negatively affecting vital plant systems. To examine if specific pools of farnesyl diphosphate are made for zealexin synthesis we made CRISPR/Cas9 knockouts of each of the three farnesyl diphosphate synthases (FPS) inZ. maysand examined the resultant impacts on different farnesyl diphosphate‐derived metabolites. We found that FPS3 (GRMZM2G098569) produced most of the farnesyl diphosphate for zealexins, while FPS1 (GRMZM2G168681) made most of the farnesyl diphosphate for the vital respiratory co‐factor ubiquinone. Indeed,fps1mutants had strong developmental phenotypes such as reduced stature and development of chlorosis. The replication and evolution of thefpsgene family inZ. maysenabled it to produce dedicated FPSs for developmentally related ubiquinone production (FPS1) or defense‐related zealexin production (FPS3). This partitioning of farnesyl diphosphate production between growth and defense could contribute to the ability ofZ. maysto produce high levels of phytoalexins without negatively impacting its growth.