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Accurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF‐Hydro) over a central California domain. Our results show WRF‐Hydro soil moisture exhibits increased correlation coefficients (r), reduced biases, and increased Kling‐Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well‐established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS‐adjusted WRF‐Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF‐Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO‐informed total soil thickness. The magnitude and timing of peak flow events are better captured,rincreases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre‐calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs.

 

We present the astrometric calibration of the Beijing–Arizona Sky Survey (BASS). The BASS astrometry was tied to the International Celestial Reference Frame via the Gaia Data Release 2 reference catalog. For effects that were stable throughout the BASS observations, including differential chromatic refraction and the low charge transfer efficiency of the CCD, we corrected for these effects at the raw image coordinates. Fourth-order polynomial intermediate longitudinal and latitudinal corrections were used to remove optical distortions. The comparison with the Gaia catalog shows that the systematic errors, depending on color or magnitude, are less than 2 milliarcseconds (mas). The position systematic error is estimated to be about −0.01 ± 0.7 mas in the region between 30° and 60° of decl. and up to −0.07 ± 0.9 mas in the region north of decl. 60°.

 

Abstract. In steep wildfire-burned terrains, intense rainfall can produce large runoff that can trigger highly destructive debris flows. However, the abilityto accurately characterize and forecast debris flow susceptibility in burned terrains using physics-based tools remains limited. Here, we augmentthe Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) to simulate both overland and channelized flows and assess postfiredebris flow susceptibility over a regional domain. We perform hindcast simulations using high-resolution weather-radar-derived precipitation andreanalysis data to drive non-burned baseline and burn scar sensitivity experiments. Our simulations focus on January 2021 when an atmospheric rivertriggered numerous debris flows within a wildfire burn scar in Big Sur – one of which destroyed California's famous Highway 1. Compared to thebaseline, our burn scar simulation yields dramatic increases in total and peak discharge and shorter lags between rainfall onset and peakdischarge, consistent with streamflow observations at nearby US Geological Survey (USGS) streamflow gage sites. For the 404 catchments located inthe simulated burn scar area, median catchment-area-normalized peak discharge increases by ∼ 450 % compared to the baseline. Catchmentswith anomalously high catchment-area-normalized peak discharge correspond well with post-event field-based and remotely sensed debris flowobservations. We suggest that our regional postfire debris flow susceptibility analysis demonstrates WRF-Hydro as a compelling new physics-basedtool whose utility could be further extended via coupling to sediment erosion and transport models and/or ensemble-based operational weatherforecasts. Given the high-fidelity performance of our augmented version of WRF-Hydro, as well as its potential usage in probabilistic hazardforecasts, we argue for its continued development and application in postfire hydrologic and natural hazard assessments. 

In the mammalian glutamate transporters, countertransported intracellular K+ is essential for relocating the glutamate binding site to the extracellular side of the membrane. This K+-dependent process is believed to be rate limiting for the transport cycle. In contrast, extracellular K+ induces glutamate release upon transporter reversal. Here, we analyzed potential K+ binding sites using molecular dynamics (MD) simulations and site-directed mutagenesis. Two candidate sites were identified by spontaneous K+ binding in MD simulations, one site (K1 site) overlapping with the Na1 Na+ binding site and the K2 site being localized under hairpin loop 2 (HP2). Mutations to conserved amino acid residues in these sites resulted in several transporters that were defective in K+-induced reverse transport and which bound K+ with reduced apparent affinity compared with the wild-type transporter. However, external K+ interaction was abolished in only one mutant transporter EAAC1D454A in the K1 site. Our results, for the first time, directly demonstrate effects of K1-site mutations on K+ binding, in contrast to previous reports on K+ binding sites based on indirect evidence. We propose that K+ binding to the K1 site is responsible for catalyzing the relocation step, whereas binding to the K2 site may have an as-of-yet unidentified regulatory function.

 

Heat and drought are two emerging climatic threats to theUSmaize and soybean production, yet their impacts on yields are collectively determined by the magnitude of climate change and rising atmosphericCO₂concentrations. This study quantifies the combined and separate impacts of high temperature, heat and drought stresses on the current and futureUSrainfed maize and soybean production and for the first time characterizes spatial shifts in the relative importance of individual stress. Crop yields are simulated using the Agricultural Production Systems Simulator (APSIM), driven by high‐resolution (12 km) dynamically downscaled climate projections for 1995–2004 and 2085–2094. Results show that maize and soybean yield losses are prominent in theUSMidwest by the late 21st century under both Representative Concentration Pathway (RCP) 4.5 andRCP8.5 scenarios, and the magnitude of loss highly depends on the current vulnerability and changes in climate extremes. Elevated atmosphericCO₂partially but not completely offsets the yield gaps caused by climate extremes, and the effect is greater in soybean than in maize. Our simulations suggest that drought will continue to be the largest threat toUSrainfed maize production underRCP4.5 and soybean production under bothRCPscenarios, whereas high temperature and heat stress take over the dominant stress of drought on maize underRCP8.5. We also reveal that shifts in the geographic distributions of dominant stresses are characterized by the increase in concurrent stresses, especially for theUSMidwest. These findings imply the importance of considering heat and drought stresses simultaneously for future agronomic adaptation and mitigation strategies, particularly for breeding programs and crop management. The modeling framework of partitioning the total effects of climate change into individual stress impacts can be applied to the study of other crops and agriculture systems.