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Abstract The predictive accuracy of regional hydrologic models often varies across both time and space. Interpreting relationships between watershed characteristics, hydrologic regimes, and model performance can reveal potential areas for model improvement. In this study, we use machine learning to assess model performance of a regional hydrologic model to forecast the occurrence of streamflow drought. We demonstrate our methodology using a regional long short‐term memory (LSTM) deep learning model developed by the U.S. Geological Survey (USGS) and data from 384 streamgages across the Colorado River Basin region. Performance was assessed by clustering catchments using: (a) physical and climatological catchment attributes, and (b) streamflow drought signatures time series. We examined the association of USGS LSTM model error measures with clusters generated by both approaches to interpret meaningful spatial and temporal information about LSTM model performance. Clustering static catchment attributes identified elevation, degree of streamflow regulation, baseflow contribution, catchment aridity, and drainage area as the most influential attributes to model performance. Clustering gages by their drought signatures revealed that catchments with significant seasonal peak runoff between January and June generally exhibited better model performance. Additionally, a Random Forest classifier was trained to successfully predict LSTM model performance (F1 score of 0.72) based on physical and climatological catchment attributes. Low degree of flow regulation was identified as a key indicator of better LSTM model performance. These findings point to the opportunities for improving the USGS LSTM model performance in future hydrologic drought prediction efforts across regional and CONUS scales. 

Abstract. In 1966, drilling at Camp Century, Greenland, recovered 3.44 meters of sub-glacial material from beneath 1350 meters of ice. Although prior analysis of this material showed that the core includes glacial sediment, ice, and sediment deposited during an interglacial, the sub-glacial material had never been thoroughly studied. To better characterize this material, we analyzed 26 of the 30 core samples remaining in the archive. We performed a multi-scale analysis including X-ray diffraction, micro-computed tomography, and scanning electron microscopy to delineate stratigraphic units and assign facies based on inferred depositional processes. At the macro-scale, quantitative X-ray diffraction revealed that quartz and feldspar dominated the sediment and that there was insignificant variation in relative mineral abundance between samples. Meso-scale evaluation of the frozen material using micro-computed tomography scans showed clear variations in the stratigraphy of the core characterized by the presence of bedding, grading, and sorting. Micro-scale grain size and shape analysis, conducted using scanning electron microscopy, showed an abundance of fine-grained materials in the lower part of the core and no correspondence between grain shape parameters and sedimentary structures. These multiscale data define 5 distinct stratigraphic units within the core based on sedimentary process; K-means clustering analysis supports this proposed unit delineation. Our observations suggest that ice retreat uncovered the Camp Century region exposing basal till, covered with a remnant of basal ice or firn (Units 1 and 2). Continued ice-free conditions led to till disruption by liquid water causing a slump deposit (Unit 3) and the development of a small fluvial system of increasing energy up core (Units 4–5). Analysis of the Camp Century sub-glacial material indicates a diverse stratigraphy preserved below the ice that recorded episodes of glaciated and deglaciated conditions in northwestern Greenland. Our physical, geochemical, and mineralogic analyses reveal a history of deposition, weathering, and sediment transport preserved under the ice and show the promise of sub-glacial materials to increase our knowledge of past ice sheet behavior over time. 

Abstract Understanding controls on solute export to streams is challenging because heterogeneous catchments can respond uniquely to drivers of environmental change. To understand general solute export patterns, we used a large‐scale inductive approach to evaluate concentration–discharge (C–Q) metrics across catchments spanning a broad range of catchment attributes and hydroclimatic drivers. We leveraged paired C–Q data for 11 solutes from CAMELS‐Chem, a database built upon an existing dataset of catchment and hydroclimatic attributes from relatively undisturbed catchments across the contiguous USA. Because C–Q relationships with Q thresholds reflect a shift in solute export dynamics and are poorly characterized across solutes and diverse catchments, we analysed C–Q relationships using Bayesian segmented regression to quantify Q thresholds in the C–Q relationship. Threshold responses were rare, representing only 12% of C–Q relationships, 56% of which occurred for solutes predominantly sourced from bedrock. Further, solutes were dominated by one or two C–Q patterns that reflected vertical solute–source distributions. Specifically, solutes predominantly sourced from bedrock had diluting C–Q responses in 43%–70% of catchments, and solutes predominantly sourced from soils had more enrichment responses in 35%–51% of catchments. We also linked C–Q relationships to catchment and hydroclimatic attributes to understand controls on export patterns. The relationships were generally weak despite the diversity of solutes and attribute types considered. However, catchment and hydroclimatic attributes in the central USA typically drove the most divergent export behaviour for solutes. Further, we illustrate how our inductive approach generated new hypotheses that can be tested at discrete, representative catchments using deductive approaches to better understand the processes underlying solute export patterns. Finally, given these long‐term C–Q relationships are from minimally disturbed catchments, our findings can be used as benchmarks for change in more disturbed catchments. 

Core Ideas Decision support systems (DSSs) are one component of precision agriculture (PA). The accuracy of DSSs may be improved by using algorithms based on machine learning. Barriers to DSSs include financial constraints, hesitancy to change, data privacy, and workforce limitations. Professional opportunities exist to overcome DSS adoption barriers. 

Core Ideas Model transparency and explainability can help foster trust between farmers and those providing artificial intelligence (AI) solutions. Assigning clear responsibility and accountability to AI decisions can improve farmers’ acceptance and use of these technologies. Development of fair and equitable AI can improve human‐machine partnerships in agriculture. Regulation or voluntary compliance with data ownership, privacy, and security is needed if AI systems are to be used by farmers. 

Abstract. Large sample datasets are transforming the catchment sciences, but there are few off-the-shelf stream water chemistry datasets with complementary atmospheric deposition, streamflow, meteorology, and catchment physiographic attributes. The existing CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) dataset includes data on topography, climate, streamflow, land cover, soil, and geology across the continental US. With CAMELS-Chem, we pair these existing attribute data for 516 catchments with atmospheric deposition data from the National Atmospheric Deposition Program and water chemistry and instantaneous discharge data from the US Geological Survey over the period from 1980 through 2018 in a relational database and corresponding dataset. The data include 18 common stream water chemistry constituents: Al, Ca, Cl, dissolved organic carbon, total organic carbon, HCO3, K, Mg, Na, total dissolved N, total organic N, NO3, dissolved oxygen, pH (field and lab), Si, SO4, and water temperature. Annual deposition loads and concentrations include hydrogen, NH4, NO3, total inorganic N, Cl, SO4, Ca, K, Mg, and Na. We demonstrate that CAMELS-Chem water chemistry data are sampled effectively across climates, seasons, and discharges for trend analysis and highlight the coincident sampling of stream constituents for process-based understanding. To motivate their use by the larger scientific community across a variety of disciplines, we show examples of how these publicly available datasets can be applied to trend detection and attribution, biogeochemical process understanding, and new hypothesis generation via data-driven techniques.