Texas experienced the most extreme one‐year drought on record in 2011 with precipitation at 40% of long‐term mean and agricultural losses of ~$7.6 billion. We assess the value of Gravity Recovery and ...Climate Experiment (GRACE) satellite‐derived total water storage (TWS) change as an alternative remote sensing‐based drought indicator, independent of traditional drought indicators based on in situ monitoring. GRACE shows depletion in TWS of 62.3 ± 17.7 km3 during the 2011 drought. Large uncertainties in simulated soil moisture storage depletion (14–83 km3) from six land surface models indicate that GRACE TWS is a more reliable drought indicator than disaggregated soil moisture or groundwater storage. Groundwater use and groundwater level data indicate that depletion is dominated by changes in soil moisture storage, consistent with high correlation between GRACE TWS and the Palmer Drought Severity Index. GRACE provides a valuable tool for monitoring statewide water storage depletion, linking meteorological and hydrological droughts.
Key Points
GRACE provides an excellent indicator of the impacts of the 2011 drought on TWS
The major contributor of TWS changes is SMS changes by comparing TWS with PDSI
GWS is a small fraction of TWS using ground‐based estimates of GWS changes
Streamflow forecasting over gauged and ungauged basins play a vital role in water resources planning, especially under the changing climate. Increased availability of large sample hydrology data ...sets, together with recent advances in deep learning techniques, has presented new opportunities to explore temporal and spatial patterns in hydrological signatures for improving streamflow forecasting. The purpose of this study is to adapt and benchmark several state‐of‐the‐art graph neural network (GNN) architectures, including ChebNet, Graph Convolutional Network (GCN), and GraphWaveNet, for end‐to‐end graph learning. We explicitly represent river basins as nodes in a graph, learn the spatiotemporal nodal dependencies, and then use the learned relations to predict streamflow simultaneously across all nodes in the graph. The efficacy of the developed GNN models is investigated using the Catchment Attributes and MEteorology for Large‐sample Studies (CAMELS) data set under two settings, fixed graph topology (transductive learning), and variable graph topology (inductive learning), with the latter applicable to prediction in ungauged basins (PUB). Results indicate that GNNs are generally robust and computationally efficient, achieving similar or better performance than a baseline model trained using the long short‐term memory (LSTM) network. Further analyses are conducted to interpret the graph learning process at the edge and node levels and to investigate the effect of different model configurations. We conclude that graph learning constitutes a viable machine learning‐based method for aggregating spatiotemporal information from a multitude of sources for streamflow forecasting
Plain Language Summary
Streamflow forecasting represents a long‐standing problem in water resources management. Only a small fraction of river segments around the world are gauged. Hydrologists typically rely on other readily available catchment attributes (e.g., elevation, slope, climatology, and vegetation) to establish hydrological similarity between gauged and ungauged basins, and then "transfer" the information through a model to predict flow in ungauged basins. This work explores the use of graph neural networks (GNNs) to perform end‐to‐end streamflow forecasting for both gauged and ungauged basins. GNN is a type of machine learning algorithm that uses nodes and edges to represent physical entities. Specifically, GNNs provide an effective means for representing and learning unstructured data sets (e.g., data sets from monitoring networks). We formulated GNN models to perform end‐to‐end spatiotemporal learning, which generates streamflow forecast at all basins. We demonstrated the feasibility of using GNN for prediction at ungauged basins, which remains one of the challenging problems in hydrology. Our systematic benchmarking results, including a large number of sensitivity studies, show that GNN models performed well on a large sample hydrology data set. An Explainable AI technique is used to interpret the learned results.
Key Points
The increasing volume and type of hydrological data calls for a more effective and automated way of extracting spatiotemporal information
This study developed and benchmarked several graph neural network (GNN) models to learn the spatiotemporal dependencies in a large data set
Our results suggest that GNN provides an effective and robust alternative for aggregating spatiotemporal information from multiple sources
Global hydrological and land surface models are increasingly used for tracking terrestrial total water storage (TWS) dynamics, but the utility of existing models is hampered by conceptual and/or data ...uncertainties related to various underrepresented and unrepresented processes, such as groundwater storage. The gravity recovery and climate experiment (GRACE) satellite mission provided a valuable independent data source for tracking TWS at regional and continental scales. Strong interests exist in fusing GRACE data into global hydrological models to improve their predictive performance. Here we develop and apply deep convolutional neural network (CNN) models to learn the spatiotemporal patterns of mismatch between TWS anomalies (TWSA) derived from GRACE and those simulated by NOAH, a widely used land surface model. Once trained, our CNN models can be used to correct the NOAH‐simulated TWSA without requiring GRACE data, potentially filling the data gap between GRACE and its follow‐on mission, GRACE‐FO. Our methodology is demonstrated over India, which has experienced significant groundwater depletion in recent decades that is nevertheless not being captured by the NOAH model. Results show that the CNN models significantly improve the match with GRACE TWSA, achieving a country‐average correlation coefficient of 0.94 and Nash‐Sutcliff efficient of 0.87, or 14% and 52% improvement, respectively, over the original NOAH TWSA. At the local scale, the learned mismatch pattern correlates well with the observed in situ groundwater storage anomaly data for most parts of India, suggesting that deep learning models effectively compensate for the missing groundwater component in NOAH for this study region.
Plain Language Summary
Global hydrological models are increasingly being used to assess water availability and sea level rise. Deficiencies in the conceptualization and parameterization in these models may introduce significant uncertainty in model predictions. GRACE satellite senses total water storage at the regional/continental scales. In this study, we applied deep learning to learn the spatial and temporal patterns of mismatch or residual between model simulation and GRACE observations. This hybrid learning approach leverages strengths of data science and hypothesis‐driven physical modeling. We show, through three different types of convolution neural network‐based deep learning models, that deep learning is a viable approach for improving model‐GRACE match. The method can also be used to fill in data gaps between GRACE missions.
Key Points
Strong interests exist in fusing GRACE satellite TWS data into global hydrological models to improve their predictive performance
We train CNN deep learning models to learn the mismatch between TWS anomalies simulated by a land surface model and that observed by GRACE
Results show deep learning models significantly improved the predictive skills of land surface model by compensating for missing components
Diamond hosts optically active color centers with great promise in quantum computation, networking, and sensing. Realization of such applications is contingent upon the integration of color centers ...into photonic circuits. However, current diamond quantum optics experiments are restricted to single devices and few quantum emitters because fabrication constraints limit device functionalities, thus precluding color center integrated photonic circuits. In this work, we utilize inverse design methods to overcome constraints of cutting-edge diamond nanofabrication methods and fabricate compact and robust diamond devices with unique specifications. Our design method leverages advanced optimization techniques to search the full parameter space for fabricable device designs. We experimentally demonstrate inverse-designed photonic free-space interfaces as well as their scalable integration with two vastly different devices: classical photonic crystal cavities and inverse-designed waveguide-splitters. The multi-device integration capability and performance of our inverse-designed diamond platform represents a critical advancement toward integrated diamond quantum optical circuits.
•Data space inversion (DSI) methods enable fast prediction of system performance.•A new DSI method is developed based on machine learning and ensemble simulation.•Our method provided accurate ...forecast results and reasonable uncertainty intervals.
Quantification of the predictive uncertainty of subsurface models has long been investigated. The traditional workflow is to calibrate prior models to match observed data, and then use the posterior models to simulate future system performance. Not only are these procedures computationally expensive, but they also have issues in maintaining geological model constraints during the calibration step. Data space inversion (DSI) was introduced recently to predict future system performance without the iterative history matching or model calibration step. In general, DSI approaches seek to establish a statistical relationship between the observed and forecast variables, as well as to quantify the predictive uncertainty of the forecast variables, by using an ensemble of uncalibrated prior models. Existing DSI approaches all require a number of complex transformation and mapping operations, which may deter their widespread use. In this study, we introduce a new and simpler DSI approach, the learning-based, data-driven forecast approach (LDFA), by combining dimension reduction and machine learning techniques to quickly provide accurate forecast results and reliably quantify corresponding uncertainty in the results. Our LDFA framework is demonstrated using two supervised learning algorithms, artificial neural network (ANN) and support vector regression (SVR), on two representative examples from reservoir engineering and geological carbon storage. Results suggest that our approach provides accurate forecast results (e.g., future oil production rate or cumulative injected CO2) and reasonable predictive uncertainty intervals. Our framework is generic and may be applied to other surface and subsurface problems.
•Portion of karst landform is the key factor for elasticity of actual E in SW China.•Actual E in karst catchments is more sensitive to P but less to E0 than in non-karst.•Karst catchments exhibited ...higher degradation stress brought by climate change.
Karst landform represents about 10% of the continental area and plays key roles in water supplies for almost a quarter of the global population. Knowledge of ecohydrological responses of karst landform to climate change is critical for both water resources management and ecological protection in these regions. This study investigated the effects of karst landform on the elasticity of actual evapotranspiration (derived by the Budyko equation), estimated the contribution of climate change and evaluated the implications, on the basis of 13 typical catchments that have different karst landform coverages in southwest China. Catchment properties, including the vegetation coverage, portion of karst landform (POK), drainage area, surface roughness, mean topographic wetness index, mean slope, and mean aspect, were selected to test the influencing factors for the elasticity of actual evapotranspiration. Results indicate that POK is the most influencing factor for the elasticity of actual evapotranspiration in this region. Moreover, the actual evapotranspiration in karst catchments is more sensitive to precipitation change and less sensitive to the potential evapotranspiration change than that in the non-karst catchments. On the other hand, the contribution of climate change to actual evapotranspiration was generally negative in this region. Furthermore, relatively large negative contributions mainly occurred in the karst-dominated catchments, suggesting that the karst catchments were exposed to higher degradation stress brought by the climate change than that in non-karst catchments.
Physical agricultural drought indices generally use soil moisture to represent root‐zone water availability (RZWA). The uncertainty in root‐zone properties, especially in deep‐rooting regions, may ...lead to significant uncertainty in the results. This study adopted a conceptual model that requires no specific root‐zone properties to model the RZWA. A RZWA‐based drought index (AgDI) was then developed by standardizing the root‐zone water deficit. A comparison over 20 catchments with different terrain and vegetation demonstrated the effectiveness of AgDI in characterizing agricultural droughts, with the correlations between vegetation indices (normalized difference vegetation index NDVI and gross primary productivity GPP) and AgDI (mean ~0.54) being significantly higher than those between the same vegetation indices and the Palmer Drought Severity Index (PDSI, ~0.22), Standardized Precipitation‐Evapotranspiration Index (SPEI, ~0.33), and Standardized Soil moisture Index (SSI, ~0.37). Compared to the SSI, the AgDI was not equally advantageous everywhere, but it generally showed better performance in deep‐rooting regions.
Plain Language Summary
Agricultural drought can adversely affect agroecosystems; therefore its detection and quantification are essential for hazard mitigation. Limited root‐zone water availability (RZWA) is the direct cause of agricultural drought. Generally, physical agricultural drought indices are based on the soil moisture of several soil layers (e.g., 0–100 cm). However, these methods may result in uncertainties because the soil moisture in such soil profiles may not fully reflect the RZWA, especially in deep‐rooting ecosystems. In addition, estimation of RZWA is challenging because of the uncertainty in root‐zone properties (e.g., roots distribution and hydraulic properties). To overcome these known limitations, we developed a conceptual model that does not require root zone properties to model the RZWA, using which a new agricultural drought index (AgDI) was then developed by standardizing the root zone water deficit. A comparison study over 20 catchments with different terrain and vegetation properties showed that the AgDI is more effective in characterizing agricultural drought than the PDSI, SPEI and SSI. Compared to the soil moisture based SSI, the AgDI is not advantageous everywhere but generally exhibits better performance in deep‐rooting regions. We also found that the meteorological droughts are the main triggers of, but do not always lead to, agricultural droughts.
Key Points
RZWA was inferred by using a conceptual model needing no specified root‐zone properties
Our RZWA‐based drought index (AgDI) is effective in characterizing agricultural droughts
The AgDI generally performs better in deep‐rooting regions than soil moisture based indices
Abstract
De novo mutations, a consequence of errors in DNA repair or replication, have been reported to accumulate with age in normal tissues of humans and model organisms. This accumulation during ...development and aging has been implicated as a causal factor in aging and age-related pathology, including but not limited to cancer. Due to their generally very low abundance mutations have been difficult to detect in normal tissues. Only with recent advances in DNA sequencing of single-cells, clonal lineages or ultra-high-depth sequencing of small tissue biopsies, somatic mutation frequencies and spectra have been unveiled in several tissue types. The rapid accumulation of such data prompted us to develop a platform called SomaMutDB (https://vijglab.einsteinmed.org/SomaMutDB) to catalog the 2.42 million single nucleotide variations (SNVs) and 0.12 million small insertions and deletions (INDELs) thus far identified using these advanced methods in nineteen human tissues or cell types as a function of age or environmental stress conditions. SomaMutDB employs a user-friendly interface to display and query somatic mutations with their functional annotations. Moreover, the database provides six powerful tools for analyzing mutational signatures associated with the data. We believe such an integrated resource will prove valuable for understanding somatic mutations and their possible role in human aging and age-related diseases.
Abstract Floods affect communities and ecosystems worldwide, emphasizing the importance of identifying their precursors and enhancing resilience to these events. Here, we calculated Antecedent Total ...Water Storage (ATWS) anomalies from the new 5-day (5D) Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) satellite solutions to enhance the detection of pre-flood and active flood conditions and to map post-flood storage anomalies. The GRACE data were compared with ~ 3300 flood events reported by the Dartmouth Flood Observatory (2002–2021), revealing distinct ATWS precursor signals in 5D solutions, in contrast to the monthly solutions. Specifically, floods caused by saturation-excess runoff—triggered by persistent rainfall, monsoonal patterns, snowmelt, or rain-on-snow events—show detectable ATWS increases 15 to 50 days before and during floods, providing a valuable opportunity to improve flood monitoring. These 5D solutions also facilitate a more rapid mapping of post-flood storage changes to assess flood recovery from tropical cyclones and sub-monthly weather extremes. Our findings show the promising potential of 5D GRACE solutions, which are still in the development phase, for future integration into operational frameworks to enhance flood detection and recovery, facilitating the rapid analysis of storage changes relative to monthly solutions.
To assess the impact of prophylactic cranial irradiation (PCI) on self-reported cognitive functioning (SRCF), a functional scale on the European Organization for Research and Treatment of Cancer Core ...Quality of Life Questionnaire (EORTC QLQ-C30).
Radiation Therapy Oncology Group (RTOG) protocol 0214 randomized patients with locally advanced non-small cell lung cancer to PCI or observation; RTOG 0212 randomized patients with limited-disease small cell lung cancer to high- or standard-dose PCI. In both trials, Hopkins Verbal Learning Test (HVLT)-Recall and -Delayed Recall and SRCF were assessed at baseline (after locoregional therapy but before PCI or observation) and at 6 and 12 months. Patients developing brain relapse before follow-up evaluation were excluded. Decline was defined using the reliable change index method and correlated with receipt of PCI versus observation using logistic regression modeling. Fisher's exact test correlated decline in SRCF with HVLT decline.
Of the eligible patients pooled from RTOG 0212 and RTOG 0214, 410 (93%) receiving PCI and 173 (96%) undergoing observation completed baseline HVLT or EORTC QLQ-C30 testing and were included in this analysis. Prophylactic cranial irradiation was associated with a higher risk of decline in SRCF at 6 months (odds ratio 3.60, 95% confidence interval 2.34-6.37, P<.0001) and 12 months (odds ratio 3.44, 95% confidence interval 1.84-6.44, P<.0001). Decline on HVLT-Recall at 6 and 12 months was also associated with PCI (P=.002 and P=.002, respectively) but was not closely correlated with decline in SRCF at the same time points (P=.05 and P=.86, respectively).
In lung cancer patients who do not develop brain relapse, PCI is associated with decline in HVLT-tested and self-reported cognitive functioning. Decline in HVLT and decline in SRCF are not closely correlated, suggesting that they may represent distinct elements of the cognitive spectrum.
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