Abstract Among several hydrological processes, river flow is an essential parameter that is vital for different water resources engineering activities. Although several methodologies have been adopted over the literature for modeling river flow, the limitation still exists in modeling the river flow time series curve. In this research, a functional quantile autoregressive of order one model was developed to characterize the entire conditional distribution of the river flow time series curve. Based on the functional principal component analysis, the regression parameter function was estimated using a multivariate quantile regression framework. For this purpose, hourly scale river flow collected from three rivers in Australia (Mary River, Lockyer Valley, and Albert River) were used to evaluate the finite‐sample performance of the proposed methodology. A series of Monte‐Carlo experiments and historical data sets were examined at three stations. Further, uncertainty analysis was adopted for the methodology evaluation. Compared with the existing methods, the proposed model provides more robust forecasts for outlying observations, non‐Gaussian and heavy‐tailed error distribution, and heteroskedasticity. Also, the proposed model has the merit of predicting the intervals of future realizations of river flow time series at the central and non‐central locations. The results confirmed the potential for predicting the river flow time series curve with a high level of accuracy in comparison with the benchmark existing functional time series methods.

Abstract Lunar seismic data are essential for understanding the Moon's internal structure and geological history. After five decades, the Apollo data set remains the only available one and continues to offer significant value for current and future lunar seismic data analyses. Recent advances in artificial intelligence for seismology have identified seismic signals that were previously unrecognized. In our study, we utilized deep learning for unsupervised clustering of lunar seismograms, leading to the discovery of a new type of long‐period lunar seismic signal that existed every lunar night from 1969 to 1976. We then conducted a thorough analysis covering the timing, frequency, polarization, and temporal distribution characteristics of this signal to study its properties, occurrence, and probable origins. This signal has a physical cause instead of artificial, such as voltage changes, according to its amplitudes during peaked and flat modes, as well as the digital converter status. Based on its relation to the lunar temperature and documents on Apollo instruments, we conclude that this signal is likely induced by the cyclic heater, with several unresolved questions that might challenge our hypothesis. Excluding interference from this newly identified signal is crucial when analyzing lunar seismic data, particularly in detecting lunar free oscillations. Our research introduced a new method for discovering new types of planetary seismic signals and helped advance our understanding of Apollo seismic data. Furthermore, the discovery of this signal holds valuable implications for the design of future planetary seismometers to avoid encountering similar issues.

Abstract The Mars Reconnaissance Orbiter and its Context Camera (CTX) have acquired more than 100,000 separate panchromatic images that capture nearly the entire surface of Mars at ∼5–6 m/pixel. This paper describes a data processing workflow used to generate the first contiguous global mosaic of CTX data, which represents a large improvement in spatial resolution over existing 100 m/pixel contiguous global mosaics. We describe the overarching strategy for the mosaic's construction, which was to maximize the scientific utility of a continuous mosaic that is 5.7 trillion pixels in size. The pipeline used for data processing prioritized traceability and reproducibility of the final mosaic, such that the provenance of all pixels is reported, equipping scientists with information to differentiate mosaic artifacts from surface landforms and to incorporate critical image metadata into their analyses. The CTX data set synthesized into a global CTX mosaic facilitates ready analysis and provides a new capability in transitioning global studies of Mars from high‐resolution investigations of individual images to systematic studies of the entire Martian surface at outcrop‐resolving quality without regard to image boundaries.

Abstract The glass phase in volcanic rocks presents a challenge to obtaining compositional data from visible and short‐wave‐infrared (VSWIR) and mid‐infrared (MIR) spectral data of remote surfaces due to its amorphous structure and variable composition. Nonetheless, glass is a common phase in volcanic materials because it forms via the rapid quench of magma and can constitute up to the entirety of a volcanic deposit. Use of partial least squares regression (PLS) to predict glass contents creates models that are insensitive to viewing geometry and sample conditions such as grain size and spectrally inactive compositional variables, enhancing the ability to detect glasses with remote sensing. PLS models are used here to predict crystallinity and oxide composition of samples from VSWIR and MIR spectral data using training spectra from natural volcanic rocks and geologically relevant synthetic samples. Three spectral resolutions of VSWIR and MIR spectra (1, 10, and 100 nm/band, and 1.9, 19, and 190 cm−1/band, respectively) were tested to assess the effects of collection configuration on different spectrometers. PLS models trained on 1 nm and 1.9 cm−1 data sets have the lowest uncertainties of glass modal abundance for VSWIR and MIR, respectively. MIR models predicting sample wt. % SiO2 and FeO, and VSWIR models of wt. % FeO provide accurate estimates (e.g., RMSE‐P of 3.4 wt. % FeO) at all spectral resolutions. Results are based on training data sets skewed to mafic compositions, which affects model accuracies.

Abstract Drought is associated with adverse environmental and societal impacts across various regions. Therefore, drought monitoring based on a single variable may lead to unreliable information, especially about the onset and persistence of drought. Previous studies show vapor pressure deficit (VPD) data can detect drought onset earlier than other drought indicators such as precipitation. On the other hand, soil moisture (SM) is a robust indicator for assessing drought persistence. This study introduces a nonparametric multivariate drought index Vapor Pressure Deficit Soil moisture standardized Drought Index (VPDSDI) which is developed by combining VPD with SM information. The performance of the multivariate index in terms of drought onset detection is compared with the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) for six major drought events across the United States including three rapidly developing drought events (this term refers to flash droughts that develop on monthly scales) and three conventional drought events. Additionally, the performance of the proposed index in detecting drought persistence is compared with the Standardized Soil moisture Index (SSI), which is an agricultural drought index. Results indicate the multivariate index detects drought onset always earlier than SPI for conventional events, but VPDSDI detects drought onset earlier than or about the same time as SPEI for rapidly developing droughts. In terms of persistence, VPDSDI detects persistence almost identical to SSI for both rapidly developing and conventional drought events. The results also show that combining VPD with SM reduces the high variability of VPD and produces a smoother index which improves the onset and persistence detection of drought events leveraging VPD and SM information.

Abstract Earthquake nowcasting has been proposed as a means of tracking the change in large earthquake potential in a seismically active area. The method was developed using observable seismic data, in which probabilities of future large earthquakes can be computed using Receiver Operating Characteristic methods. Furthermore, analysis of the Shannon information content of the earthquake catalogs has been used to show that there is information contained in the catalogs, and that it can vary in time. So an important question remains, where does the information originate? In this paper, we examine this question using stochastic simulations of earthquake catalogs. Our catalog simulations are computed using an Earthquake Rescaled Aftershock Seismicity (“ERAS”) stochastic model. This model is similar in many ways to other stochastic seismicity simulations, but has the advantage that the model has only 2 free parameters to be set, one for the aftershock (Omori‐Utsu) time decay, and one for the aftershock spatial migration away from the epicenter. Generating a simulation catalog and fitting the two parameters to the observed catalog such as California takes only a few minutes of wall clock time. While clustering can arise from random, Poisson statistics, we show that significant information in the simulation catalogs arises from the “non‐Poisson” power‐law aftershock clustering, implying that the practice of de‐clustering observed catalogs may remove information that would otherwise be useful in forecasting and nowcasting. We also show that the nowcasting method provides similar results with the ERAS model as it does with observed seismicity.

Abstract With the evolution of interferometric synthetic aperture radar into a tool for active hazard monitoring, new methods are sought to quickly and automatically interpret the large number of interferograms that are created. We present a convolutional neural network (CNN) that is able to both classify the type of deformation, and to locate the deformation within an interferogram in a single step. We achieve this through building a “two headed model,” which returns both outputs after one forward pass of an interferogram through the network. We train our model by first creating a data set of synthetic interferograms, but find that our model's performance is improved through the inclusion of real Sentinel‐1 data. We also investigate how model performance can be improved by best organizing interferograms such that they can exploit the three channel nature of computer vision models trained on very large databases of labeled color images, but find that using different data in each of the three input channels degrades performance when compared to the simple case of repeating wrapped or unwrapped phase across each channel. We also release our labeled Sentinel‐1 interferograms as a database named VolcNet, which consists of ∼500,000 labeled interferograms. VolcNet comprises of time series of unwrapped phase and labels of the magnitude, location, and duration of deformation, which allows for the automatic creation of interferograms between any two acquisitions, and greatly increases the amount of data available compared to other labeling strategies.

Abstract We present a method for creating spacecraft‐like data which can be used to train Machine Learning (ML) models to detect and classify structures in in situ spacecraft data. First, we use the Grad‐Shafranov equation to numerically solve for several magnetohydrostatic equilibria which are variations on a known analytic equilibrium. These equilibria are then used as the initial conditions for Particle‐In‐Cell simulations in which the structures of interest are observed and labeled. We then take one‐dimensional slices through the simulations to replicate what a spacecraft collecting data from the simulation would observe. This sliced data then can be used as training data for the initial training of ML models intended for use on spacecraft data. We demonstrate the method applied to the problem of detecting small‐scale plasmoids in the magnetotail, which is important for understanding complex magnetotail reconnection dynamics. The simple 1D classifier we train is able to detect more than 70% of the plasmoid points in the data set but also produces a large number of false positives. Our further work on this example problem is detailed, and further potential uses of the method are discussed.

Abstract We present a new model called Bayesian Estimated Glacial Lake Volume (BE‐GLAV) to estimate the volume of proglacial lakes. Presuming the lake cross‐section as trapezoidal, BE‐GLAV uses a Bayesian calibration approach to adjust the cross‐sectional geometry to match modeled and observed lake surface widths. We validated our model using bathymetric measurements from lakes spread across High Mountain Asia (specifically, the Himalaya and Tien‐Shan), with aerial extents ranging from 0.01 to 5.5 km2. The modeled lake volumes agreed with the measured lake volume with a root‐mean‐square absolute uncertainty of ∼14%. With minimum and maximum errors of ∼0.3% and ∼61.2%, BE‐GLAV performed well compared to 10 other models in a model inter‐comparison experiment. Using the measured set of volumes, our model can constrain both the root mean square (RMS) error and the maximum percentage error in modeled lake volume, unlike other models, some of which can compute just the RMS uncertainty.