Daily Time Scale Research Articles

Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia

ABSTRACT Missing streamflow data is a common issue in Peninsular Malaysia, as the technologies used in hydrological studies often fail to collect data accurately. Additionally, conventional methods are still widely used in the region, which are less accurate compared to artificial intelligence (AI) methods in estimating missing streamflow data. Therefore, this study aims to estimate the missing streamflow data from 11 stations in Peninsular Malaysia by using different AI methods and determine the most appropriate method. Four homogeneity tests were applied to check the quality of data, and the results of the tests indicated that the streamflow data in most stations were homogenous. Two AI methods were applied in this study, which were artificial neural network and artificial neuro-fuzzy inference systems (ANFIS). The proposed AI methods were compared with five different conventional methods. All streamflow missing data, constituting 30% of data from each year were estimated on a daily time scale, and evaluated using root mean square error, mean absolute error and correlation coefficient values. The results indicated that ANFIS was the best due to its learning abilities and the fuzzy inference systems, which enable it to handle complicated input–output patterns and provide highly accurate estimation results.

Assessing the influence of thermal structure variation on Fe and P mobility in sediments cores using Yellow Spring Instrument, diffusive gradient technology, and HR Peeper for sustainable water quality management.

The freshwater ecological characteristics in terms of the daily inventory of thermal stratification, spatial variation of O2 distribution, and the mobility of potentially toxic elements (PTEs) at the water sediment interface (WSI) are prudent freshwater assessment indices for water quality management protocol. The study conducted daily observations within a monsoon-influenced region, utilizing high-resolution techniques such as HR Peeper, Yellow Spring Instrument (YSI), and ZrO-Chelex diffusive gradient technology (DGT) to analyze PTEs, specifically phosphorus (P) and iron (Fe),within the water-sediment interface (WSI) under different temperatures and oxygen conditions. The 66-day field study showed that high thermal structure contributed significantly to production Fe ions and P from sediment under reductive dissolution of FeOOH. The study also revealed that P and Fe exhibited comparable spatial distribution patterns at the WSI, indicating a linked relationship between these PTEs. This correlation was reinforced by high Pearson correlation coefficients ranging from 0.7 to 0.9 (bilateral, p < 0.05) indicating that the concentrations of labile P were predominantly influenced by the release of phosphorus bound to iron. The fluxes of the PTEs were positive with a range of Fe, 3.3-81.5 mg/m2 day and P, 0.03-0.5 mg/m2 day showing the sediments liberated the PTEs into the benthic water. Again, high positive fluxes (Fe≈60 mg/m2 day, P≈0.5 mg/m2 day) for PTEs were obtained when stratification was high (anoxic conditions) and low (Fe≈5 mg/m2 day, P≈0.08 mg/m2 day) when stratification did not exist. This depicts that Fe/P dynamics were hinged mainly on hypoxic conditions in the benthic water under the reductive dissolution of FeOOH. The findings showed that organic materials (both solid and dissolved) correlated (> 0.7) significantly with (positive high values) Fe. This indicates that their interaction contributed to the reservoir water deterioration. However, Ca2⁺ and Mg2⁺ had little impact on the liberation of Fe-DOC-P from sediments due to their inability to compete with Fe for binding to DOC and P, as shown by their low correlation values. The research provides in-depth insights into the dynamics of PTEs on a daily timescale and offers valuable information for water management practices in inland reservoirs, particularly concerning the cycling of phosphorus (P) and its effects on ecosystem health.

Evaluation of gridded precipitation datasets in mountainous terrains of Northwestern Mexico

Study regionThe complex mountainous terrains of the Sierra Madre Occidental in northwestern Mexico. Study focusAcquiring high-resolution precipitation data in regions with limited conventional rain gauge coverage poses significant challenges. Gridded precipitation (GP) datasets, including gauge-based, satellite, and reanalysis products, provide a potential solution, but their reliability in areas with complex terrain and intricate precipitation patterns remains uncertain. This study comprehensively evaluates the performance of four GP datasets—AORC, CHIRPS, Daymet, and ERA5—in estimating precipitation. The evaluation was conducted at a daily, monthly, and seasonal timescale, further analyzing extreme precipitation, the influence of elevation, and spatial averaging across hydrologic basins, using as reference the NERN rain gauge data from 2002 to 2004. New hydrological insights for the regionResults indicate that Daymet and AORC are the most accurate GP datasets for daily and monthly timescales, respectively. All datasets improve in accuracy over longer timescales but face challenges during the wet summer monsoon months and extreme events, with Daymet performing relatively better. Terrain elevation had a minimal impact on overall dataset accuracy, though a slight improvement in precipitation detection was noted as elevation increased. This work provides valuable insights into the strengths and limitations of GP datasets in regions with complex terrain and orographically-forced convective precipitation, offering practical outcomes for climate and hydrologic studies in similar regions.

Assessing the influence of climate change on multiple climate indices in Nepal using CMIP6 global climate models

Global climate models (GCMs) serve as essential tools for projecting future climate trends, but their coarse resolution limits localized impact assessments in sectors like hydrology, agriculture, and biodiversity. Observation data with a spatial resolution of a few kilometers are crucial for downscaling and bias-correcting GCMs at finer resolutions. However, Nepal's extreme topography and organizational challenges have led to uneven distribution of meteorological stations and inconsistent data quality. Moreover, CMIP6-based climate extremes projections for the entire country are currently unavailable. To tackle these challenges, we developed a comprehensive national database for Nepal, offering high-resolution historical and projected precipitation and temperature data analyzed through 25 climate extreme indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). Initially, observation grid data were prepared at a daily timescale with a spatial resolution of 0.05° × 0.05° for baseline period (1981–2010) using the Asian Precipitation High-Resolved Observational Data Integration Toward Evaluation (APHRODITE), the fifth generation of the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), and available good quality observed climate data. This data was then utilized to downscale and bias-correct 18 CMIP6 GCMs for 2015–2100 under four SSPs (1–2.6, 2–4.5, 3–7.0, 5–8.5). Quantile mapping was employed for the bias correction of the CMIP6 GCMs. The performance of the multimodal ensemble (MME) indicated better Nash-Sutcliffe Efficiency (NSE), root mean square error ratio (RSR), and Percent Bias (PBIAS) of climate extreme indices for the historical period. A comparative analysis was conducted across Nepal's major geographic regions to account for spatial variability in regional climate systems. The finer-resolution dataset can be crucial to deepen our understanding of climate impacts, and climate change, and eventually informing the policy-making in Nepal. Moreover, the methodology can be effectively replicated in data-scarce developing nations to promote climate research and adaptation efforts.

Satellite-Based Detection of Algal Blooms in Large Alpine Lake Sevan: Can Satellite Data Overcome the Unavoidable Limitations in Field Observations?

Lake Sevan in Armenia is a unique, large, alpine lake given its surface, volume, and geographic location. The lake suffered from progressing eutrophication and, since 2018, massive cyanobacterial blooms repeatedly occurred. Although the lake is comparatively intensely monitored, the feasibility to reliably detect the algal bloom events appeared to be limited by the established in situ monitoring, mostly because algal bloom dynamics are far more dynamic than the realized monitoring frequency of monthly samplings. This mismatch of monitoring frequency and ecosystem dynamics is a notorious problem in lakes, where plankton dynamics often work at relatively short time scales. Satellite-based monitoring with higher overpass frequency, e.g., by Sentinel-3 OLCI with its daily overcasts, are expected to fill this gap. The goal of our study was therefore the establishment of a fast detection of algal blooms in Lake Sevan that operates at the time scale of days instead of months. We found that algal bloom detection in Lake Sevan failed, however, when it was only based on chlorophyll due to complications with optical water properties and atmospheric corrections. Instead, we obtained good results when true-color RGB images were analyzed or a specifically designed satellite-based HAB indicator was applied. These methods provide reliable and very fast bloom detection at a scale of days. At the same time, our results indicated that there are still considerable limitations for the use of remote sensing when it comes to a fully quantitative assessment of algal dynamics in Lake Sevan. The observations made so far indicate that algal blooms are a regular feature in Lake Sevan and occur almost always when water temperatures surpass approximately 20 °C. Our satellite-based method effectively allowed for bloom detection at short time scales and identified blooms over several years where classical sampling failed to do so, simply because of the unfortunate timing of sampling dates and blooming phases. The extension of classical in situ sampling by satellite-based methods is therefore a step towards a more reliable, faster, and more cost-effective detection of algal blooms in this valuable lake.

Implementing Superresolution of Nonstationary Tides with Wavelets: An Introduction to CWT_Multi

Abstract Tides are often nonstationary due to nonastronomical influences. Investigating variable tidal properties implies a trade-off between separating adjacent frequencies (using long analysis windows) and resolving their time variations (short analysis windows). Previous continuous wavelet transform (CWT) tidal methods resolved tidal species. Here, we present CWT_Multi, a MATLAB code that 1) uses CWT linearity (via the “response coefficient method”) to implement superresolution, i.e., resolving tidal constituents beyond the Rayleigh criterion; 2) provides a Munk–Hasselmann constituent selection criterion appropriate for superresolution; and 3) introduces an objective, time-variable form of inference (“dynamic inference”) based on time-varying data properties. CWT_Multi resolves tidal species on time scales of days, and multiple constituents per species with fortnightly filters. It outputs astronomical phase lags and admittances, analyzes multiple records, and provides power spectra of the signal(s), residual(s), and reconstruction(s); confidence limits; and signal-to-noise ratios. Artificial data and water levels from the Lower Columbia River Estuary (LCRE) and San Francisco Bay Delta (SFBD) are used to test CWT_Multi and compare it to harmonic analysis programs NS_Tide and UTide. CWT_Multi provides superior reconstruction, detiding, dynamic analysis utility, and time resolution of constituents (but with broader confidence limits). Dynamic inference resolves closely spaced constituents (like K1, S1, and P1) on fortnightly time scales, quantifying impacts of diel power peaking (with a 24-h period, like S1) on water levels in the LCRE. CWT_Multi also helps quantify the impacts of high flows and a salt barrier closing on tidal properties in the SFBD. On the other hand, CWT_Multi does not excel at prediction, and results depend on analysis details, as for any method applied to nonstationary data. Significance Statement Ocean tides, especially in coastal and estuarine systems, are often nonstationary, in the sense that the mean and standard deviation of tidal properties vary over time, usually in response to some nontidal process. We introduce here a MATLAB code, CWT_Multi, that uses wavelet transforms to resolve both tidal species and constituents on time scales from a few days to months. Our code accommodates multiple scalar time series and has typical tidal analysis features like constituent selection and inference, plus two forms of uncertainty analyses. It is flexible, allowing the user to adapt analysis properties to diverse datasets. CWT_Multi is applicable to many problems involving time-variable tides, including sea level rise, compound flooding, sediment transport, and wetland habitat analyses. Application to vector data is a straightforward extension, but further development of our uncertainty analysis is merited. Because nonstationary tidal analysis is rapidly advancing, we also define the features of a “well-formed” analysis code.

Element Formation in Radiation-hydrodynamics Simulations of Kilonovae

Understanding the details of r-process nucleosynthesis in binary neutron star merger (BNSM) ejecta is key to interpreting kilonova observations and identifying the role of BNSMs in the origin of heavy elements. We present a self-consistent, two-dimensional, ray-by-ray radiation-hydrodynamic evolution of BNSM ejecta with an online nuclear network (NN) up to a timescale of days. For the first time, an initial numerical relativity ejecta profile composed of the dynamical component and spiral-wave and disk winds is evolved including detailed r-process reactions and nuclear heating effects. A simple model for the jet energy deposition is also included. Our simulation highlights that the common approach of relating in postprocessing the final nucleosynthesis yields to the initial thermodynamic profile of the ejecta can lead to inaccurate predictions. Moreover, we find that neglecting the details of the radiation-hydrodynamic evolution of the ejecta in nuclear calculations can introduce deviations of up to 1 order of magnitude in the final abundances of several elements, including very light and second r-process peak elements. The presence of a jet affects element production only in the innermost part of the polar ejecta, and it does not alter the global nucleosynthesis results. Overall, our analysis shows that employing an online NN improves the reliability of nucleosynthesis and kilonova light-curve predictions.

A Gamma-Ray Flare from TXS 1508+572: Characterizing the Jet of a z = 4.31 Blazar in the Early Universe

Blazars can be detected from very large distances due to their high luminosity. However, the detection of γ-ray emission of blazars beyond z = 3 has only been confirmed for a small number of sources. Such observations probe the growth of supermassive black holes close to the peak of star formation in the history of galaxy evolution. As a result from a continuous monitoring of a sample of 80 z > 3 blazars with the Fermi Large Area Telescope (Fermi-LAT), we present the first detection of a γ-ray flare from the z = 4.31 blazar TXS 1508+572. This source showed high γ-ray activity from 2022 February to August, reaching a peak luminosity comparable to the most luminous flares ever detected with Fermi-LAT. We conducted a multiwavelength observing campaign involving XMM-Newton, the Neil Gehrels Swift Observatory, the Effelsberg 100 m radio telescope, and the Very Long Baseline Array. In addition, we make use of the monitoring programs by the Zwicky Transient Facility and the Near-Earth Object Wide-field Infrared Survey Explorer at optical and infrared wavelengths, respectively. We find that the source is particularly variable in the infrared band on daily timescales. The spectral energy distribution collected during our campaign is well described by a one-zone leptonic model, with the γ-ray flare originating from an increase of external Compton emission as a result of a fresh injection of accelerated electrons.

Joint ALMA/X-ray monitoring of the radio-quiet type 1 active galactic nucleus IC 4329A

The origin of a compact millimeter (mm, 100–250 GHz) emission in radio-quiet active galactic nuclei (RQ AGN) remains debated. Recent studies propose a connection with self-absorbed synchrotron emission from the accretion disk X-ray corona. We present the first joint ALMA (∼100 GHz) and X-ray (NICER/XMM-Newton/Swift; 2–10 keV) observations of the unobscured RQ AGN, IC 4329A (z = 0.016). The time-averaged mm-to-X-ray flux ratio aligns with recently established trends for larger samples, but with a tighter scatter (∼0.1 dex) compared to previous studies. However, there is no significant correlation on timescales of less than 20 days. The compact mm emission exhibits a spectral index of −0.23 ± 0.18, remains unresolved with a 13 pc upper limit, and shows no jet signatures. Notably, the mm flux density varies significantly (by factor of 3) within four days, exceeding the contemporaneous X-ray variability and showing the largest mm variations ever detected in RQ AGN over daily timescales. The high amplitude variability rules out scenarios of heated dust and thermal free–free emission, pointing toward a synchrotron origin for the mm radiation in a source of ∼1 light day (∼120 gravitational radii) size. While the exact source is not yet certain, an X-ray corona scenario emerges as the most plausible compared to a scaled-down jet or outflow-driven shocks.

Disentangling the Impacts of Environmental Factors on Evaporative Fraction Across Climate Regimes

AbstractEvaporative fraction (EF) is a useful measure for quantifying land surface energy partitioning processes and determining evaporative regimes; however, its influencing factors remain highly uncertain. Here, global data sets were compiled to disentangle the effects of environmental variables on EF variations along climate and land surface gradients. We found that (a) at annual timescales, ecosystem‐level EF could be expressed as a power law function of aridity index. The relationships of mean annual soil water content (SWC) and leaf area index (LAI) with mean annual EF resembled the traditional evaporative regime theory; (b) at daily timescales, the boosted regression tree method quantitatively revealed that the impacts of environmental variables (including meteorological variables) on EF showed equal importance, especially at humid sites, primarily due to the different response direction and magnitude of latent heat (LE) and sensible heat (H) fluxes to environmental changes. Particularly, the contrasting responses of LE (positive) and H (negative) to SWC, LAI, and relative humidity enhanced the positive effects of those influencing variables on EF; whereas, the correlations between EF and energy‐related factors (i.e., net radiation‐Rn and air temperature‐Ta) deteriorated as both LE and H showed positive response patterns to those variables; (c) meteorological factors were also found to have nonlinear effects on daily EF, further modified by climatic conditions. Rn near 150 W/m2 and Ta near 15°C appeared to be important energy‐partitioning thresholds at drier and humid sites, respectively. Moreover, changing interactions among environmental variables with climates were demonstrated to be important for better explaining EF variations.

Evaluating potential evapotranspiration methods in a rapidly warming Arctic region, SW Spitsbergen (1983–2023)

Study regionsHornsund, SW Spitsbergen, Svalbard – area representing atlantic sector of High Arctic Study focusSvalbard's warming climate significantly alters its hydrological conditions. Evapotranspiration, a crucial hydrological component, is understudied in this High Arctic environment. In this study, daily potential evapotranspiration (PET) estimates for the period 1983–2023 were calculated using meteorological data from the Polish Polar Station Hornsund (SW Spitsbergen). 11 different PET methods were applied, those include radiation-based, temperature-based, radiation-temperature-based, and combined methods. New hydrological insights for the regionThe results show a large spread in the annual sum of PET ranging from ∼20 mm/y (Kharrufa) through ∼300 mm/y (Penman-Monteith) up to ∼450 mm/y (Abtew). Trends analysis shows different outcomes depending on the length of the averaging period. Using a daily timescale, PET methods tend to show more similar patterns of changes than using monthly timescales. The changes in PET estimates differ between the models, hence classifying PET methods should consider their sensitivity to meteorological changes. PET estimates were compared with pan measurements at a daily time scale in 2022–2023. The Penman method produced the best results in relation to pan measurements. In other cases, despite a relatively high linear correlation, calibration to local conditions is needed to scale the outcomes and limit biases. This study improves understanding of how PET models perform in the rapidly changing High Arctic climate.

Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging

This study proposes a two-step probabilistic post-processing approach that combines different machine learning-based postprocessors through the Copula-Embedded Bayesian Model Averaging (COP-BMA) method to improve the performance of a hydrological model for streamflow predictions. The proposed approach serves a two-fold purpose: firstly, it aims to enhance the accuracy of streamflow predictions, and secondly, it provides probabilistic results that implicitly address the structural uncertainty inherent in different postprocessing methods. We validate our approach by applying it to the Conceptual Functional Equivalent, a lumped hydrologic model utilized for simulating extreme floods during Hurricane Harvey. The validation is conducted across twelve distinct watersheds in the Southeast Texas region at both daily and monthly scales. The findings indicate that the proposed framework significantly enhances the performance of the hydrologic model across the studied watershed. Specifically, on a daily time scale, there is a 23% and 53% improvement in the NSE and KGE respectively, while on a monthly time scale, the framework enhances NSE by 21% and KGE by 25%. Additionally, the MAE (cms) was notably reduced from 4.64 to 2.23 on the daily scale, and from 2.8 to 1.65 on the monthly scale.

Exploration of a model driven by climate to simulate pond water temperature in aquaculture systems

IntroductionInterannual climate variability in the Asian mega deltas has been posing a wide range of climate risks in the aquaculture systems of the region. Water temperature variation is one of the key risks related to disease outbreak, fish health, and loss and damage in fish production. However, Climate information can improve the ability to predict changes in pond water quality parameters at the farm level using publicly available weather and climate data. Little research has been done to translate weather data into water temperature forecasts using mechanistic models in order to provide farmers with relevant forecasting information in the context of climate services.MethodsThe advantage of mechanistic models over statistical models is that they are based on physical processes and can therefore be used in a wider range of environmental conditions. In this study, we used an energy balance model to investigate its ability to simulate pond water temperature at daily and seasonal timescales in the southwest and northeast regions of Bangladesh. The model was able to adequately simulate pond water temperature at a daily timescale using publicly available weather data, and the accuracy of the model was lower at the study site with very heavy rainfall events.ResultsSensitivity analyses showed that the model was also able to simulate the impact of air temperature cold and hot spells on the pond water temperature. Connecting the model with seasonal air temperature forecasts resulted in very small variations in the forecasted seasonal pond water temperature, in large part due to the low variability observed in water temperature at seasonal scale in the study sites.DiscussionClimate information can improve the ability to predict changes in pond water quality parameters at the farm level using publicly available weather and climate data. Hence, these improved predictions are important to help fish-farmers make informed decisions for managing associated climate risks.

Interacting Influences of Diurnal Tides, Winds, and River Discharge on a Large Coastal Plume

AbstractThe dispersal of large river plumes in the coastal ocean depends on multiple factors, and in some cases, can be categorized into distinct dynamical regimes: a tidally dominated near‐field, a rotational mid‐field, and a coastal current far‐field. In this study, observations and modeling are used to evaluate the factors controlling the variability in the buoyant plume from Mobile Bay. Rather than distinct dynamical regimes, the Mobile Bay plume depends on forcings that act at overlapping temporal and spatial scales: diurnal tides, river discharge events, and winds. Satellite synthetic aperture radar images along with shipboard in‐situ sampling and marine radar are used to observe plume fronts in spring 2021. Hydrodynamic model simulations are compared with observations and used to characterize a large coastal plume at consistent tidal phase across a range of forcing conditions. The along‐shore position of the plume depends primarily on advection by wind‐driven surface currents. The cross‐shore extent and plume area depend primarily on the tidal amplitude and river discharge, and secondarily on northerly (seaward) winds. Along‐shore winds influence the buoyancy anomaly by altering salinity in the estuary and offshore. Upwelling winds increase the buoyancy anomaly and advect previous plumes away from the mouth. Downwelling winds reduce the buoyancy anomaly by trapping previous plumes near the coast and directing freshwater discharge toward a secondary outlet. Thus, the combined, overlapping influences of the tide, wind, and discharge dominate the variability in freshwater delivery to the shelf at time scales of days and distances of tens of km.

Percolation without trapping: How Ostwald ripening during two-phase displacement in porous media alters capillary pressure and relative permeability.

Conventional measurements of two-phase flow in porous media often use completely immiscible fluids, or are performed over timescales of days to weeks. If applied to the study of gas storage and recovery, these measurements do not properly account for Ostwald ripening, significantly overestimating the amount of trapping and hysteresis. When there is transport of dissolved species in the aqueous phase, local capillary equilibrium is achieved: this may take weeks to months on the centimeter-sized samples on which measurements are performed. However, in most subsurface applications where the two phases reside for many years, equilibrium can be achieved. We demonstrate that in this case, two-phase displacement in porous media needs to be modeled as percolation without trapping. A pore network model is used to quantify how to convert measurements of trapped saturation, capillary pressure and relative permeability made ignoring Ostwald ripening to account for this effect. We show that conventional measurements overestimate the amount of capillary trapping by 20-25%.

Changes in Daytime Cirrus Properties From the ISCCP‐H Data Set and Their Impacts on the Radiation Energy Budget

AbstractThe change in clouds during the day is critical to the Earth's energy balance and climatic evolution. However, there have been relatively few studies on cloud variations at daily timescales, owing to limitations of ground‐ and satellite‐observations, especially for cirrus clouds. In this study, we examined the daytime cirrus variation (DCV) at the global scales and its associated effects on radiation budgets based on the International Satellite Cloud Climatology Project H data set. The changes in continental cirrus cover are more significant than that over the ocean, reaching a maximum of 17.3% in the afternoon. Over the tropical deep convection regions, cirrus cloud cover and optical depth exhibit large amplitudes during the daytime, closely linked to average properties of cirrus clouds. Using a process‐based radiative transfer model, we calculated the variations in daytime cirrus cloud radiative forcing (CRF). After noon, cirrus clouds over both land and ocean generate the strongest shortwave (SW) cooling and longwave (LW) warming effects at the top of the atmosphere (TOA). At the global scale, daytime cirrus clouds cause an average net CRF of 3.6 W/m2 at the TOA. If the DCV is neglected in the model, the SW cooling and LW warming effects are overestimated by 2.5 and 1.8 W/m2 at the TOA, leading to a net radiation bias of 0.7 W/m2. The findings provide key information for targeting specific aspects of the cirrus parameterization scheme in climate models.

Did the exposure of coacervate droplets to rain make them the first stable protocells?

Membraneless coacervate microdroplets have long been proposed as model protocells as they can grow, divide, and concentrate RNA by natural partitioning. However, the rapid exchange of RNA between these compartments, along with their rapid fusion, both within minutes, means that individual droplets would be unable to maintain their separate genetic identities. Hence, Darwinian evolution would not be possible, and the population would be vulnerable to collapse due to the rapid spread of parasitic RNAs. In this study, we show that distilled water, mimicking rain/freshwater, leads to the formation of electrostatic crosslinks on the interface of coacervate droplets that not only suppress droplet fusion indefinitely but also allow the spatiotemporal compartmentalization of RNA on a timescale of days depending on the length and structure of RNA. We suggest that these nonfusing membraneless droplets could potentially act as protocells with the capacity to evolve compartmentalized ribozymes in prebiotic environments.

Encoding of female mating dynamics by a hypothalamic line attractor

Females exhibit complex, dynamic behaviours during mating with variable sexual receptivity depending on hormonal status1–4. However, how their brains encode the dynamics of mating and receptivity remains largely unknown. The ventromedial hypothalamus, ventrolateral subdivision contains oestrogen receptor type 1-positive neurons that control mating receptivity in female mice5,6. Here, unsupervised dynamical system analysis of calcium imaging data from these neurons during mating uncovered a dimension with slow ramping activity, generating a line attractor in neural state space. Neural perturbations in behaving females demonstrated relaxation of population activity back into the attractor. During mating, population activity integrated male cues to ramp up along this attractor, peaking just before ejaculation. Activity in the attractor dimension was positively correlated with the degree of receptivity. Longitudinal imaging revealed that attractor dynamics appear and disappear across the oestrus cycle and are hormone dependent. These observations suggest that a hypothalamic line attractor encodes a persistent, escalating state of female sexual arousal or drive during mating. They also demonstrate that attractors can be reversibly modulated by hormonal status, on a timescale of days.

Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory-based approach.

In this study, six individual machine learning (ML) models and a stacked ensemble model (SEM) were used for daytime visibility estimation at Bangkok airport during the dry season (November-April) for 2017-2022. The individual ML models are random forest, adaptive boosting, gradient boosting, extreme gradient boosting, light gradient boosting machine, and cat boosting. The SEM was developed by the combination of outputs from the individual models. Furthermore, the impact of factors affecting visibility was examined using the Shapley Additive exPlanation (SHAP) method, an interpretable ML technique inspired by the game theory-based approach. The predictor variables include different air pollutants, meteorological variables, and time-related variables. The light gradient boosting machine model is identified as the most effective individual ML model. On an hourly time scale, it showed the best performance across three out of four metrics with the ρ = 0.86, MB = 0, ME = 0.48km (second lowest), and RMSE = 0.8km. On a daily time scale, the model performed the best for all evaluation metrics with ρ = 0.92, MB = 0.0km, ME = 0.3km, and RMSE = 0.43km. The SEM outperformed all the individual models across three out of four metrics on an hourly time scale with ρ = 0.88, MB = 0.0km, (second lowest), and RMSE = 0.75km. On the daily scale, it performed the best with ρ = 0.93, MB = 0.02km, ME = 0.27km, and RMSE = 0.4km. The seasonal average original (VISorig) and meteorologically normalized visibility (VISnorm) decrease from 2017 to 2021 but increase in 2022. The rate of decrease in VISorig is double than rate of decrease in VISnorm which suggests the effect of meteorology visibility degradation. The SHAP analysis identified relative humidity (RH), PM2.5, PM10, day of the season year (i.e., Julian day) (JD), and O3 as the most important variables affecting visibility. At low RH, visibility is not sensitive to changes in RH. However, beyond a threshold, a negative correlation between RH and visibility is found potentially due to the hygroscopic growth of aerosols. The dependence of the Shapley values of PM2.5 and PM10 on RH and the change in average visibilities under different RH intervals also suggest the effect of hygroscopic growth of aerosol on visibility. A negative relationship has been identified between visibility and both PM2.5 and PM10. Visibility is positively correlated with O3 at lower to moderate concentrations, with diminishing impact at very high concentrations. The JD is strongly negatively related to visibility during winter while weakly associated positively later in summer. Findings from this research suggest the feasibility of employing machine learning techniques for predicting visibility and understanding the factors influencing its fluctuations.

Drought Characterization With GPS: Insights Into Groundwater and Surface‐Reservoir Storage in California

AbstractDrought intensity is commonly characterized using meteorologically‐based metrics that do not provide insight into water deficits within deeper hydrologic systems. In contrast, global positioning system (GPS) displacements are sensitive to both local and regional hydrologic‐storage fluctuations. While a few studies have leveraged this sensitivity to produce geodetic drought indices, hydrologic drought characterization using GPS is not commonly accounted for in drought assessment and management. To motivate this application, we produce a new geodetic drought index (GDI) and quantify its ability to characterize hydrologic drought conditions in key surface and sub‐surface hydrologic reservoirs/pools across California. In northern California, the GDI exhibits a strong regional association with surface‐reservoir storage at the 1‐month time scale (correlation coefficient: 0.83) and groundwater levels at the 3‐month time scale (correlation coefficient: 0.87), along with moderate associations with stream discharge at the daily (instantaneous) time scale (correlation coefficient: 0.50). Groundwater in southern California is best characterized with a 12‐month GDI (correlation coefficient: 0.77), and surface‐reservoir storage is optimized with the 3‐month GDI (correlation coefficient: 0.72). Two sigma uncertainties are ±0.03. Differences between northern and southern California reveal that the GDI is sensitive to unique aquifer and drainage basin characteristics. In addition to capturing long‐term hydrologic trends, rapid changes in the GDI initiate during clusters of large atmospheric river events that closely mirror fluctuations in traditional hydrologic and meteorological observations. We show that GPS‐based hydrologic drought indices provide a significant opportunity to improve drought assessment, in California and beyond, by improving our understanding of the hydrologic cycle.