Sub-daily Resolution Research Articles

Assessment of Subseasonal-to-Seasonal (S2S) Precipitation Forecast Skill for Reservoir Operation in the Yaque Del Norte River, Dominican Republic

Operational forecasters desire information about how their reservoir and riverine systems will evolve over monthly to seasonal timescales. Seasonal traces of hydrometeorological variables at a daily or sub-daily resolution are needed to drive hydrological models at this timescale. Operationally available models such as the Climate Forecast System (CFS) provide seasonal precipitation forecasts, but their coarse spatial scale requires further processing for use in local or regional hydrologic models. We focus on three methods to generate such forecasts: (1) a bias-adjustment method, in which the CFS forecasts are bias-corrected by ground-based observations; (2) a weather generator (WG) method, in which historical precipitation data, conditioned on an index of the El Niño–Southern Oscillation, are used to generate synthetic daily precipitation time series; and (3) a historical analog method, in which the CFS forecasts are used to condition the selection of historical satellite-based mean areal precipitation (MAP) traces. The Yaque del Norte River basin in the Dominican Republic is presented herein as a case study, using an independent dataset of rainfall and reservoir inflows to assess the relative performance of the methods. The methods showed seasonal variations in skill, with the MAP historical analog method having the strongest overall performance, but the CFS and WG methods also exhibited strong performance during certain seasons. These results indicate that the strengths of each method may be combined to produce an ensemble forecast product.

A stochastic framework for rainfall intensity–time scale–return period relationships. Part ΙΙ: point modelling and regionalization over Greece

ABSTRACT In this work, we formulate a regionalization framework for rainfall intensity–time scale–return period relationships which is applied over the Greek territory. The methodology for single-site estimation is based on a stochastic framework for multi-scale modelling of rainfall intensity which is outlined in the companion paper. Five parameters are first fitted independently for each site and the resulting parameter variability is assessed. Following a systematic investigation of uncertainty and variability patterns, two parameters, i.e. the tail-index and a time scale parameter, are identified as constant in space and estimated using data pooling techniques. The other three parameters are regionalized over Greece by means of spatial interpolation and smoothing techniques that are assessed through cross-validation in a multi-model framework. The regionalization scheme is implemented in a sequential order that allows exploiting rainfall information both from rainfall stations with sub-daily resolution and from the more reliable network of daily raingauges.

Sub-daily rainfall patterns in the mountainous regions of the Island of Tahiti: Insights from a one-year rain gauge network expansion

Study regionThe Island of Tahiti in French Polynesia (17.5°S,149.5°W), with a focus on the mountainous regions of the island interior. Study focusThe high topography of Tahiti creates an obstacle for wet air masses traveling along the surface of the South Tropical Pacific Ocean, which forces air flow uplift and triggers orographic rainfall. Although the main physical processes behind orographic rain enhancement on tropical islands were identified, the ways they shape Tahitian rain fields are still not fully understood, particularly due to a lack of observations. To address the limitations posed by this shortage of rain data, this study describes the temporary expansion of the rain gauge network of Tahiti towards the mountainous island interior, and uses the resultant dataset to investigate the spatial and temporal patterns of orographic rainfall at sub-daily resolution. New hydrological insights for the regionThe analysis of the rain gauge observations on daily and shorter time-scales leads to the identification of three main types of rainfall patterns: (1) a Dry Season Trade Winds type linked to a persistent Trade Winds Inversion (TWI) and characterized by moderate orographic rainfall in windward slopes, dry conditions in most leeward areas, and localized mid-afternoon showers in the mountains downwind of the main summit of the island; (2) a Transition Season Trade Winds type linked to atmospheric disturbances weakening the TWI, and characterized by intense orographic rainfall in windward slopes and mountains, and frequent showers in leeward areas by overshoot of the main crest; and (3) a SPCZ-related type occurring when the South Pacific Convergence Zone (SPCZ) crosses Tahiti during the wet season, and characterized by intense and widespread rainfall throughout the island with clear diurnal cycle and peak intensities recorded in the mid-afternoon.

Digital Phenotypes of Instability and Fatigue Derived From Daily Standing Transitions in Persons With Multiple Sclerosis.

Impairment in persons with multiple sclerosis (PwMS) can often be attributed to symptoms of motor instability and fatigue. Symptom monitoring and queued interventions often target these symptoms. Clinical metrics are currently limited to objective physician assessments or subjective patient reported measures. Recent research has turned to wearables for improving the objectivity and temporal resolution of assessment. Our group has previously observed wearable assessment of supervised and unsupervised standing transitions to be predictive of fall-risk in PwMS. Here we extend the application of standing transition quantification to longitudinal home monitoring of symptoms. Subjects (N=23) with varying degrees of MS impairment were recruited and monitored with accelerometry for a total of ∼ 6 weeks each. These data were processed using a preexisting framework, applying a deep learning activity classifier to isolate periods of standing transition from which descriptive features were extracted for analysis. Participants completed daily and biweekly assessments describing their symptoms. From these data, Canonical Correlation Analysis was used to derive digital phenotypes of MS instability and fatigue. We find these phenotypes capable of distinguishing fallers from non-fallers, and further that they demonstrate a capacity to characterize symptoms at both daily and sub-daily resolutions. These results represent promising support for future applications of wearables, which may soon augment or replace current metrics in longitudinal monitoring of PwMS.

Optimal Temporal Filtering of the Cosmic-Ray Neutron Signal to Reduce Soil Moisture Uncertainty

Cosmic ray neutron sensors (CRNS) are increasingly used to determine field-scale soil moisture (SM). Uncertainty of the CRNS-derived soil moisture strongly depends on the CRNS count rate subject to Poisson distribution. State-of-the-art CRNS signal processing averages neutron counts over many hours, thereby accounting for soil moisture temporal dynamics at the daily but not sub-daily time scale. This study demonstrates CRNS signal processing methods to improve the temporal accuracy of the signal in order to observe sub-daily changes in soil moisture and improve the signal-to-noise ratio overall. In particular, this study investigates the effectiveness of the Moving Average (MA), Median filter (MF), Savitzky-Golay (SG) filter, and Kalman filter (KF) to reduce neutron count error while ensuring that the temporal SM dynamics are as good as possible. The study uses synthetic data from four stations for measuring forest ecosystem-atmosphere relations in Africa (Gorigo) and Europe (SMEAR II (Station for Measuring Forest Ecosystem-Atmosphere Relations), Rollesbroich, and Conde) with different soil properties, land cover and climate. The results showed that smaller window sizes (12 h) for MA, MF and SG captured sharp changes closely. Longer window sizes were more beneficial in the case of moderate soil moisture variations during long time periods. For MA, MF and SG, optimal window sizes were identified and varied by count rate and climate, i.e., estimated temporal soil moisture dynamics by providing a compromise between monitoring sharp changes and reducing the effects of outliers. The optimal window for these filters and the Kalman filter always outperformed the standard procedure of simple 24-h averaging. The Kalman filter showed its highest robustness in uncertainty reduction at three different locations, and it maintained relevant sharp changes in the neutron counts without the need to identify the optimal window size. Importantly, standard corrections of CRNS before filtering improved soil moisture accuracy for all filters. We anticipate the improved signal-to-noise ratio to benefit CRNS applications such as detection of rain events at sub-daily resolution, provision of SM at the exact time of a satellite overpass, and irrigation applications.

In situ observations of the Swiss periglacial environment using GNSS instruments

Abstract. Monitoring of the periglacial environment is relevant for many disciplines including glaciology, natural hazard management, geomorphology, and geodesy. Since October 2022, Rock Glacier Velocity (RGV) is a new Essential Climate Variable (ECV) product within the Global Climate Observing System (GCOS). However, geodetic surveys at high elevation remain very challenging due to environmental and logistical reasons. During the past decades, the introduction of low-cost global navigation satellite system (GNSS) technologies has allowed us to increase the accuracy and frequency of the observations. Today, permanent GNSS instruments enable continuous surface displacement observations at millimetre accuracy with a sub-daily resolution. In this paper, we describe decennial time series of GNSS observables as well as accompanying meteorological data. The observations comprise 54 positions located on different periglacial landforms (rock glaciers, landslides, and steep rock walls) at altitudes ranging from 2304 to 4003 ma.s.l. and spread across the Swiss Alps. The primary data products consist of raw GNSS observables in RINEX format, inclinometers, and weather station data. Additionally, cleaned and aggregated time series of the primary data products are provided, including daily GNSS positions derived through two independent processing tool chains. The observations documented here extend beyond the dataset presented in the paper and are currently continued with the intention of long-term monitoring. An annual update of the dataset, available at https://doi.org/10.1594/PANGAEA.948334 (Beutel et al., 2022),​​​​​​​ is planned. With its future continuation, the dataset holds potential for advancing fundamental process understanding and for the development of applied methods in support of e.g. natural hazard management.

High frequency root dynamics: sampling and interpretation using replicated robotic minirhizotrons.

Automating dynamic fine root data collection in the field is a longstanding challenge with multiple applications for co-interpretation and synthesis for ecosystem understanding. High frequency root data are only achievable with paired automated sampling and processing. However, automatic minirhizotron (root camera) instruments are still rare and data are often not collected in natural soils or analysed at high temporal resolution. Instruments must also be affordable for replication and robust under variable natural conditions. Here, we show a system built with off-the-shelf parts which samples at sub-daily resolution. We paired this with a neural network to analyse all images collected. We performed two mesocosm studies and two field trials alongside ancillary data collection (soil CO2 efflux, temperature, and moisture content, and 'PhenoCam'-derived above-ground dynamics). We produce robust and replicated daily time series of root dynamics under all conditions. Temporal root changes were a stronger driver than absolute biomass on soil CO2 efflux in the mesocosm. Proximal sensed above-ground dynamics and below-ground dynamics from minirhizotron data were not synchronized. Root properties extracted were sensitive to soil moisture and occasionally to time of day (potentially relating to soil moisture). This may only affect high frequency imagery and should be considered in interpreting such data.

Modeling streamflow in headwater catchments: A data-based mechanistic grounded framework

Study RegionThe Lenguazaque River Basin is a 290 km2 headwater catchment of the Fuquene Lake Watershed in the Colombian Andes. Regional stakeholders are optimizing water allocations for all users, given numerous quantity related issues such as its simultaneous use for conservation, agriculture, and coal mining. Study FocusDeveloping hydrological models in headwater catchments is challenging, especially in developing countries where technical resources and data are limited. This study addresses these challenges by proposing strategies to develop reliable yet mathematically simple models, requiring fewer inputs than data-intensive alternatives. Three stages are proposed, focused on 1) assessing hydrological data, 2) preparing datasets for modeling, and 3) developing models in daily and sub-daily resolutions. The last stage is grounded on a data-based mechanistic modeling approach and includes a novel combination of baseflow separation with digital filters and multi-objective optimization principles. Having a hydrophysical meaning, obtained models are suitable for simulating alternative scenarios. New hydrological insights for the regionResulting daily models achieved an acceptable performance, significantly better than a semi-distributed model. The performance of sub-daily models was sub-optimal, yet it can be improved by enhancing the quality of sub-daily datasets and the efficiency of computational algorithms. Due to their mathematical nature, the proposed strategies are expected to be applicable to other headwater catchments where improving water management is an imperative need.

Exploring the potential for parameter transfer from daily to hourly time step in the HYPE model for Sweden

ABSTRACT Catchments with response time shorter than a day require simulations at sub-daily resolution for flood forecasting. Long-term hydrological data with sub-daily time resolution is lacking for many catchments and, thus, it is worthwhile to investigate the potential in using hydrological model parameters calibrated at a daily temporal resolution to simulate sub-daily runoff. This study investigates the potential of directly transferring the parameters from the one-day Hydrological Predictions for the Environment (HYPE) model for Sweden to a one-hour time step version. A sensitivity analysis and calibration of the one-hour model were also performed to explore the possibility of achieving improved model performance through an additive and multiplicative scaling of the parameters. Directly transferring the parameters led to a negligible loss in performance for all 147 catchments evaluated here. Scaling of the parameters did not lead to improvement for slow-responding catchments, while the increase in performance was negligible for catchments with sub-daily response time.

Regional sub-daily stochastic weather generator based on reanalyses for surface water stress estimation in central Tunisia

We present MetGen: a sub-daily multi-variable stochastic weather generator implemented as an R library that can be used to perform gap-filling and to extend in time meteorological observation series. MetGen is tailored to provide surrogate series of air temperature, relative air humidity, global radiation and wind speed needed for surface water stress estimation that requires sub-daily resolution. Multiple gauged stations can be used to increase the calibration data although spatial dependence is not modeled. The approach relies on Generalized Linear Models that use, among their covariates, large-scale variables derived from ERA5 reanalyses. MetGen aims at preserving key features of the meteorological variables along with inter-variable dependencies. We illustrate the abilities of MetGen using a case study with three stations in central Tunisia. We consider as alternatives a univariate and a multivariate bias correction techniques along with the un-processed large-scale variables.

The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses

Abstract. Meteorological forcing plays a critical role in accurately simulating the watershed hydrological cycle. With the advancement of high-performance computing and the development of integrated watershed models, simulating the watershed hydrological cycle at high temporal (hourly to daily) and spatial resolution (tens of meters) has become efficient and computationally affordable. These hyperresolution watershed models require high resolution of meteorological forcing as model input to ensure the fidelity and accuracy of simulated responses. In this study, we utilized the Advanced Terrestrial Simulator (ATS), an integrated watershed model, to simulate surface and subsurface flow and land surface processes using unstructured meshes at the Coal Creek Watershed near Crested Butte (Colorado). We compared simulated watershed hydrologic responses including streamflow and distributed variables such as evapotranspiration, snow water equivalent (SWE), and groundwater table driven by three publicly available, gridded meteorological forcings (GMFs) – Daily Surface Weather and Climatological Summaries (Daymet), the Parameter-elevation Regressions on Independent Slopes Model (PRISM), and the North American Land Data Assimilation System (NLDAS). By comparing various spatial resolutions (ranging from 400 m to 4 km) of PRISM, the simulated streamflow only becomes marginally worse when spatial resolution of meteorological forcing is coarsened to 4 km (or 30 % of the watershed area). However, the 4 km-resolution has much worse performance than finer resolution in spatially distributed variables such as SWE. Using the temporally disaggregated PRISM, we compared models forced by different temporal resolutions (hourly to daily), and sub-daily resolution preserves the dynamic watershed responses (e.g., diurnal fluctuation of streamflow) that are absent in results forced by daily resolution. Conversely, the simulated streamflow shows better performance using daily resolution compared to that using sub-daily resolution. Our findings suggest that the choice of GMF and its spatiotemporal resolution depends on the quantity of interest and its spatial and temporal scale, which may have important implications for model calibration and watershed management decisions.

Inter-Comparison of UT1-UTC from 24-Hour, Intensives, and VGOS Sessions during CONT17.

This work focuses on the assessment of UT1-UTC estimates from various types of sessions during the CONT17 campaign. We chose the CONT17 campaign as it provides 15 days of continuous, high-quality VLBI data from two legacy networks (S/X band), i.e., Legacy-1 (IVS) and Legacy-2 (VLBA) (having different network geometry and are non-overlapping), two types of Intensive sessions, i.e., IVS and Russian Intensives, and five days of new-generation, broadband VGOS sessions. This work also investigates different approaches to optimally compare dUT1 from Intensives with respect to the 24 h sessions given the different parameterization adopted for analyzing Intensives and different session lengths. One approach includes the estimation of dUT1 from pseudo Intensives, which are created from the 24 h sessions having their epochs synchronized with respect to the Intensive sessions. Besides, we assessed the quality of the dUT1 estimated from VGOS sessions at daily and sub-daily resolution. The study suggests that a different approach should be adopted when comparing the dUT1 from the Intensives, i.e., comparison of dUT1 value at the mean epoch of an Intensive session. The initial results regarding the VGOS sessions show that the dUT1 estimated from VGOS shows good agreement with the legacy network despite featuring fewer observations and stations. In the case of sub-daily dUT1 from VGOS sessions, we found that estimating dUT1 with 6 h resolution is superior to other sub-daily resolutions. Moreover, we introduced a new concept of sub-daily dUT1-tie to improve the estimation of dUT1 from the Intensive sessions. We observed an improvement of up to 20% with respect to the dUT1 from the 24 h sessions.

Phloem water isotopically different to xylem water: Potential causes and implications for ecohydrological tracing

AbstractThe stable isotopes of hydrogen and oxygen in xylem water are often used to investigate tree water sources. But this traditional approach does not acknowledge the contribution of water stored in the phloem to transpiration and how this may affect xylem water and source water interpretations. Additionally, there is a prevailing assumption that there is no isotope fractionation during tree water transport. Here, we systematically sampled xylem and phloem water at daily and subdaily resolutions in a large lysimeter planted with Salix viminalis. Stem diurnal change in phloem water storage and transpiration rates were also measured. Our results show that phloem water is significantly less enriched in heavy isotopes than xylem water. At subdaily resolution, we observed a larger isotopic difference between xylem and phloem during phloem water refilling and under periods of tree water deficit. These findings contrast with the expectation of heavy‐isotope enriched water in phloem due to downward transport of enriched leaf water isotopic signatures. Because of previous evidence of aquaporin mediated phloem and xylem water transport and higher osmotic permeability of lighter hydrogen isotopologues across aquaporins, we propose that radial water transport across the xylem–phloem boundary may drive the relative depletion of heavy isotopes in phloem and their relative enrichment in xylem.

Fourteen years of continuous soil moisture records from plant and biocrust-dominated microsites

Drylands cover ~41% of the terrestrial surface. In these water-limited ecosystems, soil moisture contributes to multiple hydrological processes and is a crucial determinant of the activity and performance of above- and belowground organisms and of the ecosystem processes that rely on them. Thus, an accurate characterisation of the temporal dynamics of soil moisture is critical to improve our understanding of how dryland ecosystems function and are responding to ongoing climate change. Furthermore, it may help improve climatic forecasts and drought monitoring. Here we present the MOISCRUST dataset, a long-term (2006–2020) soil moisture dataset at a sub-daily resolution from five different microsites (vascular plants and biocrusts) in a Mediterranean semiarid dryland located in Central Spain. MOISCRUST is a unique dataset for improving our understanding on how both vascular plants and biocrusts determine soil water dynamics in drylands, and thus to better assess their hydrological impacts and responses to ongoing climate change.

Technical Note: Temporal disaggregation of spatial rainfall fields with generative adversarial networks

Abstract. Creating spatially coherent rainfall patterns with high temporal resolution from data with lower temporal resolution is necessary in many geoscientific applications. From a statistical perspective, this presents a high- dimensional, highly underdetermined problem. Recent advances in machine learning provide methods for learning such probability distributions. We test the usage of generative adversarial networks (GANs) for estimating the full probability distribution of spatial rainfall patterns with high temporal resolution, conditioned on a field of lower temporal resolution. The GAN is trained on rainfall radar data with hourly resolution. Given a new field of daily precipitation sums, it can sample scenarios of spatiotemporal patterns with sub-daily resolution. While the generated patterns do not perfectly reproduce the statistics of observations, they are visually hardly distinguishable from real patterns. Limitations that we found are that providing additional input (such as geographical information) to the GAN surprisingly leads to worse results, showing that it is not trivial to increase the amount of used input information. Additionally, while in principle the GAN should learn the probability distribution in itself, we still needed expert judgment to determine at which point the training should stop, because longer training leads to worse results.

Ten-year return levels of sub-daily extreme precipitation over Europe

Abstract. Information on the frequency and intensity of extreme precipitation is required by public authorities, civil security departments, and engineers for the design of buildings and the dimensioning of water management and drainage schemes. Especially for sub-daily resolutions, at which many extreme precipitation events occur, the observational data are sparse in space and time, distributed heterogeneously over Europe, and often not publicly available. We therefore consider it necessary to provide an impact-orientated data set of 10-year rainfall return levels over Europe based on climate model simulations and evaluate its quality. Hence, to standardize procedures and provide comparable results, we apply a high-resolution single-model large ensemble (SMILE) of the Canadian Regional Climate Model version 5 (CRCM5) with 50 members in order to assess the frequency of heavy-precipitation events over Europe between 1980 and 2009. The application of a SMILE enables a robust estimation of extreme-rainfall return levels with the 50 members of 30-year climate simulations providing 1500 years of rainfall data. As the 50 members only differ due to the internal variability in the climate system, the impact of internal variability on the return level values can be quantified. We present 10-year rainfall return levels of hourly to 24 h durations with a spatial resolution of 0.11∘ (12.5 km), which are compared to a large data set of observation-based rainfall return levels of 16 European countries. This observation-based data set was newly compiled and homogenized for this study from 32 different sources. The rainfall return levels of the CRCM5 are able to reproduce the general spatial pattern of extreme precipitation for all sub-daily durations with Spearman's rank correlation coefficients >0.76 for the area covered by observations. Also, the rainfall intensity of the observational data set is in the range of the climate-model-generated intensities in 60 % (77 %, 78 %, 83 %, 78 %) of the area for hourly (3, 6, 12, 24 h) durations. This results in biases between −16.3 % (hourly) to +8.2 % (24 h) averaged over the study area. The range, which is introduced by the application of 50 members, shows a spread of −15 % to +18 % around the median. We conclude that our data set shows good agreement with the observations for 3 to 24 h durations in large parts of the study area. However, for an hourly duration and topographically complex regions such as the Alps and Norway, we argue that higher-resolution climate model simulations are needed to improve the results. The 10-year return level data are publicly available (Poschlod, 2020; https://doi.org/10.5281/zenodo.3878887).

Ability of an Australian reanalysis dataset to characterise sub-daily precipitation

Abstract. The high spatio-temporal variability of precipitation is often difficult to characterise due to limited measurements. The available low-resolution global reanalysis datasets inadequately represent the spatio-temporal variability of precipitation relevant to catchment hydrology. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) provides a high-resolution atmospheric reanalysis dataset across the Australasian region. For hydrometeorological applications, however, it is essential to properly evaluate the sub-daily precipitation from this reanalysis. In this regard, this paper evaluates the sub-daily precipitation from BARRA for a period of 6 years (2010–2015) over Australia against point observations and blended radar products. We utilise a range of existing and bespoke metrics for evaluation at point and spatial scales. We examine bias in quantile estimates and spatial displacement of sub-daily rainfall at a point scale. At a spatial scale, we use the fractions skill score as a spatial evaluation metric. The results show that the performance of BARRA precipitation depends on spatial location, with poorer performance in tropical relative to temperate regions. A possible spatial displacement during large rainfall is also found at point locations. This displacement, evaluated by comparing the distribution of rainfall within a day, could be quantified by considering the neighbourhood grids. On spatial evaluation, hourly precipitation from BARRA is found to be skilful at a spatial scale of less than 100 km (150 km) for a threshold of 75th percentile (90th percentile) at most of the locations. The performance across all the metrics improves significantly at time resolutions higher than 3 h. Our evaluations illustrate that the BARRA precipitation, despite discernible spatial displacements, serves as a useful dataset for Australia, especially at sub-daily resolutions. Users of BARRA are recommended to properly account for possible spatio-temporal displacement errors, especially for applications where the spatial and temporal characteristics of rainfall are deemed very important.

Nonstationary stochastic rain type generation: accounting for climate drivers

Abstract. At subdaily resolution, rain intensity exhibits a strong variability in space and time, which is favorably modeled using stochastic approaches. This strong variability is further enhanced because of the diversity of processes that produce rain (e.g., frontal storms, mesoscale convective systems and local convection), which results in a multiplicity of space–time patterns embedded into rain fields and in turn leads to the nonstationarity of rain statistics. To account for this nonstationarity in the context of stochastic weather generators and therefore preserve the relationships between rainfall properties and climatic drivers, we propose to resort to rain type simulation. In this paper, we develop a new approach based on multiple-point statistics to simulate rain type time series conditional to meteorological covariates. The rain type simulation method is tested by a cross-validation procedure using a 17-year-long rain type time series defined over central Germany. Evaluation results indicate that the proposed approach successfully captures the relationships between rain types and meteorological covariates. This leads to a proper simulation of rain type occurrence, persistence and transitions. After validation, the proposed approach is applied to generate rain type time series conditional to meteorological covariates simulated by a regional climate model under an RCP8.5 (Representative Concentration Pathway) emission scenario. Results indicate that, by the end of the century, the distribution of rain types could be modified over the area of interest, with an increased frequency of convective- and frontal-like rains at the expense of more stratiform events.

Subdaily‐Scale Chemical Variability in a Torreites Sanchezi Rudist Shell: Implications for Rudist Paleobiology and the Cretaceous Day‐Night Cycle

AbstractThis study presents subdaily resolved chemical records through fossil mollusk shell calcite. Trace element profiles resolve periodic variability across ~40‐μm‐thin daily growth laminae in a Campanian Torreites sanchezi rudist bivalve. These high‐resolution records are combined with seasonally resolved stable isotope and trace element records that allow shell‐chemical variability to be discussed on both seasonal and daily scale. A combination of layer counting, spectral analysis of chemical cyclicity and chemical layer counting shows that the rudist precipitated 372 daily laminae per year, demonstrating that length of day has increased since the Late Cretaceous, as predicted by astronomical models. This new approach to determine the length of a solar day in geologic history through multiproxy chemical records at subdaily resolution yields considerably more control on the uncertainty of this estimate. Daily chemical variability exceeds seasonal variability in our records, and cannot be explained by diurnal temperature changes. Instead, we postulate that rudist shell chemistry is driven on a daily scale by changes in light intensity. These results together with those of stable isotope analyses provide strong evidence that Torreites rudists had photosymbionts. Bivalve shell calcite generally preserves well. Therefore, this study paves the way for daily‐scale reconstructions of paleoenvironment and sunlight intensity on geologic time scales from bivalve shells, potentially allowing researchers to bridge the gap between climate and weather reconstructions. Such reconstructions improve shell chronologies, document environmental change in warm ecosystems, and widen our understanding of the magnitude of short‐term changes during greenhouse climates.

Seasonal dynamics and profiles of soil NO concentrations in a temperate forest

Soils are known to be significant sources of atmospheric nitric oxide (NO), a key compound in atmospheric chemistry. NO is a key regulating substance for inter- and intra-species signalling and competition and affects plant growth and soil microbial metabolisms. However, little is known about NO concentration in soils and production of NO in the soil profile. Here we report on soil NO concentrations down to 65 cm soil depth and soil surface flux measurements over a 15 months period in subdaily resolution. This is supplemented by information on aerobic and anaerobic NO production in various soil layers of a spruce stand in SW Germany. NO concentrations showed a clear seasonality with highest concentrations of up to 800 nmol mol−1 (or part per billion in volume mixing ratio; ppbv) at the interface between the organic Of-Oh sub-layers in the summer. NO concentrations in the mineral subsoil (−65 cm) were approx. One order of magnitude lower than in the organic layer. Dynamic changes of soil NO concentrations were closely correlated with soil surface NO fluxes. Differences in soil NO concentrations across the soil profile reflected differences in aerobic and anaerobic NO production potential. The importance of such high NO concentrations for soil microbial and plant physiological processes remains unclear, but should be addressed in future research in order to improve our understanding of microbe-microbe and plant-microbe interactions.