Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistic forecasting of plausible debris flows from Nevado de Colima (Mexico) using data from the Atenquique debris flow, 1955

Abstract. We detail a new prediction-oriented procedure aimed at volcanic hazard assessment based on geophysical mass flow models constrained with heterogeneous and poorly defined data. Our method relies on an itemized application of the empirical falsification principle over an arbitrarily wide envelope of possible input conditions. We thus provide a first step towards a objective and partially automated experimental design construction. In particular, instead of fully calibrating model inputs on past observations, we create and explore more general requirements of consistency, and then we separately use each piece of empirical data to remove those input values that are not compatible with it. Hence, partial solutions are defined to the inverse problem. This has several advantages compared to a traditionally posed inverse problem: (i) the potentially nonempty inverse images of partial solutions of multiple possible forward models characterize the solutions to the inverse problem; (ii) the partial solutions can provide hazard estimates under weaker constraints, potentially including extreme cases that are important for hazard analysis; (iii) if multiple models are applicable, specific performance scores against each piece of empirical information can be calculated. We apply our procedure to the case study of the Atenquique volcaniclastic debris flow, which occurred on the flanks of Nevado de Colima volcano (Mexico), 1955. We adopt and compare three depth-averaged models currently implemented in the TITAN2D solver, available from https://vhub.org (Version 4.0.0 – last access: 23 June 2016). The associated inverse problem is not well-posed if approached in a traditional way. We show that our procedure can extract valuable information for hazard assessment, allowing the exploration of the impact of synthetic flows that are similar to those that occurred in the past but different in plausible ways. The implementation of multiple models is thus a crucial aspect of our approach, as they can allow the covering of other plausible flows. We also observe that model selection is inherently linked to the inversion problem.
The influence of dependence in characterizing multi-variable uncertainty for climate change impact assessments

ABSTRACT Few approaches exist that explicitly use the uncertainty associated with the spread of climate model simulations in assessing climate change impacts. An approach that does so is second-order approximation (SOA). This incorporates quantification of uncertainty to ascertain its impact on the derived response using a Taylor series expansion of the model. This study uses SOA in a statistical downscaling model of monthly streamflow, with a focus on the influence of dependence in the uncertainty of multiple atmospheric variables. Uncertainty is quantified using the square root error variance concept with a new extension that allows the inter-dependence terms among the atmospheric variable uncertainty to be specified. Applying the model to selected point locations in Australia, it is noted that the downscaling results differ considerably from downscaling that ignores uncertainty. However, when the effects of dependence in uncertainty are incorporated, the results differ according to the regional variations in dependence structure.
Experimental grain growth of quartz aggregates under wet conditions and its application to deformation in nature

Abstract. Grain growth of quartz was investigated using two quartz samples (powder and novaculite) with water under pressure and temperature conditions of 1.0–2.5 GPa and 800–1100 ∘C. The compacted powder preserved a substantial porosity, which caused a slower grain growth than in the novaculite. We assumed a grain growth law of dn-d0n=k0fH2Orexp⁡(-Q/RT)t with grain size d (µm) at time t (seconds), initial grain size d0 (µm), growth exponent n, a constant k0 (µmn MPa−r s−1), water fugacity fH2O (MPa) with the exponent r, activation energy Q (kJ mol−1), gas constant R, and temperature T in Kelvin. The parameters we obtained were n=2.5±0.4, k0=10-8.8±1.4, r=2.3±0.3, and Q=48±34 for the powder and n=2.9±0.4, k0=10-5.8±2.0, r=1.9±0.3, and Q=60±49 for the novaculite. The grain growth parameters obtained for the powder may be of limited use because of the high porosity of the powder with respect to crystalline rocks (novaculite), even if the differences between powder and novaculite vanish when grain sizes reach ∼70 µm. Extrapolation of the grain growth laws to natural conditions indicates that the contribution of grain growth to plastic deformation in the middle crust may be small. However, grain growth might become important for deformation in the lower crust when the strain rate is < 10−12 s−1.
A susceptibility-based rainfall threshold approach for landslide occurrence

Abstract. Rainfall threshold determination is a pressing issue in the landslide scientific community. While major improvements have been made towards more reproducible techniques for the identification of triggering conditions for landsliding, the now well-established rainfall intensity or event-duration thresholds for landsliding suffer from several limitations. Here, we propose a new approach of the frequentist method for threshold definition based on satellite-derived antecedent rainfall estimates directly coupled with landslide susceptibility data. Adopting a bootstrap statistical technique for the identification of threshold uncertainties at different exceedance probability levels, it results in thresholds expressed as AR = (α±Δα)⋅S(β±Δβ), where AR is antecedent rainfall (mm), S is landslide susceptibility, α and β are scaling parameters, and Δα and Δβ are their uncertainties. The main improvements of this approach consist in (1) using spatially continuous satellite rainfall data, (2) giving equal weight to rainfall characteristics and ground susceptibility factors in the definition of spatially varying rainfall thresholds, (3) proposing an exponential antecedent rainfall function that involves past daily rainfall in the exponent to account for the different lasting effect of large versus small rainfall, (4) quantitatively exploiting the lower parts of the cloud of data points, most meaningful for threshold estimation, and (5) merging the uncertainty on landslide date with the fit uncertainty in a single error estimation. We apply our approach in the western branch of the East African Rift based on landslides that occurred between 2001 and 2018, satellite rainfall estimates from the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA 3B42 RT), and the continental-scale map of landslide susceptibility of Broeckx et al. (2018) and provide the first regional rainfall thresholds for landsliding in tropical Africa.
Grazing-related nitrous oxide emissions: from patch scale to field scale

Abstract. Grazed pastures are strong sources of the greenhouse gas nitrous oxide (N2O). The quantification of N2O emissions is challenging due to the strong spatial and temporal variabilities of the emission sources and so N2O emission estimates are very uncertain. This study presents N2O emission measurements from two grazing systems in western Switzerland over the grazing season of 2016. The 12 dairy cows of each herd were kept in an intensive rotational grazing management. The diet for the two herds of cows consisted of different protein-to-energy ratios (system G: grass only diet; system M: grass with additional maize silage) resulting in different nitrogen (N) excretion rates. The N in the excretion was estimated by calculating the animal nitrogen budget taking into account the measurements of feed intake, milk yield, and body weight of the cow herds. Directly after the rotational grazing phases, background and urine patches were identified based on soil electric conductivity measurements while fresh dung patches were identified visually. The magnitude and temporal pattern of these different emission sources were measured with a fast-box (FB) chamber and the field-scale fluxes were quantified using two eddy covariance (EC) systems. The FB measurements were finally upscaled to the field level and compared to the EC measurements for quality control by using EC footprint estimates of a backward Lagrangian stochastic dispersion model. The comparison between the two grazing systems was performed during emission periods that were not influenced by fertilizer applications. This allowed the calculation of the excreta-related N2O emissions per cow and grazing hour and resulted in considerably higher emissions for system G compared to system M. Relating the found emissions to the excreta N resulted in excreta-related emission factors (EFs) of 0.74±0.26 % for system M and 0.83±0.29 % for system G. These EF values were thus significantly smaller compared to the default EF of 2 % provided by the IPCC guidelines for cattle excreta deposited on pasture. The measurements showed that urine patch emission dominated the field-scale fluxes (57 %), followed by significant background emissions (38 %), and only a small contribution of dung patch emission (5 %). The resulting source-specific EFs exhibited a clear difference between urine (1.12±0.43 %) and dung (0.16±0.06 %), supporting a disaggregation of the grazing-related EFs by excreta type in emission inventories. The study also highlights the advantage of a N-optimized diet, which resulted in reduced N2O emissions from animal excreta.
The global distribution of the M1 ocean tide

Abstract. The worldwide distribution of the small degree-3 M1 ocean tide is investigated using a quasi-global data set of over 800 tide gauge records and a global tide model. M1 is confirmed to have a geographical variation in the Atlantic consistent with the suggestion of Platzman (1984b) and Cartwright (1975) that M1 is generated in the ocean as a consequence of the spatial and temporal overlap of M1 in the tidal potential and one (or at least a small number of) diurnal ocean normal mode(s). As a consequence, it is particularly strong around the UK and on North Sea coasts (amplitudes ∼10 mm). This analysis shows that their suggestion is also consistent to a great extent with the observed small amplitudes in the Pacific and Indian oceans. However, there are differences at the regional and local level which require much further study via more sophisticated ocean tidal modelling. By contrast, what is called the M1' tide (a combination of several degree-2 lines in the tidal potential with frequencies close to that of M1) is shown to have a geographical distribution consistent with expectations from other degree-2 diurnal tides, apart from locations such as around the UK where tidal interactions introduce complications. As far as I know, this is the first time that these small tidal constituents have been mapped on a global basis and, in particular, the first time that the ocean response to the degree-3 component of the tidal potential has been investigated globally.
A synthesis of three decades of hydrological research at Scotty Creek, NWT, Canada

Abstract. Scotty Creek, Northwest Territories (NWT), Canada, has been the focus of hydrological research for nearly three decades. Over this period, field and modelling studies have generated new insights into the thermal and physical mechanisms governing the flux and storage of water in the wetland-dominated regions of discontinuous permafrost that characterises much of the Canadian and circumpolar subarctic. Research at Scotty Creek has coincided with a period of unprecedented climate warming, permafrost thaw, and resulting land cover transformations including the expansion of wetland areas and loss of forests. This paper (1) synthesises field and modelling studies at Scotty Creek, (2) highlights the key insights of these studies on the major water flux and storage processes operating within and between the major land cover types, and (3) provides insights into the rate and pattern of the permafrost-thaw-induced land cover change and how such changes will affect the hydrology and water resources of the study region.
Spatial and temporal variability of groundwater recharge in a sandstone aquifer in a semiarid region

Abstract. With the aim to understand the spatial and temporal variability of groundwater recharge, a high-resolution, spatially distributed numerical model (MIKE SHE) representing surface water and groundwater was used to simulate responses to precipitation in a 2.16 km2 upland catchment on fractured sandstone near Los Angeles, California. Exceptionally high temporal and spatial resolution was used for this catchment modeling: hourly climate data, a 20 m×20 m grid in the horizontal plane, and 240 numerical layers distributed vertically within the thick vadose zone and in the upper part of the groundwater zone. The finest practical spatial and temporal resolutions were selected to accommodate the large degree of surface and subsurface variability of catchment features. Physical property values for the different lithologies were assigned based on previous on-site investigations, whereas the parameters controlling streamflow and evapotranspiration were derived from calibration to continuous streamflow at the outfall and to average hydraulic heads from 17 wells. Confidence in the calibrated model was enhanced by validation through (i) comparison of simulated average recharge to estimates based on the applications of the chloride mass-balance method to data from the groundwater and vadose zones within and beyond the catchment, (ii) comparison of the water isotope signature (18O and 2H) in shallow groundwater to the variability of isotope signatures for precipitation events over an annual cycle, and (iii) comparison of simulated recharge time series and observed fluctuation of water levels. The average simulated recharge across the catchment for the period 1995–2014 is 16 mm yr−1 (4 % of the average annual precipitation), which is consistent with previous estimates obtained by using the chloride mass balance method (4.2 % of the average precipitation). However, one of the most unexpected results was that local recharge was simulated to vary from 0 to >1000 mm yr−1 due to episodic precipitation and overland runoff effects. This recharge occurs episodically with the major flux events at the bottom of the evapotranspiration zone, as simulated by MIKE SHE and confirmed by the isotope signatures, occurring only at the end of the rainy season. This is the first study that combines MIKE SHE simulations with the analysis of water isotopes in groundwater and rainfall to determine the timing of recharge in a sedimentary bedrock aquifer in a semiarid region. The study advances the understanding of recharge and unsaturated flow processes and enhances our ability to predict the effects of surface and subsurface features on recharge rates. This is crucial in highly heterogeneous contaminated sites because different contaminant source areas have widely varying recharge and, hence, groundwater fluxes impacting their mobility.
Comparison between upwind and downwind designs of a 10 MW wind turbine rotor

Abstract. The size of wind turbines has been steadily growing in the pursuit of a lower cost of energy by an increased wind capture. Within this trend, the vast majority of wind turbine rotors have been designed based on the conventional three-bladed upwind concept. This paper aims at assessing the optimality of this configuration with respect to a three-bladed downwind design, with and without an actively controlled variable coning used to reduce the cantilever loading of the blades. Results indicate that a conventional design appears difficult to beat even at these turbine sizes, although a downwind nonaligned configuration might be an interesting alternative.
On the choice of calibration metrics for “high-flow” estimation using hydrologic models

Abstract. Calibration is an essential step for improving the accuracy of simulations generated using hydrologic models. A key modeling decision is selecting the performance metric to be optimized. It has been common to use squared error performance metrics, or normalized variants such as Nash–Sutcliffe efficiency (NSE), based on the idea that their squared-error nature will emphasize the estimates of high flows. However, we conclude that NSE-based model calibrations actually result in poor reproduction of high-flow events, such as the annual peak flows that are used for flood frequency estimation. Using three different types of performance metrics, we calibrate two hydrological models at a daily step, the Variable Infiltration Capacity (VIC) model and the mesoscale Hydrologic Model (mHM), and evaluate their ability to simulate high-flow events for 492 basins throughout the contiguous United States. The metrics investigated are (1) NSE, (2) Kling–Gupta efficiency (KGE) and its variants, and (3) annual peak flow bias (APFB), where the latter is an application-specific metric that focuses on annual peak flows. As expected, the APFB metric produces the best annual peak flow estimates; however, performance on other high-flow-related metrics is poor. In contrast, the use of NSE results in annual peak flow estimates that are more than 20 % worse, primarily due to the tendency of NSE to underestimate observed flow variability. On the other hand, the use of KGE results in annual peak flow estimates that are better than from NSE, owing to improved flow time series metrics (mean and variance), with only a slight degradation in performance with respect to other related metrics, particularly when a non-standard weighting of the components of KGE is used. Stochastically generated ensemble simulations based on model residuals show the ability to improve the high-flow metrics, regardless of the deterministic performances. However, we emphasize that improving the fidelity of streamflow dynamics from deterministically calibrated models is still important, as it may improve high-flow metrics (for the right reasons). Overall, this work highlights the need for a deeper understanding of performance metric behavior and design in relation to the desired goals of model calibration.
Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset

Abstract. Digital elevation models (DEMs) are a gridded representation of the surface of the Earth and typically contain uncertainties due to data collection and processing. Slope and aspect estimates on a DEM contain errors and uncertainties inherited from the representation of a continuous surface as a grid (referred to as truncation error; TE) and from any DEM uncertainty. We analyze in detail the impacts of TE and propagated elevation uncertainty (PEU) on slope and aspect. Using synthetic data as a control, we define functions to quantify both TE and PEU for arbitrary grids. We then develop a quality metric which captures the combined impact of both TE and PEU on the calculation of topographic metrics. Our quality metric allows us to examine the spatial patterns of error and uncertainty in topographic metrics and to compare calculations on DEMs of different sizes and accuracies. Using lidar data with point density of ∼10 pts m−2 covering Santa Cruz Island in southern California, we are able to generate DEMs and uncertainty estimates at several grid resolutions. Slope (aspect) errors on the 1 m dataset are on average 0.3∘ (0.9∘) from TE and 5.5∘ (14.5∘) from PEU. We calculate an optimal DEM resolution for our SCI lidar dataset of 4 m that minimizes the error bounds on topographic metric calculations due to the combined influence of TE and PEU for both slope and aspect calculations over the entire SCI. Average slope (aspect) errors from the 4 m DEM are 0.25∘ (0.75∘) from TE and 5∘ (12.5∘) from PEU. While the smallest grid resolution possible from the high-density SCI lidar is not necessarily optimal for calculating topographic metrics, high point-density data are essential for measuring DEM uncertainty across a range of resolutions.
The El Niño event of 2015–2016: climate anomalies and their impact on groundwater resources in East and Southern Africa

Abstract. The impact of climate variability on groundwater storage has received limited attention despite widespread dependence on groundwater as a resource for drinking water, agriculture and industry. Here, we assess the climate anomalies that occurred over Southern Africa (SA) and East Africa, south of the Equator (EASE), during the major El Niño event of 2015–2016, and their associated impacts on groundwater storage, across scales, through analysis of in situ groundwater piezometry and Gravity Recovery and Climate Experiment (GRACE) satellite data. At the continental scale, the El Niño of 2015–2016 was associated with a pronounced dipole of opposing rainfall anomalies over EASE and Southern Africa, north–south of ∼12∘ S, a characteristic pattern of the El Niño–Southern Oscillation (ENSO). Over Southern Africa the most intense drought event in the historical record occurred, based on an analysis of the cross-scale areal intensity of surface water balance anomalies (as represented by the standardised precipitation evapotranspiration index – SPEI), with an estimated return period of at least 200 years and a best estimate of 260 years. Climate risks are changing, and we estimate that anthropogenic warming only (ignoring changes to other climate variables, e.g. precipitation) has approximately doubled the risk of such an extreme SPEI drought event. These surface water balance deficits suppressed groundwater recharge, leading to a substantial groundwater storage decline indicated by both GRACE satellite and piezometric data in the Limpopo basin. Conversely, over EASE during the 2015–2016 El Niño event, anomalously wet conditions were observed with an estimated return period of ∼10 years, likely moderated by the absence of a strongly positive Indian Ocean zonal mode phase. The strong but not extreme rainy season increased groundwater storage, as shown by satellite GRACE data and rising groundwater levels observed at a site in central Tanzania. We note substantial uncertainties in separating groundwater from total water storage in GRACE data and show that consistency between GRACE and piezometric estimates of groundwater storage is apparent when spatial averaging scales are comparable. These results have implications for sustainable and climate-resilient groundwater resource management, including the potential for adaptive strategies, such as managed aquifer recharge during episodic recharge events.