Chemical Profiles across a Jurassic Stratiform Manganese Deposit at Katsuyama in the Mino Terrane of Central Japan: Implications for Depositional Environment, Diagenetic Metal Redistribution and Paleoceanography

Abstract. Inorganic chemical compositions are determined for a series of rocks crossing an Early Jurassic stratiform manganese ore deposit in a chert‐dominant sequence at Katsuyama, in the Mino Terrane of central Japan. The lithology in the vicinity of the manganese ore bed is classified into lower bedded chert, black shale, massive chert, manganese ore and upper bedded chert, in ascending order. The rocks surrounding the manganese deposit are anomalously high in certain elements: Pb (max. 29 ppm), Ni (1140) and Co (336) in the lower bedded chert, Mo (438), As (149), Tl (29) and U (12) in the black shales, V (210) and Cr (87) in the massive chert, and MnO and W (24) in the manganese ore. The aluminum‐normalized profiles reveal a distinct zonation of redox‐sensitive elements: Pb‐Zn, Ni‐Co‐Cu(‐Zn) and U‐Cr in the lower bedded chert, Mo‐As‐Tl in the black shale, V(‐Cr) in the massive chert, and Mn‐Fe‐Ba‐W in the manganese ore, in ascending order. The lower and upper bedded cherts and manganese ore generally exhibit flat rare earth element patterns with positive Ce anomalies, whereas the uppermost part of the lower bedded chert, the black shale and massive chert have flat patterns with weak or nonexistent negative Ce anomalies and weak positive Eu anomalies. The strong enrichment in Ni, Co, W, Tl and As detected in the Katsuyama section is not recognized in other sediments, including those of anoxic deposition origin, but is identified in modern ferromanganese nodules, suggesting that metal enrichment in the Katsuyama section is essentially due to the formation of ferromanganese nodules rather than to deposition in an anoxic environment. The observed elemental zonation is well explained by equilibrium calculations, reflecting early diagenetic formation and associated gradual reduction with depth. The concentration profiles in combination with litho‐ and biostratigraphical features suggest that formation of these bedded manganese deposits was triggered by an influx of warm, saline and oxic water into a stagnant deep ocean floor basin in Panthalassa at the end of the middle Early Jurassic. Paleoceanographic environmental controls thus appear to be important factors in the formation and preservation of this type of stratiform manganese deposit.     

Statistical identification of orographic effects in the regional analysis of extreme rainfall

Regional models of extreme rainfall must address the spatial variability induced by orographic obstacles. However, the proper detection of orographic effects often depends on the availability of a well‐designed rain gauge network. The aim of this study is to investigate a new method for identifying and characterizing the effects of orography on the spatial structure of extreme rainfall at the regional scale, including where rainfall data are lacking or fail to describe rainfall features thoroughly. We analyse the annual maxima of daily rainfall data in the Campania region, an orographically complex region in Southern Italy, and introduce a statistical procedure to identify spatial outliers in a low order statistic (namely the mean). The locations of these outliers are then compared with a pattern of orographic objects that has been a priori identified through the application of an automatic geomorphological procedure. The results show a direct and clear link between a particular set of orographic objects and a local increase in the spatial variability of extreme rainfall. This analysis allowed us to objectively identify areas where orography produces enhanced variability in extreme rainfall. It has direct implications for rain gauge network design criteria and has led to promising developments in the regional analysis of extreme rainfall. Copyright © 2015 John Wiley & Sons, Ltd.     

Critical rainfall statistics for predicting watershed flood responses: rethinking the design storm concept

Recent advances have been made to modernize estimates of probable precipitation scenarios; however, researchers and engineers often continue to assume that rainfall events can be described by a small set of event statistics, typically average intensity and event duration. Given the easy availability of precipitation data and advances in desk‐top computational tools, we suggest that it is time to rethink the ‘design storm’ concept. Design storms should include more holistic characteristics of flood‐inducing rain events, which, in addition to describing specific hydrologic responses, may also be watershed or regionally specific. We present a sensitivity analysis of nine precipitation event statistics from observed precipitation events within a 60‐year record for Tompkins County, NY, USA. We perform a two‐sample Kolmogorov–Smirnov (KS) test to objectively identify precipitation event statistics of importance for two related hydrologic responses: (1) peak outflow from the Six Mile Creek watershed and (2) peak depth within the reservoir behind the Six Mile Creek Dam. We identify the total precipitation depth, peak hourly intensity, average intensity, event duration, interevent duration, and several statistics defining the temporal distribution of precipitation events to be important rainfall statistics to consider for predicting the watershed flood responses. We found that the two hydrologic responses had different sets of statistically significant parameters. We demonstrate through a stochastic precipitation generation analysis the effects of starting from a constrained parameter set (intensity and duration) when predicting hydrologic responses as opposed to utilizing an expanded suite of rainfall statistics. In particular, we note that the reduced precipitation parameter set may underestimate the probability of high stream flows and therefore underestimate flood hazard. Copyright © 2016 John Wiley & Sons, Ltd.     

Fingerprinting the 8.2 ka event climate response in a coupled climate model

Using results from coupled climate model simulations of the 8.2 ka climate event that produced a cold period over Greenland in agreement with the reconstructed cooling from ice cores, we investigate the typical pattern of climate anomalies (fingerprint) to provide a framework for the interpretation of global proxy data for the 8.2 ka climate event. For this purpose we developed an analysis method that isolates the forced temperature response and provides information on spatial variations in magnitude, timing and duration that characterise the detectable climate event in proxy archives. Our analysis shows that delays in the temperature response to the freshwater forcing are present, mostly in the order of decades (30 a over central Greenland). The North Atlantic Ocean initially cools in response to the freshwater perturbation, followed in certain parts by a warm response. This delay, occurring more than 200 a after the freshwater pulse, hints at an overshoot in the recovery from the freshwater perturbation. The South Atlantic and the Southern Ocean show a warm response reflecting the bipolar seesaw effect. The duration of the simulated event varies for different areas, and the highest probability of recording the event in proxy archives is in the North Atlantic Ocean area north of 40° N. Our results may facilitate the interpretation of proxy archives recording the 8.2 ka event, as they show that timing and duration cannot be assumed to correspond with the timing and duration of the event as recorded in Greenland ice cores. Copyright © 2011 John Wiley & Sons, Ltd.     

Severe soil erosion during a 3‐day exceptional rainfall event: combining modelling and field data for a fallow cereal field

Exceptional rainfall events cause significant losses of soil, although few studies have addressed the validation of model predictions at field scale during severe erosive episodes. In this study, we evaluate the predictive ability of the enhanced Soil Erosion and Redistribution Tool (SERT‐2014) model for mapping and quantifying soil erosion during the exceptional rainfall event (~235 mm) that affected the Central Spanish Pyrenees in October 2012. The capacity of the simulation model is evaluated in a fallow cereal field (1.9 ha) at a high spatial scale (1 × 1 m). Validation was performed with field‐quantified rates of soil loss in the rills and ephemeral gullies and also with a detailed map of soil redistribution. The SERT‐2014 model was run for the six rainfall sub‐events that made up the exceptional event, simulating the different hydrological responses of soils with maximum runoff depths ranging between 40 and 1017 mm. Predicted average and maximum soil erosion was 11 and 117 Mg ha^?1 event^?1, respectively. Total soil loss and sediment yield to the La Reina gully amounted to 16.3 and 9.0 Mg event^?1. These rates are in agreement with field estimations of soil loss of 20.0 Mg event^?1. Most soil loss (86%) occurred during the first sub‐event. Although soil accumulation was overestimated in the first sub‐event because of the large amount of detached soil, the enhanced SERT‐2014 model successfully predicted the different spatial patterns and values of soil redistribution for each sub‐event. Further research should focus on stream transport capacity. Copyright © 2014 John Wiley & Sons, Ltd.     

A nonstationary extreme value distribution for analysing the cessation of karst spring discharge

The effects of climate change and population growth in recent decades are leading us to consider their combined and potentially extreme consequences, particularly regarding hydrological processes, which can be modeled using a generalized extreme value (GEV) distribution. Most of the GEV models were based on a stationary assumption for hydrological processes, in contrast to the nonstationary reality due to climate change and human activities. In this paper, we present the nonstationary generalized extreme value (NSGEV) distribution and use it to investigate the risk of Niangziguan Springs discharge decreasing to zero. Rather than assuming the location, scale, and shape parameters to be constant as one might do for a stationary GEV distribution analysis, the NSGEV approach can reflect the dynamic processes by defining the GEV parameters as functions of time. Because most of the GEV model is designed to evaluate maxima (e.g. flooding, represented by positive numbers), and spring discharge cessation is a ?minima’, we deduced an NSGEV model for minima by applying opposite numbers, i.e. negative instead of positive numbers. The results of the model application to Niangziguan Springs showed that the probability of zero discharge at Niangziguan Springs will be 1/80 in 2025, and 1/10 in 2030. After 2025, the rate of decrease in spring discharge will accelerate, and the probability that Niangziguan Springs will cease flowing will dramatically increase. The NSGEV model is a robust method for analysing karst spring discharge. Copyright © 2013 John Wiley & Sons, Ltd.     

Analysis of the interannual variability of annual daily extreme water levels in the St Lawrence River and Lake Ontario from 1918 to 2010

We compared the interannual variability of annual daily maximum and minimum extreme water levels in Lake Ontario and the St Lawrence River (Sorel station) from 1918 to 2010, using several statistical tests. The interannual variability of annual daily maximum extreme water levels in Lake Ontario is characterized by a positive long‐term trend showing two shifts in mean (1929–1930 and 1942–1943) and a single shift in variance (in 1958–1959). In contrast, for the St Lawrence River, this interannual variability is characterized by a negative long‐term trend with a single shift in mean, which occurred in 1955–1956. As for annual daily minimum extreme water levels, their interannual variability shows no significant long‐term change in trend. However, for Lake Ontario, the interannual variability of these water levels shows two shifts in mean, which are synchronous with those for maximum water levels, and a single shift in variance, which occurred in 1965–1966. These changes in trend and stationarity (mean and variance) are thought to be due to factors both climatic (the Great Drought of the 1930s) and human (digging of the Seaway and construction of several dams and locks during the 1950s). Despite this change in means and variance, the four series are clearly described by the generalized extreme value distribution. Finally, annual daily maximum and minimum extreme water levels in the St Lawrence and Lake Ontario are negatively correlated with Atlantic multidecadal oscillation over the period from 1918 to 2010. Copyright © 2013 John Wiley & Sons, Ltd.     

Development of a rain‐on‐snow detection algorithm using passive microwave radiometry

Currently observed climate warming in the Arctic has numerous consequences. Of particular relevance, the precipitation regime is modified where mixed and liquid precipitation can occur during the winter season leading to rain‐on‐snow (ROS) events. This phenomenon is responsible for ice crust formation, which has a significant impact on ecosystems (such as biological, hydrological, ecological and physical processes). The spatially and temporally sporadic nature of ROS events makes the phenomenon difficult to monitor using meteorological observations. This paper focuses on the detection of ROS events using passive microwave (PMW) data from a modified brightness temperature (T_B) gradient approach at 19 and 37 GHz. The approach presented here was developed empirically for observed ROS events with coincident ground‐based PMW measurements in Sherbrooke, Quebec, Canada. It was then tested in Nunavik, Quebec, with the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E). We obtained a detection accuracy of 57, 71 and 89% for ROS detection for three AMSR‐E grid cells with a maximum error of 7% when considering all omissions and commissions with regard to the total number of AMSR‐E passes throughout the winter period. Copyright © 2016 John Wiley & Sons, Ltd.     

Evaluating suspended sediment and turbidity reduction projects in a glacially conditioned catchment through dynamic regression and fluvial process-based modelling

Elevated turbidity (T_n) and suspended sediment concentrations (SSC) during and following flood events can degrade water supply quality and aquatic ecosystem integrity. Streams draining glacially conditioned mountainous terrain, such as those in the Catskill Mountains of New York State, are particularly susceptible to high levels of T_n and SSC sourced from erosional contact with glacial-related sediment. This study forwards a novel approach to evaluate the effectiveness of stream restoration best management practices (BMPs) meant to reduce stream T_n and SSC, and demonstrates the approach within the Stony Clove sub-basin of the Catskills, a water supply source for New York City. The proposed approach is designed to isolate BMP effects from natural trends in T_n and SSC caused by trends in discharge and shifts in average T_n or SSC per unit discharge (Q) following large flood events. We develop Dynamic Linear Models (DLMs) to quantify how T_n-Q and SSC-Q relationships change over time at monitoring stations upstream and downstream of BMPs within the Stony Clove and in three other sub-basins without BMPs, providing observational evidence of BMP effectiveness. A process-based model, the River Erosion Model, is then developed to simulate natural, hydrology-driven SSC-Q dynamics in the Stony Clove sub-basin (absent of BMP effects). We use DLMs to compare the modelled and observed SSC-Q dynamics and isolate the influence of the BMPs. Results suggest that observed reductions in SSC and T_n in the Stony Clove sub-basin have been driven by a combination of declining streamflow and the installed BMPs, confirming the utility of the BMPs for the monitored hydrologic conditions.