The role of urban growth,climate change,and their interplay in altering runoff extremes

Changes in climate and urban growth are the most influential factors affecting hydrological characteristics in urban and extra‐urban contexts. The assessment of the impacts of these changes on the extreme rainfall–runoff events may have important implications on urban and extra‐urban management policies against severe events, such as floods, and on the design of hydraulic infrastructures. Understanding the effects of the interaction between climate change and urban growth on the generation of runoff extremes is the main aim of this paper. We carried out a synthetic experiment on a river catchment of 64 km² to generate hourly runoff time series under different hypothetical scenarios. We imposed a growth of the percentage of urban coverage within the basin (from 1.5% to 25%), a rise in mean temperature of 2.6 °C, and an alternatively increase/decrease in mean annual precipitation of 25%; changes in mean annual precipitation were imposed following different schemes, either changing rainstorm frequency or rainstorm intensity. The modelling framework consists of a physically based distributed hydrological model, which simulates fast and slow mechanisms of runoff generation directly connected with the impervious areas, a land‐use change model, and a weather generator. The results indicate that the peaks over threshold and the hourly annual peaks, used as hydrological indicators, are very sensitive to the rainstorm intensity. Moreover, the effects of climate changes dominate on those of urban growth determining an exacerbation of the fast runoff component in extreme events and a reduction of the slow and deep runoff component, thus limiting changes in the overall runoff.     

Influence of spatial distribution of sensors and observation accuracy on the assimilation of distributed streamflow data in hydrological modelling

The aim of this study is to assess the influence of sensor locations and varying observation accuracy on the assimilation of distributed streamflow observations, also taking into account different structures of semi-distributed hydrological models. An ensemble Kalman filter is used to update a semi-distributed hydrological model as a response to measured streamflow. Various scenarios of sensor locations and observation accuracy are introduced. The methodology is tested on the Brue basin during five flood events. The results of this work demonstrate that the assimilation of streamflow observations at interior points of the basin can improve the hydrological models according to the particular location of the sensors and hydrological model structure. It is also found that appropriate definition of the observation accuracy can affect model performance and consequent flood forecasting. These findings can be used as criteria to develop methods for streamflow monitoring network design.     

Geostatistical seismic inversion for non‐stationary patterns using direct sequential simulation and co‐simulation

Geostatistical seismic inversion methods are routinely used in reservoir characterisation studies because of their potential to infer the spatial distribution of the petro‐elastic properties of interest (e.g., density, elastic, and acoustic impedance) along with the associated spatial uncertainty. Within the geostatistical seismic inversion framework, the retrieved inverse elastic models are conditioned by a global probability distribution function and a global spatial continuity model as estimated from the available well‐log data for the entire inversion grid. However, the spatial distribution of the real subsurface elastic properties is complex, heterogeneous, and, in many cases, non‐stationary since they directly depend on the subsurface geology, i.e., the spatial distribution of the facies of interest. In these complex geological settings, the application of a single distribution function and a spatial continuity model is not enough to properly model the natural variability of the elastic properties of interest. In this study, we propose a three‐dimensional geostatistical inversion technique that is able to incorporate the reservoir's heterogeneities. This method uses a traditional geostatistical seismic inversion conditioned by local multi‐distribution functions and spatial continuity models under non‐stationary conditions. The procedure of the proposed methodology is based on a zonation criterion along the vertical direction of the reservoir grid. Each zone can be defined by conventional seismic interpretation, with the identification of the main seismic units and significant variations of seismic amplitudes. The proposed method was applied to a highly non‐stationary synthetic seismic dataset with different levels of noise. The results of this work clearly show the advantages of the proposed method against conventional geostatistical seismic inversion procedures. It is important to highlight the impact of this technique in terms of higher convergence between real and inverted reflection seismic data and the more realistic approximation towards the real subsurface geology comparing with traditional techniques.     

Effects of geomorphology and land use on stream water quality in southeastern Amazonia

Water quality in streams is determined by several factors, including geology, topography, climate, and anthropogenic changes. This study aimed to assess the effects of watershed physical, morphology, and precipitation seasonality on the water quality of two streams that supply drinking water to rural settlements and urban areas in the Cerrado-Amazonia transition region. We monitored 16 physico-chemical attributes of water at six different sample locations over three years (2013–2016). Our results indicate that eight of these physico-chemical attributes did not meet the standards for safe drinking water established by Brazilian legislation. Precipitation seasonality, degradation of riparian zones, stream length, and watershed slope were the most important predictors of impaired water quality. Our results highlight the importance of restoring and conserving riparian forests in order to maintain drinking water quality.     

New coring study in Augusta Bay expands understanding of offshore tsunami deposits (Eastern Sicily,Italy)

Tsunami deposits present an important archive for understanding tsunami histories and dynamics. Most research in this field has focused on onshore preserved remains, while the offshore deposits have received less attention. In 2009, during a coring campaign with the Italian Navy Magnaghi, four 1 m long gravity cores (MG cores) were sampled from the northern part of Augusta Bay, along a transect in 60 to 110 m water depth. These cores were taken in the same area where a core (MS06) was collected in 2007 about 2·3 km offshore Augusta at a water depth of 72 m below sea level. Core MS06 consisted of a 6·7 m long sequence that included 12 anomalous intervals interpreted as the primary effect of tsunami backwash waves in the last 4500 years. In this study, tsunami deposits were identified, based on sedimentology and displaced benthic foraminifera (as for core MS06) reinforced by X-ray fluorescence data. Two erosional surfaces (L1 and L2) were recognized coupled with grain-size increase, abundant Posidonia oceanica seagrass remains and a significant amount of Nubecularia lucifuga, an epiphytic sessile benthic foraminifera considered to be transported from the inner shelf. The occurrence of Ti/Ca and Ti/Sr increments, coinciding with peaks in organic matter (Mo incoherent/coherent) suggests terrestrial run-off coupled with an input of organic matter. The L1 and L2 horizons were attributed to two distinct historical tsunamis (ad 1542 and ad 1693) by indirect age-estimation methods using ²¹⁰Pb profiles and the comparison of Volume Magnetic Susceptibility data between MG cores and MS06 cores. One most recent bioturbated horizon (Bh), despite not matching the above listed interpretative features, recorded an important palaeoenvironmental change that may correspond to the ad 1908 tsunami. These findings reinforce the value of offshore sediment records as an underutilized resource for the identification of past tsunamis.     

Analyzing the spatial interactions between rainfall levels and flooding prediction in São Paulo

Rainfall is one of the primary triggers for many geological and hydrological natural disasters. While the geological events are related to mass movements in land collapse due to waterlogging, the hydrological ones are usually assigned to runoff or flooding. Studies in the literature propose predicting mass movement events as a function of accumulated rainfall levels recorded at distinct periods. According to these approaches, a two-dimensional rainfall levels feature space is segmented into the occurrence and non-occurrence decision regions by an empirical critical curve (CC). Although this scheme may easily be extended to other purposes and applications, studies in the literature need to discuss its use for flooding prediction. In light of this motivation, the present study is unfolded in (1) verifying that defining CCs in the rainfall levels feature space is a practical approach for flooding prediction and (2) analyzing how geospatial components interact with rainfall levels and flooding prediction. A database containing the rainfall levels recorded for flooding and non-flooding events in São Paulo city, Brazil, regarding the period 2015–2016, was considered in this study. The results indicate good accuracy for flooding prediction using only partial rain, which can be improved by adding physical characteristics of the flooding locations, demonstrating a direct correlation with spatial interactions, and rainfall levels.