Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland,South Africa

Soil moisture is a geophysical key observable for predicting floods and droughts, modeling weather and climate and optimizing agricultural management. Currently available in situ observations are limited to small sampling volumes and restricted number of sites, whereas measurements from satellites lack spatial resolution. Global navigation satellite system (GNSS) receivers can be used to estimate soil moisture time series at an intermediate scale of about 1000 m². In this study, GNSS signal-to-noise ratio (SNR) data at the station Sutherland, South Africa, are used to estimate soil moisture variations during 2008–2014. The results capture the wetting and drying cycles in response to rainfall. The GNSS Volumetric Water Content (VWC) is highly correlated (r ² = 0.8) with in situ observations by time-domain reflectometry sensors and is accurate to 0.05 m³/m³. The soil moisture estimates derived from the SNR of the L1 and L2P signals compared to the L2C show small differences with a RMSE of 0.03 m³/m³. A reduction in the SNR sampling rate from 1 to 30 s has very little impact on the accuracy of the soil moisture estimates (RMSE of the VWC difference 1–30 s is 0.01 m³/m³). The results show that the existing data of the global tracking network with continuous observations of the L1 and L2P signals with a 30-s sampling rate over the last two decades can provide valuable complementary soil moisture observations worldwide.     

Integrating cellular automata,artificial neural network,and fuzzy set theory to simulate threatened orchards: application to Maragheh,Iran

Urbanization processes challenge the growth of orchards in many cities in Iran. In Maragheh, orchards are crucial ecological, economical, and tourist sources. To explore orchards threatened by urban expansion, this study first aims to develop a new model by coupling cellular automata (CA) and artificial neural network with fuzzy set theory (CA–ANN–Fuzzy). While fuzzy set theory captures the uncertainty associated with transition rules, the ANN considers spatial and temporal nonlinearities of the driving forces underlying the urban growth processes. Second, the CA–ANN–Fuzzy model is compared with two existing approaches, namely a basic CA and a CA coupled with an ANN (CA–ANN). Third, we quantify the amount of orchard loss during the last three decades as well as for the upcoming years up to 2025. Results show that CA–ANN–Fuzzy with 83% kappa coefficient performs significantly better than conventional CA (with 51% kappa coefficient) and CA–ANN (with 79% kappa coefficient) models in simulating orchard loss. The historical data shows a considerable loss of 26% during the last three decades, while the CA–ANN–Fuzzy simulation reveals a considerable future loss of 7% of Maragheh’s orchards in 2025 due to urbanization. These areas require special attention and must be protected by the local government and decision-makers.     

A multi-dimensional assessment of urban vulnerability to climate change in Sub-Saharan Africa

In this paper, we develop and apply a multi-dimensional vulnerability assessment framework for understanding the impacts of climate change-induced hazards in Sub-Saharan African cities. The research was carried out within the European/African FP7 project CLimate change and Urban Vulnerability in Africa, which investigated climate change-induced risks, assessed vulnerability and proposed policy initiatives in five African cities. Dar es Salaam (Tanzania) was used as a main case with a particular focus on urban flooding. The multi-dimensional assessment covered the physical, institutional, attitudinal and asset factors influencing urban vulnerability. Multiple methods were applied to cover the full range of vulnerabilities and to identify potential response strategies, including: model-based forecasts, spatial analyses, document studies, interviews and stakeholder workshops. We demonstrate the potential of the approach to assessing several dimensions of vulnerability and illustrate the complexity of urban vulnerability at different scales: households (e.g., lacking assets); communities (e.g., situated in low-lying areas, lacking urban services and green areas); and entire cities (e.g., facing encroachment on green and flood-prone land). Scenario modeling suggests that vulnerability will continue to increase strongly due to the expected loss of agricultural land at the urban fringes and loss of green space within the city. However, weak institutional commitment and capacity limit the potential for strategic coordination and action. To better adapt to urban flooding and thereby reduce vulnerability and build resilience, we suggest working across dimensions and scales, integrating climate change issues in city-level plans and strategies and enabling local actions to initiate a ‘learning-by-doing’ process of adaptation.     

A cross-border regional earthquake early warning system: PRESTo@CE3RN

Since 2002 the Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS) in Udine (Italy), the Agencija Republike Slovenije za Okolje (ARSO) in Ljubljana (Slovenia) and the Zentralanstalt für Meteorologie und Geodynamik (ZAMG) in Vienna (Austria), are collecting, analyzing, archiving and exchanging seismic data in real time, initially in the framework of the EU Interreg IIIa Italia-Austria project “Trans-national seismological networks in the South-Eastern Alps”. As outcome of the successful cooperation, in the 2013 OGS, ARSO and ZAMG decided to officially merge their seismic monitoring efforts into the “Central and Eastern European Earthquake Research Network—CE³RN”. This work reports the results of a nine-month real-time test of the earthquake early warning (EEW) algorithm probabilistic and evolutionary early warning system carried out at the CE³RN. The study allowed identifying the actions to be implemented in order to let the CE³RN become in the next future an efficient cross-border EEW system.

     

The influence of physical and biological processes on the ichthyoplankton communities in the Gulf of Sirte (Southern Mediterranean Sea)

This paper reports on the first study on the ichthyoplankton community structure in the Gulf of Sirte and its spatial distribution in relation to environmental conditions and zooplankton abundance. The results make an important contribution to the future management of fisheries in this unexplored, but much exploited, area. Biological samples were collected in July 2008 using a Bongo40 net. In total, 1914 larvae were found and 1652 of these were identified. In particular, bathypelagic taxa were the most abundant, followed by demersal, mesopelagic, pelagic and epipelagic taxa. The ichthyoplankton community had a patchwork distribution influenced by oligotrophic conditions, the bottom depth and oceanographical features. The results suggest that environmental forcings were able to transport the ichthyoplankton to productive areas. Indeed, maximum fish egg densities were found in coastal stations in correspondence with the Atlantic Tunisian Current inflow, whereas larvae were mainly concentrated in the east side of the Gulf, probably as a result of advection by the anticyclonic circulation. Additionally, the distribution patterns of the total larvae density and the different assemblages were well matched with the abundance of the zooplankton, probably determining final larval survival, growth and recruitment.     

A robust debris-flow and GLOF risk management strategy for a data-scarce catchment in Santa Teresa,Peru

The town of Santa Teresa (Cusco Region, Peru) has been affected by several large debris-flow events in the recent past, which destroyed parts of the town and resulted in a resettlement of the municipality. Here, we present a risk analysis and a risk management strategy for debris-flows and glacier lake outbursts in the Sacsara catchment. Data scarcity and limited understanding of both physical and social processes impede a full quantitative risk assessment. Therefore, a bottom-up approach is chosen in order to establish an integrated risk management strategy that is robust against uncertainties in the risk analysis. With the Rapid Mass Movement Simulation (RAMMS) model, a reconstruction of a major event from 1998 in the Sacsara catchment is calculated, including a sensitivity analysis for various model parameters. Based on the simulation results, potential future debris-flows scenarios of different magnitudes, including outbursts of two glacier lakes, are modeled for assessing the hazard. For the local communities in the catchment, the hazard assessment is complemented by the analysis of high-resolution satellite imagery and fieldwork. Physical, social, economic, and institutional vulnerability are considered for the vulnerability assessment, and risk is eventually evaluated by crossing the local hazard maps with the vulnerability. Based on this risk analysis, a risk management strategy is developed, consisting of three complementing elements: (i) standardized risk sheets for the communities; (ii) activities with the local population and authorities to increase social and institutional preparedness; and (iii) a simple Early Warning System. By combining scientific, technical, and social aspects, this work is an example of a framework for an integrated risk management strategy in a data scarce, remote mountain catchment in a developing country.