Precipitation forecasting using classification and regression trees (CART) model: a comparative study of different approaches

Interest in semiarid climate forecasting has prominently grown due to risks associated with above average levels of precipitation amount. Longer-lead forecasts in semiarid watersheds are difficult to make due to short-term extremes and data scarcity. The current research is a new application of classification and regression trees (CART) model, which is rule-based algorithm, for prediction of the precipitation over a highly complex semiarid climate system using climate signals. We also aimed to compare the accuracy of the CART model with two most commonly applied models including time series modeling (ARIMA), and adaptive neuro-fuzzy inference system (ANFIS) for prediction of the precipitation. Various combinations of large-scale climate signals were considered as inputs. The results indicated that the CART model had a better results (with Nash–Sutcliffe efficiency, NSE?>?0.75) compared to the ANFIS and ARIMA in forecasting precipitation. Also, the results demonstrated that the ANFIS method can predict the precipitation values more accurately than the time series model based on various performance criteria. Further, fall forecasts ranked “very good” for the CART method, while the ANFIS and the time series model approximately indicated “satisfactory” and “unsatisfactory” performances for all stations, respectively. The forecasts from the CART approach can be helpful and critical for decision makers when precipitation forecast heralds a prolonged drought or flash flood.     

Climate change adaptation: a systematic review on domains and indicators

In recent years, climate change has been one of the most complicated problems that human being has faced. Climate change adaptation (CCA) is considered to be an important component of risk management. In order to achieve adaptation, it is necessary to determine the indicators influencing adaptation in each community and this requires measurement and standard tools. The aim of this study is to determine and categorize the indicators of CCA. International electronic databases including Science Direct, Web of Science, Scopus, Google Scholar were investigated for only articles published in English language. In addition, Iranian databases including Irandoc, SID, and Magiran were investigated. There was no limitation on the methods of studies. Furthermore, snowball method was used for finding more articles while the ProQuest database was searched for related dissertations. The published documents from 1990 to November 2017 were gathered in this study. Out of 4439 publications initially search, 152 full texts were investigated. Finally, a total of 45 potentially relevant citations were included for full text review; in addition, fourteen other sources were investigated. Using snowball method, we found 24 other articles that were included in our final result. From the searches, 176 indicators were identified, while seven main domains were mentioned. Since in the articles, domains of adaptation are not in the form of a model, it is better to focus on this issue in the future and it seems that prioritizing and weighting domains in adaptation in different communities with different needs are an important issue.

     

Shallow gas accumulations and seepage in the sediments of the Northeast Persian Gulf

Shallow gas accumulations and gas seepage may indicate deeper hydrocarbon reservoirs. Sonar and high resolution seismic recordings were carried out to obtain gas migration and accumulation below seabed in Northeast Persian Gulf. Results revealed some structures and reflectivity effects, for example few pockmarks, gas seepage, and other surface structures in sonar and seismic data. Most of the features observed on seismic data were located at depths between 5 and 10 m (sub-sea bed) that include acoustic blanking and turbidity, gas packets, gas chimneys, and enhanced reflection. Most of the gas is accumulated on young and thin parallel layers, but the presence of acoustic effect in Aghajari Formation indicates that the origin of the gas accumulation in shallow strata may be located in a deeper reservoir.     

Considering potential seismic sources in earthquake hazard assessment for Northern Iran

Located on the Alpine-Himalayan earthquake belt, Iran is one of the seismically active regions of the world. Northern Iran, south of Caspian Basin, a hazardous subduction zone, is a densely populated and developing area of the country. Historical and instrumental documented seismicity indicates the occurrence of severe earthquakes leading to many deaths and large losses in the region. With growth of seismological and tectonic data, updated seismic hazard assessment is a worthwhile issue in emergency management programs and long-term developing plans in urban and rural areas of this region. In the present study, being armed with up-to-date information required for seismic hazard assessment including geological data and active tectonic setting for thorough investigation of the active and potential seismogenic sources, and historical and instrumental events for compiling the earthquake catalogue, probabilistic seismic hazard assessment is carried out for the region using three recent ground motion prediction equations. The logic tree method is utilized to capture epistemic uncertainty of the seismic hazard assessment in delineation of the seismic sources and selection of attenuation relations. The results are compared to a recent practice in code-prescribed seismic hazard of the region and are discussed in detail to explore their variation in each branch of logic tree approach. Also, seismic hazard maps of peak ground acceleration in rock site for 475- and 2,475-year return periods are provided for the region.