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In this paper, bivariate statistical analysis modeling was applied and validated to derive a landslide susceptibility map of Peloponnese (Greece) at a regional scale. For this purpose, landslide-conditioning factors such as elevation, slope, aspect, lithology, land cover, mean annual precipitation (MAP) and peak ground acceleration (PGA), and a landslide inventory were analyzed within a GIS environment. A landslide dataset was realized using two main landslide inventories. The landslide statistical index method (LSI) produced a susceptibility map of the study area and the probability level of landslide occurrence was classified in five categories according to the best classification method from three different methods tested. Model performance was checked by an independent validation set of landslide events. The accuracy of the final result was evaluated by receiver operating characteristics (ROC) analysis. The prediction ability was found to be 75.2% indicating an acceptable susceptibility map obtained from the GIS-based bivariate statistical model. [ABSTRACT FROM AUTHOR]

Anatural temporary imbalance ofwater availability, consisting of persistent lower-than-average or higher-than-average precipitation, can cause extreme dry and wet conditions that adversely impact agricultural yields, water resources, infrastructure, and human systems. In this study, dry and wet periods in New Zealand were expressed using the Standardized Precipitation Index (SPI). First, both the short term (3 and 6 months) and the long term (12 and 24 months) SPI were estimated, and then, possible trends in the SPI values were detected by means of a new graphical technique, the Innovative Trend Analysis (ITA), which allows the trend identification of the low, medium, and high values of a series. Results show that, in every area currently subject to drought, an increase in this phenomenon can be expected. Specifically, the results of this paper highlight that agricultural regions on the eastern side of the South Island, as well as the north-eastern regions of the North Island, are the most consistently vulnerable areas. In fact, in these regions, the trend analysis mainly showed a general reduction in all the values of the SPI: that is, a tendency toward heavier droughts and weaker wet periods. [ABSTRACT FROM AUTHOR]

The first contact between precipitation and the land surface is often a plant canopy. The resulting precipitation partitioning by vegetation returns water back to the atmosphere (evaporation of intercepted precipitation) and redistributes water to the subcanopy surface as a "drip" flux (throughfall) and water that drains down plant stems (stemflow). Prior to the first benchmark publication of the field by Horton in 1919, European observatories and experimental stations had been observing precipitation partitioning since the mid-19th century. In this paper, we describe these early monitoring networks and studies of precipitation partitioning and show the impressive level of detail. Next to a description of the early studies, results included in this synthesis have been digitized and analyzed to compare them to recent studies. Although many early studies lack modern statistical analyses and monitoring tools that have become standard today, they had many strengths (not necessarily shared by every study, of course), including: A rigorous level of detail regarding stand characteristics (which is often lacking in modern ecohydrological studies); high-resolution spatiotemporal throughfall experiments; and chronosequential data collection and analysis. Moreover, these early studies reveal the roots of interest in precipitation partitioning processes and represent a generally forgotten piece of history shared by the hydrology, meteorology, forestry, and agricultural scientific communities. These studies are therefore relevant today and we hope modern scientists interested in plant-precipitation interactions will find new inspiration in our synthesis and evaluation of this literature. [ABSTRACT FROM AUTHOR]

The processes of isotope fractionation in the hydrological cycle naturally occur during vapor formation, vapor condensation, and moisture transportation. These processes are therefore dependent on local and regional surface and atmospheric physical features such as temperature, pressure, wind speed, and land morphology, and hence on the climate. Because of the strong influence of climate on the isotope fractionation, latitudinal and altitudinal effects on the δ18O and δ2H values of precipitation at a global scale are observed. In this study, we present and compare the processes governing precipitation isotope fractionation from two contrasting climatic regions: Virunga in Central-Eastern Africa and the Central Mediterranean (Stromboli and Sicily, Italy). While Virunga is a forested rainy tropical region located between Central and Eastern Africa, the Mediterranean region is characterized by a rainy mild winter and a dry hot summer. The reported δ18O and δ2H dataset are from precipitation collected on rain gauges sampled either on a monthly or an approximately bimonthly basis and published in previous papers. Both regions show clearly defined temporal and altitudinal variations of δ18O and δ2H, depending on precipitation amounts. The Central Mediterranean shows a clear contribution of local vapor forming at the sea–air interface, and Virunga shows a contribution from both local and regional vapor. The vapor of Virunga is from two competing sources: the first is the continental recycled moisture from soil/plant evaporation that dominates during the rainy season, and the second is from the East African Great Lakes evaporation that dominates during the dry season. [ABSTRACT FROM AUTHOR]

Climate change is one of the prominent factors that causes an increased severity of extreme precipitation which, in turn, has a huge impact on drainage systems by means of flooding. Intensity–duration–frequency (IDF) curves play an essential role in designing robust drainage systems against extreme precipitation. It is important to incorporate the potential threat from climate change into the computation of IDF curves. Most existing works that have achieved this goal were based on Generalized Extreme Value (GEV) analysis combined with various circulation model simulations. Inspired by recent works that used machine learning algorithms for spatial downscaling, this paper proposes an alternative method to perform projections of precipitation intensity over short durations using machine learning. The method is based on temporal downscaling, a downscaling procedure performed over the time scale instead of the spatial scale. The method is trained and validated using data from around two thousand stations in the US. Future projection of IDF curves is calculated and discussed. [ABSTRACT FROM AUTHOR]

Floods typically occur due to ice jams in the winter or extended periods of precipitation in the spring and summer seasons. An increase in the rate of water discharge in the river coincides with a flood event. This research combines the time series decomposition and the time series regression model for the flood prediction in Mohawk River at Schenectady, New York. The time series decomposition has been applied to separate the different frequencies in hydrogeological and climatic data. The time series data have been decomposed into the long-term, seasonal-term, and short-term components using the Kolmogorov-Zurbenko filter. For the application of the time series regression model, we determine the lags of the hydrogeological and climatic variables that provide the maximum performance for the model. The lags applied in the predictor variables of the model have been used for the physical interpretation of the model to strengthen the relationship between the water discharge and the climatic and hydrogeological variables. The overall model accuracy has been increased up to 73%. The results show that using the lags of the variables in the time regression model, and the forecasting accuracy has been increased compared to the raw data by two times. [ABSTRACT FROM AUTHOR]

Glacier area and volume changes were quantified through the use of historical aerial photographs in the Wind River Range, Wyoming. Forty-four glaciers in the Wind River Range were analyzed using orthorectified aerial photography from 2012. This is an update to the work of Thompson et al. [1] in which the surface area changes of the 44 glaciers were estimated from 1966 to 2006. The total surface area of the glaciers was estimated to be 27.8 ± 0.8 km², a decrease of 39% from 1966 and a decrease of 2% from 2006. The 2012 volume changes for the 44 glaciers were estimated using the Bahr et al. [2] volume-area scaling technique. The total glacier volume in 2012 was calculated to be 1.01 ± 0.21 km³, a decrease of 63% from 1966. These results, once compared to temperature and snowpack trends, suggest that the downward trend in snowpack as well as increasing temperatures seem to be the most likely driver of the glacier recessions. With Global Circulation Models (GCMs) forecasting higher temperatures and lower precipitation in the western U.S., it is likely that glaciers will continue to recede.Glacier area and volume changes were quantified through the use of historical aerial photographs in the Wind River Range, Wyoming. Forty-four glaciers in the Wind River Range were analyzed using orthorectified aerial photography from 2012. This is an update to the work of Thompson et al. [1] in which the surface area changes of the 44 glaciers were estimated from 1966 to 2006. The total surface area of the glaciers was estimated to be 27.8 ± 0.8 km², a decrease of 39% from 1966 and a decrease of 2% from 2006. The 2012 volume changes for the 44 glaciers were estimated using the Bahr et al. [2] volume-area scaling technique. The total glacier volume in 2012 was calculated to be 1.01 ± 0.21 km³, a decrease of 63% from 1966. These results, once compared to temperature and snowpack trends, suggest that the downward trend in snowpack as well as increasing temperatures seem to be the most likely driver of the glacier recessions. With Global Circulation Models (GCMs) forecasting higher temperatures and lower precipitation in the western U.S., it is likely that glaciers will continue to recede. [ABSTRACT FROM AUTHOR]

The Northeast region of Brazil (NRB) is the most populous semiarid area in the world and is extremely susceptible to droughts. The severity and duration of these droughts depend on several factors, and they do not necessarily follow the same behavior. The aim of this work is to evaluate the frequency of droughts in the NRB and calculate the return period of each drought event using the copula technique, which integrates the duration and severity of the drought in the NRB in a joint bivariate distribution. Monthly precipitation data from 96 meteorological stations spatially distributed in the NRB, ranging from 1961 to 2017, are used. The copula technique is applied to the Standardized Precipitation Index (SPI) on the three-month time scale, testing three families of Archimedean copula functions (Gumbel–Hougaard, Clayton and Frank) to reveal which model is best suited for the data. Averagely, the most frequent droughts observed in the NRB are concentrated in the northern sector of the region, with an observed duration varying from three and a half to five and a half months. However, the eastern NRB experiences the most severe droughts, lasting for 14 to 24 months. The probability distributions that perform better in modeling the series of severity and duration of droughts are exponential, normal and lognormal. The observed severity and duration values show that, for average values, the return period across the region is approximately 24 months. Still in this regard, the southernmost tip of the NRB stands out for having a return period of over 35 months. Regarding maximum observed values of severity and duration, the NRB eastern strip has the longest return period (>60 months), mainly in the southeastern portion where a return period above 90 months was observed. The northern NRB shows the shortest return period (~45 months), indicating that it is the NRB sector with the highest frequency of intense droughts. These results provide useful information for drought risk management in the NRB. [ABSTRACT FROM AUTHOR]

In the basin of the Amur River in the Russian Far East, the influence of watershed areas covered by forests on the river basin has a complex nature, and no strict functional dependency has been established yet between these two factors. A study of the Amur River watershed in the current conditions, between 2000 and 2016 (climate, forest coverage, fires, and felling), has been conducted using the ground and satellite observations. The purpose of the study was to identify their influence on the river behaviour (flow, flooding, and levels of water). The study of hydrological regime of rivers was conducted in conjunction with the analysis of the dynamics of forest and burns areas over the synchronised periods of time. A special attention was given to the changing nature of the species composition of the forests (coniferous and deciduous forests separately) from 2000 to 2016, and climatic parameters over thirty years (atmospheric temperature, dew point, precipitation). New facts have been obtained, which provide an explanation of the reasons for predominant prolonged trends in the dynamics of the summer streamflow. In the view of the general tendency toward increased forest coverage combining all species of forest stand, the trend in the dynamics of the coniferous species areas is negative. Therefore, a conclusion can be made, that one of the major factors in the increase of the river flood flow (alongside the atmospheric precipitation), is deforestation of primary coniferous forests on the watershed areas, in contrast with the deciduous forests, where the trend is positive. Practicability of such conclusions can be justified, as different types of forests have different root systems, which mellow the ground and facilitate partial loss of the atmospheric precipitation and its transformation into the groundwater flow. Besides, coniferous forests attract more frequent and intensive fires, more subjected to felling, have longer regeneration period, and also, use larger volumes of ground waters for growing and functioning. Consequently, with their disappearance, an increase in streamflow should be expected. No changes in surface temperature and humidity of the forest cover in the watersheds during 1980-2016 despite global warming. Therefore, annual variability of forested areas of watersheds is greatly influenced by fires and felling. There are reasons to assume, that because of the tendency for decreasing areas of coniferous forests, the conditions contributing to the increases in rivers' flood flow and flood risks during monsoon and frontal cyclonic rainfalls will remain. [ABSTRACT FROM AUTHOR]

Almost every year, north Atlantic tropical cyclones (TCs) are responsible for significant socioeconomic losses across the Mid-Atlantic USA. However, the extent to which TC activity contributes to the changes in the probability distributions of the extreme precipitation have not yet been comprehensively characterized for this region. In this study, a quantile regression method was used to investigate the trends of the lower (τ = 0.2) and upper (τ = 0.8) quantiles of annual and seasonal daily maximum precipitation series for the region using the station-based daily precipitation data for the period 1950–2011. Results show that the rates of changes in the upper quantile have greatly strengthened for the region. Analysis of the spatial pattern of the lower and upper quantile trends for TC and non-TC extreme precipitation series shows that trends have larger magnitudes in most of the sites for TC precipitation series as compared with the non-TC precipitation series for both the lower and upper quantiles. Additionally, the highest trends are observed in the upper quantile for TC time series indicating that TC precipitation is contributing more to the upper tails of the extreme precipitation distribution as compared to the non-TC precipitation. Results from this study have implications for the improved design and reassessment of flood-controlling infrastructure. [ABSTRACT FROM AUTHOR]

This study provides a unique procedure for validating and reconstructing temperature and precipitation data. Although developed from data in Middle Italy, the validation method is intended to be universal, subject to appropriate calibration according to the climate zones analysed. This research is an attempt to create shared applicative procedures that are most of the time only theorized or included in some software without a clear definition of the methods. The purpose is to detect most types of errors according to the procedures for data validation prescribed by the World Meteorological Organization, defining practical operations for each of the five types of data controls: gross error checking, internal consistency check, tolerance test, temporal consistency, and spatial consistency. Temperature and precipitation data over the period 1931–2014 were investigated. The outcomes of this process have led to the removal of 375 records (0.02%) of temperature data from 40 weather stations and 1286 records (1.67%) of precipitation data from 118 weather stations, and 171 data points reconstructed. In conclusion, this work contributes to the development of standardized methodologies to validate climate data and provides an innovative procedure to reconstruct missing data in the absence of reliable reference time series. [ABSTRACT FROM AUTHOR]

The investigation of the statistical links between changes in temperature and rainfall, though not widely achieved in the past, is an interesting issue because their physical interdependence is difficult to point out. Aiming at detecting possible trends with a pooled approach, a correlative analysis of temperature and rainfall has been carried out by comparing changes in their standardized anomalies from two different 30-year time periods. The procedure has been applied to the time series of seasonal mean temperature and cumulative rainfall observed in four sites of the Calabria region (Southern Italy), with reference to the series which verify the normality hypothesis. Specifically, the displacements of the ellipses, representing the probability density functions of the bivariate normal distribution assumed for the climatic variables, have been quantified and tested for each season, passing from the first subperiod to the following one. The main results concern a decreasing trend of both the temperature and the rainfall anomalies, predominantly in the winter and autumn seasons. [ABSTRACT FROM AUTHOR]