Station Elevation Research Articles

Tropical West Pacific moisture dynamics and climate controls on rainfall isotopic ratios in southern Papua, Indonesia

AbstractUnderstanding the controls on stable isotopologues of tropical rainfall is critical for paleoclimatic reconstruction from tropical ice core records. The southern Papua region, Indonesia, has a unique climate regime that allows for the evaluation of the influence of precipitation and convective activity on seasonal rainfall δ18O. The influence of the El Niño–Southern Oscillation (ENSO) on interannual rainfall δ18O variation is also important for paleoclimate reconstruction. Here we present stable isotope analyses of 1332 rain samples collected daily during the period from January 2013 to February 2014 (ENSO‐normal) and December 2014 to September 2015 (El Niño) at various elevation stations (9 to 3945 m above sea level) on the southern slope of the central mountain ranges in Papua. The results suggest an altitude effect with an isotopic lapse rate for δ18O (δD) of −2.4‰/km (−18.2‰/km). The temporal δ18O variability (daily to interannual) is controlled mostly by regional convective activity rather than local/regional precipitation amount. The intraseasonal δ18O variation resembles the Madden‐Julian Oscillation cycle with major δ18O depletion events associated with active (wet) phases. Moisture origins, transport pathways, moisture convergence, and raindrop evaporation appear to have no significant seasonal effects on δ18O, leading to the conclusion that condensation temperature controls δ18O depletion associated with convective activity. Seasonal δ18O variation is likely associated with atmospheric temperature at the mean condensation level as indicated by the altitude of latent heat release in the troposphere. Rainfall δ18O (δD) is generally enriched by 1.6‰–2‰ (11‰–15‰) during El Niño than during ENSO‐normal periods.

Factors controlling stable isotope composition of precipitation in arid conditions: an observation network in the Tianshan Mountains, central Asia

Approximately one-third of the Earth's arid areas are distributed across central Asia. The stable isotope composition of precipitation in this region is affected by its aridity, therefore subject to high evaporation and low precipitation amount. To investigate the factors controlling stable water isotopes in precipitation in arid central Asia, an observation network was established around the Tianshan Mountains in 2012. Based on the 1052 event-based precipitation samples collected at 23 stations during 2012–2013, the spatial distribution and seasonal variation of δD and δ18O in precipitation were investigated. The values of δD and δ18O are relatively more enriched in the rainfall dominant summer months (from April to October) and depleted in the drier winter months (from November to March) with low D-excess due to subcloud evaporation observed at many of the driest low elevation stations. The local meteoric water line (LMWL) was calculated to be δD=7.36δ18O – 0.50 (r2=0.97, p<0.01) based on the event-based samples, and δD=7.60δ18O+2.66 (r2=0.98, p<0.01) based on the monthly precipitation-weighted values. In winter, the data indicate an isotopic rain shadow effect whereby rainout leads to depletion of precipitation in the most arid region to the south of the Tianshan Mountains. The values of δ18O significantly correlate with air temperature for each station, and the best-fit equation is established as δ18O=0.78T – 16.01 (r2=0.73, p<0.01). Using daily air temperature and precipitation derived from a 0.5° (latitude)×0.5° (longitude) gridded data set, an isoscape of δ18O in precipitation was produced based on this observed temperature effect.

Remote sensing of interannual boreal forest NDVI in relation to climatic conditions in interior Alaska

Climate has warmed substantially in interior Alaska and several remote sensing studies have documented a decadal-scale decline in the normalized difference vegetation index (NDVI) termed a ‘browning trend’. Reduced summer soil moisture due to changing climatic factors such as earlier springs, less snowpack, and summer drought may reduce boreal productivity and NDVI. However, the relative importance of these climatic factors is poorly understood in boreal interior Alaska. In this study, I used the remotely sensed peak summer NDVI as an index of boreal productivity at 250 m pixel size from 2000 to 2014. Maximum summer NDVI was related to last day of spring snow, early spring snow water equivalent (SWE), and a summer moisture index. There was no significant correlation between early spring SWE and peak summer NDVI. There was a significant correlation between the last day of spring snow and peak summer NDVI, but only for a few higher elevation stations. This was likely due to snowmelt occurring later at higher elevations, thus having a greater effect on summer soil moisture relative to lower elevation sites. For most of boreal interior Alaska, summer drought was likely the dominant control on peak summer NDVI and this effect may persist for several years. Peak summer NDVI declined at all 26 stations after the 2004 drought, and the decline persisted for 2 years at all stations. Due to the shallow rooting zone of most boreal plants, even cool and moist sites at lower elevations are likely vulnerable to drought. For example the peak summer NDVI response following the 2004 drought was similar for adjacent cold and warm watershed basins. Thus, if frequent and severe summer droughts continue, moisture stress effects are likely to be widespread and prolonged throughout most of interior boreal Alaska, including relatively cool, moist sites regardless of spring snowpack conditions or spring phenology.

Analysis of long-term weather, snow and avalanche data at Glacier National Park, B.C., Canada

Long-term changes of the global climate system have been observed. However, the effect of long-term changes in the climate system on avalanche hazard in mountainous areas remains inconclusive. For this study we analyzed long-term weather, snow cover, and avalanche data from Glacier National Park. Weather and snow cover data were measured at two sites (1315m and 1905ma.s.l.). The avalanche data were observed along the section of the Trans Canada Highway within the park. Meteorological data were analyzed by winter season, i.e. early, mid and late winter, represented by three-month periods between September and May. Increasing trends were found for the mean seasonal air temperature at both stations during the mid season. Trends for the solid precipitation rate were not significant, indicating no trend towards more rain events. Decreasing trends of the maximum snow depth were only found for the lower elevation station at Rogers Pass for the mid and late season, which is consistent with decreasing trends for all seasons of the mean 24-hour new snow amounts at the lower elevation and for the mid season at the higher elevation station at Mt. Fidelity. Due to uncertainty arising from changes in explosive control, we draw no conclusions regarding the regional change of avalanche activity. However, the weather and snowpack trends observed in Glacier National Park are consistent with longer time series from mountains with similar latitudes and elevations in France and Switzerland.

Changes in annual, seasonal and monthly precipitation events and their link with elevation in Sichuan province, China

ABSTRACTThis study performs the spatial and temporal trend analysis of the annual, seasonal and monthly time series of a set of uniformly distributed 36 stations precipitation data in Sichuan Province during 1960–2013. The result indicates that the annual precipitation amount and precipitation days across the region have decreasing trends, but the last is significant. The annual precipitation intensity and maximum 1‐day precipitation have slightly increasing trends. Although annual precipitation amount and precipitation days decline, annual precipitation intensity and maximum 1‐day precipitation rise. This reflects that droughts and floods increase in Sichuan Province in recent years. Seasonally, precipitation amount has decreased in all seasons, while autumn precipitation amount is statistically significant. Regionally average trends in precipitation days also show large differences in all seasons. The temporal variability in regional precipitation intensity increases except for autumn, but significant increases are found in spring and summer. Regional maximum 1‐day precipitation has significantly increasing trends in spring and summer, but slightly decreasing trend in autumn and winter. On a monthly basis, trends of precipitation amount show tremendous differences. Spatial changes in annual, seasonal and monthly precipitation events are not the same, and they are not clustered, either. Additionally, the correlations between elevation and the magnitudes of the trends in annual precipitation amount and precipitation days are statistically significant whereas the correlations are not perfect in precipitation intensity and maximum 1‐day precipitation. As can be seen from the seasonal and monthly precipitation amount, the correlations between elevation and the magnitudes of the trends in precipitation amount are higher in spring and autumn, but not perfect in summer and winter. April, October and December precipitation amount shows the strongest positive correlations with elevation. On the whole, the increasing trend of precipitation is relatively prominent at higher elevations, while the decrease trend is more significant at low elevation stations.

THE ROLE OF SATELLITE DERIVED DATA FOR FLOOD INUNDATION MAPPING USING GIS

Abstract. River flooding in planar region is the most significant type of natural disaster that modern society is exposed to, affecting several thousand people each year. Recent flood events, population growth concerns have augmented the call for global methods which utilise both spatial and temporal dynamics. Object oriented classification approaches based on the segmentation are being adopted for extraction of variety of thematic information from high resolution satellite images. Generation of landuse/cover map which is one of the important inputs to the model for flood inundation mapping and for accurate assessment of damage due to floods requires advanced methods of image classification. The Cartosat-1 (PAN) satellite data has been fused with the LISS-III (MX) to obtain the color image containing both high spatial and spectral information. The fused image is further classified to obtain the landuse/cover map using object based classification approach. The classification results are assessed by calculating overall accuracy and kappa index with the help of error matrix. The overall accuracy of classification has been obtained 86.00% with kappa index 0.7815. The objective of this study is to develop a GIS aided model for flood inundation mapping of the surrounding of the part of the Yamuna River which flows through the two districts i.e. Yamuna Nagar and Saharanpur in states of Haryana and Uttar Pradesh respectively. The model considers the five parameters viz. topography (slope, elevation) information, landuse/cover, time series data of surface water elevation, river geometry and location of the rain gauge station. Field survey has been conducted to validate the positional accuracy of the DEM and landuse/cover classes using DGPS. A final flood inundation map has been prepared by combining all weighted layers with in GIS environment. The flood inundation maps can further be used for quick identification of areas of potential flood hazard to minimize the flood losses.

A comprehensive examination of global atmospheric CO2 teleconnections using wavelet-based multi-resolution analysis

Interannual variability of the atmospheric CO2 accumulation is at a similar magnitude of decadal average CO2 accumulation in the atmosphere. Part of observed variability in the atmospheric CO2 can be interpreted by large-scale climate variability. Teleconnection between the atmospheric CO2 anomalous tendency and selected large-scale coupled oceanic and atmospheric oscillations (AO, NAO, PDO, IOD, ENSO and SAM) are examined using wavelet-based multi-resolution analysis. The results indicate that all six oscillation indices appear to be associated with monthly CO2 concentration at most of the 13 examined stations. About 70 % of interannual variability of CO2 anomalous tendency at Mauna Loa and South Pole can be interpreted by the climatic indices. The temperature effects on the CO2 anomalous tendency are overall positive. At the monthly scale, they interpret 14 % variability in CO2 anomalous tendency in 1980–2011. At two high elevation stations (Mauna Loa and South Pole) with large footprints, the teleconnection signal is stronger (interpreting 20 and 25 % variability in monthly CO2 anomalous tendency, respectively). Including global mean monthly air temperature time series, slightly but significantly in the statistical sense, improves the proportion of monthly CO2 anomalous tendency being interpreted at most of the examined stations. The signs (positive vs. negative) in the association between monthly CO2 anomalous tendency and the significant climatic indices appear to reflect physical mechanisms leading to such teleconnections.

Mapping of maximum snow load values for the 50-year return period for Croatia

Snow load is an important climatic element that is, together with minimum and maximum temperatures and wind load, a part of the national annex of standards for the design of structures. In particular, the characteristic snow load, defined as the maximum snow load at the ground for the 50-year return period, has to be estimated and a map has to be supplied as part of the national annex. The method for the estimation of this parameter at the station locations is presented, followed by the geostatistical mapping procedure used to estimate this parameter for the whole territory of Croatia. Snow load is defined as a product of snow density, snow depth and gravitational acceleration. Although snow depths are measured at a large number of meteorological stations, snow loads are not easy to estimate because of very few snow density measurements. To overcome this, snow density was fitted to snow depth data using linear regression according to the daily pairs of values from 13 meteorological stations. Since snow density changes with elevations and seasons, the stations are divided into three subsets according to station elevation (lower than 600 m, 600–1000 m high and higher than 1000 m) and the regression equations are also built on a monthly basis. These modelling results allowed the estimation of the snow load at 96 low-elevation stations, eight higher elevation stations and one high-altitude station based on the monthly maximum snow depths and the model estimated snow densities. Based on the monthly maximum snow loads, the series of maximum annual snow loads can be calculated for 118 locations. These series of annual maximum snow loads are the basis for the estimation of the annual maximum snow load values for the 50-year return period, by means of an extreme value distribution.Finally, the estimated annual maximum snow load values for the 50-year return period at 118 stations constitute the input for the geostatistical procedure of mapping this parameter for the territory of Croatia.

LiDAR Elevation and DEM Errors in Forested Settings

igital elevation models (DEMs) form the basis of LiDAR derived tree height measurements and other topographic modeling needs within natural resource applications. We compared 2873 digital total station elevations to the closest discrete LiDAR elevation point and DEM raster cell across several forest and topographic settings. We also examined limiting comparisons to points within 0.5 m and within one meter. Using all nearest LiDAR points, average total station plot elevation differences ranged from -0.06 m (SD 0.40) to -0.59 m (SD 0.23) indicating that LiDAR elevations are higher than actual elevations. LiDAR DEM differences ranged from -0.09 (SD 0.41) to -0.56 m (SD 0.70). We also compared mapping-grade GPS receiver measurements to LiDAR point elevation and DEMs. Average plot GPS elevation differences ranged from 0.24 (SD 1.55) to 2.82 m (SD 4.58) for the nearest LiDAR point, and from 0.27 (SD 2.33) to 2.69 m (SD 5.06) for LiDAR DEMs. We believe that our efforts represent one of the most robust studies of LiDAR measurement errors available in published literature. The relatively small measurement differences that we found between LiDAR elevations and our most reliable field-based method of elevations, the digital total station, demonstrate the potential for LiDAR in forestry and natural resource applications.

Temporal trends in δ18O composition of precipitation in Germany: insights from time series modelling and trend analysis

AbstractTemporal and spatial variations of stable oxygen (18O) and hydrogen (2H) isotope measurements in precipitation act as important proxies for changing hydro‐meteorological and regional and global climate patterns. Temporal trends in time series of the stable isotope composition in precipitation were rarely observed, and they are poorly understood. These might be a result of a lack of proper trend detection tools and effort for exploring trend processes. Here, we investigate temporal trends of δ18O in precipitation at 17 observation stations in Germany between 1978 and 2009. We test if significant trends in the isotope time series from different models can be observed. Mann–Kendall trend tests are applied on the isotope series, using general multiplicative seasonal autoregressive integrate moving average (ARIMA) models, which account for first and higher order serial correlations. Effects of temperature, precipitation, and geographic parameters on isotope trends are also investigated in the proposed models. To benchmark our proposed approach, the ARIMA results are compared with a trend‐free pre‐whitening procedure, the state of the art method for removing the first order autocorrelation in environmental trend studies. Moreover, we further explore whether higher order serial correlations in isotope series affects our trend results. Overall, three out of the 17 stations show significant changes when higher order autocorrelation are adjusted, and four show a significant trend when temperature and precipitation effects are considered. The significant trends in the isotope time series generally occur only at low elevation stations. Higher order autoregressive processes are shown to be important in the isotope time series analysis. Results suggest that the widely used trend analysis with only the first order autocorrelation adjustment may not adequately take account of the high order autocorrelated processes in the stable isotope series. The investigated time series analysis method including higher autocorrelation and external climate variable adjustments is shown to be a better alternative. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Sensitivity of snowpack storage to precipitation and temperature using spatial and temporal analog models

AbstractEmpirical sensitivity analyses are important for evaluation of the effects of a changing climate on water resources and ecosystems. Although mechanistic models are commonly applied for evaluation of climate effects for snowmelt, empirical relationships provide a first‐order validation of the various postulates required for their implementation. Previous studies of empirical sensitivity for April 1 snow water equivalent (SWE) in the western United States were developed by regressing interannual variations in SWE to winter precipitation and temperature. This offers a temporal analog for climate change, positing that a warmer future looks like warmer years. Spatial analogs are used to hypothesize that a warmer future may look like warmer places, and are frequently applied alternatives for complex processes, or states/metrics that show little interannual variability (e.g., forest cover). We contrast spatial and temporal analogs for sensitivity of April 1 SWE and the mean residence time of snow (SRT) using data from 524 Snowpack Telemetry (SNOTEL) stations across the western U.S. We built relatively strong models using spatial analogs to relate temperature and precipitation climatology to snowpack climatology (April 1 SWE, R2=0.87, and SRT, R2=0.81). Although the poorest temporal analog relationships were in areas showing the highest sensitivity to warming, spatial analog models showed consistent performance throughout the range of temperature and precipitation. Generally, slopes from the spatial relationships showed greater thermal sensitivity than the temporal analogs, and high elevation stations showed greater vulnerability using a spatial analog than shown in previous modeling and sensitivity studies. The spatial analog models provide a simple perspective to evaluate potential futures and may be useful in further evaluation of snowpack with warming.

Assessment of Population Density and Disparity of Village Weaverbirds ( Ploceus cucullatus ) Along Three Selected Road Axis in Ogun State, Nigeria

Study of Village weaverbirds Ploceus cucullatus along the roads was necessary in order to provide prospect of their population, density and platform for monitoring their distribution. Data were collected through on-site observations and pointcount method at thirty-four (34) point-count stations. Collected data were subjected to descriptive statistics and one-way Analysis of Variance (ANOVA). 58.8% of the point-count stations were recorded along Abeokuta/Ibadan road axis, 26.5% along Abeokuta/Shagamu road axis and 14.7% along Ijebu-Ode/Ibadan road axis. Twelve different tree species representing ten families were found colonized by the bird but Cassia spp, Mangifera indica an d Terminalia catappa were most preferred for habitation. Habitat use classifications indicated that Village weaverbirds predominantly nested in human settlements (94.2%). Total population of 1269 Village weaverbirds were recorded along the three road axis and highest population of it was from Abeokuta-Ibadan road axis. Overall, population density for the three road axis was 18 Village weaverbirds per kilometre and among the roads; Abeokuta-Ibadan road axis recorded the highest density. The total population mean Village weaverbirds was 223±13 Village weaverbirds while the total nest population was 129±nest. Total elevation value was 141±6 asl and the mean total of the tree species was 5±1 tree species. Maximum and minimum values of Village weaverbirds population, nest counts, elevation and tree species were recorded along Abeokuta/Ibadan road axis than other road axis. Elevation of point-count station was significantly different (P< 0.05). Elevation of the study locations contributed to Village weaverbirds population, density and population disparity along the road axis. ©JASEM

The Tosontsengel Mongolia world record sea‐level pressure extreme: spatial analysis of elevation bias in adjustment‐to‐sea‐level pressures

ABSTRACTA World Meteorological Organization (WMO) committee evaluated the record sea‐level pressure (SLP) measurement of 1089.4 hPa on 30 December 2004 in Tosontsengel, Mongolia (1724.6 m). Although instrumentation and data collection procedures were properly followed according to the assessment of the committee, concern was raised regarding the reliability of SLP adjustment from such a high‐elevation station. This paper addresses this concern with a number of analyses that look at relationships between SLP extremes and corresponding station elevation and temperature. First, we selected data from stations extracted from the Integrated Surface Database (ISD‐Lite) of NOAA's National Climate Data Center. A spatial analysis indicates that elevation shows little to no association (R2 values essentially zero) to extreme SLP. However, a second analysis between extreme SLP and air temperature indicates that high regionalism exists in spatial correlations (local R2) between those two variables. This relationship to temperature is likely the result of differences in SLP adjustment formulae used around the world. Based on this analysis, on the need to differentiate the SLP values adjusted using extremely cold temperatures (and generally high elevation), and following past WMO SLP guidelines, the WMO Rapporteurs for Climate and Weather Extremes therefore have created two distinct SLP records: (a) highest adjusted SLP (below 750 m), currently 1083.3 hPa recorded on 31 December 1968 at Agata, Evenhiyskiy, Russia; and (b) highest adjusted SLP (above 750 m), currently 1089.4 hPa (by Russian method; 1089.1 hPa by WMO formula) on 30 December 2004 in Tosontsengel, Mongolia. Future WMO guidance regarding SLP adjustment may lead to re‐evaluation of this and other SLP records.

Sensitivity of the Snowmelt Runoff Model to snow covered area and temperature inputs

Snowpack is an important source of freshwater. Snowmelt runoff models provide a means to predict the timing and magnitude of spring snowmelt. This study assessed the sensitivity of the conceptual, degree-day Snowmelt Runoff Model (SRM) to snow covered area and temperature inputs in a small mountainous catchment in Utah, USA. It was found that snow cover products from the Moderate Resolution Imaging Spectroradiometer (MODIS) underestimate snow covered area during the second half of the melt season, leading to inadequate modeling of spring snowmelt with SRM (Nash-Sutcliffe Efficiency = 0.59; bias of −26.9%). Incorporation of ancillary snow covered area information provided by Landsat ETM+ imagery greatly improved streamflow simulations (Nash-Sutcliffe Efficiency = 0.92; bias of −0.01%). For the temperature input, SRM appeared more sensitive to the elevation and location of the temperature reference station than to the lapse rate scenario used to extrapolate temperatures throughout the watershed. These findings will be useful for snowmelt runoff research and water resource management.

Implementation of a webGIS service platform for high mountain climate research: the SHARE GeoNetwork project

The implementation of a webGIS service platform dedicated to the management and sharing of climatological data acquired by high elevation stations is the core of the Station at High Altitude for Research on the Environment (SHARE) GeoNetwork project, promoted by the Ev‐K2 CNR Committee. The web platform basically will provide three types of services: structured metadata archive, data and results from high‐altitude environments research and projects; access to high‐altitude Ev‐K2 CNR stations and creation of a network of existing stations; dedicated webGIS for geo‐referenced data collected during the research. High elevation environmental and territorial data and metadata are catalogued in a single integrated platform to get access to the information heritage of the SHARE project, using open‐source tools: Geonetwork for the metadata catalogue and webGIS resources, and the open‐source Weather and Water Database (WDB), developed by the Norwegian Meteorological Institute, for the database information system implementation. The information system is designed to have a main node, with the possibility to install relocated subsystems based on the same technology, named focal point of SHARE, which will contain metadata and data connected to the main node. In this study, a new structure of metadata for the description of the climatological stations is proposed and WDB adaptation and data preprocessing are described in detail, giving code and script samples.

Systematic errors of mapping functions which are based on the VMF1 concept

Precise global navigation satellite system (GNSS) positioning requires an accurate mapping function (MF) to model the tropospheric delay. To date, the most accurate MF is the Vienna mapping function 1 (VMF1). It utilizes data from a numerical weather model and therefore captures the short-term variability of the atmosphere. Still, the VMF1, or any other MF that is based on the VMF1 concept, is a parameterized mapping approach, and this means that it is tuned for specific elevation angles, station heights, and orbital altitudes. In this study, we analyze the systematic errors caused by such tuning on a global scale. We find that, in particular, the parameterization of the station height dependency is a major concern regarding the application in complex terrain or airborne applications. At this time, we do not provide an improved parameterized mapping approach to mitigate the systematic errors but instead we propose a (ultra-) rapid direct mapping approach, the so-called Potsdam mapping factors (PMFs). Since for any station---satellite link the ratio of the tropospheric delay in the slant and zenith direction is computed directly, the PMFs effectively eliminate the systematic errors.

Lowermost Mantle Velocity Estimations Beneath the Central North Atlantic Area from Pdif Observed at Balkan, East Mediterranean, and American Stations

Lowermost mantle velocity in the area 15°S–70°N latitude/60°W–5° W longitude is estimated using two groups of observations, complementary to each other. There are 894 Pdif observations at stations in the Balkan and Eastern Mediterranean areas from 15 major earthquakes in Central and South America. Another 218 Pdif observations are associated with four earthquakes in Greece/Turkey and one event in Africa, recorded by American stations. A Pdif slowness tomographic approach of the structures immediately above the core-to-mantle boundary (CMB) is used, incorporating corrections for ellipticity, station elevation and velocity perturbations along the ray path. A low-velocity zone above CMB with a large geographical extent, approximately in the area (35–65°N) × (40–20°W), appears to have the velocity perturbations exceeding the value actually assumed by some global models. Most likely, it is extended beneath western Africa. A high-velocity area is observed west of the low-velocity zone. The results suggest that both Cape Verde and Azorean islands are located near transition areas from low-to-high velocity values in the lowermost mantle.

Evaluation of a multi-site weather generator in simulating precipitation in the Qiantang River Basin, East China

Recent years have seen a surge in assessment of potential impacts of climate change. As one of the most important tools for generating synthetic hydrological model inputs, weather generators have played an important role in climate change impact analysis of water management. However, most weather generators like statistical downscaling model (SDSM) and long Ashton research station weather generator (LARS-WG) are designed for single site data generation. Considering the significance of spatial correlations of hydro-meteorological data, multi-site weather data generation becomes a necessity. In this study we aim to evaluate the performance of a new multi-site stochastic model, geo-spatial temporal weather generator (GiST), in simulating precipitation in the Qiantang River Basin, East China. The correlation matrix, precipitation amount and occurrence of observed and GiST-generated data are first compared for the evaluation process. Then we use the GiST model combined with the change factor method (CFM) to investigate future changes of precipitation (2071–2100) in the study area using one global climate model, Hadgem2_ES, and an extreme emission scenario RCP 8.5. The final results show that the simulated precipitation amount and occurrence by GiST matched their historical counterparts reasonably. The correlation coefficients between simulated and historical precipitations show good consistence as well. Compared with the baseline period (1961–1990), precipitation in the future time period (2071–2100) at high elevation stations will probably increase while at other stations decreases will occur. This study implies potential application of the GiST stochastic model in investigating the impact of climate change on hydrology and water resources.

Precipitation isoscape of high reliefs: interpolation scheme designed and tested for monthly resolved precipitation oxygen isotope records of an Alpine domain

Abstract. Stable oxygen isotope composition of atmospheric precipitation (δ18Op) was scrutinized from 39 stations distributed over Switzerland and its border zone. Monthly amount-weighted δ18Op values averaged over the 1995–2000 period showed the expected strong linear altitude dependence (−0.15 to −0.22‰ per 100 m) only during the summer season (May–September). Steeper gradients (~ −0.56 to −0.60‰ per 100 m) were observed for winter months over a low elevation belt, while hardly any altitudinal difference was seen for high elevation stations. This dichotomous pattern could be explained by the characteristically shallower vertical atmospheric mixing height during winter season and provides empirical evidence for recently simulated effects of stratified atmospheric flow on orographic precipitation isotopic ratios. This helps explain "anomalous" deflected altitudinal water isotope profiles reported from many other high relief regions. Grids and isotope distribution maps of the monthly δ18Op have been calculated over the study region for 1995–1996. The adopted interpolation method took into account both the variable mixing heights and the seasonal difference in the isotopic lapse rate and combined them with residual kriging. The presented data set allows a point estimation of δ18Op with monthly resolution. According to the test calculations executed on subsets, this biannual data set can be extended back to 1992 with maintained fidelity and, with a reduced station subset, even back to 1983 at the expense of faded reliability of the derived δ18Op estimates, mainly in the eastern part of Switzerland. Before 1983, reliable results can only be expected for the Swiss Plateau since important stations representing eastern and south-western Switzerland were not yet in operation.

Analysis of Temperature Trends in Sutluj River Basin, India

Surface air temperature is an important climatic variable that drives the hydrological cycle. Precipitation and runoff production are impacted by surface air temperature changes and are, therefore, important for water resources planning, irrigation and agriculture. Focusing on the Sutlej Basin in the Himalayan region, the present research is aimed at utilizing observational evidence to evaluate the response of the region to global warming through investigation of temperature trends. Trends in long-term average annual and seasonal surface air temperature, and several temperature indices at eight stations in the Sutlej River basin have been examined using Mann-Kendall non-parametric test. Six stations exhibited increasing trends in annual average maximum temperature with two being statistically significant. The trends in annual average minimum temperature were mixed; three of eight stations exhibited statistically significant decreasing trends. Some higher elevation stations exhibited clear warming trends both in maximum and minimum temperatures. Analysis of seasonal temperature data indicated that the warming was more pronounced in winter and spring than in summer and autumn. Trends in temperature indices considered in this paper were predominantly increasing. The predominant pattern of increased warming in the basin could have implications for water availability as the snow and glacier melt contribution to annual runoff at Bhakra reservoir is estimated at 59%. The analysis shows that even within a small area, there is variability in the magnitude and direction of historic temperature trends. Some of these variations could be partially attributed to data reliability.