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The accuracy of hydrological assessments in mountain regions is often hindered by the low density of gauges coupled with complex spatial variations in climate. Increasingly, spatial datasets (i.e. satellite and other products) and new computational tools are merged with ground observations to address this problem. This paper presents a comparison of approaches of different complexities to spatially interpolate monthly precipitation and daily temperature time series in the upper Aconcagua catchment in central Chile. A generalised linear mixed model (GLMM) whose parameters are estimated through approximate Bayesian inference is compared with simpler alternatives: inverse distance weighting (IDW), lapse rates (LRs), and two methods that analyse the residuals between observations and WorldClim (WC) data or Climate Hazards Group Infrared Precipitation with Station data (CHIRPS). The assessment is based on a leave-one-out cross validation (LOOCV), with the root-mean-squared error (RMSE) being the primary performance criterion for both climate variables, while the probability of detection (POD) and false-alarm ratio (FAR) are also used for precipitation. Results show that for spatial interpolation of temperature and precipitation, the approaches based on the WorldClim or CHIRPS residuals may be recommended as being more accurate, easy to apply and relatively robust to tested reductions in the number of estimation gauges. The GLMM has comparable performance when all gauges were included and is better for estimating occurrence of precipitation but is more sensitive to the reduction in the number of gauges used for estimation, which is a constraint in sparsely monitored catchments. [ABSTRACT FROM AUTHOR]

The snowpack in the central Andes (30°–37°S) is the primary source for streamflow in central Chile and central-western Argentina, but few published studies are available on snowpack variability in the region. This paper develops the first regional snowpack series (1951–2005) from Chilean and Argentinean snow course records. This series shows a strong regional signal, marked interannual variability, and a positive, though nonsignificant, linear trend. Correlations with local precipitation and temperature records reveal a marked association with conditions in central Chile. High snow accumulation is generally concurrent with El Niño events in the tropical Pacific, but only 5 of the 10 driest years coincided with La Niña events. Evaluation of 500-hPa geopotential height anomaly maps during extreme snow years highlights the crucial significance of tropospheric conditions in the subtropical and southeast Pacific in modulating snowfall. Correlations with gridded SST and SLP data and multiple regressions with large-scale climatic indices corroborate a Pacific ENSO-related influence largely concentrated during the austral winter months. This hampers the predictability of snowpack before the onset of the cold season. Annual and warm-season river discharges on both sides of the cordillera are significantly correlated with the regional snowpack record and show positive linear trends over the 1951–2004 common period, probably related to a greater frequency of above-average snowpacks during recent decades. Future demand and competition for water resources in these highly populated regions will require detailed information about temporal and spatial variations in snow accumulation over the Andes. The results indicate that the relationships between snowpack and atmospheric circulation patterns prior to the winter season are complex, and more detailed analyses are necessary to improve prediction of winter snowfall totals. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) emissions affect precipitation worldwide. The response is commonly described by two timescales linked to different processes: a rapid adjustment to radiative forcing, followed by a slower response to surface warming. However, additional timescales exist in the surface-warming response, tied to the time evolution of the sea-surface-temperature (SST) response. Here, we show that in climate model projections, the rapid adjustment and surface mean warming are insufficient to explain the time evolution of the hydro-climate response in three key Mediterranean-like areas--namely, California, Chile, and the Mediterranean. The time evolution of those responses critically depends on distinct shifts in the regional atmospheric circulation associated with the existence of distinct fast and slow SST warming patterns. As a result, Mediterranean and Chilean drying are in quasiequilibrium with GHG concentrations, meaning that the drying will not continue after GHG concentrations are stabilized, whereas California wetting will largely emerge only after GHG concentrations are stabilized. The rapid adjustment contributes to a reduction in precipitation, but has a limited impact on the balance between precipitation and evaporation. In these Mediterranean-like regions, future hydro-climate-related impacts will be substantially modulated by the time evolution of the pattern of SST warming that is realized in the real world. [ABSTRACT FROM AUTHOR]

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In recent decades, Southern Hemisphere midlatitude regions such as southern Africa, southeastern Australia, and southern Chile have experienced a reduction in austral autumn precipitation; the cause of which is poorly understood. This study focuses on the ability of global climate models that form part of the Coupled Model Intercomparison Project phase 5 to simulate these trends, their relationship with extratropical and subtropical processes, and implications for future precipitation changes. Models underestimate both the historical autumn poleward expansion of the subtropical dry zone and the positive southern annular mode (SAM) trend. The multimodel ensemble (MME) is also unable to capture the spatial pattern of observed precipitation trends across semiarid midlatitude regions. However, in temperate regions that are located farther poleward such as southern Chile, the MME simulates observed precipitation declines. The MME shows a strong consensus in twenty-first-century declines in autumn precipitation across southern Chile in both the medium-low and high representative concentration pathway (RCP) scenarios and across southern Africa in the high RCP scenario, but little change across southeastern Australia. Projecting a strong positive SAM trend and continued subtropical dry-zone expansion, the models converge on large SAM and dry-zone-expansion-induced precipitation declines across southern midlatitudes. In these regions, the strength of future precipitation trends is proportional to the strength of modeled trends in these phenomena, suggesting that unabated greenhouse gas-induced climate change will have a large impact on austral autumn precipitation in such midlatitude regions. [ABSTRACT FROM AUTHOR]

Central Chile is facing dramatic projections of climate change, with a consensus for declining precipitation, negatively affecting hydropower generation and irrigated agriculture. Rising from sea level to 6000 m within a distance of 200 km, precipitation characterization is difficult because of a lack of long-term observations, especially at higher elevations. For understanding current mean and extreme conditions and recent hydroclimatological change, as well as to provide a baseline for downscaling climate model projections, a temporally and spatially complete dataset of daily meteorology is essential. The authors use a gridded global daily meteorological dataset at 0.25° resolution for the period 1948-2008, adjusted by monthly precipitation observations interpolated to the same grid using a cokriging method with elevation as a covariate. For validation, daily statistics of the adjusted gridded precipitation are compared to station observations. For further validation, a hydrology model is driven with the gridded 0.25° meteorology and streamflow statistics are compared with observed flow. The high elevation precipitation is validated by comparing the simulated snow extent to Moderate Resolution Imaging Spectroradiometer (MODIS) images. Results show that the daily meteorology with the adjusted precipitation can accurately capture the statistical properties of extreme events as well as the sequence of wet and dry events, with hydrological model results displaying reasonable agreement with observed streamflow and snow extent. This demonstrates the successful use of a global gridded data product in a relatively data-sparse region to capture hydroclimatological characteristics and extremes. [ABSTRACT FROM AUTHOR]

Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)