Spatiotemporal Variability Of Precipitation Research Articles

The large-scale spatio-temporal variability of precipitation over Sweden observed from the weather radar network

Abstract. Using measurements from the national network of 12 weather radar stations for the 11-year period 2000–2010, we investigate the large-scale spatio-temporal variability of precipitation over Sweden. These statistics provide useful information to evaluate regional climate models as well as for hydrology and energy applications. A strict quality control is applied to filter out noise and artifacts from the radar data. We focus on investigating four distinct aspects: the diurnal cycle of precipitation and its seasonality, the dominant timescale (diurnal versus seasonal) of variability, precipitation response to different wind directions, and the correlation of precipitation events with the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO). When classified based on their intensity, moderate- to high-intensity events (precipitation > 0.34 mm/3 h) peak distinctly during late afternoon over the majority of radar stations in summer and during late night or early morning in winter. Precipitation variability is highest over the southwestern parts of Sweden. It is shown that the high-intensity events (precipitation > 1.7 mm/3 h) are positively correlated with NAO and AO (esp. over northern Sweden), while the low intensity events are negatively correlated (esp. over southeastern parts). It is further observed that southeasterly winds often lead to intense precipitation events over central and northern Sweden, while southwesterly winds contribute most to the total accumulated precipitation for all radar stations. Apart from its operational applications, the present study demonstrates the potential of the weather radar data set for studying climatic features of precipitation over Sweden.

Spatio-temporal variations of precipitation in arid and semiarid regions of China: The Yellow River basin as a case study

Daily precipitation data from 64 precipitation gauging stations within the Yellow River basin were analyzed by defining 17 precipitation indices with the aim to investigate space and time changes in precipitation characteristics. The modified Mann–Kendall trend test method was used to detect trends in the precipitation series. The influence of Interdecadal Pacific Oscillation (IPO) on the basin precipitation was also investigated. Results indicate that: (1) the Yellow River basin is dominated by decreasing precipitation and precipitation deficit may further deteriorate the shortage of water resources. The middle Yellow River basin may encounter increasingly serious shortage of water resources and higher risk of floods would be expected in the lower Yellow River basin; (2) a higher risk of droughts can be expected in spring and autumn and precipitation in winter is increasing, which imply evident seasonality and seasonal shifts of precipitation changes within the basin; (3) short-duration consecutive precipitation events are prevalent in the basin and frequency and amount of short-duration consecutive precipitation events are increasing; (4) precipitation extremes are not dominant in the Yellow River basin, except for some regions in the North China Plain and rainstorm events are generally decreasing; and (5) no evident impact of IPO on the precipitation in the Yellow River basin is found. In this case, precipitation deficit would be a major problem the basin-scale water resources management may face in the future.

Caractérisation de la Variabilité Climatique : Cas du Bassin Versant de La Medjerda ( Nord-Est Algérien ) = Characterization of Climate Variability : Case of Watershed Medjerda ( North East of Algeria )

جفاف | تغير المناخ | تجزئة هطول الأمطار | حوض مجردة | شرق الجزائر |Sécheresse | Variabilité Climatique | Fraction Pluviométrique | Bassin Versant Medjerda | Est de L'Algérie | Drought | Climate Variability | Rainfall Fraction | Watershed Medjerda | East of Algeria

Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation

Abstract. There are a number of statistical techniques that downscale coarse climate information from general circulation models (GCMs). However, many of them do not reproduce the small-scale spatial variability of precipitation exhibited by the observed meteorological data, which is an important factor for predicting hydrologic response to climatic forcing. In this study a new downscaling technique (Bias-Correction and Stochastic Analog method; BCSA) was developed to produce stochastic realizations of bias-corrected daily GCM precipitation fields that preserve both the spatial autocorrelation structure of observed daily precipitation sequences and the observed temporal frequency distribution of daily rainfall over space. We used the BCSA method to downscale 4 different daily GCM precipitation predictions from 1961 to 1999 over the state of Florida, and compared the skill of the method to results obtained with the commonly used bias-correction and spatial disaggregation (BCSD) approach, a modified version of BCSD which reverses the order of spatial disaggregation and bias-correction (SDBC), and the bias-correction and constructed analog (BCCA) method. Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spatial correlation indices, and variograms for wet (June through September) and dry (October through May) seasons were calculated for each method. Results showed that (1) BCCA underestimated mean daily precipitation for both wet and dry seasons while the BCSD, SDBC and BCSA methods accurately reproduced these characteristics, (2) the BCSD and BCCA methods underestimated temporal variability of daily precipitation and thus did not reproduce daily precipitation standard deviations, transition probabilities or wet/dry spell lengths as well as the SDBC and BCSA methods, and (3) the BCSD, BCCA and SDBC methods underestimated spatial variability in daily precipitation resulting in underprediction of spatial variance and overprediction of spatial correlation, whereas the new stochastic technique (BCSA) replicated observed spatial statistics for both the wet and dry seasons. This study underscores the need to carefully select a downscaling method that reproduces all precipitation characteristics important for the hydrologic system under consideration if local hydrologic impacts of climate variability and change are going to be reasonably predicted. For low-relief, rainfall-dominated watersheds, where reproducing small-scale spatiotemporal precipitation variability is important, the BCSA method is recommended for use over the BCSD, BCCA, or SDBC methods.

A principal components/singular spectrum analysis approach to ENSO and PDO influences on rainfall in western Iran

This paper analyzes the spatio-temporal variability of precipitation in western Iran by means of the Standardized Precipitation Index (SPI), calculated in annual and seasonal aggregations for wet and dry conditions over 46 synoptic stations with monthly data from 1957 to 2008. Regions of homogeneous SPI realizations were delimited using principal components analysis (PCA) to highlight major variation modes distinguishable in the basin and singular spectrum analysis (SSA) was performed over the reconstructed values of SPI to identify their oscillation modes. Extreme SPI values associated with El Niño–Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) were also evaluated. Results show two significant oscillations around 4 and 8 years on the SPI with an increase in precipitation variability since 1990 and a tendency to have less rain during the cold season and more rain during the warm season. We calculated standardized products among SPI, ENSO, and PDO to determine years in which the indices reinforce each other, 1986, 1992, 1999, and 2008 being particularly significant in the associations. These results are important for water managers in western Iran because they indicate significant changes in precipitation regimes associated with ENSO and PDO signals that will help to assess the occurrence of droughts and floods in the area.

Spatio-temporal variation of precipitation in the Three-River Headwater Region from 1961 to 2010

Based on a monthly dataset of precipitation time series (1961–2010) from 12 meteorological stations across the Three-River Headwater Region (THRHR) of Qinghai Province, China, the spatio-temporal variation and abrupt change analysis of precipitation were examined by using moving average, linear regression, spline interpolation, the Mann-Kendall test and so on. Major conclusions were as follows. (1) The long-term annual and seasonal precipitation in the study area indicated an increasing trend with some oscillations during 1961–2010; however, the summer precipitation in the Lantsang (Lancang) River Headwater Region (LARHR), and the autumn precipitation in the Yangtze River Headwater Region (YERHR) of the THRHR decreased in the same period. (2) The amount of annual precipitation in the THRHR and its three sub-headwater regions was greater in the 1980s and 2000s. The springs were fairly wet after the 1970s, while the summers were relatively wet in the 1960s, 1980s and 2000s. In addition, the amount of precipitation in the autumn was greater in the 1970s and 1980s, but it was relatively less for the winter precipitation, except in the 1990s. (3) The normal values of spring, summer, winter and annual precipitation in the THRHR and its three sub-headwater regions all increased, but the normal value of summer precipitation in the LARHR had a negative trend and the normal value of winter precipitation declined in general. (4) The spring and winter precipitation increased in most of the THRHR. The summer, autumn and annual precipitation increased mainly in the marginal area of the west and north and decreased in the regions of Yushu, Zaduo, Jiuzhi and Banma. (5) The spring and winter precipitation in the THRHR and its three sub-headwater regions showed an abrupt change, except for the spring precipitation in the YARHR. The abrupt changes of spring precipitation were mainly in the late 1980s and early 1990s, while the abrupt changes of winter precipitation were primary in the mid-to late 1970s. This research would be helpful for further understanding the trends and periodicity of precipitation and for watershed-based water resource management in the THRHR.

An Independent Assessment of the Monthly PRISM Gridded Precipitation Product in Central Oklahoma

Accurate, long-term records of precipitation are required for the development of climate-informed decision support tools for agriculture. But rain gauges are too sparsely located to meet this need, and radar-derived precipitation measurements are too recent in duration. Using all readily available station records, spatiotemporally continuous estimates of precipitation were created by the PRISM Climate Group to address this problem. As with all interpolated data, the validity of the gridded PRISM product requires validation, and given the extreme spatiotemporal variability of precipitation, such validation is essential. Previous work comparing the monthly precipitation product against contributing rain gauge data revealed inconsistencies that prompted the analysis reported herein. As a basis for checking the accuracy of the PRISM product, independent precipitation data gathered at a USDA research laboratory in central Oklahoma were quality controlled, including comparison to a co-located automated rain gauge operated by the Oklahoma Mesonet. Results indicate that the independent USDA gauge data are of sufficient quality to use in the evaluation of the PRISM product. The area average of the independent USDA data over a matching size area was then used to validate colocated gridded PRISM estimates. The validation results indicate that the monthly gridded PRISM precipitation estimates are close to the independent observed data in terms of means (smaller by 3% to 4.5%) and cumulative probability distributions (within ~4%), but with variances too small by 7% to 11%. From the point of view of agricultural decision support, these results indicate that PRISM estimates might be useful for probabilistic applications, such as downscaling climate forecasts or driving weather generators, assuming appropriate corrections to the higher-order statistics were applied. However, the number of months with potentially significant differences precludes the use of PRISM estimates for any retrospective month-by-month analyses of possible interactions between climate, crop management, and productivity.

Long-Term (1951–2007) Rainfall Trends around Six Indian Cities: Current State, Meteorological, and Urban Dynamics

The present study focuses on analyzing the precipitation trends over six Indian cities during the summer monsoon (June–September) covering the period 1951–2007 and also attempting to investigate possible urban forcing and dynamics by examining the variation in precipitation in the upwind and downwind directions. The analysis shows negative trends in the total number of rainy days over Hyderabad (−10.4%), Kanpur (−7.1%), Jaipur (−10.5%), and Nagpur (−4.8%) and positive trends over Delhi (7.4%) and Bangalore (22.9%). On the other hand, decreases of −21.3%, −5.9%, −14.2%, and −14.6% in seasonal rainfall are found over Delhi, Hyderabad, Jaipur, and Kanpur, respectively, whereas Bangalore and Nagpur show 65.8% and 13.5% increase. The lesser rainfall and rainy days, along with the mostly declining trend, in the downwind directions of the cities may imply an urban influence in precipitation associated with the increased anthropogenic emissions due to expansion of the urban areas and the increase of population. However, the large spatiotemporal variability of precipitation and the lack of statistical significance in the vast majority of the trends do not allow the extraction of safe conclusion concerning the aerosol-precipitation interactions around Indian cities.

Testing gridded NWS 1-day observed precipitation analysis in a daily irrigation scheduler

One of the inputs required by daily decision support tools for scheduling irrigation is the amount of water supplied by rainfall. In-field measurements of daily precipitation are expensive or laborious, while measurements from gauges within a few kilometers are frequently not representative due to the high spatiotemporal variability of precipitation. Online radarbased precipitation analyses from NOAA’s National Weather Service (NWS) have obvious potential to provide the needed data, but are known to have varying degrees of accuracy with location and conditions. The NWS precipitation analysis is computed on a 4 km × 4 km grid, so differences should also be expected between the product and individual gauge measurements under each grid cell. In order to test the utility of the NWS precipitation analysis in a daily irrigation scheduler, daily data were gathered in July 2012 from 18 weather stations under 2 NWS precapitation analysis grid cells across instru-mented research and production fields in the Mississippi Delta. Differences between individual station measurements and the NWS precipitation analysis are examined, and root-zone daily soil water deficits computed using both station data and the NWS precipitation analysis. Sub-grid spatial variability between gauge locations, and differences in precipitation between gauges and the gridded NWS analysis, are found to be similar to those reported elsewhere. Differences between time series of soil water deficit computed using the two different precipitation data sources are noted, but prove to be of limited impact on the decision to irrigate or not to irrigate. It is also noted that profile-filling rainfalls limit the impact of accumulating error, resetting the modeled soil water to “full”. Given the Delta-local practice of irrigating to replace full evapotranspirational water used, use of the NWS daily precipitation analysis data as input for a daily irrigation scheduler is judged not only acceptable, but also preferable to other sources of daily precipitation data.

Evaluation of spatial and spatiotemporal estimation methods in simulation of precipitation variability patterns

Identification of spatial and spatiotemporal precipitation variations plays an important role in different hydrological applications such as missing data estimation. In this paper, the results of Bayesian maximum entropy (BME) and ordinary kriging (OK) are compared for modeling spatial and spatiotemporal variations of annual precipitation with and without incorporating elevation variations. The study area of this research is Namak Lake watershed located in the central part of Iran with an area of approximately 90,000 km2. The BME and OK methods have been used to model the spatial and spatiotemporal variations of precipitation in this watershed, and their performances have been evaluated using cross-validation statistics. The results of the case study have shown the superiority of BME over OK in both spatial and spatiotemporal modes. The results have shown that BME estimates are less biased and more accurate than OK. The improvements in the BME estimates are mostly related to incorporating hard and soft data in the estimation process, which resulted in more detailed and reliable results. Estimation error variance for BME results is less than OK estimations in the study area in both spatial and spatiotemporal modes.

Simulations over South Asia using the Weather Research and Forecasting model with Chemistry (WRF-Chem): set-up and meteorological evaluation

Abstract. The configuration and evaluation of the meteorology is presented for simulations over the South Asian region using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Temperature, water vapor, dew point temperature, zonal and meridional wind components, precipitation and tropopause pressure are evaluated against radiosonde and satellite-borne (AIRS and TRMM) observations along with NCEP/NCAR reanalysis fields for the year 2008. Chemical fields, with focus on tropospheric ozone, are evaluated in a companion paper. The spatial and temporal variability in meteorological variables is well simulated by the model with temperature, dew point temperature and precipitation showing higher values during summer/monsoon and lower during winter. The index of agreement for all the parameters is estimated to be greater than 0.6 indicating that WRF-Chem is capable of simulating the variations around the observed mean. The mean bias (MB) and root mean square error (RMSE) in modeled temperature, water vapor and wind components show an increasing tendency with altitude. MB and RMSE values are within ±2 K and 1–4 K for temperature, 30% and 20–65% for water vapor and 1.6 m s−1 and 5.1 m s−1 for wind components. The spatio-temporal variability of precipitation is also reproduced reasonably well by the model but the model overestimates precipitation in summer and underestimates precipitation during other seasons. Such a behavior of modeled precipitation is in agreement with previous studies on South Asian monsoon. The comparison with radiosonde observations indicates a relatively better model performance for inland sites as compared to coastal and island sites. The MB and RMSE in tropopause pressure are estimated to be less than 25 hPa. Sensitivity simulations show that biases in meteorological simulations can introduce errors of ±(10–25%) in simulations of tropospheric ozone, CO and NOx. Nevertheless, a comparison of statistical metrics with benchmarks indicates that the model simulated meteorology is of sufficient quality for use in chemistry simulations.

Quantitative Spatiotemporal Evaluation of Dynamically Downscaled MM5 Precipitation Predictions over the Tampa Bay Region, Florida

AbstractThis research quantitatively evaluated the ability of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) to reproduce observed spatiotemporal variability of precipitation in the Tampa Bay region over the 1986–2008 period. Raw MM5 model results were positively biased; therefore, the raw model precipitation outputs were bias corrected at 53 long-term precipitation stations in the region using the cumulative distribution function (CDF) mapping approach. CDF mapping effectively removed the bias in the mean daily, monthly, and annual precipitation totals and improved the RMSE of these rainfall totals. Observed daily precipitation transition probabilities were also well predicted by the bias-corrected MM5 results. Nevertheless, significant error remained in predicting specific daily, monthly, and annual total time series. After bias correction, MM5 successfully reproduced seasonal geostatistical precipitation patterns, with higher spatial variance of daily precipitation in the wet season and lower spatial variance of daily precipitation in the dry season. Bias-corrected daily precipitation fields were kriged over the study area to produce spatiotemporally distributed precipitation fields over the dense grids needed to drive hydrologic models in the Tampa Bay region. Cross validation at the 53 long-term precipitation gauges showed that kriging reproduced observed rainfall with average RMSEs lower than the RMSEs of individually bias-corrected point predictions. Results indicate that although significant error remains in predicting actual daily precipitation at rain gauges, kriging the bias-corrected MM5 predictions over a hydrologic model grid produces distributed precipitation fields with sufficient realism in the daily, seasonal, and interannual patterns to be useful for multidecadal water resource planning in the Tampa Bay region.

Spatio-temporal variability of precipitation, temperature and agricultural drought indices in Central Italy

The agricultural sector is probably the one that will suffer most directly from the climatic variations expected at the global level. In particular, the analysis of the changes expected in water availability and demand is fundamental in order to correctly establish both the present water resource management and the definition of new strategies. In this paper the time series of some climatic and agro-climatic indices in the Region of Umbria (Central Italy) have been analyzed with the aim of finding signs of climate changes and identifying the potential impacts on the agricultural water balance. The aforesaid indices include the precipitation, the mean maximum and minimum temperatures ( Tmin, Tmax), the mean temperature range (Δ T), the reference evapotranspiration ( ET0) and two drought indices, Standardized Precipitation Index ( SPI) and Standardized Deficit Index ( SDI, based on the difference between ET0 and precipitation). These indices were analyzed with reference to different periods (in particular the average growing and irrigation seasons). Furthermore, more specific information was obtained by analyzing the simulated water requirement ( CWR) and evapotranspiration deficit ( ED) of two typical annual crops (corn and sunflower). The time series of the indices were quantified for 38 stations in the region and they were then analyzed with non-parametric tests both at single sites and at the regional level. The tendencies in cumulated precipitation are generally decreasing (particularly during the wet period) and they are also characterized by a defined spatial pattern. The rainfall reduction during the irrigation season, although less widespread, could have the most important practical consequences. The significant trends detected for both the Tmax and Tmin are mainly positive, and they are more evident for Tmin, often resulting in a reduction of Δ T. ET0 shows a prevailing stationary condition due to the counteracting effects of the prevalent reduction observed for the Δ T and the increment of the mean temperature. At any rate, with reference to the irrigation season, ET0 trends are mainly positive. The results obtained for SPI and SDI are in accordance with the tendencies of non-standardized indices with an expectation of extreme drought event occurrence doubled or even tripled over a 30-year time span. Finally, the analysis of the CWR for corn and the ED for sunflower shows a relevant presence of significant positive trends whose impacts can be estimated in respective mean increments of about 23% and 44% over a 50-year time span.

Sensitivity of Heavy Precipitation Forecasts to Small Modifications of Large-Scale Weather Patterns for the Elbe River

Abstract Extreme flood events are caused by long-lasting and/or intensive precipitation. The detailed knowledge of the distribution, intensity, and spatiotemporal variability of precipitation is, therefore, a prerequisite for hydrological flood modeling and flood risk management. For hydrological modeling, temporal and spatial high-resolution precipitation data can be provided by meteorological models. This study deals with the question of how small changes in the synoptic situation affect the characteristics of extreme forecasts. For that purpose, two historic extreme precipitation events were hindcasted using the Consortium for Small Scale Modeling (COSMO) model of the German Weather Service (DWD) with different grid resolutions (28, 7, and 2.8 km), where the domains with finer resolutions were nested into the ones with coarser resolution. The results show that the model is capable of simulating such extreme precipitation events in a satisfactory way. To assess the impact of small changes in the synoptic situations on extreme precipitation events, the large-scale atmospheric fields were shifted to north, south, east, and west with respect to the orography by about 28 and 56 km, respectively, in one series of runs while in another series, the relative humidity and temperature were increased to modify the amount of precipitable water. Both series were performed for the Elbe flood events in August 2002 and January 2003, corresponding to two very different synoptic situations. The results show that the modeled precipitation can be quite sensitive to small changes of the synoptic situation with changes in the order of 20% for the maximum daily precipitation and that the types of synoptic situations play an important role. While van Bebber weather conditions, of Mediterranean origin, were quite sensitive to modifications, more homogeneous weather patterns were less sensitive.

Spatiotemporal Variability of Precipitation, Modeled Soil Moisture, and Vegetation Greenness in North America within the Recent Observational Record

Abstract Dominant spatiotemporal patterns of precipitation, modeled soil moisture, and vegetation are determined in North America within the recent observational record (late twentieth century onward). These data are from a gridded U.S.–Mexico precipitation product, retrospective long-term integrations of two land surface models, and satellite-derived vegetation greenness. The analysis procedure uses three statistical techniques. First, all the variables are normalized according to the standardized precipitation index procedure. Second, dominant patterns of spatiotemporal variability are determined using multitaper method–singular value decomposition for interannual and longer time scales. The dominant spatiotemporal patterns of precipitation generally conform to known and distinct Pacific SST forcing in the cool and warm seasons. Two specific time scales in precipitation at 9 and 6–7 yr correspond to significant variability in soil moisture and vegetation, respectively. The 9-yr signal is related to precipitation in late fall to early winter, whereas the 6–7-yr signal is related to earlysummer precipitation. Canonical correlation analysis is finally used to confirm that strong covariability between land surface variables and precipitation exists at these specific times of the year. Both signals are strongest in the central and western United States and are consistent with prior global modeling and paleoclimate studies that have investigated drought in North America.

Climate change projections for the tropical Andes using a regional climate model: Temperature and precipitation simulations for the end of the 21st century

High‐elevation tropical mountain regions may be more strongly affected by future climate change than their surrounding lowlands. In the tropical Andes a significant increase in temperature and changes in precipitation patterns will likely affect size and distribution of glaciers and wetlands, ecosystem integrity, and water availability for human consumption, irrigation, and power production. However, detailed projections of future climate change in the tropical Andes are not yet available. Here we present first results for the end of the 21st century (2071–2100) using a regional climate model (RCM) based on two different emission scenarios (A2 and B2). The model adequately simulates the spatiotemporal variability of precipitation and temperature but displays a cool and wet bias, in particular along the eastern Andean slope during the wet season, December–February. Projections of changes in the 21st century indicate significant warming in the tropical Andes, which is enhanced at higher elevations and further amplified in the middle and upper troposphere. Temperature changes are spatially similar in both scenarios, but the amplitude is significantly higher in RCM‐A2. The RCM‐A2 scenario also shows a significant increase in interannual temperature variability, while it remains almost unchanged in RCM‐B2 when compared to a 20th century control run. Changes in precipitation are spatially much less coherent, with both regions of increased and decreased precipitation across the Andes. These results provide a first attempt at quantifying future climate change in the tropical Andes and could serve as input for impact models to simulate anticipated changes in Andean glaciation, hydrology, and ecosystem integrity.

Verification of Quantitative Precipitation Forecasts via Stochastic Downscaling

Abstract The use of dense networks of rain gauges to verify the skill of quantitative numerical precipitation forecasts requires bridging the scale gap between the finite resolution of the forecast fields and the point measurements provided by each gauge. This is usually achieved either by interpolating the numerical forecasts to the rain gauge positions, or by upscaling the rain gauge measurements by averaging techniques. Both approaches are affected by uncertainties and sampling errors due to the limited density of most rain gauge networks and to the high spatiotemporal variability of precipitation. For this reason, an estimate of the sampling errors is crucial for obtaining a meaningful comparison. This work presents the application of a stochastic rainfall downscaling technique that allows a quantitative comparison between numerical forecasts and rain gauge measurements, in both downscaling and upscaling approaches, and allows a quantitative assessment of the significance of the results of the verification procedure.

Investigating the impact of remotely sensed precipitation and hydrologic model uncertainties on the ensemble streamflow forecasting

In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies have actually implemented SDA methods using realistic input error models for precipitation. In this study we use particle filtering as a SDA method to propagate input errors through a conceptual hydrologic model and quantify the state, parameter and streamflow uncertainties. Recent progress in satellite‐based precipitation observation techniques offers an attractive option for considering spatiotemporal variation of precipitation. Therefore, we use the PERSIANN‐CCS precipitation product to propagate input errors through our hydrologic model. Some uncertainty scenarios are set up to incorporate and investigate the impact of the individual uncertainty sources from precipitation, parameters and also combined error sources on the hydrologic response. Also probabilistic measures are used to quantify the quality of ensemble prediction.

Variability of precipitation in the Atacama Desert: its causes and hydrological impact

An analysis of the variability of rainfall at 27 stations and run-off at 4 stations between 18° and 28°S in the Atacama Desert has been carried out. A diagonal boundary zone between summer- and winter-dominated areas is related to the provenance of the rainfall: Amazonia to the north and east, and Pacific moisture to the south. It is shown that winter rainfall tends to be higher during El Nino years, while heavy summer rainfall tends to be more common during La Nina. However, rather than the precipitation being directly controlled by El Nino-Southern Oscillation (ENSO), previous studies have shown that it is the regional synoptic conditions towards the source areas that largely control temporal precipitation variations, and these are in turn either facilitated or inhibited by ENSO. The spatio-temporal variability of precipitation leads to a complex hydrological regime. Perennial rivers in the north and central Atacama Desert tend to flood in summer, especially during La Nina conditions, from source to sea. Perennial rivers in the south tend to flood in summer, but as a result of melt from the previous years snowfall, especially during El Nino conditions, again from source to sea. However, while inland areas may also experience flooding of ephemeral rivers in summer associated with La Nina, coastal areas on the other hand experience winter flooding of ephemeral rivers associated with El Nino. Surface water flood events, and groundwater recharge events reported in the literature, are generally less frequent than ENSO events, confirming the requirement for specific synoptic conditions and making the use of averages unsound for present-day hydrological studies and water resource evaluations. Copyright © 2006 Royal Meteorological Society.

Regression-based downscaling of spatial variability for hydrologic applications

There is an obvious imbalance between, on the one hand, the importance of spatio-temporal variability of precipitation for river flows and, on the other, their representation in current empirical downscaling models that are applied for climate scenarios. The imperfect variability results from incomplete forcing of the large scales. The last IPCC report mentioned three regression-based methods that try to overcome the imperfection of point-wise variability: randomization, inflation, and expanded downscaling, Here, we analyze and compare these methods with respect to their spatial variability and how that relates to river runoff. Using the downscaled temperature and precipitation for observed and simulated large-scale forcings (climate scenarios), we applied the hydrologic model HBV for two river basins in Germany. We discuss the obvious and hidden model imperfections regarding present and future precipitation climate, along with their relevance for runoff. The overall picture is quite diverse, and it appears that temporal characteristics, i.e. time-lagged effects, are at least as important as spatial characteristics. We conclude that, although the models agree in a number of essential projections for river flow, a more consistent picture requires the full spatio-temporal variability as it depends on the large scale atmosphere.