Search

Numerous circulation indices have been applied in practical climate services focused on regional precipitation. It is beneficial to identify the most influential or decisive indices, but this is difficult with conventional correlation analyses because of the underlying nonlinear mechanisms for precipitation. This paper demonstrates a set of the most influential indices for July–August precipitation in North China, based on the recursive random forest (RRF) method. These decisive circulation indices include the Polar–Eurasia teleconnection, North African subtropical high ridge position, India–Burma trough, Antarctic Oscillation, Northern Hemisphere polar vortex central latitude, North Atlantic Oscillation, and western Pacific subtropical high northern boundary position. Some of these factors have been recognized as directly influential to the regional precipitation, for example, those of the northwestern Pacific subtropical high; however, some are not easily understood. Decision tree (DT) models using these indices were developed to facilitate composite analyses to explain the RRF results. Taking one of the most interesting DT rules as an example, when the North African subtropical high ridge position is sufficiently far south, an anomalous anticyclone occurs in the upstream and an anomalous cyclone in the downstream of North China. This is unfavorable for northward moisture transport in eastern China and hence causes less precipitation in North China than climatology. The present results are not only helpful for improving diagnostic models of regional precipitation, but also enlightening for exploring how global climate change could impact a region by modulating large-scale circulation patterns. [ABSTRACT FROM AUTHOR]

Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months. [ABSTRACT FROM AUTHOR]

Middle and eastern routes of the South-North Water Diversion Project (SNWDP) of China, which are approximately located within the area 28°-42°N and 110°-122°E, are being constructed. This paper investigates the past climatic variations on various time scales using instrumental and proxy data. It is found that annual mean surface air temperature has increased significantly during the past 50-100 years, and winter and spring temperatures in the northern part of the region have undergone the most significant changes. A much more significant increase occurs for annual mean minimum temperature and extreme low temperature than for annual mean maximum temperature and extreme high temperature. No significant trend in annual precipitation is found for the region as a whole for the last 50 and 100 years, although obvious decadal and spatial variation is detectable. A seesaw pattern of annual and summer precipitation variability between the north and the south of the region is evident. Over the last 100 years, the Haihe River basin has witnessed a significant negative trend of annual precipitation, but no similar trend is detected for the Yangtze and Huaihe River basins. Pan evaporation has significantly decreased since the mid-1960s in the region in spite of the fact that the trend appears to have ended in the early 1990s. The negative trend of pan evaporation is very significant in the plain area between the Yangtze and Yellow Rivers. There was a notable series of dry intervals lasting decades in the north of the region. The northern drought of the past 30 years is not the most severe in view of the past 500 years; however, the southern drought during the period from the 1960s to the 1980s may have been unprecedented. The dryness-wetness index (DWI) shows significant oscillations with periodicities of 9.5 and 20 years in the south and 10.5 and 25 years in the north. Longer periodicities in the DWI series include 160-170- and 70-80-yr oscillations in the north, and 100-150-yr oscillations in the south. The observed climate change could have implications for the construction and management of the SNWDP. The official approval and start of the hydro project was catalyzed by the severe multiyear drought of 1997-2003 in the north, and the operation and management of the project in the future will also be influenced by climate change-in particular by precipitation variability. This paper provides a preliminary discussion of the potential implications of observed climate change for the SNWDP. [ABSTRACT FROM AUTHOR]

In a changing climate, there is an interest in predicting how extreme rainfall events may change. Using historical records, several recent papers have evaluated whether high-intensity precipitation scales with temperature in accordance with the Clausius--Clapeyron (C--C) relationship. For varying locations in Europe, these papers have identified both super C--C relationships as well as a breakdown of the C--C relationship under dry conditions. In this paper, a similar analysis is carried out for the United States using data from 14 weather stations clustered in four different hydroclimatic regions: the coastal northeast, interior New York, the central plains, and the western plains. In all regions except interior New York state, 99th percentile 1-h precipitation generally followed the C--C relation. In interior New York, there was evidence that intensity scaled with a super C--C relationship. For the 99.9th percentile precipitation, interior New York displayed some moderate evidence of a super C--C relationship, the western plains showed little relation between precipitation and temperature, and the remainder of sites generally scaled with the C--C relationship. Also, if only July, August, and September precipitation is considered, all stations except those in interior New York have little relation between temperature and precipitation, suggesting that precipitation intensity during summer months may not be well constrained by the C--C relationship. Overall, the C--C relationship (or a variation thereof) does not appear to constrain extreme precipitation in all regions and in all seasons, and its ability to aid in constraining future predictions of extreme precipitation may only be relevant to certain locales and time periods. [ABSTRACT FROM AUTHOR]

Because of the limitations of coarse-resolution general circulation models (GCMs), delta change (DC) methods are generally used to derive scenarios of future climate as inputs into impact models. In this paper, the impact of future climate change on irrigation was investigated over China using the Community Land Model, version 4 (CLM4), which was calibrated against observed irrigation water demand (IWD) at the provincial level. The results show large differences in projected changes of IWD variability, extremes, timing, and regional responses between the DC and bias-corrected (BC) methods. For example, 95th-percentile IWD increased by 62% in the BC method compared to only a 28% increase in the DC method. In addition, a shift of seasonal IWD peaks (averaged over the country) to one month later in the year was projected when using the BC method, whereas no evident changes were predicted when using the DC method. Furthermore, low-percentile runoff has larger impacts in the BC method compared with proportional changes in the DC method, indicating that hydrological droughts seem to be exacerbated by increased climate variability. The discrepancies between the two methods were potentially due to the inability of the DC method to capture the changes in precipitation variability. Therefore, the authors highlight the potential effects of climate variability and the sensitivity to the choice of particular strategy-adjusting climate projection in assessing climate change impacts on irrigation. Some caveats, however, should be placed around interpretation of simulated percentage changes for all of China since a large model bias was found in southern China. [ABSTRACT FROM AUTHOR]

Results from a regional climate model simulation show substantial increases in future flood risk (2040-69) in many Pacific Northwest river basins in the early fall. Two primary causes are identified: 1) more extreme and earlier storms and 2) warming temperatures that shift precipitation from snow to rain dominance over regional terrain. The simulations also show a wide range of uncertainty among different basins stemming from localized storm characteristics. While previous research using statistical downscaling suggests that many areas in the Pacific Northwest are likely to experience substantial increases in flooding in response to global climate change, these initial estimates do not adequately represent the effects of changes in heavy precipitation. Unlike statistical downscaling techniques applied to global climate model scenarios, the regional model provides an explicit, physically based simulation of the seasonality, size, location, and intensity of historical and future extreme storms, including atmospheric rivers. This paper presents climate projections from the ECHAM5/Max Planck Institute Ocean Model (MPI-OM) global climate model dynamically downscaled using the Weather Research and Forecasting (WRF) Model implemented at 12-km resolution for the period 1970-2069. The resulting daily precipitation and temperature data are bias corrected and used as input to a physically based Variable Infiltration Capacity (VIC) hydrologic model. From the daily time step simulations of streamflow produced by the hydrologic model, probability distributions are fit to the extreme events extracted from each water year and flood statistics for various return intervals are estimated. [ABSTRACT FROM AUTHOR]

According to the mean seasonal and annual precipitation from 30 meteorological stations in the periods of 1961-2008, the precipitation trends are analyzed by using the Mann-Kendall (MK) test in the Jinsha River basin (JRB). Both the temporal and spatial distribution characteristics of precipitation trends in different regions in the JRB are studied for the first time in this paper. There is a slight and insignificant increasing trend in seasonal and annual precipitation except for autumn precipitation, and the annual precipitation has increased by 0.7634 mm yr−1 during the last 48 years. The increasing precipitation trends in spring seem more significant than those in the other three seasons, and autumn is the only season showing a slight and insignificant decreasing precipitation trend. There are more than 80% of stations exhibiting increasing trends for annual precipitation, and it goes to 90% for spring precipitation. The increasing precipitation trends in the headwater and the middle reaches are more dominant than those in the upper and lower reaches. The largest increase magnitude occurred in the less precipitation area, while the largest decrease magnitude occurred in the more precipitation area. The increasing trend of minimum precipitation series and decreasing trend of maximum precipitation series could result in a decreasing trend for the range series in the JRB. In general, the increasing trends of precipitation in the tributary (the Yalong River) are more significant than those in the mainstream (the Jinsha River). The results of this study will provide further knowledge for understanding on the climate change in the JRB. [ABSTRACT FROM AUTHOR]

The rapid expansion of irrigation in the Great Plains since World War II has resulted in significant water table declines, threatening the long-term sustainability of the Ogallala Aquifer. As discussed in Part I of this paper, the Weather Research and Forecasting Model (WRF) was modified to simulate the effects of irrigation at subgrid scales. Simulations of nine April-October periods (three drought, three normal, and three pluvial) over the Great Plains were completed to assess the full impact of irrigation on the water budget. Averaged over all simulated years, irrigation over the Great Plains contributes to May-September evapotranspiration increases of approximately 4% and precipitation increases of 1%, with localized increases of up to 20%. Results from these WRF simulations are used along with a backward trajectory analysis to identify where evapotranspiration from irrigated fields falls as precipitation (i.e., irrigation-induced precipitation) and how irrigation impacts precipitation recycling. On average, only 15.8% of evapotranspiration from irrigated fields falls as precipitation over the Great Plains, resulting in 5.11 mm of May-September irrigation-induced precipitation and contributing to 6.71 mm of recycled precipitation. Reductions in nonrecycled precipitation suggest that irrigation reduces precipitation of moisture advected into the region. The heaviest irrigation-induced precipitation is coincident with simulated and observed precipitation increases, suggesting that observed precipitation increases in north-central Nebraska are strongly related to evapotranspiration of irrigated water. Water losses due to evapotranspiration are much larger than irrigation-induced precipitation and recycled precipitation increases, confirming that irrigation results in net water loss over the Great Plains. [ABSTRACT FROM AUTHOR]

Satellite-derived high-resolution precipitation products (HRPP) have been developed to address the needs of the user community and are now available with 0.25° × 0.25° (or less) subdaily resolutions. This paper evaluates a number of commonly available satellite-derived HRPPs covering northwest Europe over a 6-yr period. Precipitation products include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center (CPC) morphing (CMORPH) technique, the CPC merged microwave technique, the Naval Research Laboratory (NRL) blended technique, and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) technique. In addition, the Geosynchronous Operational Environmental Satellite (GOES) precipitation index (GPI) and the European Centre for Medium-Range Weather Forecasting (ECMWF) operational forecast model products are included for comparison. Surface reference data from the European radar network is used as ground truth, supported by the Global Precipitation Climatology Centre (GPCC) precipitation gauge analysis and gauge data over the United Kingdom. Measures of correlation, bias ratio, probability of detection, and false alarm ratio are used to evaluate the products. Results show that satellite products generally exhibit a seasonal cycle in correlation, bias ratio, probability of detection, and false alarm ratio, with poorer statistics during the winter. The ECMWF model also shows a seasonal cycle in the correlation, although the results are poorer during the summer, while the bias ratio, probability of detection, and false alarm ratio are consistent through all seasons. Importantly, all the satellite HRPPs underestimate precipitation over northwest Europe in all seasons. [ABSTRACT FROM AUTHOR]

Precipitation has a high spatial variability, and thus some modeling applications require high-resolution data (<10 km). Unfortunately, in some cases, such as meteorological forecasts and future regional climate projections, only spatial averages over large areas are available. While some attention has been given to the disaggregation of mean areal precipitation estimates, the computation of a disaggregated field with a realistic spatial structure remains a difficult task. This paper describes the development of a statistical disaggregation model based on Gibbs sampling. The model disaggregates 45.6-km-resolution rainfall fields to grids with pixel sizes ranging from 3.8 to 22.8 km. The model is conceptually simple, as the algorithm is straightforward to compute with only a few parameters to estimate. The rainfall depth at each grid pixel is related to the depths of the neighboring pixels, while the spatial variability is related to the convective available potential energy (CAPE) field. The model is developed using daily rainfall data over a 40 000-km2 area located in the southeastern United States. Four-kilometer-resolution rainfall estimates obtained from NCEP's stage IV analysis were used to estimate the model parameters (2002-04) and as a reference to validate the disaggregated fields (2005/06). Results show that the model accurately simulates rainfall depths and the spatial structure of the observed field. Because the model has low computational requirements, an ensemble of disaggregated data series can be generated. [ABSTRACT FROM AUTHOR]

The U.S. Climate Reference Network (USCRN) was deployed between 2001 and 2008 for the purpose of yielding high-quality and temporally stable in situ climate observations in pristine environments over the twenty-first century. Given this mission, USCRN stations are engineered to operate largely autonomously with great reliability and accuracy. A triplicate approach is used to provide redundant measurements of temperature and precipitation at each location, allowing for observations at a specific time to be compared for quality control. This approach has proven to be robust in the most extreme environments, from extreme cold (−49°C) to extreme heat (++52°C), in areas of heavy precipitation (4700 mm yr−1), and in locations impacted by strong winds, freezing rain, and other hazards. In addition to a number of stations enduring extreme winter environments in Alaska and the northern United States, seven of the USCRN stations are located at elevations over 2000 m, including stations on Mauna Loa, Hawaii (3407 m) and on Niwot Ridge above Boulder, Colorado (2996 m). The USCRN temperature instruments and radiation shield have also been installed and run successfully at a station on the Quelccaya Ice Cap in Peru (5670 m). This paper reviews the performance of the USCRN station network during its brief lifetime and the potential utility of its triplicate temperature instrument configuration for measuring climate change at elevation. [ABSTRACT FROM AUTHOR]

Recently, a single-site stochastic precipitation model called ''the mixture of generalized chain-dependent processes conditioned on a climate variable'' was developed. The model can effectively eliminate overdispersion--that is, underestimation in variance of seasonal precipitation total. In this paper, the single-site model is further developed into a multisite stochastic precipitation model by driving a collection of individual single-site models, but with spatial dependence following a method proposed by D. S. Wilks. Specifically, a computationally effective algorithm for estimating the spatial dependence of precipitation occurrence is developed to replace the construction of the empirical curves in the Wilks method. An effective and straightforward approach for correcting the bias of the spatial correlation of precipitation intensity is also proposed. This model is tested on a small network of sites from a significant hydroelectric power generation region of South Island, New Zealand. [ABSTRACT FROM AUTHOR]

This paper presents a validation study for a high-resolution version of the Regional Climate Model version 3 (RegCM3) over the Carpathian basin and its surroundings. The horizontal grid spacing of the model is 10 km--the highest reached by RegCM3. The ability of the model to capture temporal and spatial variability of temperature and precipitation over the region of interest is evaluated using metrics spanning a wide range of temporal (daily to climatology) and spatial (inner domain average to local) scales against different observational datasets. The simulated period is 1961--90. RegCM3 shows small temperature biases but a general overestimation of precipitation, especially in winter; although, this overestimate may be artificially enhanced by uncertainties in observations. The precipitation bias over the Hungarian territory, the authors'' main area of interest, is mostly less than 20%%. The model captures well the observed late twentieth-century decadal-to-interannual and interseasonal variability. On short time scales, simulated daily temperature and precipitation show a high correlation with observations, with a correlation coefficient of 0.9 for temperature and 0.6 for precipitation. Comparison with two Hungarian station time series shows that the model performance does not degrade when going to the 10-km gridpoint scale. Finally, the model reproduces the spatial distribution of dry and wet spells over the region. Overall, it is assessed that this high-resolution version of RegCM3 is of sufficiently good quality to perform climate change experiments over the Carpathian region--and, in particular, the Hungarian territory--for application to impact and adaptation studies. [ABSTRACT FROM AUTHOR]

Overlying the challenge of managing within natural hydroclimatic variability is the likely modification of runoff variability along with average runoff due to anthropogenic enhancement of greenhouse gas concentrations. In this paper analytical models are developed in which runoff mean and variability, the latter defined by the variance (or standard deviation) of annual runoff, are related to the variances and the covariance of annual precipitation and potential evapotranspiration, and the aridity index (mean annual potential evapotranspiration divided by mean annual precipitation). The method was validated using observed runoff data for 699 worldwide catchments. It was concluded that combining the Schreiber function, which relates the ratio of annual actual evapotranspiration to annual precipitation, with the analytical models provided satisfactory estimates of observed annual runoff mean and interannual variability. It was also concluded that estimates of annual runoff variability based on the simplified model of Koster and Suarez were unsatisfactory. By way of illustrating the new methodology, the approach was applied to projected annual values of precipitation from the Hadley Centre Global Environment Model version 1 (HadGEM) and it showed that considerable changes in reservoir yield are likely to occur if climate change projections of precipitation from HadGEM are realistic. Finally, further simplifications of the equations, based on the Schreiber function, are developed to estimate the mean and standard deviation of annual runoff that allow climate analysts to estimate the impact of potential climate changes on annual runoff characteristics and reservoir yield performance without having to resort to the calibration and application of a rainfall-runoff model or rely on the runoff output from general circulation models to examine such characteristics. [ABSTRACT FROM AUTHOR]

The integration of climate change projections into hydrological and other response models used for water resource planning and management is challenging given the varying spatial resolutions of the different models. In general, climate models are generated at spatial ranges of hundreds of kilometers, while hydrological models are generally watershed specific and based on input at the station or local level. This paper focuses on techniques applied to downscale large-scale climate model simulations to the spatial scale required by local response models (hydrological, agricultural, soil). Specifically, results were extracted from a regional climate model (RegCM) simulation focused on the Middle East, which was downscaled to a scale appropriate for input into a local watershed model [the Hydrological Model for Karst Environment (HYMKE)] calibrated for the upper Jordan River catchment. With this application, the authors evaluated the effect of future climate change on the amount and form of precipitation (rain or snow) and its effect on streamflow in the Jordan River and its tributaries—the major water resources in the region. They found that the expected changes in the form of precipitation are nearly insignificant in terms of changing the timing of streamflow. Additionally, the results suggest a future increase in evaporation and decrease in average annual rainfall, supporting expected changes based on global models in this region. [ABSTRACT FROM AUTHOR]

The future rainfall series used to drive hydrological models in most climate change impact studies is informed by global climate models (GCMs). This paper compares future runoff projections in ∼11 000 0.25° grid cells across Australia from a daily rainfall–runoff model driven with future daily rainfall series obtained using three simple scaling methods, informed by 14 GCMs. In the constant scaling and daily scaling methods, the historical daily rainfall series is scaled by the relative difference between GCM simulations for the future and historical climates. The constant scaling method scales all the daily rainfall by the same factor, and the daily scaling method takes into account changes in the daily rainfall distribution by scaling the different daily rainfall amounts differently. In the daily translation method, the GCM future daily rainfall series is translated to a 0.25° gridcell rainfall series using the relationship established between the historical GCM-scale rainfall and 0.25° gridcell rainfall data. The daily scaling and daily translation methods generally give higher extreme and annual runoff than the constant scaling method because they take into account the increase in extreme daily rainfall (which generates significant runoff) simulated by the large majority of the GCMs. However, the difference between the mean annual runoff simulated with future daily rainfall series obtained using the constant versus daily scaling methods is generally less than 5%, which is relatively smaller than the range of runoff results from the different GCMs of 30%–40%. [ABSTRACT FROM AUTHOR]

Despite continued interest in meteorological influences on the thermal variability of river systems, there are few detailed studies of stream temperature dynamics during storm events. This paper reports high-resolution (15 min) water column and streambed temperature data for storm events of contrasting magnitude, duration, and intensity for three streams (draining glacier, snow, and groundwater sources) across an alpine river system during summers 2002 and 2003. The results demonstrate clear spatial and temporal differences in water column and streambed thermal responses to precipitation events and streamflow peaks. Analysis of all storms across the three sites showed a decrease in water column temperature for 75% of events, with significant negative relationships between stream temperature and precipitation magnitude, precipitation intensity, and stream discharge peaks. Temperature decreases of 10.4°C were recorded, but temperature increases were less marked at up to 2.3°C. Temperature response to precipitation was dampened with increasing depth into the streambed at all sites. Spatial and temporal differences in thermal response to storm events were controlled by precipitation and stream discharge peak characteristics (above) plus antecedent basin conditions, which together determine the nature and rapidity of hydrological response. In this steep alpine basin, stream temperature variability appears to be enhanced by quick routing of precipitation to the river channel (i.e., direct precipitation/channel interception, rapid surface flow over impermeable bedrock/thin alpine soils, and subsurface flow through highly weathered scree slopes). This research highlights the need for integrated hydrometeorological research of precipitation event–hydrological response–stream temperature interactions to advance understanding of runoff generation processes driving event-scale thermal dynamics in alpine and other river systems. [ABSTRACT FROM AUTHOR]

This paper attempts to provide some new understanding of the mechanical as well as thermal effects of the Tibetan Plateau (TP) on the circulation and climate in Asia through diagnosis and numerical experiments. The air column over the TP descends in winter and ascends in summer and regulates the surface Asian monsoon flow. Sensible heating on the sloping lateral surfaces appears from the authors’ experiments to be the major driving source. The retarding and deflecting effects of the TP in winter generate an asymmetric dipole zonal-deviation circulation, with a large anticyclone gyre to the north and a cyclonic gyre to the south. Such a dipole deviation circulation enhances the cold outbreaks from the north over East Asia, results in a dry climate in south Asia and a moist climate over the Indochina peninsula and south China, and forms the persistent rainfall in early spring (PRES) in south China. In summer the TP heating generates a cyclonic spiral zonal-deviation circulation in the lower troposphere, which converges toward and rises over the TP. It is shown that because the TP is located east of the Eurasian continent, in summertime the meridional winds and vertical motions forced by the Eurasian continental-scale heating and the TP local heating are in phase over the eastern and central parts of the continent. The monsoon in East Asia and the dry climate in middle Asia are therefore intensified. [ABSTRACT FROM AUTHOR]

Variability in the global distribution of precipitation is recognized as a key element in assessing the impact of climate change for life on earth. The response of precipitation to climate forcings is, however, poorly understood because of discrepancies in the magnitude and sign of climatic trends in satellite-based rainfall estimates. Quantifying and ultimately removing these biases is critical for studying the response of the hydrologic cycle to climate change. In addition, estimates of random errors owing to variability in algorithm assumptions on local spatial and temporal scales are critical for establishing how strongly their products should be weighted in data assimilation or model validation applications and for assigning a level of confidence to climate trends diagnosed from the data. This paper explores the potential for refining assumed drop size distributions (DSDs) in global radar rainfall algorithms by establishing a link between satellite observables and information gleaned from regional validation experiments where polarimetric radar, Doppler radar, and disdrometer measurements can be used to infer raindrop size distributions. By virtue of the limited information available in the satellite retrieval framework, the current method deviates from approaches adopted in the ground-based radar community that attempt to relate microphysical processes and resultant DSDs to local meteorological conditions. Instead, the technique exploits the fact that different microphysical pathways for rainfall production are likely to lead to differences in both the DSD of the resulting raindrops and the three-dimensional structure of associated radar reflectivity profiles. Objective rain-type classification based on the complete three-dimensional structure of observed reflectivity profiles is found to partially mitigate random and systematic errors in DSDs implied by differential reflectivity measurements. In particular, it is shown that vertical and horizontal reflectivity structure obtained from spaceborne radar can be used to reproduce significant differences in Zdr between the easterly and westerly climate regimes observed in the Tropical Rainfall Measuring Mission Large-scale Biosphere–Atmosphere (TRMM-LBA) field experiment as well as the even larger differences between Amazonian rainfall and that observed in eastern Colorado. As such, the technique offers a potential methodology for placing locally observed DSD information into a global framework. [ABSTRACT FROM AUTHOR]