Scott M. Robeson

Analyzing the discharge regime of a large tropical river through remote sensing, ground‐based climatic data, and modeling

This study demonstrates the potential for applying passive microwave satellite sensor data to infer the discharge dynamics of large river systems using the main stem Amazon as a test case. The methodology combines (1) interpolated ground-based meteorological station data, (2) horizontally and vertically polarized temperature differences (HVPTD) from the 37-GHz scanning multichannel microwave radiometer (SMMR) aboard the Nimbus 7 satellite, and (3) a calibrated water balance/water transport model (WBM/WTM). Monthly HVPTD values at 0.25° (latitude by longitude) resolution were resampled spatially and temporally to produce an enhanced HVPTD time series at 0.5° resolution for the period May 1979 through February 1985. Enhanced HVPTD values were regressed against monthly discharge derived from the WBM/WTM for each of 40 grid cells along the main stem over a calibration period from May 1979 to February 1983 to provide a spatially contiguous estimate of time-varying discharge. HVPTD-estimated flows generated for a validation period from March 1983 to February 1985 were found to be in good agreement with both observed arid modeled discharges over a 1400-km section of the main stem Amazon. This span of river is bounded downstream by a region of tidal influence and upstream by low sensor response associated with dense forest canopy. Both the WBM/WTM and HVPTD-derived flow rates reflect the significant impact of the 1982–1983 El Nino-;Southern Oscillation (ENSO) event on water balances within the drainage basin.

Evaluation and comparison of statistical forecast models for daily maximum ozone concentrations

Abstract Three statistical models that estimate daily maximum ozone (O3) concentrations in the lower Fraser Valley of British Columbia (BC) are specified using measured concentrations from two monitoring stations during the time period 1978–1985. The three models are (1) a univariate deterministic/stochastic model, (2) a univariate autoregressive integrated moving average (ARIMA) model, and (3) a bivariate temperature and persistence based regression model. The three models as well as a persistence forecast are tested by comparison with O3 concentrations observed during 1986; it is concluded that the bivariate model is superior to both unvariate models and persistence. The ARIMA model has nearly the same predictive capability as persistance while the mixed deterministic/stochastic model performs the worst. This suggests that the traditional time series technique of decomposing a series into a trend, a cycle and a stochastic component may not be appropriate for O3 air quality forecasting.

Increasing Growing-Season Length in Illinois during the 20th Century

Using daily minimum air-temperature (Tmin) data from the state of Illinois, the dates of spring and fall freezes – and the resulting growing-season length – are examined for trends during theperiod 1906–1997. Of the stations in the Daily Historical Climate Network, mostshow trends toward earlier spring freezes; however, trends in fall freezes are not consistent over the station network. Although the time series are highly variable (noisy), results suggest that the growing-season length in Illinois became roughly one week longer during the 20thcentury. To examine how changing freeze-date statistics relate to changing air-temperature probability distributions, percentiles of Tmin formoving 10-year periods were analyzed for trends during the typical times for spring and fall freezes in Illinois (i.e., the months of April and October). The lower portion of the April probability distribution shows substantially larger warming (0.5–0.7 ° C/100 yrs) than the upper portion of the distribution (0.2–0.3 ° C/100 yrs), suggesting that although cold events are warming during April, warm events are not warming as fast. Conversely, the lower portion of the October probability distribution shows modest cooling in Tmin (–0.2 ° C/100yrs for the 10th percentile), while middle and upper portions of the distribution show very large rates of cooling (up to –1.5 ° C/100 yrs for the 40th–70th percentiles). Analysis ofthe entire probability distribution provides a more-comprehensive perspective on climatic change than does the traditional focus on central tendency.

Settlement Design, Forest Fragmentation, and Landscape Change in Rondônia, Amazônia

Deforestation and colonization in Amazonia have attracted substantial attention. This article focuses on an area of 3,000 km 2 within the Brazilian State of Rondonia. Two adjacent settlements were compared to assess the role of their different designs in landscape change. Anari was planned following an orthogonal road network. Machadinho was designed with attention to topography in laying out roads and farm properties, while including communal reserves. Field research was undertaken in conjunction with multi-temporal classifications of remotely sensed data (1988, 1994, and 1998) and landscape ecology methods. The results indicate that large patches of communal reserves play an important role in maintaining lower levels of fragmentation. Analyses of landscape structure confirmed that forest patches in Machadinho are less fragmented, more complex, and preserve more interior habitat. By comparing the effects of different settlement designs on landscape change and forest fragmentation, this article contributes to the debate about colonization strategies in

Revisiting the recent California drought as an extreme value

Spatially weighted averages of Palmer Drought Severity Index (PDSI) over central and southern California show that the 1 year 2014 drought was not as severe as previously reported, but it still is the most severe in the 1895–2014 instrumental record. Using the typical adjustment procedure that matches the mean and standard deviation of tree ring PDSI values to those of instrumental data shows over 10 droughts from 800 to 2006 that were more severe than the 1 year 2014 drought, with the 2014 drought having a return period of 140–180 years. Quantile mapping allows for a closer correspondence between instrumental and tree ring PDSI probability distributions and produces return periods of 700–900 years for the 1 year 2014 drought. Associated cumulative 3 and 4 year droughts, however, are estimated to be much more severe. The 2012–2014 drought is nearly a 10,000 year event, while the 2012–2015 drought has an almost incalculable return period and is completely without precedent.

Statistical Characteristics of Daily Precipitation: Comparisons of Gridded and Point Datasets

Gridding of daily precipitation data alleviates many of the limitations of data that are derived from point observations, such as problems associated with missing data and the lack of spatial coverage. As a result, gridded precipitation data can be valuable for applied climatological research and monitoring, but they too have limitations. To understand the limitations of gridded data more fully (especially when they are used as surrogates for station data), annual precipitation total, rain-day frequency, and annual maxima are calculated and compared for five Midwestern grid points from the Climate Prediction Center’s Unified Rain Gauge Dataset (URD) and those of its nearest (rain gauge) station. To further examine differences between the two datasets, return periods of daily precipitation were calculated over a region encompassing Illinois and Indiana. These analyses reveal that the gridding process used to create the URD produced nearly the same annual totals as the rain gauge data; however, the gridding significantly increased the frequency of low-precipitation events while greatly reducing the frequency of heavy-precipitation events. Extreme precipitation values also were greatly reduced in the gridded precipitation data. While smoothing nearly always occurs when data are gridded, the gridding of discrete variables such as daily precipitation can produce datasets with statistical characteristics that are very different from those of the original observations.

Spherical Methods for Spatial Interpolation: Review and Evaluation

Global change research has placed new demands on methods of spatial analysis. In particular, spherical methods for spatial interpolation are required when spatial analyses are performed over large areas of the Earths surface. In this article, spherical spatial interpolation procedures are reviewed, compared, and evaluated. Three classes of spherical interpolants are evaluated in detail: distance weighting, functional minimization, and tesselation. The strengths and weaknesses of a method from each of these classes—inverse-distance weighting, thin-plate splines, and surfaces fit to triangulated patches—are evaluated using a hypothetical mathematical surface and a global scale representation of topography. For smooth functions, such as the hypothetical mathematical surface, thin-plate splines produce a visually pleasing surface and have low interpolation error. For non-smooth surfaces, such as global topography, inverse-distance weighting, interpolating thin-plate splines, and triangulated C0 patches appea...

Influence of spatially variable instrument networks on climatic averages

Instrument networks for measuring surface air temperature (T) and precipitation (P)have varied consid- erably over the last century. Inadequate observing-station locations have produced incomplete, uneven, and biased samples of the spatial variability in climate and, in turn, terrestrial and global scale averages of T and P have been biased. New high-resolution climatologies (Legates and Willmort, 1990a; 1990b) are intensively sampled and inte- grated to illustrate the effects of these nontrivial sampling biases. Since station networks may not represent spatial climatic variability adequately, their ability to represent climate through time is suspect.

COMPARISON OF APPROACHES FOR ESTIMATING TIME-AVERAGED PRECIPITATION USING DATA FROM THE USA

Spatial and temporal sampling errors inherent in large-scale, weather-station (raingauge) climatologies of precipitation are evaluated. A primary goal is to assess whether more representative large-scale precipitation climatologies emerge when (i) more station means are included, even when they are based on unequal periods of record, or (ii) fewer station means are included but all are derived from the same period of record. Observations drawn from the Historical Climatology Network (HCN) are used to estimate temporally averaged precipitation over lo-, 20-, and 30-year intervals at 457 stations within the USA. Two strategies for estimating these ‘observed’ means are examined, one based on temporal ‘substitution’ within each station record, and the other based on spatial interpolation from surrounding stations. Temporally estimated m-year means were obtained by substituting other m-year means, from within the same station record, for each ‘observed’ m-year mean, where m is the length of the averaging period of interest. Spatially interpolated m-year means were estimated from m-year means associated with nearby stations. Climatologies containing a greater number of station averages, even if they are computed over unequal averaging periods, appear to better represent the space-time variability in mean precipitation than climatologies containing fewer, but tem orall indicate that the within-station-record substitution of means is about 1.3 to 2.5 times more accurate than is interpolation from surrounding station means. Within-station substitution errors-associated with estimating any 10-year mean precipitation from any other 10-year mean-for example, were about 8 per cent of the long-term spatial precipitation mean for the USA, or 67.6 mm. Spatially interpolated 10-year means, from nearby stations, were in error by more than 10 per cent, or 88.8 mm on average. Much of the space-time variability in mean precipitation was not resolved adequately by the 457 HCN stations, especially high-frequency spatial variability caused by orographic and convective mechanisms. For many regions of the world, temporally homogeneous precipitation station networks are considerably more sparse than in the USA, further degrading the reliability of interpolated and spatially integrated mean precipitation fields derived solely from those networks.

SPATIAL COHERENCE AND DECAY OF WIND SPEED AND POWER IN THE NORTH-CENTRAL UNITED STATES

Hourly wind data from a network of climate stations in the north-central United States (drawn from the states of Illinois, lowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota, and Wisconsin) are analyzed to evaluate the efficacy of spatial analyses of near-surface wind speed and power. Spatial autocorrelation functions (acfs) were calculated at a number of timescales: annual, monthly, daily, and hourly. Annual wind speeds have virtually no coherent distance-decay relationship; monthly data produce a more consistent relationship, but still exhibit a large amount of scatter. Both daily and hourly data have classical decay with increasing distance between stations and there appears to be an optimal level of temporal aggregation, near the daily timescale, for spatial analysis of wind. In general, however, spatial acfs overestimate the spatial coherence of both wind speed and power. Temporal nonstationarities in wind data (i.e., diurnal and annual cycles) bias spatial autocorrelation functi...