Yang, W., D. W. Hyndman, J. A. Winkler, A. Viña, J. Deines, F. Lupi, L. Luo, Y. Li, B. Basso, C. Zheng, D. Ma, S. Li, X. Liu, H. Zheng, G. Cao, Q. Meng, Z. Ouyang, and J. Liu. 2016. Urban water sustainability: framework and application. Ecology and Society 21(4):4.http://dx.doi.org/10.5751/ES-08685-210404

A synoptic climatological approach was used to investigate 1) the airflow characteristics of southerly low-level wind maxima in the Great Plains, 2) the typical thermodynamic environments in which low-level wind maxima form, 3) the convergence fields accompanying the wind maxima, and 4) the associated patterns of cloud-to-ground lightning activity. A total of 260 low-level jet events that occurred at either 0000 or 1200 UTC during the warm seasons (Apr–Sep) of 1991–92 were employed in the analysis. The spatial configuration of southerly low-level jets was shown to be considerably more complex than previously portrayed. A subjective classification of the jet events—based on streamline curvature, latitudinal extent, and the orientation of confluence and deformation zones—resulted in 12 distinct configuration types. Only 37% of the events were broadly classified as anticyclonically curving jets, even though the majority of previous case studies focused on this type of wind maximum. Another unanticipated finding was the substantial variation in wind direction with height, usually to more anticyclonic flow, observed for over 50% of the low-level wind maxima. These events were termed multilevel jets, although it is not clear whether they are characterized by distinct, multiple airstreams separated in the vertical or a more gradual change in airflow with height. Composite temperature and humidity fields indicated that the thermodynamic conditions are fairly similar for the different jet types. All types were associated with a collocated humidity gradient and thermal ridge southwest of the jet axis. In contrast, the composite lower-tropospheric convergence fields varied considerably between types and reflect the complex airflow configurations. The spatial distribution of cloud-to-ground lightning activity corresponded well with local maxima in the composite convergence fields. This study contributes to a more complete understanding of the climatology of low-level jets. It provides a unique perspective in that no previous climatological study systematically investigated the variability in the airflow configurations of jet events or explicitly related the spatial pattern of convection to the airflow configuration and resulting convergence fields. In Part II of this study, the typical synoptic and subsynoptic-scale environments in which wind maxima with different configurations occur are described.

Research has shown that varying spatial scale through the selection of the total extent of investigation and the grain size of environmental predictor variables has effects on species distribution model (SDM) results and accuracy, but there has been minimal investigation into the interactive effects of extent and grain. To do this, we used a consistently sampled range-wide dataset of giant panda occurrence across southwest China and modeled their habitat and distribution at 4 extents and 7 grain sizes. We found that increasing grain size reduced model accuracy at the smallest extent, but that increasing extent negated this effect. Increasing extent also generally increased model accuracy, but the models built at the second-largest (mountain range) extent were more accurate than those built at the largest, geographic range-wide extent. When predicting habitat suitability in the smallest nested extents (50 km²), we found that the models built at the next-largest extent (500 km²) were more accurate than the smallest-extent models but that further increases in extent resulted in large decreases in accuracy. Overall, this study highlights the impacts of the selection of spatial scale when evaluating species’ habitat and distributions, and we suggest more explicit investigations of scale effects in future modeling efforts.

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.

The Haines Index is an operational tool for evaluating the potential contribution of dry, unstable air to the development of large or erratic plume-dominated wildfires. The index has three variants related to surface elevation, and is calculated from temperature and humidity measurements at atmospheric pressure levels. To effectively use the Haines Index, fire forecasters and managers must be aware of the climatological and statistical characteristics of the index for their location. However, a detailed, long-term, and spatially extensive analysis of the index does not currently exist. To meet this need, a 40-year (1961–2000) climatology of the Haines Index was developed for North America. The climatology is based on gridded (2.5° latitude × 2.5° longitude) temperature and humidity fields from the NCEP/NCAR reanalysis. The climatology illustrates the large spatial variability in the Haines Index both within and between regions using the different index variants. These spatial variations point to the limitations of the index and must be taken into account when using the Haines Index operationally.