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A thorough understanding of the peak flows under urbanization and climate change-with the associated uncertainties-is indispensable for mitigating the negative social, economic, and environmental impacts from flooding. In this paper, a case study was conducted by applying the Distributed Hydrology Soil Vegetation Model (DHSVM) to the San Antonio River basin (SARB), Texas. Historical and future land-cover maps were assembled to represent the urbanization process. Future climate and its uncertainties were represented by a series of designed scenarios using the Change Factor (CF) method. The factors were calculated by comparing the model ensemble from phase 5 of the Coupled Model Intercomparison Project (CMIP5) with baseline historical climatology during two future periods (2020-49, period 1; 2070-99, period 2). It was found that with urban impervious areas increasing alone, annual peak flows may increase from 601 (period 1) to 885 m3 s−1 (period 2). With regard to climate change, annual peak flows driven by forcings from maximum, median, and minimum CFs under four representative concentration pathways (RCPs) were analyzed. While the median values of future annual peak flows-forced by the median CF values-are very similar to the baseline under all RCPs, in each case the uncertainty range (calculated as the difference between annual peak flows driven by the maximum and minimum CFs) is very large. When urbanization and climate change coevolve, these averaged annual peak flows from the four RCPs will increase from 447 (period 1) to 707 m3 s−1 (period 2), with the uncertainties associated with climate change more than 3 times greater than those from urbanization. [ABSTRACT FROM AUTHOR]

Hourly surface temperature differences between Dallas-Fort Worth, Texas, metropolitan and rural sites have been used to calculate the urban heat island from 2001 to 2011. The heat island peaked after sunset and was particularly strong during the drought and heat wave in July 2011, reaching a single-day instantaneous maximum value of 5.4°C and a monthly mean maximum of 3.4°C, as compared with the 2001-11 July average of 2.4°C. This severe drought caused faster warming of rural locations relative to the metropolitan area in the morning as a result of lower soil moisture content, which led to an average negative heat island in July 2011 of −2.3°C at 1100 central standard time. The ground-based assessment of canopy air temperature at screening level has been supported by a remotely sensed surface estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite, highlighting a dual-peak maximum heat island in the major city centers of Dallas and Fort Worth. Both ground-based and remotely sensed spatial analyses of the maximum heat island indicate a northwest shift, the result of southeast winds in July 2011 of ~2 m s−1 on average. There was an overall positive trend in the urban heat island of 0.14°C decade−1 in the Dallas-Fort Worth metropolitan area from 2001 to 2011, due to rapid urbanization. Superimposed on this trend are significant interannual and decadal variations that influence the urban climate. [ABSTRACT FROM AUTHOR]

The record-setting 2011 Texas drought/heat wave is examined to identify physical processes, underlying causes, and predictability. October 2010-September 2011 was Texas's driest 12-month period on record. While the summer 2011 heat wave magnitude (2.9°C above the 1981-2010 mean) was larger than the previous record, events of similar or larger magnitude appear in preindustrial control runs of climate models. The principal factor contributing to the heat wave magnitude was a severe rainfall deficit during antecedent and concurrent seasons related to anomalous sea surface temperatures (SSTs) that included a La Niña event. Virtually all the precipitation deficits appear to be due to natural variability. About 0.6°C warming relative to the 1981-2010 mean is estimated to be attributable to human-induced climate change, with warming observed mainly in the past decade. Quantitative attribution of the overall human-induced contribution since preindustrial times is complicated by the lack of a detected century-scale temperature trend over Texas. Multiple factors altered the probability of climate extremes over Texas in 2011. Observed SST conditions increased the frequency of severe rainfall deficit events from 9% to 34% relative to 1981-2010, while anthropogenic forcing did not appreciably alter their frequency. Human-induced climate change increased the probability of a new temperature record from 3% during the 1981-2010 reference period to 6% in 2011, while the 2011 SSTs increased the probability from 4% to 23%. Forecasts initialized in May 2011 demonstrate predictive skill in anticipating much of the SST-enhanced risk for an extreme summer drought/heat wave over Texas. [ABSTRACT FROM AUTHOR]

An understanding of the vertical structure of clouds is important for remote sensing of precipitation from space and for the parameterization of cloud microphysics in numerical weather prediction (NWP) models. The representation of cloud hydrometeor profiles in high-resolution NWP models has direct applications in inversion-type precipitation retrieval algorithms [e.g., the Goddard profiling (GPROF) algorithm used for retrieval of precipitation from passive microwave sensors] and in quantitative precipitation forecasting. This study seeks to understand how the vertical structure of hydrometeors (liquid and frozen water droplets in a cloud) produced by high-resolution NWP models with explicit microphysics, henceforth referred to as cloud-resolving models (CRMs), compares to observations. Although direct observations of 3D hydrometeor fields are not available, comparisons of modeled and observed radar echoes can provide some insight into the vertical structure of hydrometeors and, in turn, into the ability of CRMs to produce precipitation structures that are consistent with observations. Significant differences are revealed between the vertical structure of observed and modeled clouds of a severe midlatitude storm over Texas for which the surface precipitation is reasonably well captured. Possible reasons for this discrepancy are presented, and the need for future research is highlighted. [ABSTRACT FROM AUTHOR]