The rational method for peak discharge ($Q_p$ ) estimation was introduced in the 1880s. Although the rational method is considered simplistic, it remains an effective method for estimating peak discharge for small watersheds. The runoff coefficient ($C$ ) is a key parameter for the rational method and can be estimated in various ways. Literature-based $C$ values ($C_{\mathrm{lit}}$ ) are listed for different land-use/land cover (two words, no hyphen) (LULC) conditions in various design manuals and textbooks; however, these $C_{\mathrm{lit}}$ values were developed with little basis on observed rainfall and runoff data. In this paper, $C_{\mathrm{lit}}$ values were derived for 90 watersheds in Texas by using LULC data for 1992 and 2001; the $C_{\mathrm{lit}}$ values derived from the two data sets were essentially the same. Also for this study, volumetric runoff coefficients ($C_v$ ) were estimated by using observed rainfall and runoff depths from more than 1,600 events observed in the watersheds. Watershed-median and watershed-average $C_v$ values were computed, and both are consistent with data from the National Urban Runoff Program. In addition, $C_v$ values were estimated by using rank-ordered pairs of rainfall and runoff depths (i.e., frequency matching). As anticipated, $C$ values derived by all three methods (literature based, event totals, and frequency matching) consistently had larger values for developed watersheds than for undeveloped watersheds. Two regression equations of $C_v$ versus percent impervious area were developed and combined into a single equation that can be used to rapidly estimate $C_v$ values for similar Texas watersheds.

In the southeastern US, Atlanta is always the focus of attention, despite the rapid expansion of small and medium-sized cities (SMSCs) in the region. Clearly, larger cities have more people, resulting in more loss during disasters. However, SMSCs also face natural calamities and must be made robust and sustainable. Keeping this in mind, this study chooses to focus on ten SMSCs in Alabama (Population > 40,000) which have encountered at least a 6% increase in population size between 1990 and 2020, out of which two large cities (Population > 180,000) which experienced loss during the same time. This paper examines the change in urban built-up area between 1990 and 2020 using the random forest algorithm in Google Earth Engine (GEE) and estimates future 2050 urban expansion scenarios using the Cellular Automata (CA) Markov model in TerrSet’s Land Change Modeler (LCM). The results revealed urban built-up areas grew rapidly from 1990 to 2020, with some cities doubling or tripling in size due to population growth. The future growth model predicted growth for most cities and urban expansion along transportation networks. The outcome of this research showcases the importance of proper planning and building sustainably in SMSCs for future natural disaster events.

The 1980 eruptions of Mount St. Helens provided an excellent opportunity for scientists to investigate the recovery of vegetation communities following a major geologic disturbance. An important and often overlooked aspect in these studies is the human factor in recovery processes, and specifically, the different management approaches taken towards re-establishment of vegetation on lands under the control of various owners. This study examines vegetation changes throughout the 1980 blast zone using a time series of Landsat-derived Normalized Difference Vegetation Index (NDVI) images and change detection methods to assess the changes over 25 years, from 1980 to 2005, as a function of human management combined with ecological factors. This long-term tracking of change indicates that differences in the speed of vegetation re-establishment and consequent rates of change substantially reflect human involvement and varying management strategies.

We analyze thermal emission spectra using the 2001 Mars Odyssey Thermal Emission Imaging System and the Mars Global Surveyor Thermal Emission Spectrometer to characterize grain size and mineralogical composition of dunes at Hargraves crater, Mars. Thermal inertia and bulk composition of the dunes were compared to inferred provenances from the thermal infrared response of surface constituent materials. We use a Markov Chain Monte Carlo technique to estimate the bulk amount of mineralogy contributed by each inferred provenance to the dune field composition. An average thermal inertia value of 238 ± 17 Jm⁻² K⁻¹ s^−0.5 was found for the dunes, corresponding to a surface composed of an average effective grain size of ∼391 ± 172 μm. This effective particle size suggests the presence of mostly medium sand-sized materials mixed with fine and coarse grain sands. The dunes are likely composed of a weakly indurated surface mixed with unconsolidated materials. Compositional analysis specifies that the dunes are composed of a mixture of feldspar, olivine, pyroxene, and relatively low bulk-silica content. Dune materials were likely derived from physical weathering, especially aeolian erosion, predominantly from the crater ejecta unit at the crater, mixed with a small amount from the crater floor and crater rim and wall lithologies—indicating that the dune materials were likely sourced locally.

The Mars Global Digital Dune Database (MGD³) contains information on Martian dune fields and prepared manually from the Thermal Emission Imaging System (THEMIS; 100 m/pixel) images. Although the MGD³ outlines dune fields, it overlooks the recognition of smaller dune forms. This paper aims to identify individual dunes from a semiautomated object‐based image analysis technique and characterize dune materials at Hargraves crater, Mars. MGD³ would benefit to be updated for an improved understanding of the Martian surface and its atmospheric mechanisms at a local scale. An object‐based image analysis technique was applied here to the Context Camera (CTX; 6 m/pixel) data set to extract dune data in a more efficient, reliable, and accurate fashion. This study is a test case in validating a remote sensing method that has wide applicability to the entire Martian surface resulting in an update to the dune database at a higher spatial resolution—providing a better understanding of surface and atmospheric behavior of Mars at the local scale. We also explored the wind flow and dune stability—presenting an insight into the dune modification mechanism—within the crater. The prevailing wind inside the crater flows to the west‐northwest. The dunes are labeled as active (stability index of 2) and do not appear to have been influenced by subsurface water ice or volatiles. We emphasize that the technique used here has a wide prospect in temporal monitoring of dune sediment flux, dune migration or erosion rates, improving near‐surface airflow modeling, and dune stability analysis.