Adam Luke

Increased nuisance flooding along the coasts of the United States due to sea level rise: Past and future

Author(s): Moftakhari, HR; AghaKouchak, A; Sanders, BF; Feldman, DL; Sweet, W; Matthew, RA; Luke, A | Abstract:

Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States

Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies on predictions of out-of-sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split-sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log-Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLAB® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference.

Utilizing complementarity of active/passive microwave observations at L-band for soil moisture studies in sandy soils

In this study, sensitivity of active and passive (AP) observations at L-band to near-surface SM was analyzed for bare sandy soils. The complementarity of AP microwave observations was used to obtain realistic SM profile that matched well with both AP observations during dynamic moisture conditions. Active observations exhibit less sensitivity to SM changes and higher sensitivity to surface roughness than passive observations. Based upon these findings, the observed brightness temperatures (TBs) were used to estimate a SM profile using an emission model. The backscatter (σ°) observations were used to estimate root mean square height (s) and correlation length (cl) using a backscatter model. The estimated SM profile, s, and cl resulted in RMSDs of 4.55K and 0.81dB between the observed and modeled TB and σ° values, respectively, for the rough surface. This study demonstrates the integrated use of AP to improve SM estimates.

The Influence of Hazard Maps and Trust of Flood Controls on Coastal Flood Spatial Awareness and Risk Perception

Understanding the impact of digital, interactive flood hazard maps and flood control systems on public flood risk perception could enhance risk communication and management. This study analyzed a survey of residents living near California’s Newport Bay Estuary and found that self-rated nonspatial perceptions of dread or concern over future flood impacts were positively associated with spatial awareness of flood-prone areas. Trust in flood control systems was associated with greater spatial flood hazard awareness but weaker nonspatial dread or concern, suggesting residents who witnessed and trust flood control systems developed a confident sense of flood-prone areas and that this confidence reduced the overall nonspatial sense of flood dread and concern. Viewing a flood hazard map eliminated differences in spatial hazard awareness between subgroups that existed prior to viewing a map, and viewing a map with estimated flood depth and greater spatial differentiation was associated with higher levels of postmap spatial awareness.