Steven M. Quiring

Soil moisture: A central and unifying theme in physical geography

Soil moisture is a critical component of the earth system and plays an integrative role among the various subfields of physical geography. This paper highlights not just how soil moisture affects atmospheric, geomorphic, hydrologic, and biologic processes but that it lies at the intersection of these areas of scientific inquiry. Soil moisture impacts earth surface processes in such a way that it creates an obvious synergistic relationship among the various subfields of physical geography. The dispersive and cohesive properties of soil moisture also make it an important variable in regional and microclimatic analyses, landscape denudation and change through weathering, runoff generation and partitioning, mass wasting, and sediment transport. Thus, this paper serves as a call to use research in soil moisture as an integrative and unifying theme in physical geography.

Developing Objective Operational Definitions for Monitoring Drought

Abstract Drought is a complex phenomenon that is difficult to accurately describe because its definition is both spatially variant and context dependent. Decision makers in local, state, and federal agencies commonly use operational drought definitions that are based on specific drought index thresholds to trigger water conservation measures and determine levels of drought assistance. Unfortunately, many state drought plans utilize operational drought definitions that are derived subjectively and therefore may not be appropriate for triggering drought responses. This paper presents an objective methodology for establishing operational drought definitions. The advantages of this methodology are demonstrated by calculating meteorological drought thresholds for the Palmer drought severity index, the standardized precipitation index, and percent of normal precipitation using both station and climate division data from Texas. Results indicate that using subjectively derived operational drought definitions may ...

Estimating the spatial distribution of power outages during hurricanes in the Gulf coast region

Hurricanes have caused severe damage to the electric power system throughout the Gulf coast region of the US, and electric power is critical to post-hurricane disaster response as well as to long-term recovery for impacted areas. Managing power outage risk and preparing for post-storm recovery efforts requires accurate methods for estimating the number and location of power outages. This paper builds on past work on statistical power outage estimation models to develop, test, and demonstrate a statistical power outage risk estimation model for the Gulf Coast region of the US. Previous work used binary hurricane-indicator variables representing particular hurricanes in order to achieve a good fit to the past data. To use these models for predicting power outages during future hurricanes, one must implicitly assume that an approaching hurricane is similar to the average of the past hurricanes. The model developed in this paper replaces these indicator variables with physically measurable variables, enabling future predictions to be based on only well-understood characteristics of hurricanes. The models were developed using data about power outages during nine hurricanes in three states served by a large, investor-owned utility company in the Gulf Coast region.

Comparison and Validation of Statistical Methods for Predicting Power Outage Durations in the Event of Hurricanes

This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy.

Improving the predictive accuracy of hurricane power outage forecasts using generalized additive models.

Electric power is a critical infrastructure service after hurricanes, and rapid restoration of electric power is important in order to minimize losses in the impacted areas. However, rapid restoration of electric power after a hurricane depends on obtaining the necessary resources, primarily repair crews and materials, before the hurricane makes landfall and then appropriately deploying these resources as soon as possible after the hurricane. This, in turn, depends on having sound estimates of both the overall severity of the storm and the relative risk of power outages in different areas. Past studies have developed statistical, regression-based approaches for estimating the number of power outages in advance of an approaching hurricane. However, these approaches have either not been applicable for future events or have had lower predictive accuracy than desired. This article shows that a different type of regression model, a generalized additive model (GAM), can outperform the types of models used previously. This is done by developing and validating a GAM based on power outage data during past hurricanes in the Gulf Coast region and comparing the results from this model to the previously used generalized linear models.

Prestorm Estimation of Hurricane Damage to Electric Power Distribution Systems

Hurricanes frequently cause damage to electric power systems in the United States, leading to widespread and prolonged loss of electric service. Restoring service quickly requires the use of repair crews and materials that must be requested, at considerable cost, prior to the storm. U.S. utilities have struggled to strike a good balance between over- and underpreparation largely because of a lack of methods for rigorously estimating the impacts of an approaching hurricane on their systems. Previous work developed methods for estimating the risk of power outages and customer loss of power, with an outage defined as nontransitory activation of a protective device. In this article, we move beyond these previous approaches to directly estimate damage to the electric power system. Our approach is based on damage data from past storms together with regression and data mining techniques to estimate the number of utility poles that will need to be replaced. Because restoration times and resource needs are more closely tied to the number of poles and transformers that need to be replaced than to the number of outages, this pole-based assessment provides a much stronger basis for prestorm planning by utilities. Our results show that damage to poles during hurricanes can be assessed accurately, provided that adequate past damage data are available. However, the availability of data can, and currently often is, the limiting factor in developing these types of models in practice. Opportunities for further enhancing the damage data recorded during hurricanes are also discussed.

Study Abroad Field Trip Improves Test Performance through Engagement and New Social Networks

Although study abroad trips provide an opportunity for affective and cognitive learning, it is largely assumed that they improve learning outcomes. The purpose of this study is to determine whether a study abroad field trip improved cognitive learning by comparing test performance between the study abroad participants (n = 20) and their peers who did not participate (n = 365). Test performance was statistically identical between these groups before and immediately after the study abroad program. On the final exam, the study abroad participants scored significantly higher. Qualitative methods were used to identify increased engagement with the course material and the creation of new social networks as likely explanations.

A Comparison of Soil Moisture Models Using Soil Climate Analysis Network Observations

Abstract Because of the lack of field measurements, models are often used to monitor soil moisture conditions. Therefore, it is important to find a model that can accurately simulate soil moisture under a variety of land surface conditions. In this paper, three models of varying complexities [the Variable Infiltration Capacity (VIC), Decision Support System for Agrotechnology Transfer (DSSAT), and Climatic Water Budget (CWB) models] that are commonly used for simulating soil moisture were evaluated and compared using soil moisture data (1997–2005) from three Soil Climate Analysis Network (SCAN) sites (Bushland, Texas; Prairie View, Texas; Powder Mill, Maryland). Results demonstrated that DSSAT and VIC simulated soil moisture more accurately than CWB at the three SCAN sites. DSSAT and VIC both accurately simulated the annual cycle of soil moisture and the wetting and drying in response to weather conditions, as evidenced by the relatively strong correlations, but could not accurately simulate the actual so...

Does Afternoon Precipitation Occur Preferentially over Dry or Wet Soils in Oklahoma

AbstractSoil moisture is an integral part of the climate system and can drive land–atmosphere interactions through the partitioning of latent and sensible heat. Soil moisture feedback to precipitation has been documented in several regions of the world, most notably in the southern Great Plains. However, the impact of soil moisture on precipitation, particularly at short (subdaily) time scales, has not been resolved. Here, in situ soil moisture observations and satellite-based precipitation estimates are used to examine if afternoon precipitation falls preferentially over wet or dry soils in Oklahoma. Afternoon precipitation events during the warm season (May–September) in Oklahoma from 2003 and 2012 are categorized by how favorable atmospheric conditions are for convection, as well as the presence or absence of the Great Plains low-level jet. The results show afternoon precipitation falls preferentially over wet soils when the Great Plains low-level jet is absent. In contrast, precipitation falls prefere...

Predicting Hurricane Power Outages to Support Storm Response Planning

Hurricanes regularly cause widespread and prolonged power outages along the U.S. coastline. These power outages have significant impacts on other infrastructure dependent on electric power and on the population living in the impacted area. Efficient and effective emergency response planning within power utilities, other utilities dependent on electric power, private companies, and local, state, and federal government agencies benefit from accurate estimates of the extent and spatial distribution of power outages in advance of an approaching hurricane. A number of models have been developed for predicting power outages in advance of a hurricane, but these have been specific to a given utility service area, limiting their use to support wider emergency response planning. In this paper, we describe the development of a hurricane power outage prediction model applicable along the full U.S. coastline using only publicly available data, we demonstrate the use of the model for Hurricane Sandy, and we use the model to estimate what the impacts of a number of historic storms, including Typhoon Haiyan, would be on current U.S. energy infrastructure.