Gregory J. Carbone

Feeling at risk matters: water managers and the decision to use forecasts.

Experts contend that weather and climate forecasts could have an important role in risk management strategies for community water systems. Yet, most water managers make minimal use of these forecasts. This research explores the determinants of the use of weather and climate forecasts by surveying managers of community water systems in two eastern American states (South Carolina and the Susquehanna River Basin of Pennsylvania). Assessments of the reliability of weather and climate forecasts are not driving their use as water managers who find forecasts reliable are no more likely to use them than are managers who find them unreliable. Although larger systems and those depending on surface water are more likely to use forecasts for some (but not all) purposes, the strongest determinant of forecast use is risk perceptions. Water managers who expect to face problems from weather events in the next decade are much more likely to use forecasts than are water managers who expect few problems. Their expectations of future problems are closely linked with past experience: water managers who have had problems with specific types of weather events (e.g., flood emergencies) in the last 5 years are likely to expect to experience problems in the next decade. Feeling at risk, regardless of the specific source of that weather-related risk, stimulates a decision to use weather and climate forecasts.

Response of Soybean and Sorghum to Varying Spatial Scales of Climate Change Scenarios in the Southeastern United States

This study examines how uncertainty associated with the spatial scale of climate change scenarios influences estimates of soybean and sorghum yield response in the southeastern United States. We investigated response using coarse (300-km, CSIRO) and fine (50-km, RCM) scale climate change scenarios and considering climate changes alone, climate changes with CO2 fertilization, and climate changes with CO2 fertilization and adaptation. Relative to yields simulatedunder a current, control climate scenario, domain-wide soybean yield decreased by 49% with the coarse-scale climate change scenario alone, and by26% with consideration for CO2 fertilization. By contrast, thefine-scale climate change scenario generally exhibited higher temperatures and lower precipitation in the summer months resulting in greater yield decreases (69% for climate change alone and 54% with CO2fertilization). Changing planting date and shifting cultivars mitigated impacts, but yield still decreased by 8% and 18% respectively for the coarse andfine climate change scenarios. The results were similar for sorghum. Yield decreased by 51%, 42%, and 15% in response to fine-scaleclimate change alone, CO2 fertilization, and adaptation cases, respectively– significantly worse than with the coarse-scale (CSIRO) scenarios. Adaptation strategies tempered the impacts of moisture and temperature stress during pod-fill and grain-fill periods and also differed with respect to the scale of the climate change scenario.

Ensemble Forecasts of Drought Indices Using a Conditional Residual Resampling Technique

Abstract The historical climate record and seasonal temperature and precipitation records provide useful datasets for making short-term drought predictions. A variety of methods have exploited these resources, but few have quantitatively measured uncertainties associated with predictions of drought index values commonly used in management plans. In this paper, stochastic approaches for estimating uncertainty are applied to drought index predictions. National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) seasonal forecasts and resampling of nearest-neighbor residuals are incorporated to measure uncertainty in monthly forecasts of Palmer drought severity index (PDSI) and standardized precipitation index (SPI) in central South Carolina. Kuiper skill scores of PDSI indicate good forecast performance with up to 3-month lead time and improvements for 1-month-lead SPI forecasts. NOAA CPC climate outlook improved the forecast skill by as much as 40%, and the degree of improvement v...

A Comparison of Weekly Monitoring Methods of the Palmer Drought Index

Abstract A method for weekly monitoring of the Palmer Drought Index (PDI) by using four parallel month-long calculation chains in rotation (“ROLLING” method) was tested for the Kansas Northwest Climate Division and the South Carolina Southern Climate Division and compared to two other methods, a modified version of the Climate Prediction Center’s weekly Palmer Drought Index monitoring method with a modified set of coefficients (“WEEKLY” method) and the National Climatic Data Center’s (NCDC’s) projected monthly Palmer Drought Index method using long-term historical daily normal temperature and precipitation (“NORMALS” method). The results for the Kansas Northwest Climate Division and the South Carolina Southern Climate Division generally agreed. The weekly method produced drought severity values that differ most from standard monthly PDI values despite using a modified set of coefficients. The method recently adopted by NCDC successfully estimated Palmer Modified Drought Index (PMDI) values late in the mon...

Drought Index Mapping at Different Spatial Units

Abstract This paper investigates the influence of spatial interpolation and aggregation of data to depict drought at different spatial units relevant to and often required for drought management. Four different methods for drought index mapping were explored, and comparisons were made between two spatial operation methods (simple unweighted average versus spatial interpolation plus aggregation) and two calculation procedures (whether spatial operations are performed before or after the calculations of drought index values). Deterministic interpolation methods including Thiessen polygons, inverse distance weighted, and thin-plate splines as well as a stochastic and geostatistical interpolation method of ordinary kriging were compared for the two methods that use interpolation. The inverse distance weighted method was chosen based on the cross-validation error. After obtaining drought index values for different spatial units using each method in turn, differences in the empirical binned frequency distributi...

Considerations of Meteorological Time Series in Estimating Regional-Scale Crop Yield

Abstract The sensitivity of simulated soybean yield to spatial averaging of meteorological data was analyzed for the central United States during a 23-year period. Regional yield was simulated using the physiological model, SOYGRO, in two sets of experiments. In the first set, yield was simulated using meteorological data at individual stations within grid cells ranging from 2° latitude ×2° longitude to 5° latitude ×5° longitude. In the second set, the daily meteorological time series were adjusted through spatial averaging over grid cells. Spatial averaging caused bias ranging from 18% in 2° latitude ×2° longitude grid cells to 28% in 5° latitude ×5° longitude grid cells when averaged over the study period. During individual years such averaging caused bias exceeding 80% of simulated yield. While spatial averaging provides a means of characterizing regional-scale climate, and has been used with empirical crop yield models, the sensitivity of physiological models to the timing of meteorological events req...

Estimating Drought Conditions for Regions with Limited Precipitation Data

Abstract Three closely related issues that affect drought estimation in regions with limited precipitation data are addressed by investigating methods for filling missing daily precipitation data, handling short-term records, and deriving drought information for unsampled locations. The analysis yields three general conclusions: 1) it is better to conduct spatial interpolation prior to calculating drought index values, 2) using weather stations with moderate lengths of records (usually at least 10 years) improves the spatial–temporal characterization of drought, and 3) alternative precipitation sources of the National Weather Service multisensor precipitation rainfall estimates and the Tropical Rainfall Measuring Mission (TRMM) satellite monthly rainfall product (3B43) do not outperform spatially interpolated daily precipitation data in most regions, except in the western United States where the TRMM-based precipitation data work better than the spatially interpolated values for drought monitoring.

A high performance query analytical framework for supporting data-intensive climate studies

Abstract Climate observations and model simulations produce vast amounts of data. The unprecedented data volume and the complexity of geospatial statistics and analysis requires efficient analysis of big climate data to investigate global problems such as climate change, natural disasters, diseases, and other environmental issues. This paper introduces a high performance query analytical framework to tackle these challenges by leveraging Hive and cloud computing technologies. With this framework, we propose grid transformation, a new perspective for complex climate analysis that applies a series of atomic transformations to terabytes of climate data using SQL-style query (HiveQL). Specifically, we introduce four types of grid transformations (temporal, spatial, local, and arithmetic) to support a broad range of climate analyses, from the basic spatiotemporal aggregation to more sophisticated anomaly detection. Each query is processed as MapReduce tasks in a highly scalable Hadoop cluster as the parallel processing engine. Big climate data are directly stored and managed in a Hadoop Distributed File System without any data format conversion. A prototype is developed to evaluate the feasibility and performance of the framework. Experimental results show that complex and data-intensive climate analysis can be conducted using intuitive SQL queries with good flexibility and performance. This research provides a building block and practical insights in establishing a cyberinfrastructure that provides a high performance and collaborative environment for data-intensive geospatial applications in climate science.

Managing climate change scenarios for societal impact studies

Dealing with the potential consequences of climate change on society requires scenarios that accurately project future climate. Uncertainties about future greenhouse gas emissions, climate sensitivity to radiative forcing, and limits to simulating a complex system constrain this objective. This paper reviews literature outlining the inherent challenges of creating future climate scenarios from general circulation models; it examines methods used to improve their interpretation and use; and it explores approaches taken to recognize and address uncertainty when investigating interactions between climate and society.

Flood Simulation in South Carolina Watersheds Using Different Precipitation Inputs

Flooding induced by extreme rainfall events causes tremendous loss of life and property and infrastructure failure. Accurate representation of precipitation, which has high variation in space and time, is critical to hydrologic model simulations and flood analyses. In this study, we examined responses of differently sized United States Geological Survey (USGS) hydrologic units to heavy precipitation using three different data sets. The first consists of rainfall observed at individual meteorological gauges. The second uses the National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC) 4 km gridded radar-estimated precipitation (GRIB) Stage IV data. The third one derives from the method we developed that blends gauge data with the spatial coverage of the Parameter-elevation Relationships on Independent Slopes Model (PRISM) data. We examined how two watersheds in South Carolina respond to the three different representations of heavy rainfall, using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) developed by the U.S. Army Corps of Engineers. We found that the latter two precipitation inputs that consider spatial representation of rainfall yielded similar performance and improved simulated streamflow as compared to simulation using rainfall observed at individual meteorological gauges. The method we developed overcomes the spatial sparsity of rain gauges required for interpolation and extends availability of precipitation surfaces. Our study advances the understanding of advantages and limitations of different precipitation products for flood simulation.