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The relationship between the satellite-based Vegetation Condition Index (VCI) and frequently used agricultural drought indices like Palmer Drought Severity Index, Palmer’s Z-index, Standard Precipitation Index, percent normal and deciles was evaluated using a comparative correlation analysis. These indices were compared at the county level for all 254 Texas counties for the growing-season months (March to August) using monthly data from 1982-1999. The evaluation revealed that the VCI was most strongly correlated with the 6-month SPI and the PDSI. This suggests that the VCI is most similar to drought indices that account for antecedent moisture conditions. There was also significant spatial variability in the magnitude of the correlations between the VCI and the drought indices. The reasons for this variability were explored by utilizing additional data such as irrigation, prevalent landuse/landcover, water table depth, soil moisture levels and soil hydrologic/hydraulic properties. The results demonstrated that mean annual precipitation, soil moisture, landuse/landcover, and depth of the water table accounted for a significant amount of the spatial variability (explaining more than 75% of the variance) in the relationship between the VCI and traditional drought indices.

Master of Science / Department of Agronomy / Xiaomao Lin / The high-quality climate data and high-resolution soil property data in Kansas and adjacent states were used to develop drought datasets for the monthly Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), and the Standardized Precipitation-Evapotranspiration Index (SPEI) over 1900 to 2014. The statistical analysis of these multiple drought indices were conducted to assess drought occurrence, duration, severity, intensity, and return period. Results indicated that the PDSI exhibited a higher frequency for every category of drought in central and western Kansas than the SPEI by up to 10%. Severe and extreme drought frequency was the highest in southwest Kansas around the Arkansas River lowlands and lowest in the southeast. The mean total drought frequency for eastern, central, and western Kansas was 36%, 39%, and 44%, respectively. The regional mean correlations between the SPI and SPEI were greater than or equal to 0.95 for all regions, but due to statistically significant increases in potential evaporation in western Kansas, the PDSI and SPEI are recommended over the SPI for meteorological and hydrological drought analysis. Drought variability of the last 115 years was analyzed through the Empirical Orthogonal Functions (EOFs) techniques and their Varimax rotations from 1900 to 2014 in Kansas. Large-scale synoptic patterns primarily dominated the Kansas spatial drought structures, especially during long-duration events. The EOFs indicated that the first principal components of drought explained approximately 70% of the drought variability across the state and demonstrated a statistically significant wetting trend over the last 115 years, oscillating at a period of about 14 years for all drought indices. The 99° W meridian line acted as the dominant transitional line demarcating the areas of Kansas’ climate and vegetation relationship as spatial drought presented. The Multivariate El Nino Index (MEI) signal , which modulates global and regional climate variabilities, provided a low-frequency indicator to couple with Kansas drought’s leading modes by varying leads of 3 to 7 months depending on the use of drought index and time steps selected. Large-scale predictors of surface temperature and precipitation are evaluated from the monthly forecasts in Climate Forecast System version 2.0 (CFSv2) from North Dakota down through central Texas (32.6 - 47.7°N and 92.8 - 104.1°W). By using singular value decomposition (SVD), the CFSv2 monthly forecasts of precipitation and 2-m temperature were statistically downscaled using ensemble mean predictions of reforecasts from 1982-2010. Precipitation skill was considerably less than temperature, and the highest skill occurred during the wintertime for 1-month lead time. Only the central and northern plains had statistically significant correlations between observed and modeled precipitation for 1-month lead time. Beyond a 1-month lead time, prediction skill was regionally and seasonally dependent. For the 3-month lead time, only the central plains demonstrated statistically significant mean anomaly correlation. After three-month lead times, the ensemble means of forecasts have shown limited reliable predictions which could make the forecast skill too low to be useful in practice for precipitation. However, temperature forecasts at lead times greater than five months showed some skill in predicting wintertime temperatures.

Made available in DSpace on 2018-08-01T22:33:10Z (GMT). No. of bitstreams: 1 tese_9008_Vitor Heringer.pdf: 1038279 bytes, checksum: 64e5aa0ed12c68d26d3b0b8108d94b8d (MD5) Previous issue date: 2016-07-29 / A seca é um dos fenômenos climáticos que mais causam prejuízos na agricultura influenciando de forma negativa o desenvolvimento socioeconômico. Ela ocorre quando a precipitação apresenta valores abaixo da normal climatológica para determinada região. O diagnóstico da seca pode ser feito por meio de índices quantificadores de seca e análises estatísticas com base em um serie climatológica. Os índices de seca identificam os períodos de seca ou umidade em uma área pontual ou regional a partir de equações empíricas e permitem determinar a intensidade, duração e frequência que ocorrem. Eles relacionam vários anos de variáveis climatológicas como: precipitação, temperatura do ar, evapotranspiração, escoamento superficial e umidade do solo. O objetivo deste trabalho foi avaliar a relação do Índice de Severidade de Seca de Palmer (PDSI) com a produtividade de café no Espírito Santo (ES) e da soja nos estados do Mato Grosso (MT), Paraná (PR) e Rio Grande do Sul (RS). Os cálculos foram realizados por meio do código adaptado de Jacobi et al. (2013), em que são necessários evapotranspiração, precipitação e capacidade de campo. Os dados de produtividade utilizados foram do IBGE, a série histórica de dados utilizada foi no período de 1990 a 2013. O PDSI foi calculado para o ES, RS, PR e MT. Após foi analisado a relação da produtividade com o índice de Palmer. No ES o PDSI foi avaliado com a produtividade de café, no PR, MT e RS o PDSI foi avaliado com a produtividade de soja. De acordo com a regressão linear simples conclui-se que o PDSI não foi significativo na correlação com a produtividade de café no ES, para a cultura da soja o PDSI foi significativo para o estado do RS e MT.

Theme and purpose of this diploma thesis was to compile a recent oak standard chronology for the region of Northern Moravia and to perform a dendroclimatological analysis. At three locations within the region of Northern Moravia, 195 samples from recent oaks have been collected using the Pressler increment borer. Tree-ring widths have been measured and processed in PAST 32 programme. From well-correlated tree-ring curves, a standard chronology has been successfully established with the range from 1895 to 2015. Removal of age-related trend from tree-ring curves and creation of residual standard index oak chronology has been performed in ARSTAN programme. Resulted residual chronology was used for modelling an impact of climatic variables on the radial increment in DendroClim programme, whilst also an analysis of significant negative years has been performed. Radial increment showed statistical and significant positive correlation with rainfalls occurred in last September and in March of this year. Thickness increment indicates statistically negative correlation with Palmer Drought Severity Index (PDSI) from April to August last year. The most significant negative years for radial increment, when more than 60% of all trees responded, were the following years: 1941, 1942, 1943, 1956, and 1998. These most significant negative years were caused by a lower rate of monthly rainfall accumulations and low temperatures during the growing season.

<p>A tree-ring width chronology from Scots pine (<em>Pinus sylvestris</em> L.) was constructed from a xeric site in Stockholm to investigate the relationships between climate and tree growth and to reconstruct past moisture variability. The measure of moisture conditions employed here is a self-calibrating Palmer Drought Severity Index (PDSI). The index is derived from temperature, precipitation, and available water capacity of the soil, and assesses the intensity and duration of drought. It is widely used in tree-ring based climate reconstructions, a method which has never before been tested in the Nordic countries.</p><p>The comparison of the Stockholm tree-ring chronology with monthly temperature and precipitation data from a nearby meteorological station shows that tree growth is reduced by high summer temperatures, whereas high precipitation at the beginning of the growing season favours growth. The comparison with a PDSI calculated from this meteorological data shows that negative PDSI values are associated with narrow rings. Although tree growth in the humid climate of central Sweden is generally not limited by precipitation, the trees sampled for this study prove to be sensitive to changes in water supply. Their rings thus provide a record of past moisture variability and enable the reconstruction of precipitation and drought. The transfer function models for the reconstructions are calibrated using linear regression. A detailed verification of the results using the more than 200-year long meteorological record from Stockholm affirms the good model performance. May–June precipitation sums and the July PDSI could be reconstructed back to 1625.</p><p>The Palmer Drought Severity Index is found to be a useful tool in a tree-ring based reconstruction of past moisture variability, approximating the fraction of rainfall which is actually available to the tree, by including soil moisture storage, runoff, and the influence of temperature on evapotranspiration. It cannot completely account for the combined temperature and precipitation forcing of tree growth, and the use of the index does not improve the reconstruction compared to using precipitation alone. However, a reconstruction of both precipitation and the PDSI is possible when selecting an adequate sample site.</p>

A tree-ring width chronology from Scots pine (Pinus sylvestris L.) was constructed from a xeric site in Stockholm to investigate the relationships between climate and tree growth and to reconstruct past moisture variability. The measure of moisture conditions employed here is a self-calibrating Palmer Drought Severity Index (PDSI). The index is derived from temperature, precipitation, and available water capacity of the soil, and assesses the intensity and duration of drought. It is widely used in tree-ring based climate reconstructions, a method which has never before been tested in the Nordic countries. The comparison of the Stockholm tree-ring chronology with monthly temperature and precipitation data from a nearby meteorological station shows that tree growth is reduced by high summer temperatures, whereas high precipitation at the beginning of the growing season favours growth. The comparison with a PDSI calculated from this meteorological data shows that negative PDSI values are associated with narrow rings. Although tree growth in the humid climate of central Sweden is generally not limited by precipitation, the trees sampled for this study prove to be sensitive to changes in water supply. Their rings thus provide a record of past moisture variability and enable the reconstruction of precipitation and drought. The transfer function models for the reconstructions are calibrated using linear regression. A detailed verification of the results using the more than 200-year long meteorological record from Stockholm affirms the good model performance. May–June precipitation sums and the July PDSI could be reconstructed back to 1625. The Palmer Drought Severity Index is found to be a useful tool in a tree-ring based reconstruction of past moisture variability, approximating the fraction of rainfall which is actually available to the tree, by including soil moisture storage, runoff, and the influence of temperature on evapotranspiration. It cannot completely account for the combined temperature and precipitation forcing of tree growth, and the use of the index does not improve the reconstruction compared to using precipitation alone. However, a reconstruction of both precipitation and the PDSI is possible when selecting an adequate sample site.

Archaeologists describe the Uinta Fremont (A.D. 0 – 1300) as a mixed foraging-farming society that underwent a dramatic social change from A.D. 700 – 1000. Researchers observe through different architectural styles and subsistence activity a change from large, aggregated settlements to more dispersed and defensively oriented villages and hamlets. The Ideal Free Distribution (IFD) model provides an explanatory framework through which to interpret these changes. IFD predicts the order in which people or animals will occupy habitats based on a habitat’s relative suitability and suggests hypothetical behaviors that people or animals might engage in to improve or maintain the relative suitability of a habitat. One prediction of IFD is that behaviors indicating resource competition will become more frequent when population density increases. I test whether this hypothesis explains changes in storage features by considering storage behavior as a manifestation of resource competition, and I investigate whether storage feature frequency correlates with periods of Fremont population increases and paleoenvironmental degradation. These tests explain aspects of Fremont culture change and suggest future research possibilities. Storage feature frequency, representing resource competition, remains low from A.D. 0 – 700, suggesting that the habitats could absorb growing Fremont populations. After A.D. 700, however, resource competition rose and remained high, a condition that likely spurred the defensive architecture and dispersed settlements that became increasingly common after A.D. 1000. The successes and limitations of applying IFD to the archaeological record point the way toward future uses of the model to investigate settlement spacing and reaffirm the use of radiocarbon data in archaeological science.

Farming communities in the United States and around the world lose billions of dollars every year due to drought. Drought Indices such as the Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) are widely used by the government agencies to assess and respond to drought. These drought indices are currently monitored at a large spatial resolution (several thousand km2). Further, these drought indices are primarily based on precipitation deficits and are thus good indicators for monitoring large scale meteorological drought. However, agricultural drought depends on soil moisture and evapotranspiration deficits. Hence, two drought indices, the Evapotranspiration Deficit Index (ETDI) and Soil Moisture Deficit Index (SMDI), were developed in this study based on evapotranspiration and soil moisture deficits, respectively. A Geographical Information System (GIS) based approach was used to simulate the hydrology using soil and land use properties at a much finer spatial resolution (16km2) than the existing drought indices. The Soil and Water Assessment Tool (SWAT) was used to simulate the long-term hydrology of six watersheds located in various climatic zones of Texas. The simulated soil water was well-correlated with the Normalized Difference Vegetation Index NDVI (r ~ 0.6) for agriculture and pasture land use types, indicating that the model performed well in simulating the soil water. Using historical weather data from 1901-2002, long-term weekly normal soil moisture and evapotranspiration were estimated. This long-term weekly normal soil moisture and evapotranspiration data was used to calculate ETDI and SMDI at a spatial resolution of 4km ?? 4km. Analysis of the data showed that ETDI and SMDI compared well with wheat and sorghum yields (r > 0.75) suggesting that they are good indicators of agricultural drought. Rainfall is a highly variable input both spatially and temporally. Hence, the use of NEXRAD rainfall data was studied for simulating soil moisture and drought. Analysis of the data showed that raingages often miss small rainfall events that introduce considerable spatial variability among soil moisture simulated using raingage and NEXRAD rainfall data, especially during drought conditions. The study showed that the use of NEXRAD data could improve drought monitoring at a much better spatial resolution.

Farming communities in the United States and around the world lose billions of dollars every year due to drought. Drought Indices such as the Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) are widely used by the government agencies to assess and respond to drought. These drought indices are currently monitored at a large spatial resolution (several thousand km2). Further, these drought indices are primarily based on precipitation deficits and are thus good indicators for monitoring large scale meteorological drought. However, agricultural drought depends on soil moisture and evapotranspiration deficits. Hence, two drought indices, the Evapotranspiration Deficit Index (ETDI) and Soil Moisture Deficit Index (SMDI), were developed in this study based on evapotranspiration and soil moisture deficits, respectively. A Geographical Information System (GIS) based approach was used to simulate the hydrology using soil and land use properties at a much finer spatial resolution (16km2) than the existing drought indices. The Soil and Water Assessment Tool (SWAT) was used to simulate the long-term hydrology of six watersheds located in various climatic zones of Texas. The simulated soil water was well-correlated with the Normalized Difference Vegetation Index NDVI (r ~ 0.6) for agriculture and pasture land use types, indicating that the model performed well in simulating the soil water. Using historical weather data from 1901-2002, long-term weekly normal soil moisture and evapotranspiration were estimated. This long-term weekly normal soil moisture and evapotranspiration data was used to calculate ETDI and SMDI at a spatial resolution of 4km ?? 4km. Analysis of the data showed that ETDI and SMDI compared well with wheat and sorghum yields (r > 0.75) suggesting that they are good indicators of agricultural drought. Rainfall is a highly variable input both spatially and temporally. Hence, the use of NEXRAD rainfall data was studied for simulating soil moisture and drought. Analysis of the data showed that raingages often miss small rainfall events that introduce considerable spatial variability among soil moisture simulated using raingage and NEXRAD rainfall data, especially during drought conditions. The study showed that the use of NEXRAD data could improve drought monitoring at a much better spatial resolution.