A Global Gridded Dataset of GRACE Drought Severity Index for 2002–14: Comparison with PDSI and SPEI and a Case Study of the Australia Millennium Drought

A new monthly global drought severity index (DSI) dataset developed from satellite-observed time-variable terrestrial water storage changes from the Gravity Recovery and Climate Experiment (GRACE) is presented. The GRACE-DSI record spans from 2002 to 2014 and will be extended with the ongoing GRACE and scheduled GRACE Follow-On missions. The GRACE-DSI captures major global drought events during the past decade and shows overall favorable spatiotemporal agreement with other commonly used drought metrics, including the Palmer drought severity index (PDSI) and the standardized precipitation evapotranspiration index (SPEI). The assets of the GRACE-DSI are 1) that it is based solely on satellite gravimetric observations and thus provides globally consistent drought monitoring, particularly where sparse ground observations (especially precipitation) constrain the use of traditional model-based monitoring methods; 2) that it has a large footprint (~350 km), so it is suitable for assessing regional- and global-scale drought; and 3) that it is sensitive to the overall terrestrial water storage component of the hydrologic cycle and therefore complements existing drought monitoring datasets by providing information about groundwater storage changes, which affect soil moisture recharge and drought recovery. In Australia, it is demonstrated that combining GRACE-DSI with other satellite environmental datasets improves the characterization of the 2000s “Millennium Drought” at shallow surface and subsurface soil layers. Contrasting vegetation greenness response to surface and underground water supply changes between western and eastern Australia is found, which might indicate that these regions have different relative plant rooting depths

IMPACTOS DE LA VARIABILIDAD CLIMÁTICA SOBRE LA VEGETACIÓN DE LA CUENCA DEL RÍO SAUCE GRANDE (ARGENTINA)

El cambio climático ha generado impactos profundos en los ecosistemas terrestres, siendo la vegetación uno de los elementos más afectados. El objetivo de este trabajo fue analizar los impactos de variabilidad climática sobre la vegetación de la cuenca del río Sauce Grande (Argentina) mediante la aplicación del índice estandarizado de precipitación y evapotranspiración (SPEI, por sus siglas en inglés) y el índice de vegetación de diferencia normalizada (NDVI, por sus siglas en inglés). La metodología incluyó el análisis de datos de tres puntos del NDVI de febrero y octubre y el SPEI de dos meses durante el período 2000-2020. Se aplicó una correlación de Pearson entre ambos índices y se calculó la tendencia y variaciones a partir del test de Mann Kendall y el estimador de pendiente de Sen, respectivamente. Los resultados indicaron que la escala de SPEI de dos meses (SPEI-2) fue la más apropiada para analizar la dinámica de la vegetación del área de estudio, dado que el coeficiente de correlación fue superior a 0,782 con alto grado de significancia estadística (p< 0,01) en los tres sectores de la cuenca. Durante el período 2000-2020, el SPEI-2 presentó tendencia negativa y estadísticamente significativa en todo el área de estudio. Por lo tanto, se evidenció un aumento en la frecuencia de los períodos secos y un incremento en la magnitud de estos eventos, que fue creciente en sentido N-S durante el mes febrero y opuesto durante octubre. El NDVI de febrero y octubre también presentaron tendencia negativa y significancia estadística en toda la cuenca. Esta situación indicó que la vegetación presentó procesos de deterioro como consecuencia del incremento de los períodos secos. La cuenca inferior fue la que reflejó los procesos de deterioro más importantes, ya que el NDVI de febrero presentó una tasa de disminución de -0,032, mientras que el de octubre fue de -0,044 en los 21 años analizados. Los resultados encontrados aportan información fundamental para los tomadores de decisión y los productores agropecuarios, dado que servirá de base para la planificación de las actividades agroeconómicas, para el ordenamiento del territorio y para orientar las políticas públicas destinadas a conservar los recursos naturales de la cuenca del río Sauce Grande

Significance of soil moisture on vegetation greenness in the African Sahel from 1982 to 2008

Popular science The Sahel is a semi-arid eco-climatic transition zone in northern Africa separating the Sahara desert from the Africa’s tropical forest. The Sahel word in Arabic language means “shore” which is linguistically describes the appearances of vegetation as a shoreline defining the boundary of the Sahara desert. Soil moisture (rainwater accumulated over a period of time in soil) is considered one of the most important factors on vegetation growth in Sahel as the agriculture droughts occurs due to soil moisture deficiency. The projected number of Africans in semi-arid locations will suffer from increasing water stress by 2020s is between 75-250 million and this number is projected to increase to be between 350-600 million by 2050s. This study aims to evaluate the relationship between soil moisture and vegetation growth in Sahel region during 1982-2008 at different time lags. Land cover and soil texture data were used to investigate whether the relationship between soil moisture and vegetation growth are related to land cover and soil type or not. Satellite remote sensing data (vegetation index), modelled soil moisture data land cover map and soil type map were mainly used to achieve the purpose of this study. The most important findings of this study is the best correlations between vegetation greenness and soil moisture occurred at lag0 (no time lag differences), lag1 (one month time lag) and lag2 (two months’ time lags). The correlation relationship varied between low and moderate values in Sahel region indicating that soil moisture variable is not only the main driver for vegetation dynamics in the study area and maybe other factors such as human impacts could have a great contribution on vegetation changes in Sahel. Croplands and Grasslands are the main land cover types that increasing the correlation relationship between soil moisture and vegetation growth, whereas Entisols (occur in flood plains and steep slopes) and Alfisols (occur under forest and mixed vegetation cover) are the main soil types showing a positive effect on the correlation relationship between soil moisture and vegetation dynamics. Finally, good understanding the temporal relationship between water availability and vegetation dynamics can help us to know water affects plant growth and to predict the future relationship within a season between vegetation growth and soil moisture which can be used for detecting famine possibilities.This study investigates the temporal correlation relationship between vegetation greenness and soil moisture in the African Sahel from 1982 to 2008 at different time lags (maximum five lags used in this study) and determines the extent which soil moisture explains vegetation dynamics in the Sahel. Monthly composites of remotely sensed Normalized Difference Vegetation Index (NDVI) from National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA-AVHRR) were used in this study as a proxy for vegetation growth, whereas modeled soil moisture data (1.6m column depth) provided by the NOAA National Centers for Environmental Predictions (NCEP) Climate Prediction Center (CPC) Global Monthly high resolution Soil Moisture (GMSM) was used as an indicator of moisture availability for plants. The analyses were applied for all-year months data (dry season included) and only for growing months season (from July to October) to estimate the effect of long dry season on the association between vegetation growth and soil moisture. Trends in vegetation greenness, soil moisture and NDVI residuals were calculated separately in Sahel to investigate the changes occurred in vegetation growth and soil moisture during the study period. The correlations relationship were evaluated against land cover and soil texture data to estimate the influences of land cover and soil type on the strength of correlation relationship between vegetation growth and soil moisture. The results showed a significant correlation relationship between vegetation greenness and soil moisture at lag0 (no time lag differences), lag1 (one month time lag) and lag2 (two months’ time lag) with a better association in northern parts of Sahel region by using only the growing season data. However, the significant correlations covered a larger area by using all the year data (long dry season included). The results indicated that using AVHRR NDVI data for studying the vegetation growth in response to soil moisture availability is limited in the southern parts of the study area. The significant correlation coefficients (r) are varied between low and moderate values (0.1-0.6) in the study area, suggesting that soil moisture is not only the main driver of vegetation dynamics in Sahel. Vegetation greenness showed a significant increase during the study period in many locations in Sahel region (center of Chad, Senegal and south of Mali), whereas soil moisture showed a small significant locations in the study area (center of Sudan, center of Mali and east of Mauritania) during the study period from 1982-2008. Land cover type (Croplands and Grasslands) and soil texture (Entisols and Alfisols) showed a significant association and high influences on the correlation relationship between vegetation greenness and soil moisture at lag0, lag1 and lag2.Scientific abstract Soil moisture (rainwater accumulated over a period of time in soil) is considered one of the most important factors on vegetation growth in Sahel as the agriculture droughts usually associated with soil moisture deficiency. This thesis study investigates the correlation relationship between soil moisture and vegetation greenness in the Sahel region from 1982 to 2008 at different time lags (maximum five lags used in our analysis). Monthly time series data of remotely sensed Normalized Difference Vegetation Index (NDVI) from National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (NOAA-AVHRR) was used in this study as a proxy for vegetation growth, whereas the monthly modeled soil moisture data was provided by the NOAA National Centers for Environmental Predictions (NCEP) Climate Prediction Center (CPC) Global Monthly high resolution Soil Moisture (GMSM). Land cover map and soil map data of Sahel region were used to investigate the effect of land cover and soil type on the correlation relationship. The results were based on pixel by pixel analysis for two time frames: all-year data (dry season included) and only growing season (from July to October). The best correlation between NDVI and soil moisture occurred at lag0 (no time lag difference), lag1 (one month time lag) and lag2 (two month time lag) and the strength of relationship is decreasing by increasing time lags (lag0, lag1 and lag2 are the dominant in the study area). The degree of association between NDVI and soil moisture increased in the northern part of Sahel region by using only the growing season data and this relationship was vague in central and southern part of Sahel region. The significant correlation coefficients varied between low and moderate (0.1-0.6) across the study area suggesting that soil moisture is not only the main driver factor on the vegetation dynamics in Sahel region. Trends of vegetation showed a significant increase during the study period in many locations (center of Chad, Senegal and south of Mali), whereas soil moisture showed small significant locations (center of Sudan, center of Mali and east of Mauritania) from 1982-2008. Land cover type (Croplands and Grasslands) and soil type (Entisols and Alfisols) showed a significant influence on the correlation relationship between vegetation greenness and soil moisture

A Combined Satellite-Derived Drought Indicator to Support Humanitarian Aid Organizations

Governments, aid organizations and researchers are struggling with the complexity of detecting and monitoring drought events, which leads to weaknesses regarding the translation of early warnings into action. Embedded in an advanced decision-support framework for Doctors without Borders (Médecins sans Frontières), this study focuses on identifying the added-value of combining different satellite-derived datasets for drought monitoring and forecasting in Ethiopia. The core of the study is the improvement of an existing drought index via methodical adaptations and the integration of various satellite-derived datasets. The resulting Enhanced Combined Drought Index (ECDI) links four input datasets (rainfall, soil moisture, land surface temperature and vegetation status). The respective weight of each input dataset is calculated for every grid point at a spatial resolution of 0.25 degrees (roughly 28 kilometers). In the case of data gaps in one input dataset, the weights are automatically redistributed to other available variables. Ranking the years 1992 to 2014 according to the ECDI-based warning levels allows for the identification of all large-scale drought events in Ethiopia. Our results also indicate a good match between the ECDI-based drought warning levels and reported drought impacts for both the start and the end of the season

Measuring and modeling deep drainage, streamflow, and soil moisture in Oklahoma

This dissertation examines multiple components of the Oklahoma water balance in order to answer three independent research questions:i) Can long-term soil moisture monitoring data be used to estimate potential groundwater recharge rates? Daily drainage rates from the root zone were estimated for 78 sites using up to 17 years of soil moisture data from the Oklahoma Mesonet. Mean annual drainage rates ranged from 6 to 266 mm yr-1, with a statewide median of 67 mm yr-1. Drainage estimates were also modeled for four focus sites using HYDRUS1-D. Soil moisture-based drainage rates and HYDRUS1-D drainage rates agreed within 10 mm yr-1 at two drier sites but had discrepancies of >150 mm yr-1 at two sites with >1000 mm yr-1 precipitation.ii) Does incorporating soil moisture information improve seasonal streamflow forecast accuracy? A modified version of the standard Natural Resources Conservation Service (NRCS) principal component analysis and regression (PCR) model was developed to forecast streamflow in four rainfall-dominated watersheds. This model incorporated antecedent precipitation and soil moisture data from long-term monitoring networks into PCR analysis to predict seasonal streamflow volumes at 0-, 1-, 2-, and 3-month lead times. Including soil moisture data improved forecast accuracy by more than 50% over precipitation-based forecasts.iii) Can root zone soil moisture under diverse land cover types be effectively estimated by integrating ground-based meteorological data and remotely-sensed vegetation index data? Estimates of root zone soil moisture were made for four focus locations - a mixed hardwood forest, a loblolly pine plantation, cropland, and tallgrass prairie - by integrating ground-based meteorological data and basal crop coefficient curves derived from remotely-sensed vegetation index data within a soil water balance model. Results show that the model is able to estimate plant available water dynamics moderately well at the four focus locations, but needs further improvements before it can be used operationally

Development and Extrapolation of a General Light Use Efficiency Model for the Gross Primary Production

The global carbon cycle is one of the large biogeochemical cycles spanning all living and non-living compartments of the Earth system. Against the background of accelerating global change, the scientific community is highly interested in analyzing and understanding the dynamics of the global carbon cycle and its complex feedback mechanism with the terrestrial biosphere. The international network FLUXNET was established to serve this aim with measurement towers around the globe. The overarching objective of this thesis is to exploit the powerful combination of carbon flux measurements and satellite remote sensing in order to develop a simple but robust model for the gross primary production (GPP) of vegetation stands. Measurement data from FLUXNET sites as well as remote sensing data from the NASA sensor MODIS are exploited in a data-based model development approach. The well-established concept of light use efficiency is chosen as modeling framework. As a result, a novel gross primary production model is established to quantify the carbon uptake of forests and grasslands across a broad range of climate zones. Furthermore, an extrapolation scheme is derived, with which the model parameters calibrated at FLUXNET sites can be regionalized to pave the way for spatially continuous model applications

Surface water hydrologic modeling using remote sensing data for natural and disturbed lands

Doctor of PhilosophyDepartment of Biological & Agricultural EngineeringStacy L. HutchinsonThe Soil Conservation Service-Curve Number (SCS-CN) method is widely used to estimate direct runoff from rainfall events; however, the method does not account for the dynamic rainfall-runoff relationship. This study used back-calculated curve numbers (CNs) and Normalized Difference Vegetation Index (NDVI) to develop NDVI-based CNs (CN[subscript]NDV) using four small northeastern Kansas grassland watersheds with average areas of 1 km² and twelve years (2001–2012) of daily precipitation and runoff data. Analysis indicated that the CN[subscript]NDVI model improved runoff predictions compared to the SCS-CN method. The CN[subscript]NDVI also showed greater variability in CNs, especially during growing season, thereby increasing the model’s ability to estimate relatively accurate runoff from rainfall events since most rainfall occurs during the growing season. The CN[subscript]NDVI model was applied to small, disturbed grassland watersheds to assess the model’s ability to detect land cover change impact for military maneuver damage and large, diverse land use/cover watersheds to assess the impact of scaling up the model. CN[subscript]NDVI application was assessed using a paired watershed study at Fort Riley, Kansas. Paired watersheds were identified through k-means and hierarchical-agglomerative clustering techniques. At the large watershed scale, Daymet precipitation was used to estimate runoff, which was compared to direct runoff extracted from stream flow at gauging points for Chapman (grassland dominated) and Upper Delaware (agriculture dominated) watersheds. In large, diverse watersheds, CN[subscript]NDVI performed better in moderate and overall flow years. Overall, CN[subscript]NDVI more accurately simulated runoff compared to SCS-CN results: The calibrated model increased by 0.91 for every unit increase in observed flow (r = 0.83), while standard CN-based flow increased by 0.506 for every unit increase in observed flow (r = 0.404). Therefore, CN[subscript]NDVI could help identify land use/cover changes and disturbances and spatiotemporal changes in runoff at various scales. CN[subscript]NDVI could also be used to accurately estimate runoff from precipitation events in order to instigate more timely land management decisions