scholarly journals Spatio-Temporal Patterns of Drought and Impact on Vegetation in North and West Africa Based on Multi-Satellite Data

2020 ◽  
Vol 12 (23) ◽  
pp. 3869
Author(s):  
Malak Henchiri ◽  
Qi Liu ◽  
Bouajila Essifi ◽  
Tehseen Javed ◽  
Sha Zhang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
West Africa ◽  
Land Surface ◽  
Condition Index ◽  
Mitigation Strategies ◽  
Drought Severity ◽  
Drought Risk ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Drought Prediction

Studying the significant impacts of drought on vegetation is crucial to understand its dynamics and interrelationships with precipitation, soil moisture, and temperature. In North and West Africa regions, the effects of drought on vegetation have not been clearly stated. Therefore, the present study aims to bring out the drought fluctuations within various types of Land Cover (LC) (Grasslands, Croplands, Savannas, and Forest) in North and West Africa regions. The drought characteristics were evaluated by analyzing the monthly Self-Calibrating Palmer Drought Severity Index (scPDSI) in different timescale from 2002 to 2018. Then, the frequency of droughts was examined over the same period. The results have revealed two groups of years (dry years and normal years), based on drought intensity. The selected years were used to compare the shifting between vegetation and desert. The Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), the Precipitation Condition Index (PCI), and the Soil Moisture Condition Index (SMCI) were also used to investigate the spatiotemporal variation of drought and to determine which LC class was more vulnerable to drought risk. Our results revealed that Grasslands and Croplands in the West region, and Grasslands, Croplands, and Savannas in the North region are more sensitive to drought. A higher correlation was observed among the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Tropical Rainfall Measuring Mission (TRMM), and Soil Moisture (SM). Our findings suggested that NDVI, TRMM, and SM are more suitable for monitoring drought over the study area and have a reliable accuracy (R2 > 0.70) concerning drought prediction. The outcomes of the current research could, explicitly, contribute progressively towards improving specific drought mitigation strategies and disaster risk reduction at regional and national levels.

scholarly journals Soil moisture obtained through remote sensing to assess drought events

2020 ◽  
Vol 24 (9) ◽  
pp. 575-580 ◽  
Author(s):  
Tiago de M. Inocêncio ◽  
Alfredo Ribeiro Neto ◽  
Alzira G. S. S. Souza
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Condition Index ◽  
Semiarid Region ◽  
Moisture Index ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Northeast Region ◽  
Promising Tool ◽  
Soil Moisture Index

ABSTRACT The sequence of drought events in the Northeast of Brazil in recent decades raises attention to the importance of studying this phenomenon. The objective of this study was to evaluate the duration and severity of drought events from 1988 to 2018 in hydrographic basins of the state of Pernambuco, Brazil, using two drought indexes: Standardized Soil Moisture Index and Soil Moisture Condition Index, calculated based on data of the Soil Moisture Project of the European Space Agency’s Climate Change Initiative. The duration of the droughts was determined considering the months between their beginning and end, and their severity was based on the area formed in the graph between the curve of the index and the x-axis. The soil moisture database showed to be a promising tool for the analysis and monitoring of drought events in the Northeast region of Brazil, mainly for analysis and monitoring of drought events. The indexes allowed the evaluation of the drought phenomenon over the 30-year period, showing increases from 2012, which were more pronounced in the Semiarid region. The hydrographic basins responded differently to a same event, depending on the climate characteristics of the region in which they are located. Consecutive years with rainfall below the historical mean increased the magnitude of the droughts, as found for the 2012-2017 period, in which the indexes presented delays to return to more favorable values, showing the effect that one drought year has on the following year.

scholarly journals A multitemporal remote sensing approach to parsimonious streamflow modeling in a southcentral Texas watershed, USA

2007 ◽  
Vol 4 (1) ◽  
pp. 1-33 ◽  
Author(s):  
B. P. Weissling ◽  
H. Xie ◽  
K. E. Murray
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Remotely Sensed ◽  
Convective Precipitation ◽  
Precipitation Event ◽  
Streamflow Prediction ◽  
Remotely Sensed Data ◽  
Moisture Condition ◽  
Soil Moisture Condition

Abstract. Soil moisture condition plays a vital role in a watershed's hydrologic response to a precipitation event and is thus parameterized in most, if not all, rainfall-runoff models. Yet the soil moisture condition antecedent to an event has proven difficult to quantify both spatially and temporally. This study assesses the potential to parameterize a parsimonious streamflow prediction model solely utilizing precipitation records and multi-temporal remotely sensed biophysical variables (i.e.~from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra satellite). This study is conducted on a 1420 km2 rural watershed in the Guadalupe River basin of southcentral Texas, a basin prone to catastrophic flooding from convective precipitation events. A multiple regression model, accounting for 78% of the variance of observed streamflow for calendar year 2004, was developed based on gauged precipitation, land surface temperature, and enhanced vegetation Index (EVI), on an 8-day interval. These results compared favorably with streamflow estimations utilizing the Natural Resources Conservation Service (NRCS) curve number method and the 5-day antecedent moisture model. This approach has great potential for developing near real-time predictive models for flood forecasting and can be used as a tool for flood management in any region for which similar remotely sensed data are available.

scholarly journals Diagnosing the Sensitivity of Local Land–Atmosphere Coupling via the Soil Moisture–Boundary Layer Interaction

2011 ◽  
Vol 12 (5) ◽  
pp. 766-786 ◽  
Author(s):  
Joseph A. Santanello ◽  
Christa D. Peters-Lidard ◽  
Sujay V. Kumar
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
Land Surface ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Coupled Systems ◽  
Thermodynamic Quantities ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Boundary Layer Interaction

Abstract The inherent coupled nature of earth’s energy and water cycles places significant importance on the proper representation and diagnosis of land–atmosphere (LA) interactions in hydrometeorological prediction models. However, the precise nature of the soil moisture–precipitation relationship at the local scale is largely determined by a series of nonlinear processes and feedbacks that are difficult to quantify. To quantify the strength of the local LA coupling (LoCo), this process chain must be considered both in full and as individual components through their relationships and sensitivities. To address this, recent modeling and diagnostic studies have been extended to 1) quantify the processes governing LoCo utilizing the thermodynamic properties of mixing diagrams, and 2) diagnose the sensitivity of coupled systems, including clouds and moist processes, to perturbations in soil moisture. This work employs NASA’s Land Information System (LIS) coupled to the Weather Research and Forecasting (WRF) mesoscale model and simulations performed over the U.S. Southern Great Plains. The behavior of different planetary boundary layers (PBL) and land surface scheme couplings in LIS–WRF are examined in the context of the evolution of thermodynamic quantities that link the surface soil moisture condition to the PBL regime, clouds, and precipitation. Specifically, the tendency toward saturation in the PBL is quantified by the lifting condensation level (LCL) deficit and addressed as a function of time and space. The sensitivity of the LCL deficit to the soil moisture condition is indicative of the strength of LoCo, where both positive and negative feedbacks can be identified. Overall, this methodology can be applied to any model or observations and is a crucial step toward improved evaluation and quantification of LoCo within models, particularly given the advent of next-generation satellite measurements of PBL and land surface properties along with advances in data assimilation schemes.

Impact of climate change on soil moisture condition in Nova Scotia

2012 ◽  
Author(s):  
Raheleh Malekian ◽  
Robert Gordon ◽  
Ali Madani ASABE Member ◽  
Seyyed Ebrahim Hashemi
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Nova Scotia ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Impact Of Climate Change

scholarly journals Effect of soil moisture condition on the conversion rate of oxamyl.

1979 ◽  
Vol 27 (3) ◽  
pp. 191-198
Author(s):  
J.H. Smelt ◽  
A. Dekker ◽  
M. Leistra
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Conversion Rate ◽  
Soil Moisture Content ◽  
Loamy Sand ◽  
Clay Loam ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Incubation Experiments ◽  
Wilting Point

The decomposition of oxamyl in four soils under moist conditions was measured in incubation experiments at 15 deg C. Half-lives of oxamyl in soils with moisture tensions of approx. -9.8 X 103 Pa were 13 days in a clay loam, 14 days in a loamy sand, 34 days in a peaty sand and 39 days in a humic loamy sand. The rate of oxamyl decomposition in the clay loam decreased with decreasing soil moisture content down to values for below wilting point. Oxamyl decomposition in the humic loamy sand decreased with decreasing soil moisture content, but increased sharply in the very dry range. (Abstract retrieved from CAB Abstracts by CABI’s permission)

Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature

2010 ◽  
Vol 2 (2) ◽  
Author(s):  
Diandong Ren
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Land Surface ◽  
Surface Soil ◽  
Land Surface Model ◽  
Initial Guess ◽  
Surface Model ◽  
Soil Moisture Condition ◽  
Wide Range ◽  
Surface Soil Temperature

AbstractBased on a 2-layer land surface model, a rather general variational data assimilation framework for estimating model state variables is developed. The method minimizes the error of surface soil temperature predictions subject to constraints imposed by the prediction model. Retrieval experiments for soil prognostic variables are performed and the results verified against model simulated data as well as real observations for the Oklahoma Atmospheric Surface layer Instrumentation System (OASIS). The optimization scheme is robust with respect to a wide range of initial guess errors in surface soil temperature (as large as 30 K) and deep soil moisture (within the range between wilting point and saturation). When assimilating OASIS data, the scheme can reduce the initial guess error by more than 90%, while for Observing Simulation System Experiments (OSSEs), the initial guess error is usually reduced by over four orders of magnitude.Using synthetic data, the robustness of the retrieval scheme as related to information content of the data and the physical meaning of the adjoint variables and their use in sensitivity studies are investigated. Through sensitivity analysis, it is confirmed that the vegetation coverage and growth condition determine whether or not the optimally estimated initial soil moisture condition leads to an optimal estimation of the surface fluxes. This reconciles two recent studies.With the real data experiments, it is shown that observations during the daytime period are the most effective for the retrieval. Longer assimilation windows result in more accurate initial condition retrieval, underlining the importance of information quantity, especially for schemes assimilating noisy observations.

Flash Drought Characteristics by Different Severities in Humid Subtropical Basins: A Case Study in the Gan River Basin, China

2021 ◽  
pp. 1-44
Author(s):  
Yuqing Zhang ◽  
Qinglong You ◽  
Guangxiong Mao ◽  
Changchun Chen ◽  
Xin Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Vapour Pressure Deficit ◽  
Severity Index ◽  
Drought Severity ◽  
Rapid Decline ◽  
Drought Risk ◽  
Drought Event ◽  
Drought Period ◽  
Drought Intensity

AbstractIt is essential to assess flash drought risk based on a reliable flash drought intensity (severity) index incorporating comprehensive information of the rapid decline (“flash”) in soil moisture towards drought conditions and soil moisture thresholds belonging to the “drought” category. In this study, we used the Gan River Basin as an example to define a flash drought intensity index that can be calculated for individual time steps (pentads) during a flash drought period over a given grid (or station). The severity of a complete flash drought event is the sum of the intensity values during the flash drought. We explored the spatial and temporal characteristics of flash droughts with different grades based on their respective severities. The results show that decreases in total cloud cover, precipitation, and relative humidity, as well as increases in 500 hPa geopotential height, convective inhibition, temperature, vapour pressure deficit, and wind speed can create favorable conditions for the occurrence of flash droughts. Although flash droughts are relatively frequent in the central and southern parts of the basin, the severity is relatively high in the northern part of the basin due to longer duration. Flash drought severity shows a slightly downward trend due to decreases in frequency, duration, and intensity from 1961 to 2018. Extreme and exceptional flash droughts decrease significantly while moderate and severe flash droughts trend slightly upward. Flash drought severity appears to be more affected by the interaction between duration and intensity as the grade increases from mild to severe. The frequency and duration of flash droughts are higher in July to October. The southern part of the basin is more prone to moderate and severe flash droughts, while the northern parts of the basin are more vulnerable to extreme and exceptional flash droughts due to longer durations and greater severities than other parts. Moderate, severe, extreme, and exceptional flash droughts occurred approximately every 3-6, 5-15, 10-50, and 30-200 year intervals, respectively, based on the copula analysis.

Soil moisture condition and soil nitrogen dynamics in a pure Alnus japonica forest in Korea

2010 ◽  
Vol 7 (1) ◽  
pp. 93-99 ◽  
Author(s):  
Kyung Won Seo ◽  
Su Jin Heo ◽  
Yowhan Son ◽  
Nam Jin Noh ◽  
Sue Kyoung Lee ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Nitrogen ◽  
Nitrogen Dynamics ◽  
Alnus Japonica ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Soil Nitrogen Dynamics

Soil Moisture Drought in China, 1950–2006

2011 ◽  
Vol 24 (13) ◽  
pp. 3257-3271 ◽  
Author(s):  
Aihui Wang ◽  
Dennis P. Lettenmaier ◽  
Justin Sheffield
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Agricultural Drought ◽  
Central China ◽  
Spatial Extent ◽  
Drought Severity ◽  
Spatial And Temporal Variability ◽  
The Tibetan Plateau ◽  
Small Areas ◽  
Physically Based

Abstract Four physically based land surface hydrology models driven by a common observation-based 3-hourly meteorological dataset were used to simulate soil moisture over China for the period 1950–2006. Monthly values of total column soil moisture from the simulations were converted to percentiles and an ensemble method was applied to combine all model simulations into a multimodel ensemble from which agricultural drought severities and durations were estimated. A cluster analysis method and severity–area–duration (SAD) algorithm were applied to the soil moisture data to characterize drought spatial and temporal variability. For drought areas greater than 150 000 km2 and durations longer than 3 months, a total of 76 droughts were identified during the 1950–2006 period. The duration of 50 of these droughts was less than 6 months. The five most prominent droughts, in terms of spatial extent and then duration, were identified. Of these, the drought of 1997–2003 was the most severe, accounting for the majority of the severity–area–duration envelope of events with areas smaller than 5 million km2. The 1997–2003 drought was also pervasive in terms of both severity and spatial extent. It was also found that soil moisture in north central and northeastern China had significant downward trends, whereas most of Xinjiang, the Tibetan Plateau, and small areas of Yunnan province had significant upward trends. Regions with downward trends were larger than those with upward trends (37% versus 26% of the land area), implying that over the period of analysis, the country has become slightly drier in terms of soil moisture. Trends in drought severity, duration, and frequency suggest that soil moisture droughts have become more severe, prolonged, and frequent during the past 57 yr, especially for northeastern and central China, suggesting an increasing susceptibility to agricultural drought.

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