Soil Moisture and Climate Variability

2015 ◽  
2021 ◽  
Author(s):  
Yanghang Ren ◽  
Kun Yang ◽  
Han Wang

<p>As region that is highly sensitive to global climate change, the Tibetan Plateau (TP) experiences an intra-seasonal soil water deficient due to the reduced precipitation during the South Asia monsoon (SAM) break. Few studies have investigated the impact of the SAM break on TP ecological processes, although a number of studies have explored the effects of inter-annual and decadal climate variability. In this study, the response of vegetation activity to the SAM break was investigated. The data used are: (1) soil moisture from in situ, satellite remote sensing and data assimilation; and (2) the Normalized Difference Vegetation Index (NDVI) and Solar-Induced chlorophyll Fluorescence (SIF). We found that in the region impacted by SAM break, which is distributed in the central-eastern part of TP, photosynthesis become more active during the SAM break. And temporal variability in the photosynthesis of this region is controlled mainly by solar radiation variability and has little sensitivity to soil moisture. We adopted a diagnostic process-based modeling approach to examine the causes of enhanced plant activity during the SAM break on the central-eastern TP. Our analysis indicates that active photosynthetic behavior in the reduced precipitation is stimulated by increases in solar radiation absorbed and temperature. This study highlights the importance of sub-seasonal climate variability for characterizing the relationship between vegetation and climate.</p>


2015 ◽  
Vol 120 (16) ◽  
pp. 8144-8164 ◽  
Author(s):  
Sarah E. Perkins ◽  
Daniel Argüeso ◽  
Christopher J. White

2012 ◽  
Vol 9 (12) ◽  
pp. 13773-13803 ◽  
Author(s):  
B. Orlowsky ◽  
S. I. Seneviratne

Abstract. Recent years have seen a number of severe droughts in different regions around the world, causing agricultural and economic losses, famines and migration. Despite their devastating consequences, the Standardised Precipitation Index (SPI) of these events lies within the range of internal climate variability, which we estimate from simulations from the 5th phase of the Coupled Model Intercomparison Project (CMIP5). In terms of drought magnitude, regional trends of SPI over the last decades remain mostly inconclusive in observations and CMIP5 simulations, although Soil Moisture Anomalies (SMAs) in CMIP5 simulations hint at increased drought in a few regions (e.g. the Mediterranean, Central America/Mexico, the Amazon, North-East Brazil and South Africa). Also for the future, projections of meteorological (SPI) and agricultural (SMA) drought in CMIP5 display large uncertainties over all time frames, generally impeding trend detection. Analogue analyses of the frequencies rather than magnitudes of future drought display, however, more robust signal-to-noise ratios with detectable trends towards more frequent drought until the end of the 21st century in the Mediterranean, South Africa and Central America/Mexico. Other present-day hot spots are projected to become less drought-prone, or to display unsignificant changes in drought occurrence. A separation of different sources of uncertainty in drought projections reveals that for the near term, internal climate variability is the dominant source, while the formulation of Global Climate Models (GCMs) generally becomes the dominant source of uncertainty by the end of the 21st century, especially for agricultural (soil moisture) drought. In comparison, the uncertainty in Green-House Gas (GHG) concentrations scenarios is negligible for most regions. These findings stand in contrast to respective analyses for a heat wave indicator, for which GHG concentrations scenarios constitute the main source of uncertainty. Our results highlight the inherent difficulty of drought quantification and the uncertainty of drought projections. However, high uncertainty should not be equated with low drought risk, since potential scenarios include large drought increases in key agricultural and ecosystem regions.


Author(s):  
I.C. Blair

With increasing climate variability, a reliable method of estimating pasture growth has eluded farmers. Rain, temperature, evapotranspiration, radiation and soil moisture status are components which interact and affect pasture production. In 1992, soil moisture monitoring in Marlborough vineyards was extended to pasture. Keywords: soil moisture, pasture production, models, Southern Oscillation Index, Pacific Decadal Oscillation


2020 ◽  
Author(s):  
Jacqueline Boutin ◽  
Nicolas Reul ◽  
Julia Koehler ◽  
Adrien Martin ◽  
Rafael Catany ◽  
...  

<p>Sea Surface Salinity (SSS) is an Essential Climate Variable (ECV) that plays a fundamental role in the density-driven global ocean circulation, the water cycle, and climate. The satellite SSS observation from the Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (SMAP) missions have provided an unprecedented opportunity to map SSS over the global ocean since 2010 at 40-150km scale with a revisit every 2 to 3 days. This observation capability has no historic precedent and has brought new findings concerning the monitoring of SSS variations related with climate variability such as El Niño-Southern Oscillation, Indian Ocean Dipole, and Madden-Julian Oscillation, and the linkages of the ocean with different elements of the water cycle such as evaporation and precipitation and continental runoff. It has enhanced the understanding of various ocean processes such as tropical instability waves, Rossby waves, mesoscale eddies and related salt transport, salinity fronts, hurricane haline wake, river plume variability, cross-shelf exchanges. There are also emerging use of satellite SSS to study ocean biogeochemistry, e.g. linked to air-sea CO<sub>2</sub> fluxes.</p><p>Following the success of the initial oceanographic studies implementing this new variable, the European Space Agency (ESA) Climate Change Initiative CCI+SSS project (2018-2020) aims at generating improved calibrated global SSS fields over 10 years period (2010-2019) from all available satellite L-band radiometer measurements, extended at regional scale to 2002-2019 from C-band radiometer measurements. It fully exploits the ESA/Earth explorer SMOS mission complemented with SMAP and AQUARIUS satellite missions. The project gathers teams involved in earth observation remote sensing, in the validation of satellite data and in climate variability study. In this presentation, we will present the first CCI+SSS product released to the scientific community (https://catalogue.ceda.ac.uk/uuid/9ef0ebf847564c2eabe62cac4899ec41). The comparisons with in situ ground truth indicate much better performances than the ones obtained with a single satellite data product, with global rmsd against in situ references of 0.16 pss. Large scale interannual variability is successfully reproduced and SSS variability in very variable regions like the Bay of Bengale and in river plumes in the Atlantic Ocean is very satisfactory, confirming the usefulness of these products for scientific studies. Nevertheless we also identify some caveats that will be discussed as well as the ways envisaged to resolve part of them in the next version of the product to be delivered publicly in Summer 2020.</p><p>The ESA CCI+SSS consortium gathers scientists and engineers from various European research institutes and companies (LOCEAN/IPSL, LOPS, University of Hamburg, NOC, ICM, ARGANS, ACRI-st, ODL) and is conducted in collaboration with US colleagues from NASA and Remote Sensing System.</p>


2020 ◽  
Vol 12 (17) ◽  
pp. 7023 ◽  
Author(s):  
Netrananda Sahu ◽  
Atul Saini ◽  
Swadhin Behera ◽  
Takahiro Sayama ◽  
Sridhara Nayak ◽  
...  

The impact of Indo-Pacific climate variability in the South Asian region is very pronounced and their impact on agriculture is very important for the Indian subcontinent. In this study, rice productivity, climatic factors (Rainfall, Temperature and Soil Moisture) and associated major Indo-Pacific climate indices in Bihar were investigated. Bihar is one of the major rice-producing states of India and the role of climate variability and prevailing climate indices in six events (between 1991–2014) with severer than −10% rice productivity are analyzed. The Five-year moving average, Pearson’s Product Moment Correlation, Partial Correlation, Linear Regression Model, Mann Kendall Test, Sen’s Slope and some other important statistical techniques were used to understand the association between climatic variables and rice productivity. Pearson’s Product Moment Correlation provided an overview of the significant correlation between climate indices and rice productivity. Whereas, Partial Correlation provided the most refined results on it and among all the climate indices, Niño 3, Ocean Niño Index and Southern Oscillation Index are found highly associated with years having severer than −10% decline in rice productivity. Rainfall, temperature and soil moisture anomalies are analyzed to observe the importance of climate factors in rice productivity. Along with the lack of rainfall, lack of soil moisture and persistent above normal temperature (especially maximum temperature) are found to be the important factors in cases of severe loss in rice productivity. Observation of the dynamics of ocean-atmosphere coupling through the composite map shows the Pacific warming signals during the event years. The analysis revealed a negative (positive) correlation of rice productivity with the Niño 3 and Ocean Niño Index (Southern Oscillation Index).


2006 ◽  
Vol 10 (17) ◽  
pp. 1-27 ◽  
Author(s):  
Weile Wang ◽  
Bruce T. Anderson ◽  
Nathan Phillips ◽  
Robert K. Kaufmann ◽  
Christopher Potter ◽  
...  

Abstract Feedbacks of vegetation on summertime climate variability over the North American Grasslands are analyzed using the statistical technique of Granger causality. Results indicate that normalized difference vegetation index (NDVI) anomalies early in the growing season have a statistically measurable effect on precipitation and surface temperature later in summer. In particular, higher means and/or decreasing trends of NDVI anomalies tend to be followed by lower rainfall but higher temperatures during July through September. These results suggest that initially enhanced vegetation may deplete soil moisture faster than normal and thereby induce drier and warmer climate anomalies via the strong soil moisture–precipitation coupling in these regions. Consistent with this soil moisture–precipitation feedback mechanism, interactions between temperature and precipitation anomalies in this region indicate that moister and cooler conditions are also related to increases in precipitation during the preceding months. Because vegetation responds to soil moisture variations, interactions between vegetation and precipitation generate oscillations in NDVI anomalies at growing season time scales, which are identified in the temporal and the spectral characteristics of the precipitation–NDVI system. Spectral analysis of the precipitation–NDVI system also indicates that 1) long-term interactions (i.e., interannual and longer time scales) between the two anomalies tend to enhance one another, 2) short-term interactions (less than 2 months) tend to damp one another, and 3) intermediary-period interactions (4–8 months) are oscillatory. Together, these results support the hypothesis that vegetation may influence summertime climate variability via the land–atmosphere hydrological cycles over these semiarid grasslands.


Earth ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 556-585
Author(s):  
Kassim Ramadhani Mussa ◽  
Ibrahimu Chikira Mjemah ◽  
Revocatus Lazaro Machunda

The response of aquifers with contrasting climate and geology to climate and land cover change perturbations through natural groundwater recharge remains inadequately understood. In Tanzania and elsewhere in the world, studies have been conducted to assess the impact of climate change and variability, and land use/cover changes on stream flow using different models, but similar studies on groundwater dynamics are inadequate. This study, therefore, examined the influence of land use/cover and climate dynamics on natural groundwater recharge in basins with contrasting climate and geology in Tanzania, applying the modified soil moisture balance method, coupled with the curve number (CN). The method hinges on the balance between the incoming water from precipitation and the outflow of water by evapotranspiration. The different parameters in the soil moisture balance method were computed using the Thornthwaite Water Balance software. The potential evapotranspiration (PET) was calculated using the daily maximum and minimum temperatures, utilizing two-temperature-based PET methods, Penman–Monteith (PM) and Hargreaves–Samani (HS). The rainfall data were obtained from the gauging stations under the Tanzania Meteorological Agency and some additional data were acquired from climate observatories management by water basins. The results show that there has been a quasi-stable CN in the Singida semi-arid, fractured crystalline basement aquifer (74.2 in 1997, 73.64 in 2005, and 73.87 in 2018). In the Kimbiji, humid, Neogene sedimentary aquifer, the CN has been steadily increasing (66.69 in 1997, 69.08 in 2008, and 71.42 in 2016), indicating the rapid land cover changes in the Kimbiji aquifer as compared to the Singida aquifer. For the Kimbiji humid aquifer, the PET calculated using the Penman–Monteith (PM) method for the 1996/1997, 2007/2008, and 2015/2016 hydrological years were 1156.5, 1079.5, and 1143.9 mm/year, respectively, while for the Hargreaves–Samani (HS) method, the PET was found to be 1046.1, 1138.3, and 1204.4 mm/year for the 1996/1997, 2007/2008, and 2015/2016 hydrological years, respectively. For the Singida semi-arid aquifer, the PM PET method resulted in 2083.3, 2053.6, and 1875.4 mm/year for the 1996/1997, 2004/2005, and 2017/2018 hydrological years, respectively. The HS method produced relatively lower PET values for the semi-arid area (1839.4, 1814.7, and 1710.2 mm/year) for the 1996/1997, 2004/2005, and 2017/2018 hydrological years, respectively. It was equally revealed that the recharge and aridity indices correspond with the PET calculated using two temperature-dependent methods. The decline of certain land covers (forests) and increase in others (built-up areas) have contributed to the increase in surface runoff in each study area, possibly resulting in the decreasing trend of groundwater recharge. An overestimation of the PET using the HS method in the Kimbiji humid aquifer was observed, which was relatively smaller than the overestimation of the PET using the PM method in the Singida semi-arid aquifer. Despite the difference in climate and geology, the response of the two aquifers to rainfall is similar. The combined influence of climate and land cover changes on natural groundwater recharge was observed to be prominent in the Kimbiji aquifer, while only climate variability appreciably influences natural groundwater recharge in the Singida semi-arid aquifer. El Nino and the Southern Oscillation as part of the climate variability phenomenon dwarfed the time lags between rainfall and recharge in the two basins, regardless of their difference in climate and geology.


2007 ◽  
Vol 34 (6) ◽  
Author(s):  
Adriaan J. Teuling ◽  
François Hupet ◽  
Remko Uijlenhoet ◽  
Peter A. Troch

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