Vegetation indices as a proxy for spatio-temporal variations in water availability in the semi-arid Rio Santa valley (Callejón de Huaylas, Peru)

2021 ◽  
Author(s):  
Lorenz Hänchen ◽  
Cornelia Klein ◽  
Fabien Maussion ◽  
Wolfgang Gurgiser ◽  
Georg Wohlfahrt
Keyword(s):  
Time Series ◽  
Plant Growth ◽  
Water Availability ◽  
Vegetation Indices ◽  
Growing Season ◽  
Rainfall Data ◽  
Season Length ◽  
Wet Season ◽  
Semi Arid ◽  
Over Time

<p>In the semi-arid Peruvian Andes, the agricultural growing season is mostly determined by the timing of the onset and cessation of the wet season, to which annual crop yields are highly sensitive. Recently, local farmers in the Rio Santa valley (Callejón de Huaylas) bordered by the glaciated Coordillera Blanca to the east and the unglaciated Coordillera Negra to the west, reported increasing challenges in the predictability of the onset, more frequent dry spells and extreme precipitation events during the wet season. Previous studies based on time-series of local rain gauges however did not show any significant changes in either timing or intensity of the wet season. Both in-situ and satellite rainfall data for the region lack the necessary spatial resolution to capture the highly variable rainfall distribution typical for complex terrain, and are often of questionable quality and temporal consistency. As in other Andean valleys, there remains considerable uncertainty in the Rio Santa basin regarding hydrological changes over the last decades. These changes are of a great concern for the local society and the lacking knowledge about changes in water availability (i.e. rainfall) and water demand (i.e. land use practices) hinder the assessment of relevant factors for the development of adaption strategies.</p><p>The over-archiving goal of this study was to better understand variability and recent changes of plant growth and rainfall seasonality and the interactions between them in the Rio Santa basin. Specifically, we aimed to illustrate how satellite-derived information on vegetation greenness can be exploited to infer a robust and highly resolved picture of recent changes in rainfall and vegetation across the region: As the semi-arid climate causes water availability (i.e. precipitation) to be the key limiting factor for plant growth, patterns of precipitation occurrence and the seasonality of vegetation indices (VIs) are tightly coupled. Therefore, these indices can serve as an integrated proxy of rainfall. By combining a 20 year time series of MODIS Aqua and Terra VIs (from 2000 to today) and datasets of precipitation (both remote-sensing and observations) we explore recent spatial and temporal changes in vegetation and water availability by combining VIs timeseries and derived land surface phenology (LSP) with measures of wet season onset and cessation from rainfall data. Furthermore, we analyse the interaction of El Niño Southern Oscillation (ENSO) and the wet and growing season.</p><p>We find spatially variable but significant greening over the majority of the Rio Santa valley domain. This greening is particularly pronounced during the the dry season (Austral winter) and indicates an overall increase of plant available water over time. The start of the growing season (SOS) is temporally highly variable and dominates the variability of growing season length over time. Peak and end of season (POS, EOS) are significantly delayed in the 20 year analysis. By partitioning the results into periods of three stages of ENSO (neutral, Niño, Niña) we find an earlier onset of the rainy and growing season and an overall increased season length in years associated with El Niño.</p>

scholarly journals Vegetation indices as proxies for spatio-temporal variations in water availability in the Rio Santa valley (Peruvian Andes)

2021 ◽  
Author(s):  
Lorenz Hänchen ◽  
Cornelia Klein ◽  
Fabien Maussion ◽  
Wolfgang Gurgiser ◽  
Pierluigi Calanca ◽  
...  
Keyword(s):  
Water Availability ◽  
Rainy Season ◽  
Vegetation Indices ◽  
Growing Season ◽  
Dry Season ◽  
Season Length ◽  
Wet Season ◽  
Peruvian Andes ◽  
Spatio Temporal ◽  
Semi Arid

Abstract. In the semi-arid Peruvian Andes, the growing season is mostly determined by the timing of the onset and retreat of the wet season, to which annual crop yields are highly sensitive. Recently, local farmers in the Rio Santa basin (RSB) reported decreasing predictability of the onset of the rainy season and further challenges related to changes in rainfall characteristics. Previous studies based on time series of local rain gauges however, did not find any significant changes in either the timing or intensity of the wet season. Both in-situ and satellite rainfall data for the region lack the necessary spatial resolution to capture the highly variable rainfall distribution typical for complex terrain, and are often questionable in terms of quality and temporal consistency. To date, there remains considerable uncertainty in the RSB regarding hydrological changes over the last decades. In this study, we overcome this limitation by exploiting satellite-derived information on vegetation greenness to reveal a robust and highly resolved picture of recent changes in rainfall and vegetation phenology across the region: As the semi-arid climate causes water availability (i.e. precipitation) to be the key limiting factor for plant growth, patterns of precipitation occurrence and the seasonality of vegetation indices (VIs) are tightly coupled. Therefore, VIs can serve as an integrated proxy of rainfall. By combining MODIS Aqua and Terra VIs for 2000–2020 and several datasets of precipitation, we explore recent spatio-temporal changes in vegetation and water availability. Furthermore, we examine their links to El Niño Southern Oscillation (ENSO). While different rainfall datasets tend to be incoherent in the period of observation, we find significant greening over the majority of the RSB domain in VI data, particularly pronounced during the dry season (Austral winter). This indicates an overall increase of plant available water over time. The rainy season onset and consequently the start of the growing season (SOS) exhibits high inter-annual variability and dominates the growing season length (LOS). The end of the growing season (EOS) is significantly delayed in the analysis which matches the observed dry-season greening. By partitioning the results into periods of three stages of ENSO (neutral, Niño, Niña), we find an earlier SOS and an overall increased season length in years associated with El Niño. However, the appearance of Niño/Niña events during the analysed period cannot explain the observed greening and delayed EOS. While our study could not corroborate anecdotal evidence for recent changes in the SOS, we confirm that the SOS is highly variable and conclude that rainfed farming in the RSB would profit from future efforts being directed towards improving medium-range forecasts of the rainy season onset.

scholarly journals Identifying environmental controls on vegetation greenness phenology through model-data integration

2014 ◽  
Vol 11 (7) ◽  
pp. 10917-11025
Author(s):  
M. Forkel ◽  
N. Carvalhais ◽  
S. Schaphoff ◽  
W. v. Bloh ◽  
M. Migliavacca ◽  
...  
Keyword(s):  
Time Series ◽  
Observational Data ◽  
Water Availability ◽  
Boreal Forests ◽  
Growing Season ◽  
The Arctic ◽  
Data Sets ◽  
Vegetation Greenness ◽  
Environmental Controls ◽  
Dynamics Of Vegetation

Abstract. Existing dynamic global vegetation models (DGVMs) have a~limited ability in reproducing phenology and decadal dynamics of vegetation greenness as observed by satellites. These limitations in reproducing observations reflect a poor understanding and description of the environmental controls on phenology, which strongly influence the ability to simulate longer term vegetation dynamics, e.g. carbon allocation. Combining DGVMs with observational data sets can potentially help to revise current modelling approaches and thus to enhance the understanding of processes that control seasonal to long-term vegetation greenness dynamics. Here we implemented a~new phenology model within the LPJmL (Lund Potsdam Jena managed lands) DGVM and integrated several observational data sets to improve the ability of the model in reproducing satellite-derived time series of vegetation greenness. Specifically, we optimized LPJmL parameters against observational time series of the fraction of absorbed photosynthetic active radiation (FAPAR), albedo and gross primary production to identify the main environmental controls for seasonal vegetation greenness dynamics. We demonstrated that LPJmL with new phenology and optimized parameters better reproduces seasonality, inter-annual variability and trends of vegetation greenness. Our results indicate that soil water availability is an important control on vegetation phenology not only in water-limited biomes but also in boreal forests and the arctic tundra. Whereas water availability controls phenology in water-limited ecosystems during the entire growing season, water availability co-modulates jointly with temperature the beginning of the growing season in boreal and arctic regions. Additionally, water availability contributes to better explain decadal greening trends in the Sahel and browning trends in boreal forests. These results emphasize the importance of considering water availability in a new generation of phenology modules in DGVMs in order to correctly reproduce observed seasonal to decadal dynamics of vegetation greenness.

scholarly journals Impacts of the seasonal distribution of rainfall on vegetation productivity across the Sahel

2017 ◽  
Author(s):  
Wenmin Zhang ◽  
Martin Brandt ◽  
Xiaoye Tong ◽  
Qingjiu Tian ◽  
Rasmus Fensholt
Keyword(s):  
Rainfall Intensity ◽  
Arid Zone ◽  
Growing Season ◽  
Seasonal Distribution ◽  
Seasonal Rainfall ◽  
Wet Season ◽  
Rainfall Events ◽  
Heavy Rainfall Events ◽  
Semi Arid ◽  
Rainy Days

Abstract. Climate change in drylands has caused alterations in the seasonal distribution of rainfall including increased heavy rainfall events, longer dry spells, and a shifted timing of the wet season. Yet, the aboveground net primary productivity (ANPP) in drylands is usually explained by annual rainfall sums, disregarding the influence of the seasonal distribution of rainfall. This study tests the importance of seasonal rainfall metrics (onset and cessation of the wet season, number of rainy days, rainfall intensity, number of consecutive dry days and heavy rainfall events) on growing season ANPP. We focus on the Sahel and north-Sudanian region (100–800 mm year−1) and apply daily satellite based rainfall estimates (RFE-2.0) and growing season integrated NDVI (MODIS) as a proxy for ANNP over the study period 2001–2015. Growing season ANPP in the arid zone (100–300 mm year−1) was found to be rather insensitive to variations in the seasonal rainfall metrics, whereas vegetation in the semi-arid zone (300–700 mm year−1) was significantly impacted by most metrics, especially by the number of rainy days and timing (start and cessation) of the wet season. We analyzed critical breakpoints for all metrics, showing that growing season ANPP is particularly negatively impacted after > 10 consecutive dry days and that a rainfall intensity of 7 mm day−1 is detected for optimum growing season ANPP. We conclude that number of rainy days and the timing of the wet season are seasonal rainfall metrics being decisive for favorable vegetation growth in semi-arid Sahel which needs to be considered when modelling primary productivity from rainfall in the dryland's of Sahel and elsewhere.

New Ornamental Garden

2011 ◽  
Author(s):  
Simon Rickard
Keyword(s):  
Drought Tolerance ◽  
Plant Growth ◽  
Water Availability ◽  
Evaporation Rate ◽  
Cold Water ◽  
Growing Season ◽  
Exotic Plants ◽  
Root Competition ◽  
Temperate Climate ◽  
Rainfall Patterns

This book takes a fresh look at garden-worthy plants for Australian conditions. It will help gardeners to reappraise their climate, select appropriate plants and modify gardening practices to create beautiful gardens featuring native and exotic plants with proven drought tolerance, reliability and minimal weed potential. The New Ornamental Garden shows how heat, cold, water availability, rainfall patterns, length of growing season, evaporation rate and humidity influence plant growth in Australia, from the wet sub-tropics to the temperate climate of southern Australia. It also discusses the influence of microclimates within a garden: dry sun, dry shade, moist sun, moist shade, seaside conditions, exposed sites, urban situations and root competition from eucalyptus and allelopaths. The main focus of the book is the plant index, which contains notes on hundreds of plant varieties and how they function in the garden. All gardeners will benefit from reading this book!

Revealing climate and vegetation indices interactions through Cross Recurrence Techniques

2021 ◽  
Author(s):  
Andrés Felipe Almeida Ñauñay ◽  
Rosa María Benito Zafrilla ◽  
Miguel Quemada Sáenz-Badillos ◽  
Juan Carlos Losada ◽  
Ana María Tarquis Alfonso
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Observation Data ◽  
Time Step ◽  
Agricultural Insurance ◽  
Complexity Measures ◽  
Temperature And Precipitation ◽  
Multiple Variables ◽  
Over Time

<p>Grasslands are one of the world's major ecosystems groups many of them are now being acknowledged as having a multifunctional role in producing food and rehabilitating croplands, in environmental management and cultural heritage. Multiple studies showed pasture grasslands as a complex agroecological system, depending on multiple variables with a nonlinear dynamic greatly affected by climate fluctuations over time. Remote sensing methods proved to be one of the most effective techniques for monitoring variations over wide areas. In this line, vegetation indices (VIs) demonstrated to be an appropriate indicator of vegetation cover condition. This study aims to perform a method to visualize and quantify the complexity between semiarid grasslands and climate. With this goal, VIs and climate time series are analysed simultaneously with non-linear techniques to reveal the dynamic behaviour of both series over time and their interaction.</p><p>A semi-arid grassland area characterized by a Mediterranean climate with a continental character and low precipitation throughout the year were chosen. VIs time series were constructed from MODIS TERRA (MOD09Q1.006) product. Multispectral images composed by 8-days were acquired from 2002 till 2018 and four pixels with a spatial resolution of 250 x 250 m<sup>2</sup> were chosen in the selected area. Normalized Difference Vegetation Index (NDVI) and Modified Soil-Adjusted Vegetation Index (MSAVI) were calculated based on these images. Temperature and precipitation series were acquired from a near meteorological station and adapted to 8-day time step.</p><p>Cross-Recurrence plots (CRP) and recurrence quantification analysis (RQA) were performed to analyse the climate and vegetation dynamics simultaneously. To achieve this goal, several measures of complexity were computed, such as Determinism (DET), average diagonal length (LT) and entropy (ENT).</p><p>Our results showed different CRPs depending on the climate variable and the utilized VIs. Temperature and VIs CRPs showed a periodical pattern, implying the temperature seasonality over time. In contrast, precipitation and VIs CRPs showed more chaotical behaviour, due to the occurrence of extreme events and seasonal shifts. These results are quantified by the DET and ENTR values.</p><p>Our results indicate that temperature and precipitation present a dynamical complexity that is revealed in VIs response. Both indices showed different results of complexity measures, implying that MSAVI has a higher complexity than NDVI. This fact is probably due to the addition of a variable soil adjustment factor. Consequently, MSAVI should be considered as a potential alternative to NDVI in semiarid areas.</p><p><strong>Reference</strong></p><p>Almeida-Ñauñay, A. F., Benito, R. M., Quemada, M., Losada, J. C., & Tarquis, A. M. Complexity of the Vegetation-Climate System Through Data Analysis. In International Conference on Complex Networks and Their Applications. Springer, Cham., 609-619, 2020</p><p><strong>Acknowledgements</strong></p><p>The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330 and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020.</p>

Modelling and mapping soil pH in Andalusia (Spain) using phenological products as predictor features

2020 ◽  
Author(s):  
Francisco M. Canero ◽  
Victor Rodriguez-Galiano ◽  
Aaron Cardenas-Martinez ◽  
Juan Antonio Luque-Espinar
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Soil Ph ◽  
Growing Season ◽  
Remotely Sensed ◽  
Monthly Precipitation ◽  
Season Length ◽  
Entire Study ◽  
Maximum Peak ◽  
Growing Season Length

<p>Soil pH is one of the most important soil parameters, due to its importance for for soil management and food security. Spatial distribution of pH could altered by the different environmental conditions, such as geology, climate or soil-vegetation interactions. pH has an ecological function in controlling spatial distribution of plant species, conditioning absence or presence of different species due to soil pH ability or modifying mineral solubility. Hence, pH and remotely sensed land surface phenology (LSP) could be associated. The objective of this work was two-folded: i) mapping the soil pH of Andalusian soils and ii) the evaluation of new features derived from remote sensing which are related to seasonal cycles of vegetation applied to digital soil mapping</p><p>We developed a pH model using 3215 pH measurements at different locations together with three types of predictor features: terrain (elevation, slope, hydrological attributes…), climatic (annual and monthly precipitation and maximum and minimum temperatures) and phenological features extracted from remotely sensed vegetation indices time series (date of the start of spring, date of the end of senescence, growing season length, end of the growing season, length of the growing season, maximum peak, and large seasonal integral as a proxy of productivity). The LSP features were obtained from time series of NDVI that were computed from the MODIS weekly surface reflectance product (MOD09Q1 v6) at a spatial resolution of 250 for the entire study period. The performance of  multiple lineal regression (MLR) and Random Forest was evaluated within the framework of a high dimensional feature space.</p><p>The results showed that RF outperformed MLR (R<sup>2</sup>: 0.66 and 0.58; RMSE: 0.76 and 0.83). ph and feature pairwise correlations were higher for the phenological features: median of large integral (-0.55); median of maximum peak (-0.51); valley depth (0.48); median of date of start of spring (-0.47), median of value on the date of start of spring (-0.46). The most important features in RF prediction were almost the same: the median of large integral, valley depth, maximum temperatures in September and median of maximum peak, showing that LSP features were relevant in pH spatial modelling, with an better performance of RF model.</p>

Evaluation of winter wheat management practices under semi-arid conditions

1992 ◽  
Vol 72 (1) ◽  
pp. 1-12 ◽  
Author(s):  
G. P. Lafond
Keyword(s):  
Plant Growth ◽  
Winter Wheat ◽  
Nitrogen Fertilizer ◽  
Growing Season ◽  
Late Season ◽  
Fungicide Application ◽  
Residual Nitrogen ◽  
Growing Conditions ◽  
Semi Arid ◽  
Growing Season Precipitation

A study was conducted to evaluate European cereal management techniques in winter wheat under semi-arid growing conditions. Combinations of rates and split applications of ammonium nitrate fertilizer with a plant growth regulator and/or a late season fungicide application were investigated using no-till "stubbled-in" production practices in two winter wheat cultivars, Norwin and Norstar at two locations over 3 yr. Nitrogen fertilizer gave the maximum yield when it was applied in mid-April. Split applications of nitrogen did not improve grain yields or grain protein concentration. A height reduction was observed with the use of plant growth regulators in both cultivars but no benefits were incurred due to the lack of lodging. The late season fungicide application had some effect on increasing kernel weight in both cultivars but rarely translated into a higher yield. Nitrogen and growing conditions had the largest effects on yield and the dilemma faced by producers is to correctly match nitrogen rates with environmental conditions given that the nitrogen has to be applied early in the spring. Available spring soil moisture and soil residual nitrogen provided little help in determining the rate of nitrogen giving the maximum economic yield because assumptions on growing season precipitation have to be made. It is suggested that nitrogen management be based on a risk analysis which would involve determining the probability of different levels of growing season precipitation for various climatic zones and soil types and the corresponding yield levels expected. Rates of nitrogen fertilizer would then be adjusted according to soil residual nitrogen levels and the risk the producer is willing to assume. This will require more extensive research and development of crop production models.Key words: Nitrogen fertilizer, Triticum aestivum L., intensive cereal management, propiconazole, chlormequat chloride, ethephon

scholarly journals Nitrogen supply affects root:shoot ratio in corn and velvetleaf (Abutilon theophrasti)

Weed Science ◽  
2005 ◽  
Vol 53 (5) ◽  
pp. 670-675 ◽  
Author(s):  
Kimberly D. Bonifas ◽  
Daniel T. Walters ◽  
Kenneth G. Cassman ◽  
John L. Lindquist
Keyword(s):  
Plant Growth ◽  
Growing Season ◽  
Belowground Biomass ◽  
Nutrient Supply ◽  
Nitrogen Supply ◽  
Abutilon Theophrasti ◽  
Normal Plant ◽  
Root:Shoot Ratio ◽  
N Supply ◽  
Over Time

Competitive outcome between crops and weeds is affected by partitioning of new biomass to above- and belowground plant organs in response to nutrient supply. This study determined the fraction of biomass partitioned to roots vs. shoots in corn and velvetleaf in response to nitrogen (N) supply. Pots measuring 28 cm in diam and 60 cm deep were embedded in the ground and each contained one plant of either corn or velvetleaf. Each plant received one of three N treatments: 0, 1, or 3 g N applied as ammonium nitrate in 2001, and 0, 2, or 6 g N in 2002. Measurements of total above- and belowground biomass were made at 10 sampling dates during each growing season. The root:shoot ratio decreased over time for both corn and velvetleaf as a result of normal plant growth and as N supply increased. Root:shoot ratio was greater for corn than for velvetleaf at comparable stages of development and at all levels of N supply. Both corn and velvetleaf display true plasticity in biomass partitioning patterns in response to N supply. Velvetleaf root:shoot ratio increased by 46 to 82% when N was limiting in 2001 and 2002, respectively, whereas corn root:shoot ratio increased by only 29 to 45%. The greater increase in biomass partitioned to roots by velvetleaf might negatively impact its ability to compete with corn for light when N supply is limited.

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