Interannual memory effects for spring NDVI in semi-arid South Africa

Almost 20 years of Normalized Difference Vegetative Index (NDVI) and precipitation (PPT) data are analysed to better understand the interannual memory effects on vegetation dynamics observed at regional scales in Southern Africa (SA). The study focuses on a semi-arid region (25°S–31°S; 21°E–26°E) during the austral early summer (September–December). The memory effects are examined using simple statistical approaches (linear correlations and regressions) which require the definition of an early summer vegetation predictand (December NDVI minus September NDVI) and a consistent set of potential predictors (rainfall amount, number of rainy days, rainfall intensity, NDVI and Rain-Use-Efficiency) considered with 4 to 15-month time-lag. An analysis over six SA sub-regions, corresponding to the six major land-cover types of the area reveals two distinct memory effects. A “negative” memory effect (with both rainfall and vegetation) is detected at 7 to 10-month time-lag while a “positive” memory effect (with vegetation only) is observed at 12 to 14-month time-lag. These results suggest that interannual memory effects in early summer vegetation dynamics of semi-arid South Africa may preferably be driven by biological rather than hydrological mechanisms

ENSO effects on primary productivity in Southern Atacama desert

International audienceIn the winter-rain southern Atacama Desert of the Coquimbo Region of Chile, El Niño - Southern Oscillation (ENSO) events modulate primary productivity. In this region, there are important changes in water availability between La Niña (dry) and El Niño (rainy) years. Using inter-annual comparisons of LANDSAT images from 30° to 31° S latitude, we observed changes in primary productivity between dry and rainy years at the regional level. There were also significant, negative correlations between productivity and elevation, with changes occurring first at low elevation during rainy years. The limiting factors to dryland vegetation primary productivity is different in regard to elevation. Rain during an El Niño year is the main factor that explains the increase in primary productivity at low elevation, while lower temperatures reduce and delay the net primary productivity at mid elevation

Etude des variations interannuelles de la production herbacée des pâturages sahéliens (exemple du Gourma malien)

TOULOUSE3-BU Sciences (315552104) / SudocSudocFranceF

Testing a sahelian grassland functioning model against herbage mass measurements

International audienceA simple grassland process model is tested against ground measurements collected during 15 years in the sahelian zone in Mali. The model uses standard daily meteorological variables and a limited number of site specific parameters to simulate herbage growth and canopy functioning. The model has two calibration parameters. Due probably to the strong interannual variation of both the herbaceous layer in terms of floristic composition and soil nutrient mineralization, the use of default values for the two calibration parameters leads to large errors in the prediction of herbage yields. In contrast, once the parameters have been optimised, a good concordance is found between the model outputs and the seasonal variation of the herbage standing mass in sites on sandy soils located along a north-south bioclimatic gradient

Testing a sahelian grassland functioning model against herbage mass measurements

International audienceA simple grassland process model is tested against ground measurements collected during 15 years in the sahelian zone in Mali. The model uses standard daily meteorological variables and a limited number of site specific parameters to simulate herbage growth and canopy functioning. The model has two calibration parameters. Due probably to the strong interannual variation of both the herbaceous layer in terms of floristic composition and soil nutrient mineralization, the use of default values for the two calibration parameters leads to large errors in the prediction of herbage yields. In contrast, once the parameters have been optimised, a good concordance is found between the model outputs and the seasonal variation of the herbage standing mass in sites on sandy soils located along a north-south bioclimatic gradient

Multi-month memory effects on early summer vegetative activity in semi-arid South Africa and their spatial heterogeneity.

20 pagesInternational audienceIn semi-arid African regions (annual rainfall between 200 and 600 mm), variability of vegetative activity is mainly due to the rainfall of the current rainy season. In most of South Africa, the rainy season occurs from October to March. On average, vegetative activity lags rainfall by 1 to 2 months. The interannual variability in early summer (December to September) normalized difference vegetation index (NDVI) depends primarily on precipitation at the beginning (October to November) of the rainy season. However, once this primary control is removed, the residual interannual variability in NDVI highlights a double memory effect: a 1-year effect, referred to as Mem1, and a 7- to 10-month effect, referred to as Mem2. This article aims at better describing the influence of soil and vegetation characteristics on these two memory effects. The data sets used in this study are as follows: (1) a 19-year NDVI time series from National Oceanic and Atmospheric Administration (NOAA) satellites, (2) rainfall records from a network of 1160 raingauge stations compiled by the Water Research Commission (WRC), (3) vegetation types from Global Land Cover (GLC) 2000 and (4) soil characteristics from the soil and terrain database for Southern Africa (SOTERSAF). Results indicate that among 20-30% of NDVI variance that is not explained by the concurrent rainfall, one-third is explained by the two memory effects. Mem1 is found to have maximum effect in the northwest of our study domain, near the Botswana boundary, in the South Kalahari. Associated conditions are open grasslands growing on Arenosols. Mem1 is less important in the southeast, particularly in open grassland with shrubs growing on Cambisols. Thus, Mem1 mainly depends on soil texture. Mem2 is more widespread and its influence is the greatest in the centre, the south and the east of our domain. It is related to rainfall from January to April, which controls, beyond the intervening dry season, the interannual variations of NDVI (December to September) at the beginning of the next rainy season. Through these new findings, this article emphasizes again the high potential of remote-sensing techniques to monitor and understand the dynamics of semi-arid environments

Using coarse remote sensing radar observations to control the trajectory of a simple Sahelian land suiface model

In the Sahel, land surface processes are significantly interacting with climate dynamics. In this paper, we present an original method to control a simple Sahelian land surface model coupled to a radiative transfer model (RTM) on the basis of ERS wind scatterometer (WSC) observations. In a first step, a sensitivity study is implemented to identify those parameters of the land surface model that can be estimated through the assimilation of WSC data. The assimilation scheme relies on evolution strategies (ES) algorithm that aims at solving the parameter evaluation problem. These algorithms are particularly well suited for complex (nonlinear) inverse problems. The assimilation scheme is applied to several study sites located in the Sahelian mesoscale site of the African Monsoon Multidisciplinary Analysis Project (Gourma region, Mali). The results are compared with ground observations of herbaceous mass. After the WSC data assimilation, the simulated herbaceous mass curves compare well with observations [187 kilogram of dry matter per hectare (kg DM/ha) of average error]. The simulated water fluxes exhibit a behaviour in agreement with ground measurements performed over similar ecosystems during the Hapex Sahel experiment. The accuracy of estimated herbaceous mass and water fluxes resulting from uncertainties on climatic forcing variable is evaluated using a stochastic approach. The average error on the herbaceous mass values mainly depends on the rainfall estimate accuracy and ranges from 139 to 268 kg DM/ha that compares well with a previous study based on the sole inversion of the radiative transfer model. Finally, this study underlines the need for a multispectral assimilation approach to get a better constraint on water fluxes estimation. D 2004 Elsevier Inc. All rights reserved

Using coarse remote sensing radar observations to control the trajectory of a simple Sahelian land suiface model

In the Sahel, land surface processes are significantly interacting with climate dynamics. In this paper, we present an original method to control a simple Sahelian land surface model coupled to a radiative transfer model (RTM) on the basis of ERS wind scatterometer (WSC) observations. In a first step, a sensitivity study is implemented to identify those parameters of the land surface model that can be estimated through the assimilation of WSC data. The assimilation scheme relies on evolution strategies (ES) algorithm that aims at solving the parameter evaluation problem. These algorithms are particularly well suited for complex (nonlinear) inverse problems. The assimilation scheme is applied to several study sites located in the Sahelian mesoscale site of the African Monsoon Multidisciplinary Analysis Project (Gourma region, Mali). The results are compared with ground observations of herbaceous mass. After the WSC data assimilation, the simulated herbaceous mass curves compare well with observations [187 kilogram of dry matter per hectare (kg DM/ha) of average error]. The simulated water fluxes exhibit a behaviour in agreement with ground measurements performed over similar ecosystems during the Hapex Sahel experiment. The accuracy of estimated herbaceous mass and water fluxes resulting from uncertainties on climatic forcing variable is evaluated using a stochastic approach. The average error on the herbaceous mass values mainly depends on the rainfall estimate accuracy and ranges from 139 to 268 kg DM/ha that compares well with a previous study based on the sole inversion of the radiative transfer model. Finally, this study underlines the need for a multispectral assimilation approach to get a better constraint on water fluxes estimation. D 2004 Elsevier Inc. All rights reserved

The Impact of Agricultural Activities on Fog Formation in an Arid Zone of Chile

Special Issue: Fog researchInternational audienceThe aim of this article is to investigate the impact of irrigated areas on the local and regional climate in a valley in Chile's dry zone, using the KAMM model for a late spring and a late summer situation. For both situations, two different simulations are performed, featuring the actual state of the region, i.e. the valley floor used for agricultural activities with irrigation (Moist Valley Simulation MVS), and the original natural state of the region, i.e. the valley floor covered with natural vegetation (Dry Valley Simulation DVS). Agricultural activities modify the energy balance components: near-surface temperature and the thermally induced wind fields – not only in the valley, but also in some surrounding areas. With increased soil moisture and vegetation cover of the valley floor the albedo decreases and, consequently, net radiation is higher in the MVS case. An important part of the available energy is used for evaporation, and during the day the turbulent latent heat flux is higher in the MVS case; less energy is available for transformation into sensible heat flux. In the MVS case, the amplitude of the diurnal cycle of near-surface temperature on the valley floor is smaller. In the early morning, the temperature is higher while after noon it is lower in the MVS case. Outside the valley, there are zones with highly positive differences (around 2oC) which coincide with zones of positive anomalies of turbulent sensible heat flux. Near the coast, in the early morning, the intensity of the sea wind is higher while, after noon, it is lower in the MVS case. Agricultural activities enhance up-slope and upvalley winds in the daytime. With increased water availability in the soil of the valley, near-surface specific humidity is higher, too. Due to advective processes this also affects neighbouring areas. In the early morning and in the late afternoon the relative humidity in the cultivated areas reaches near-saturation values which may in fact induce fog formation at these times of the day. Nevertheless, we have to note that fog is not simulated explicitly in KAMM. Only threshold values for relative humidity have been used to indicate areas where fog formation is likely after changes in land use. In order to model fog formation more adequately, in addition to the dynamic and vegetation models, especially cloud and aerosol microphysics as well as radiation have to be considered. Efficient parameterisation schemes for radiation and cloud physics should be implemented in KAMM to represent fog conditions and their geographical distribution in more detail