Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to ﬁll this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment – the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.
How to cite: Guiomar Ruiz-Pérez, Julian Koch, Salvatore Manfreda, Kelly Caylor and Félix Francés, Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI, Hydrology and Earth System Sciences (doi: 10.5194/hess-21-6235-2017) 2017. [pdf]
In the present work, the role played by vegetation parameters, necessary to the hydrological distributed modeling, is investigated focusing on the correct use of remote sensing products for the evaluation of hydrological losses in the soil water balance. The research was carried out over a medium-sized river basin in Southern Italy, where the vegetation status is characterised through a data-set of multi- temporal NDVI images. The model adopted uses one layer of vegetation whose status is deﬁned by the Leaf Area Index (LAI), which is often obtained from NDVI images. The inherent problem is that the vegetation heterogeneity – including soil disturbances – has a large inﬂuence on the spectral bands and so the relation between LAI and NDVI is not unambiguous. We present a rationale for the basin scale calibration of a non-linear NDVI-LAI regression, based on the comparison between NDVI values and literature LAI estimations of the vegetation cover in recognized landscape elements of the study catchment. Adopting a process-based model (DREAM) with a distributed parameterisation, the inﬂuence of different NDVI-LAI regression models on main features of water balance predictions is investigated. The results show a signiﬁcant sensitivity of the hydrological losses and soil water regime to the alternative LAI estimations. These crucially affects the model performances especially in low ﬂows simulation and in the identiﬁcation of the intermittent regime.
How to cite: Gigante V., P. Milella, V. Iacobellis, S. Manfreda, and I. Portoghese, Influences of Leaf Area Index estimations on the soil water balance predictions in Mediterranean regions, Natural Hazard and Earth System Sciences, 9, 979-991, (doi:10.5194/nhess-9-979-2009), 2009. [pdf]
Savanna grass cover is dynamic and its annual extent resonates with wet season rainfall, as shown by satellite observations of normalized diﬀerence vegetation index (NDVI) time series for the Kalahari Transect (KT) in southern Africa. We explore the hydrological signiﬁcance of the dynamic grass cover by applying a soil moisture model to the water-limited portion of the KT, which spans a north-south gradient in mean wet season rainfall, r’, from approximately 700 to 300 mm. Satellite-derived tree fractional cover, xt, is shown to be highly correlated with ground meteorological measurements of r’ (R2 =0,94) in this region. By implementing a simple expression for grass growth and decay in the model that factored in only xt and near-surface soil moisture, we were able to eﬀectively reproduce the satellite-derived fractional grass cover, xg , along the transect over a 16-year period (1983–1998). We compared the results from dynamic grass model with those yielded by a static grass cover model in which xg was set to its 16-year average for each simulation. The dynamic quality of the grass was found to be important for reducing tree stress during dry years and for reducing the amount of water that is lost from the overall root zone during the wet years, relative to the static grass case. We ﬁnd that the dynamic grass cover acts as a buﬀer against variability in wet season precipitation, and in doing so helps to maximize ecosystem water use. The model results indicate that mixed tree/grass savanna ecosystems are ideally suited to reach a dynamic equilibrium with respect to the use of a ﬂuctuating limiting resource (water) by having functional components that respond to variability in rainfall over long timescales (trees) and short timescales (grasses).
How to cite: Scanlon, T.M., K.K. Caylor, S. Manfreda, S.A. Levin, I. Rodríguez-Iturbe, Dynamic Response of Grass Cover to Rainfall Variability: Implications the Function and Persistence of Savanna Ecosystems, Advances in Water Resources, 28(3), 291-302, (doi: 10.1016/j.advwatres.2004.10.014), 2005. [pdf]