Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI

Abstract

Abstract. 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 spatiotemporal 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 datascarce 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.

Assessment of Remotely Sensed Near-Surface Soil Moisture for Distributed Eco-Hydrological Model Implementation

“…It is important to note that to perform basin modeling, TETIS can include a dynamic vegetation sub-model. This sub-model [27,53] has 11 parameters (maximum interception storage, effective depth of the first layer, effective depth of the second layer, light use efficiency, coverage factor, distribution Internally, TETIS presents a separate structure from its effective parameters by using a scalar correction factor for each parameter type. This correction factor is common for all cells and multiplies the map of values of the previously estimated corresponding parameter in each cell [51,52].…”

“…It is important to note that to perform basin modeling, TETIS can include a dynamic vegetation sub-model. This sub-model [27,53] has 11 parameters (maximum interception storage, effective depth of the first layer, effective depth of the second layer, light use efficiency, coverage factor, distribution of roots, maximum LAI supported by the system, light extinction coefficient, specific leaf area, optimal temperature, respiration rate) for each type of vegetation cover. In the case of this study, it was decided to calibrate the three most influential parameters in the hydrological cycle: maximum interception storage, coverage factor and distribution of roots.…”

“…For this purpose, the methodology of the Empirical Orthogonal Functions (EOF) was chosen to analyze the data set of the SM. EOFs have been widely used in climate sciences [19,36,[54][55][56][57] and with recent applications in hydrology [27,58]. Other studies have suggested alternative metrics to evaluate the spatial accuracy of such variables, such as the use of Map Curves [59] or the concordance coefficient [60].…”

“…Hence, the selected soil moisture-based approach showed an acceptable performance in terms of discharges, presenting a negligible decrease in the validation period (∆NSE = 0.1) and greater sensitivity to the spatio-temporal variables' spatial representation.Currently there is a high availability of satellite data, almost in real time, with sufficient spatio-temporal resolutions (30 m-25 km) for ecohydrology in most cases and with a spatial distribution covering the entire earth. Among the sources of satellite information that can be used in ecohydrology, the following stand out: real evapotranspiration [19][20][21][22], land surface temperature [23,24], different vegetation indices [25][26][27], near-surface soil moisture (hereafter SM), [28][29][30][31][32] and more recently total water storage anomaly [33].Soil moisture plays a key role in the hydrological cycle, due to its influence on many processes that directly or indirectly affect the water balance, such as: vegetation growth, hydraulic properties of the ground, evapotranspiration, runoff generation and the processes of infiltration and deep percolation [13,30,[34][35][36][37]. Despite their importance, SM in-situ measurements are still uncommon in time and space [38,39].…”

“…Currently there is a high availability of satellite data, almost in real time, with sufficient spatio-temporal resolutions (30 m-25 km) for ecohydrology in most cases and with a spatial distribution covering the entire earth. Among the sources of satellite information that can be used in ecohydrology, the following stand out: real evapotranspiration [19][20][21][22], land surface temperature [23,24], different vegetation indices [25][26][27], near-surface soil moisture (hereafter SM), [28][29][30][31][32] and more recently total water storage anomaly [33].…”