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This work is distributed under the Creative Commons Attribution 3.0 License., The normalised difference vegetation index (NDVI) is a useful tool for studying vegetation activity and ecosystem performance at a large spatial scale. In this study we use the Gravity Recovery and Climate Experiment (GRACE) total water storage (TWS) estimates to examine temporal variability of the NDVI across Australia. We aim to demonstrate a new method that reveals the moisture dependence of vegetation cover at different temporal resolutions. Time series of monthly GRACE TWS anomalies are decomposed into different temporal frequencies using a discrete wavelet transform and analysed against time series of the NDVI anomalies in a stepwise regression. The results show that combinations of different frequencies of decomposed GRACE TWS data explain NDVI temporal variations better than raw GRACE TWS alone. Generally, the NDVI appears to be more sensitive to interannual changes in water storage than shorter changes, though grassland-dominated areas are sensitive to higher-frequencies of water-storage changes. Different types of vegetation, defined by areas of land use type, show distinct differences in how they respond to the changes in water storage, which is generally consistent with our physical understanding. This unique method provides useful insight into how the NDVI is affected by changes in water storage at different temporal scales across land use types.

As the availability of spatially distributed data sets for distributed rainfall–runoff modelling is strongly growing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis (SA) method for snow cover fraction input data (SCF) for a distributed rainfall–runoff model to investigate if the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focused on the relation between the SCF sensitivity and the physical, spatial parameters and processes of a distributed rainfall–runoff model. The methodology is tested for the Biebrza River catchment, Poland for which a distributed WetSpa model is setup to simulate two years of daily runoff. The SA uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which uses different response functions for each 4 km × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different environmental factors such as: geomorphology, soil texture, land-use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for the spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model.

Outcropping sediments can be used as easily accessible analogues for studying subsurface sediments, especially to determine the small-scale spatial variability of hydrogeological parameters. The use of cost-effective in situ measurement techniques potentially makes the study of outcrop sediments even more attractive. We investigate to what degree air permeameter measurements on outcrops of unconsolidated sediments can be a proxy for aquifer saturated hydraulic conductivity (K) heterogeneity. The Neogene aquifer in northern Belgium, known as a major groundwater resource, is used as case study. K and grain size data obtained from different outcropping sediments are compared with K and grain size data from aquifer sediments obtained either via laboratory analyses on undisturbed borehole cores (K and grain size) or via large-scale pumping tests (K only). This comparison shows a pronounced and systematic difference between outcrop and aquifer sediments. Part of this difference is attributed to grain size variations and earth surface processes specific to outcrop environments, including root growth, bioturbation, and weathering. Moreover, palaeoenvironmental conditions such as freezing-drying cycles and differential compaction histories will further alter the initial hydrogeological properties of the outcrop sediments. A linear correction is developed for rescaling the outcrop data to the subsurface data. The spatial structure pertaining to outcrops complements that obtained from the borehole cores in several cases. The higher spatial resolution of the outcrop measurements identifies small-scale spatial structures that remain undetected in the lower resolution borehole data. Insights in stratigraphic and K heterogeneity obtained from outcrop sediments improve developing conceptual models of groundwater flow and transport.

Vegetation parameters derived from the geostationary satellite MSG/SEVIRI have been distributed at a daily frequency since 2007 over Europe, Africa and part of South America, through the LSA-SAF facility. We propose here a method to handle two new remote sensing products from LSA-SAF, leaf area index and Fractional Vegetation Cover, noted LAI and FVC respectively, for land surface models at MSG/SEVIRI scale. The developed method relies on an ordinary least-square technique and a land cover map to estimate LAI for each model plant functional types of the model spatial unit. The method is conceived to be applicable for near-real time applications at continental scale. Compared to monthly vegetation parameters from a vegetation database commonly used in numerical weather predictions (ECOCLIMAP-I), the new remote sensing products allows a better monitoring of the spatial and temporal variability of the vegetation, including inter-annual signals, and a decreased uncertainty on LAI to be input into land surface models. We assess the impact of using LSA-SAF vegetation parameters compared to ECOCLIMAP-I in the land surface model H-TESSEL at MSG/SEVIRI scale. Comparison with in-situ observations in Europe and Africa shows that the results on evapotranspiration are mostly improved, and especially in semi-arid climates. At last, the use of LSA-SAF and ECOCLIMAP-I is compared with simulations over a North-South Transect in Western Africa using LSA-SAF radiation forcing derived from remote sensing, and differences are highlighted.