Simon Stisen

Evaluation of AVHRR PAL and GIMMS 10‐day composite NDVI time series products using SPOT‐4 vegetation data for the African continent

Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized difference vegetation index) 10‐day composite data sets have been used for numerous local to global scale vegetation time series studies during recent years. AVHRR Pathfinder (PAL) NDVI was available from 1981 until 2001, and the new AVHRR GIMMS NDVI was available from 1981 to the present time. A number of aspects potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. NDVI from SPOT‐4 VGT data is considered an improvement over AVHRR, and for this reason it is important to examine how and if the differences in sensor design and processing influence continental scale NDVI composite products. In this study, the quality of these AVHRR NDVI time series are evaluated by the continental scale 1 km resolution SPOT‐4 vegetation (VGT) 10‐day composite (S10) NDVI data. Three years of AVHRR PAL (1998–2000) and seven years of GIMMS (1998–2004) have been compared to 8 km resampled SPOT‐4 VGT (1998–2004) data. The dynamic range of SPOT‐4 VGT NDVI tends to be higher than the AVHRR PAL NDVI, whereas there is an exact match between AVHRR GIMMS NDVI and SPOT‐4 VGT NDVI. Ortho‐regression analysis on annually integrated values of AVHRR PAL/GIMMS and SPOT‐4 VGT on a continental scale reveals high correlations amongst the AVHRR and the SPOT data set, with lowest RMSE (root mean square error) on the GIMMS/SPOT‐4 VGT compared to the PAL/SPOT‐4 VGT. Analyses on decade data likewise show that a linear relation exists between Spot‐4 VGT NDVI and the two AVHRR composite products; GIMMS explaining most of the Spot‐4 VGT NDVI variance compared to PAL. These results show that the AVHRR GIMMS NDVI is more consistent with Spot‐4 VGT NDVI compared to AVHRR PAL versus Spot‐4 VGT NDVI (in terms of RMSE and dynamic range) and can therefore be considered the more accurate long time AVHRR data record. Analyses performed on monthly maximum composites and decade composite data, however, reveal intra‐annual variations in the correlation between SPOT‐4 VGT and the two AVHRR data sets, which are attributed to different cloud masking algorithms. The SPOT‐4 VGT cloud‐screening algorithm is insufficient, thereby suppressing the rainy season NDVI.

Assimilation of SMOS‐derived soil moisture in a fully integrated hydrological and soil‐vegetation‐atmosphere transfer model in Western Denmark

Real surface soil moisture retrieved from the Soil Moisture and Ocean Salinity (SMOS) satellite is downscaled and assimilated in a fully integrated hydrological and soil-vegetation-atmosphere transfer (MIKE SHE SW-ET) model using a bias aware ensemble transform Kalman filter (Bias-ETKF). Satellite-derived soil moisture assimilation in a catchment scale model is typically restricted by two challenges: (1) passive microwave is too coarse for direct assimilation and (2) the data tend to be biased. The solution proposed in this study is to disaggregate the SMOS bias using a higher resolution land cover classification map that was derived from Landsat thermal images. Using known correlations between SMOS bias and vegetation type, the assimilation filter is adapted to calculate biases online, using an initial bias estimate. Real SMOS-derived soil moisture is assimilated in a precalibrated catchment model in Denmark. The objective is to determine if any additional gains can be achieved by SMOS surface soil moisture assimilation beyond the optimized model. A series of assimilation experiments were designed to (1) determine how effectively soil moisture corrections propagate downward in the soil column, (2) compare the efficacy of in situ versus SMOS assimilation, and (3) determine how soil moisture assimilation affects fluxes and discharge in the catchment. We find that assimilation of SMOS improved R2 soil moisture correlations in the upper 5 cm compared to a network of 30 in situ sensors for most land cover classes. Assimilation also brought modest gains in R2 at 25 cm depth but slightly degraded the correlation at 50 cm depth. Assimilation overcorrected discharge peaks.

Assessing the impact of model spin‐up on surface water‐groundwater interactions using an integrated hydrologic model

Integrated land surface-groundwater models are valuable tools in simulating the terrestrial hydrologic cycle as a continuous system and exploring the extent of land surface-subsurface interactions from catchment to regional scales. However, the fidelity of model simulations is impacted not only by the vegetation and subsurface parameterizations, but also by the antecedent condition of model state variables, such as the initial soil moisture, depth to groundwater, and ground temperature. In land surface modeling, a given model is often run repeatedly over a single year of forcing data until it reaches an equilibrium state: the point at which there is minimal artificial drift in the model state or prognostic variables (most often the soil moisture). For more complex coupled and integrated systems, where there is an increased computational cost of simulation and the number of variables sensitive to initialization is greater than in traditional uncoupled land surface modeling schemes, the challenge is to minimize the impact of initialization while using the smallest spin-up time possible. In this study, multicriteria analysis was performed to assess the spin-up behavior of the ParFlow.CLM integrated groundwater-surface water-land surface model over a 208 km2 subcatchment of the Ringkobing Fjord catchment in Denmark. Various measures of spin-up performance were computed for model state variables such as the soil moisture and groundwater storage, as well as for diagnostic variables such as the latent and sensible heat fluxes. The impacts of initial conditions on surface water-groundwater interactions were then explored. Our analysis illustrates that the determination of an equilibrium state depends strongly on the variable and performance measure used. Choosing an improper initialization of the model can generate simulations that lead to a misinterpretation of land surface-subsurface feedback processes and result in large biases in simulated discharge. Estimated spin-up time from a series of spin-up functions revealed that 20 (or 21) years of simulation were sufficient for the catchment to equilibrate according to at least one criterion at the 0.1% (0.01%) threshold level. Amongst a range of convergence metrics examined, percentage changes in monthly values of groundwater and unsaturated zone storages produced a slow system convergence to equilibrium, whereas criteria based on ground temperature allowed a more rapid spin-up. Slow convergence of unsaturated and saturated zone storages is a result of the dynamic adjustment of the water table in response to a physically arbitrary or inconsistent initialization of a spatially uniform water table. Achieving equilibrium in subsurface storage ensured equilibrium across a spectrum of other variables, hence providing a good measure of system-wide equilibrium. Overall, results highlight the importance of correctly identifying the key variable affecting model equilibrium and also the need to use a multicriteria approach to achieve a rapid and stable model spin-up.

Stakeholder driven update and improvement of a national water resources model

It is generally acknowledged that water management must be based on an integrated approach, considering the entire freshwater cycle. This has in particularly been endorsed in Europe by the European Water Framework Directive (WFD) imposing integrated management considering all waters. Although not prescribed by the WFD, integrated hydrological modelling may be necessary to support the management according to the directive as also suggested by several research projects initiated by the EU commission. To ensure a coherent and consistent management across various institutions and authorities, having different responsibilities and operating at various scales, a common tool integrating all relevant knowledge and data is imperative. By the end of 2003, a numerical national water resources model was constructed for Denmark, which has been applied in several national assessments. At the regional level there has, however, been some reluctance to use the model, primarily because the model did not contain the most recent data and understanding obtained from detailed local studies. The model has therefore been subject to a comprehensive update focussing on utilising the system understanding from the local studies. This process was largely stakeholder driven by involvement of predominantly the technical staff at the regional water authorities. Local knowledge is continuously improved urging the model update to be an on-going process. Based on experience from the update of the Danish national water resources model, three levels of model updating have been identified: 1) Basic data update - keeping the model up-to-date with respect to input data, 2) improving the model description by including new or more detailed data, and 3) reconstructing the model concept. The three levels vary with respect to technical tasks, challenges and stakeholder involvement. Two utility programs developed to optimise the updating process and support the uptake of data and knowledge from local users are furthermore presented. Finally, some of the challenges in operating a national model with multiple users belonging to different institutions with varying demands are discussed.

Comparisons of compositing period length for vegetation index data from polar-orbiting and geostationary satellites for the cloud-prone region of West Africa

Land surface data from MODIS and AVHRR have been extensively used for vegetation monitoring. In cloud-prone areas like West Africa the use of Normalized Difference Vegetation Index (NDVI) data for vegetation monitoring is hampered by persistent cloud cover especially during the rainy season. The new geostationary satellite Meteosat Second Generation (SEVIRI MSG) is the first geostationary satellite suited for vegetation monitoring allowing NDVI to be derived with a 15-minute temporal resolution. For West Africa, MODIS (combined TERRA and AQUA) produce above 85 percent cloud-free pixels in the scene during the entire rainy season using 16-day composite periods. SEVIRI MSG data produces � 98 percent cloud-free pixels during the entire season using a 3-day composite period. Therefore, there is a much higher probability for producing high quality cloud free data using SEVIRI MSG data for a short time composite period compared to Polar Orbiting Environmental Satellite (POES) data, which is expected to substantially improve various applications of satellite based natural resource management, including vegetation monitoring, in West Africa.

Toward a true spatial model evaluation in distributed hydrological modeling: Kappa statistics, Fuzzy theory, and EOF-analysis benchmarked by the human perception and evaluated against a modeling case study

The hydrological modeling community is aware that the validation of distributed hydrological models has to move beyond aggregated performance measures, like hydrograph assessment by means of Nash-Suitcliffe efficiency toward a true spatial model validation. Remote sensing facilitates continuous data and can be measured on a similar spatial scale as the predictive scale of the hydrological model thereby it can serve as suitable data for the spatial validation. The human perception is often described as a very reliable and well-trained source for pattern comparison, which this study wants to exploit. A web-based survey that is interpreted based on approximately 200 replies reflects the consensus of the human perception on map comparisons of a reference map and 12 synthetic perturbations. The resulting similarity ranking can be used as a reference to benchmark various spatial performance metrics. This study promotes Fuzzy theory as a suitable approach because it considers uncertainties related to both location and value in the simulated map. Additionally, an EOF-analysis (Empirical Orthogonal Function) is conducted to decompose the map comparison into its similarities and dissimilarities. A modeling case study serves to further examine the metrics capability to assess the goodness of fit between simulated and observed land surface temperature maps. The EOF-analysis unambiguously identifies a systematic depth to groundwater table-related model deficiency. Kappa statistic extended by Fuzziness is a suitable and commonly applied measure for map comparison. However, its apparent bias sensitivity limits its capability as a diagnostic tool to detect the distinct deficiency.

The integrated hydrologic model intercomparison project, IH‐MIP2: A second set of benchmark results to diagnose integrated hydrology and feedbacks

Emphasizing the physical intricacies of integrated hydrology and feedbacks in simulating connected, variably saturated groundwater-surface water systems, the Integrated Hydrologic Model Intercomparison Project initiated a second phase (IH-MIP2), increasing the complexity of the benchmarks of the first phase. The models that took part in the intercomparison were ATS, Cast3M, CATHY, GEOtop, HydroGeoSphere, MIKE-SHE, and ParFlow. IH-MIP2 benchmarks included a tilted v-catchment with 3-D subsurface; a superslab case expanding the slab case of the first phase with an additional horizontal subsurface heterogeneity; and the Borden field rainfall-runoff experiment. The analyses encompassed time series of saturated, unsaturated, and ponded storages, as well as discharge. Vertical cross sections and profiles were also inspected in the superslab and Borden benchmarks. An analysis of agreement was performed including systematic and unsystematic deviations between the different models. Results show generally good agreement between the different models, which lends confidence in the fundamental physical and numerical implementation of the governing equations in the different models. Differences can be attributed to the varying level of detail in the mathematical and numerical representation or in the parameterization of physical processes, in particular with regard to ponded storage and friction slope in the calculation of overland flow. These differences may become important for specific applications such as detailed inundation modeling or when strong inhomogeneities are present in the simulation domain.

Spatial validation of large‐scale land surface models against monthly land surface temperature patterns using innovative performance metrics

Land surface models (LSMs) are a key tool to enhance process understanding and to provide predictions of the terrestrial hydrosphere and its atmospheric coupling. Distributed LSMs predict hydrological states and fluxes, such as land surface temperature (LST) or actual evapotranspiration (aET), at each grid cell. LST observations are widely available through satellite remote sensing platforms that enable comprehensive spatial validations of LSMs. In spite of the great availability of LST data, most validation studies rely on simple cell to cell comparisons and thus do not regard true spatial pattern information. The core novelty of this study is the development and application of two innovative spatial performance metrics, namely, empirical orthogonal function (EOF) and connectivity analyses, to validate predicted LST patterns by three LSMs (Mosaic, Noah, Variable Infiltration Capacity (VIC)) over the contiguous United States. The LST validation data set is derived from global High-Resolution Infrared Radiometric Sounder retrievals for a 30 year period. The metrics are bias insensitive, which is an important feature in order to truly validate spatial patterns. The EOF analysis evaluates the spatial variability and pattern seasonality and attests better performance to VIC in the warm months and to Mosaic and Noah in the cold months. Further, more than 75% of the LST variability can be captured by a single pattern that is strongly correlated to air temperature. The connectivity analysis assesses the homogeneity and smoothness of patterns. The LSMs are most reliable at predicting cold LST patterns in the warm months and vice versa. Lastly, the coupling between aET and LST is investigated at flux tower sites and compared against LSMs to explain the identified LST shortcomings

Assessment of climate change impacts on the quantity and quality of a coastal catchment using a coupled groundwater–surface water model

The hydrology of coastal catchments is influenced by both sea level and climate. Hence, a comprehensive assessment of the impact of climate change on coastal catchments is a challenging task. In the present study, a coupled groundwater–surface water model is forced by dynamically downscaled results from a general circulation model. The effects on water quantity and quality of a relatively large lake used for water supply are analyzed. Although stream inflow to the lake is predicted to decrease during summer, the storage capacity of the lake is found to provide a sufficient buffer to support sustainable water abstraction in the future. On the other hand, seawater intrusion into the stream is found to be a significant threat to the water quality of the lake, possibly limiting its use for water supply and impacting the aquatic environment. Additionally, the results indicate that the nutrient load to the lake and adjacent coastal waters is likely to increase significantly, which will increase eutrophication and have negative effects on the surface water ecology. The hydrological impact assessment is based on only one climate change projection; nevertheless, the range of changes generated by other climate models indicates that the predicted results are a plausible realization of climate change impacts. The problems identified here are expected to be relevant for many coastal regimes, where the hydrology is determined by the interaction between saline and fresh groundwater and surface water systems.

Spatial Sensitivity Analysis of Simulated Land Surface Patterns in a Catchment Model Using a Set of Innovative Spatial Performance Metrics

AbstractDistributed hydrological models simulate states and fluxes of water and energy in the terrestrial hydrosphere at each cell. The predicted spatial patterns result from complex nonlinear relationships and feedbacks. Spatial patterns are often neglected during the modeling process, and therefore a spatial sensitivity analysis framework that highlights their importance is proposed. This study features a comprehensive analysis of spatial patterns of actual evapotranspiration (ET) and land surface temperature (LST), with the aim of quantifying the extent to which forcing data and model parameters drive these patterns. This framework is applied on a distributed model [MIKE Systeme Hydrologique Europeen (MIKE SHE)] coupled to a land surface model [Shuttleworth and Wallace–Evapotranspiration (SW-ET)] of a catchment in Denmark. Twenty-two scenarios are defined, each having a simplified representation of a potential driver of spatial variability. A baseline model that incorporates full spatial detail is used...