We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the ...flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.
Large‐scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system, in particular the unsaturated zone, remains ...uncalibrated. Soil moisture observations from satellites have the potential to fill this gap. Here we evaluate the added value of remotely sensed soil moisture in calibration of large‐scale hydrological models by addressing two research questions: (1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? (2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to calibration based only on discharge observations, such that this leads to improved simulations of soil moisture content and discharge? A dual state and parameter Ensemble Kalman Filter is used to calibrate the hydrological model LISFLOOD for the Upper Danube. Calibration is done using discharge and remotely sensed soil moisture acquired by AMSR‐E, SMOS, and ASCAT. Calibration with discharge data improves the estimation of groundwater and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate identification of parameters related to land‐surface processes. For the Upper Danube upstream area up to 40,000 km2, calibration on both discharge and soil moisture results in a reduction by 10–30% in the RMSE for discharge simulations, compared to calibration on discharge alone. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models, leading to a better simulation of soil moisture content throughout the catchment and a better simulation of discharge in upstream areas.
Key Points
Satellite soil moisture holds potential for calibration of hydrological models
Soil moisture observations enable better calibration of land‐surface parameters
Errors in discharge simulations are reduced using soil moisture for calibration
Ethanol production in Brazil is projected to double between 2012 and 2030 in order to meet increased global demand, resulting in the expansion of sugarcane cultivation. Sugarcane expansion drives ...both direct and indirect land-use changes, and subsequent changes in hydrology may exacerbate problems of (local) water scarcity. This study assesses the impacts of projected ethanol-driven sugarcane expansion on agricultural and hydrological drought in Brazil. Drought due to sugarcane expansion is modelled using a spatial terrestrial hydrological model (PCR-GLOBWB) with spatiotemporally variable land-use change and climate change scenarios as input. We compare an ethanol scenario with increased ethanol demand to a reference situation in which ethanol demand does not increase.
The results show that, on average, 29% of the Centre West Cerrado region is projected to experience agricultural drought between 2012 and 2030, and the drought deficit in this region is projected to be 7% higher in the ethanol scenario compared to the reference. The differences between the ethanol and the reference scenario are small when averaged over macro-regions, but can be considerable at a local scale. Differences in agricultural and hydrological drought between the ethanol and reference scenario are most notable in the Centre West Cerrado and Southeast regions. Locally, considerable changes may also occur in other regions, including the Northeast Coast and Northern Amazon region.
Because the South East and Centre West Cerrado regions are responsible for a large proportion of agricultural production, increased agricultural drought may result in significant economic losses, while increased hydrological drought could exacerbate existing problems of water supply to large metropolitan areas in these regions. The identification of areas at risk of increased droughts can be important information for policy makers to take precautionary measures to avoid negative hydrological impacts of increased ethanol demand.
•Hydrological impacts of increased ethanol demand show strong spatial variation.•Ethanol-driven sugarcane expansion may lead to increased problems of water scarcity.•Sugarcane expansion is expected to result in more intense agricultural drought by 2030.•High ethanol demand results in >50% increase in water use for irrigation in 2030.
► We compare the streamflow of a past agricultural catchment (A) and a forested one (B). ► Topography is similar so we can separate the soil and vegetation effect on streamflow. ► In A peak flows ...were greater, response time faster and recession limbs shorter. ► Runoff was higher in A under dry conditions but lower under the wettest conditions. ► Hydrological differences are mainly determined by the presence of degraded areas in A.
The stream flow response of two neighboring catchments in the central Spanish Pyrenees was compared for 26 rainstorms covering both catchments: one catchment (2.84
km
2) was extensively used for agriculture in the past, and the other (0.92
km
2) is covered by dense natural forest. Their similarity in terms of lithology and topography enabled us to separate the effects of soil and land cover on their hydrological responses. Relative to the forested catchment, peak flows in the past agricultural catchment were always greater (566
vs. 119
l
s
−1
km
−2), the response time was 2- to 3-fold faster (131
vs. 356
min), and the recession limbs were 1–2 orders of magnitude shorter (7
vs. 72
h). Storm flow was usually greater in the former agricultural catchment, especially for low–intermediate sized flood events; only for larger events the storm flow in the forested catchment was sometimes greater. Storm flow differences were closely related to catchment wetness conditions and showed a marked seasonal pattern, with higher values in the past agricultural catchment under dry conditions, and usually higher values in the forested catchment under wet conditions. In the past agricultural catchment, runoff was generated during the entire water year, through both surface (i.e. infiltration excess and saturation excess overland flow) and subsurface flow. We suggest that the forested catchment can be characterized by a dual (or switching) behavior controlled by soil moisture conditions, which regulates the hydrological connectivity and favors the release of large amounts of subsurface flow. Differences in soil depth and permeability, together with differences in vegetation cover, may explain the contrasting dominant runoff generation processes operating in each catchment, and consequently the differences between their hydrograph characteristics.
GEOMORPHOLOGICAL MAPPING OF INTERTIDAL AREAS Lu, M.; Groeneveld, L.; Karssenberg, D. ...
International archives of the photogrammetry, remote sensing and spatial information sciences.,
06/2021, Volume:
XLIII-B3-2021
Journal Article, Conference Proceeding
Peer reviewed
Open access
Spatiotemporal geomorphological mapping of intertidal areas is essential for understanding system dynamics and provides information for ecological conservation and management. Mapping the ...geomorphology of intertidal areas is very challenging mainly because spectral differences are oftentimes relatively small while transitions between geomorphological units are oftentimes gradual. Also, the intertidal areas are highly dynamic. Considerable challenges are to distinguish between different types of tidal flats, specifically, low and high dynamic shoal flats, sandy and silty low dynamic flats, and mega-ripple areas. In this study, we harness machine learning methods and compare between machine learning methods using features calculated in classical Object-Based Image Analysis (OBIA) vs. end-to-end deep convolutional neural networks that derive features directly from imagery, in automated geomorphological mapping. This study expects to gain us an in-depth understanding of features that contribute to tidal area classification and greatly improve the automation and prediction accuracy. We emphasise model interpretability and knowledge mining. By comparing and combing object-based and deep learning-based models, this study contributes to the development and integration of both methodology domains for semantic segmentation.
Modern and forecasted flooding of deltas is accelerated by subsidence of Holocene deposits. Subsidence caused by tectonics, isostasy, sediment compaction and anthropogenic processes, combined with ...eustatic sea‐level rise, results in drowning and increased flood risk within densely populated deltas. Many deltaic sedimentary successions include substantial amounts of peat, which is highly compressible compared to clay, silt and sand. Peat compaction, therefore, may contribute considerably to total delta subsidence. Existing studies are inadequate for quantifying peat compaction across deltas. We present a numerical peat compaction model calibrated with an extensive field dataset. The model quantifies spatial and temporal trends in peat compaction within fluvial‐dominated Holocene flood basin sequences of different compositions. Subsidence due to peat compaction is highly variable in time and space, with local rates of up to 15 mm/yr, depending on sedimentary sequence. This is extremely important information for developing sound delta management strategies. Artificial groundwater table lowering may cause substantial additional subsidence. Subsidence due to peat compaction might even exceed estimates of relative sea‐level rise, and thus, may seriously increase the risk of delta drowning and human vulnerability to flooding.
Key Points
Subsidence due to peat compaction highly depends on sequence build‐up
Compaction rates averaged over long periods may underestimate current rates
(Peat compaction) models should be calibrated using an extensive field dataset
This article applies error propagation in a Monte Carlo simulation for a spatial-based fuzzy logic multi-criteria evaluation (MCE) in order to investigate the output uncertainty created by the input ...data sets and model structure. Six scenarios for quantifying uncertainty are reviewed. Three scenarios are progressively more complex in defining observational data (attribute uncertainty); while three other scenarios include uncertainty in observational data (position of boundaries between map units), weighting of evidence (fuzzy membership assignment), and evaluating changes in the MCE model (fuzzy logic operators). A case study of petroleum exploration in northern South America is used. Despite the resources and time required, the best estimate of input uncertainty is that based on expert-defined values. Uncertainties for fuzzy membership assignment and boundary transition zones do not affect the results as much as the attribute assignment uncertainty. The MCE fuzzy logic operator uncertainty affects the results the most. Confidence levels of 95% and 60% are evaluated with threshold values of 0.7 and 0.5 and show that accepting more uncertainty in the results increases the total area available for decision-making. Threshold values and confidence levels should be predetermined, although a series of combinations may yield the best decision-making support.
Accurate quantification of the amount and spatial variation of evapotranspiration is important in a wide range of disciplines. Remote sensing based surface energy balance models have been developed ...to estimate turbulent surface energy fluxes at different scales. The objective of this study is to evaluate the Surface Energy Balance System (SEBS) model on a landscape scale, using tower-based flux measurements at different land cover units during an overpass of the ASTER sensor over the SPARC 2004 experimental site in Barrax (Spain). A sensitivity analysis has been performed in order to investigate to which variable the sensible heat flux is most sensitive. Taking into account their estimation errors, the aerodynamic parameters (hc , z0M and d0 ) can cause large deviations in the modelling of sensible heat flux. The effect of replacement of empirical derivation of these aerodynamic parameters in the model by field estimates or literature values is investigated by testing two scenarios: the Empirical Scenario in which empirical equations are used to derive aerodynamic parameters and the Field Scenario in which values from field measurements or literature are used to replace the empirical calculations of the Empirical Scenario. In the case of a homogeneous land cover in the footprints of the measurements, the Field Scenario only resulted in a small improvement, compared to the Empirical Scenario. The Field Scenario can even worsen the result in the case of heterogeneous footprints, by creating sharp borders related to the land cover map. In both scenarios modelled fluxes correspond better with flux measurements over uniform land cover compared to cases where different land covers are mixed in the measurement footprint. Furthermore SEBS underestimates sensible heat flux especially over dry and sparsely vegetated areas, which is common in single-source models.
In climate models, lateral terrestrial water fluxes are usually neglected. We estimated the contribution of vertical and lateral groundwater fluxes to the land surface water budget at a ...subcontinental scale, by modeling convergence of groundwater and surfacewater fluxes. We present a hydrological model of the entire Danube Basin at 5 km resolution, and use it to show the importance of groundwater for the surface climate. Results show that the contribution of groundwater to evaporation is significant, and can locally be higher than 30 % in summer. We demonstrate through the same model that this contribution also has important temporal characteristics. A wet episode can influence groundwater contribution to summer evaporation for several years afterwards. This indicates that modeling groundwater flow has the potential to augment the multi-year memory of climate models. We also show that the groundwater contribution to evaporation is local by presenting the groundwater travel times and the magnitude of groundwater convergence. Throughout the Danube Basin the lateral fluxes of groundwater are negligible when modeling at this scale and resolution. This suggests that groundwater can be adequately added in land surface models by including a lower closed groundwater reservoir of sufficient size with two-way interaction with surface water and the overlying soil layers.
This study presents an application of a multiple point geostatistics (MPS) to map landforms. MPS uses information at multiple cell locations including morphometric attributes at a target mapping ...cell, i.e. digital elevation model (DEM) derivatives, and non-morphometric attributes, i.e. landforms at the neighboring cells, to determine the landform. The technique requires a training data set, consisting of a field map of landforms and a DEM. Mapping landforms proceeds in two main steps. First, the number of cells per landform class, associated with a set of observed attributes discretized into classes (e.g. slope class), is retrieved from the training image and stored in a frequency tree, which is a hierarchical database. Second, the algorithm visits the non-mapped cells and assigns to these a realization of a landform class, based on the probability function of landforms conditioned to the observed attributes as retrieved from the frequency tree. The approach was tested using a data set for the Buëch catchment in the French Alps. We used four morphometric attributes extracted from a 37.5-m resolution DEM as well as two non-morphometric attributes observed in the neighborhood. The training data set was taken from multiple locations, covering 10% of the total area. The mapping was performed in a stochastic framework, in which 35 map realizations were generated and used to derive the probabilistic map of landforms. Based on this configuration, the technique yielded a map with 51.2% of correct cells, evaluated against the field map of landforms. The mapping accuracy is relatively high at high elevations, compared to the mid-slope and low-lying areas. Debris slope was mapped with the highest accuracy, while MPS shows a low capability in mapping hogback and glacis. The mapping accuracy is highest for training areas with a size of 7.5–10% of the total area. Reducing the size of the training images resulted in a decreased mapping quality, as the frequency database only represents local characteristics of landforms that are not representative for the remaining area. MPS outperforms a rule-based technique that only uses the morphometric attributes at the target mapping cell in the classification (i.e. one-point statistics technique), by 15% of cell accuracy.
•Introduction of multiple point geostatistics (MPS) for automated landform mapping.•MPS uses DEM information and landforms in surroundings to map unvisited locations.•A geomorphological field map is used to train the interpolation technique.•Quality of the automated map increases with training image size up to 28km2.•High mapping uncertainty in the valleys where many landform types are observed
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