Hydrology and Earth System Sciences Discussions, 2013
ABSTRACT This paper presents an evaluation of the closure relation for Hortonian runoff that expl... more ABSTRACT This paper presents an evaluation of the closure relation for Hortonian runoff that explicitly accounts for sub-REW process heterogeneity and scale effects, proposed in Vannametee et al. (2012). We apply the closure relation, which is embedded in an event-based rainfall-runoff model developed under the REW framework, to a 15 km2 catchment in the French Alps. The scaling parameters in the closure relation are directly estimated using local and thus observable REW properties and rainstorm characteristics. Evaluation of the simulation results against the observed discharge indicates good performance in reproducing the hydrograph and discharge volume, even without calibration. The discharge prediction exhibits a significant improvement when the closure relation is calibrated with catchment-scale runoff. Our closure relation also yields better predictions when compared with results from a benchmark closure relation that does not consider scale effects. Calibration is done by only changing one of the REW observables, i.e. hydraulic conductivity, as that determines the scaling parameters, using a single prefactor for the entire catchment. This enables the calibration of the (semi)distributed modelling framework in this study to use only a single parameter. The results without calibration suggest that, in the absence of discharge observations, reasonable estimates of catchment-scale runoff responses are possibly based on observations at the sub-REW (i.e. plot) scale. Thus, our study provides a platform for the future development of low-dimensional and robust semi-distributed, physically-based discharge models in ungauged catchments.
ABSTRACT This study presents an application of a multiple point geostatistics (MPS) to map landfo... more ABSTRACT 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.
ABSTRACT A number of aquifers worldwide are being depleted, mainly by agricultural activities, ye... more ABSTRACT A number of aquifers worldwide are being depleted, mainly by agricultural activities, yet groundwater stress has not been explicitly linked to specific agricultural crops. Using the newly-developed concept of the groundwater footprint (the area required to sustain groundwater use and groundwater-dependent ecosystem services), we develop a methodology to derive crop-specific groundwater footprints. We illustrate this method by calculating high resolution groundwater footprint estimates of crops in two heavily used aquifer systems: the Central Valley and High Plains, USA. In both aquifer systems, hay and haylage, corn and cotton have the largest groundwater footprints, which highlights that most of the groundwater stress is induced by crops meant for cattle feed. Our results are coherent with other studies in the High Plains but suggest lower groundwater stress in the Central Valley, likely due to artificial recharge from surface water diversions which were not taken into account in previous estimates. Uncertainties of recharge and irrigation application efficiency contribute the most to the total relative uncertainty of the groundwater footprint to aquifer area ratios. Our results and methodology will be useful for hydrologists, water resource managers and policy makers concerned with which crops are causing the well-documented groundwater stress in semi-arid to arid agricultural regions around the world.
Hydrology and Earth System Sciences Discussions, 2013
ABSTRACT This paper presents an evaluation of the closure relation for Hortonian runoff that expl... more ABSTRACT This paper presents an evaluation of the closure relation for Hortonian runoff that explicitly accounts for sub-REW process heterogeneity and scale effects, proposed in Vannametee et al. (2012). We apply the closure relation, which is embedded in an event-based rainfall-runoff model developed under the REW framework, to a 15 km2 catchment in the French Alps. The scaling parameters in the closure relation are directly estimated using local and thus observable REW properties and rainstorm characteristics. Evaluation of the simulation results against the observed discharge indicates good performance in reproducing the hydrograph and discharge volume, even without calibration. The discharge prediction exhibits a significant improvement when the closure relation is calibrated with catchment-scale runoff. Our closure relation also yields better predictions when compared with results from a benchmark closure relation that does not consider scale effects. Calibration is done by only changing one of the REW observables, i.e. hydraulic conductivity, as that determines the scaling parameters, using a single prefactor for the entire catchment. This enables the calibration of the (semi)distributed modelling framework in this study to use only a single parameter. The results without calibration suggest that, in the absence of discharge observations, reasonable estimates of catchment-scale runoff responses are possibly based on observations at the sub-REW (i.e. plot) scale. Thus, our study provides a platform for the future development of low-dimensional and robust semi-distributed, physically-based discharge models in ungauged catchments.
ABSTRACT This study presents an application of a multiple point geostatistics (MPS) to map landfo... more ABSTRACT 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.
ABSTRACT A number of aquifers worldwide are being depleted, mainly by agricultural activities, ye... more ABSTRACT A number of aquifers worldwide are being depleted, mainly by agricultural activities, yet groundwater stress has not been explicitly linked to specific agricultural crops. Using the newly-developed concept of the groundwater footprint (the area required to sustain groundwater use and groundwater-dependent ecosystem services), we develop a methodology to derive crop-specific groundwater footprints. We illustrate this method by calculating high resolution groundwater footprint estimates of crops in two heavily used aquifer systems: the Central Valley and High Plains, USA. In both aquifer systems, hay and haylage, corn and cotton have the largest groundwater footprints, which highlights that most of the groundwater stress is induced by crops meant for cattle feed. Our results are coherent with other studies in the High Plains but suggest lower groundwater stress in the Central Valley, likely due to artificial recharge from surface water diversions which were not taken into account in previous estimates. Uncertainties of recharge and irrigation application efficiency contribute the most to the total relative uncertainty of the groundwater footprint to aquifer area ratios. Our results and methodology will be useful for hydrologists, water resource managers and policy makers concerned with which crops are causing the well-documented groundwater stress in semi-arid to arid agricultural regions around the world.
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Papers by Marc Bierkens