RN
R.C. Nijzink
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
13 records found
1
Abstract
(2018)
-
Remko Nijzink, S. Susana Almeida, Ilias Pechlivanidis, René Capell, David Gustafsson, Berit Arheimer, Hubert Savenije, Ronald van Nooijen, Markus Hrachowitz, More authors...
Calibrating hydrological models without stream flow observations is still difficult, and the simultaneous, combined use of additional data, such as remotely sensed products, for calibration has not been exhaustively tested thus far. It is hypothesized that the combined use of products can improve model performances, internal model dynamics and the representation of hydrological signatures, in comparison with traditional calibration on stream flow.
...
Calibrating hydrological models without stream flow observations is still difficult, and the simultaneous, combined use of additional data, such as remotely sensed products, for calibration has not been exhaustively tested thus far. It is hypothesized that the combined use of products can improve model performances, internal model dynamics and the representation of hydrological signatures, in comparison with traditional calibration on stream flow.
Selecting model formulations and parameterizations
Reducing the need for calibration using open data and landscape characteristics
The need for calibration of conceptual hydrological models on river discharge is still large, and the scope of this research is to reduce this need based on new parameter estimation techniques, additional information sources and hydrological understanding. In a first step, a regularized model and an adjusted regularized model with sub-grid variability based on the landscape, both constrained and unconstrained, were calibrated for seven catchments. Four catchments were also simultaneously calibrated and their feasible parameters transferred to the remaining three receiver catchments. Small improvements were observed by introducing sub-grid variability, whereas the semi-quantitative constraints led to moderate improvements compared to the unconstrained model. Especially low flow statistics improved and suitable prior constraints can aid model transferability. Subsequently, it was assessed how well the key parameter of root-zone storage capacity could be estimated in three deforested catchments. A recently introduced method based on rainfall and an estimate of transpiration was used to reproduce the temporal evolution of root-zone storage capacities. These values were compared to the values from four hydrological models calibrated for consecutive 2-year windows. Water-balance derived root-zone storage capacities showed a similar signal compared to the calibrated values of the models with a sharp decline in root-zone storage capacity after deforestation, followed by a gradual recovery of 5 to 13 years. The added value of several combinations of remotely sensed products for parameter estimation was assessed for five different hydrological models in 27 catchments across Europe. A parameter selection process was applied for 1023 possible combinations of ten different data sources, ranging from using 1 to all 10 of these products. High probabilities of improvement, with regard to commonly applied model performance metrics, were obtained when combinations included AMSR-E and ASCAT soil moisture, and GRACE total water storage anomalies. The evaporation products of LSA-SAF and MODIS were less effective for deriving meaningful posterior parameter distributions. In a last step, a large-scale hydrological model with landscape-derived sub-grid variability was run with 50 random parameterizations for the European continent, with and without semi-quantitative prior parameter constraints. A variable pattern in improvements/deteriorations was observed when evaluated for 397 gauging stations, which shows that the prior parameter constraints were not sufficient to limit the search space effectively. Concluding, this thesis presents several ways to better optimize different hydrological models without using observed river discharge for varying spatial scales and changing circumstances. In this way, this thesis serves as a stepping stone towards fully predicting in ungauged catchments.
...
The need for calibration of conceptual hydrological models on river discharge is still large, and the scope of this research is to reduce this need based on new parameter estimation techniques, additional information sources and hydrological understanding. In a first step, a regularized model and an adjusted regularized model with sub-grid variability based on the landscape, both constrained and unconstrained, were calibrated for seven catchments. Four catchments were also simultaneously calibrated and their feasible parameters transferred to the remaining three receiver catchments. Small improvements were observed by introducing sub-grid variability, whereas the semi-quantitative constraints led to moderate improvements compared to the unconstrained model. Especially low flow statistics improved and suitable prior constraints can aid model transferability. Subsequently, it was assessed how well the key parameter of root-zone storage capacity could be estimated in three deforested catchments. A recently introduced method based on rainfall and an estimate of transpiration was used to reproduce the temporal evolution of root-zone storage capacities. These values were compared to the values from four hydrological models calibrated for consecutive 2-year windows. Water-balance derived root-zone storage capacities showed a similar signal compared to the calibrated values of the models with a sharp decline in root-zone storage capacity after deforestation, followed by a gradual recovery of 5 to 13 years. The added value of several combinations of remotely sensed products for parameter estimation was assessed for five different hydrological models in 27 catchments across Europe. A parameter selection process was applied for 1023 possible combinations of ten different data sources, ranging from using 1 to all 10 of these products. High probabilities of improvement, with regard to commonly applied model performance metrics, were obtained when combinations included AMSR-E and ASCAT soil moisture, and GRACE total water storage anomalies. The evaporation products of LSA-SAF and MODIS were less effective for deriving meaningful posterior parameter distributions. In a last step, a large-scale hydrological model with landscape-derived sub-grid variability was run with 50 random parameterizations for the European continent, with and without semi-quantitative prior parameter constraints. A variable pattern in improvements/deteriorations was observed when evaluated for 397 gauging stations, which shows that the prior parameter constraints were not sufficient to limit the search space effectively. Concluding, this thesis presents several ways to better optimize different hydrological models without using observed river discharge for varying spatial scales and changing circumstances. In this way, this thesis serves as a stepping stone towards fully predicting in ungauged catchments.
Journal article
(2018)
-
R. C. Nijzink, S. Almeida, R. R.P. van Nooijen, H. H.G. Savenije, M. Hrachowitz, I. G. Pechlivanidis, R. Capell, D. Gustafssons, B. Arheimer, J. Parajka, J. Freer, D. Han, T. Wagener
The calibration of hydrological models without streamflow observations is problematic, and the simultaneous, combined use of remotely sensed products for this purpose has not been exhaustively tested thus far. Our hypothesis is that the combined use of products can (1) reduce the parameter search space and (2) improve the representation of internal model dynamics and hydrological signatures. Five different conceptual hydrological models were applied to 27 catchments across Europe. A parameter selection process, similar to a likelihood weighting procedure, was applied for 1,023 possible combinations of 10 different data sources, ranging from using 1 to all 10 of these products. Distances between the two empirical distributions of model performance metrics with and without using a specific product were determined to assess the added value of a specific product. In a similar way, the performance of the models to reproduce 27 hydrological signatures was evaluated relative to the unconstrained model. Significant reductions in the parameter space were obtained when combinations included Advanced Microwave Scanning Radiometer - Earth Observing System and Advanced Scatterometer soil moisture, Gravity Recovery and Climate Experiment total water storage anomalies, and, in snow-dominated catchments, the Moderate Resolution Imaging Spectroradiometer snow cover products. The evaporation products of Land Surface Analysis - Satellite Application Facility and MOD16 were less effective for deriving meaningful, well-constrained posterior parameter distributions. The hydrological signature analysis indicated that most models profited from constraining with an increasing number of data sources. Concluding, constraining models with multiple data sources simultaneously was shown to be valuable for at least four of the five hydrological models to determine model parameters in absence of streamflow.
...
The calibration of hydrological models without streamflow observations is problematic, and the simultaneous, combined use of remotely sensed products for this purpose has not been exhaustively tested thus far. Our hypothesis is that the combined use of products can (1) reduce the parameter search space and (2) improve the representation of internal model dynamics and hydrological signatures. Five different conceptual hydrological models were applied to 27 catchments across Europe. A parameter selection process, similar to a likelihood weighting procedure, was applied for 1,023 possible combinations of 10 different data sources, ranging from using 1 to all 10 of these products. Distances between the two empirical distributions of model performance metrics with and without using a specific product were determined to assess the added value of a specific product. In a similar way, the performance of the models to reproduce 27 hydrological signatures was evaluated relative to the unconstrained model. Significant reductions in the parameter space were obtained when combinations included Advanced Microwave Scanning Radiometer - Earth Observing System and Advanced Scatterometer soil moisture, Gravity Recovery and Climate Experiment total water storage anomalies, and, in snow-dominated catchments, the Moderate Resolution Imaging Spectroradiometer snow cover products. The evaporation products of Land Surface Analysis - Satellite Application Facility and MOD16 were less effective for deriving meaningful, well-constrained posterior parameter distributions. The hydrological signature analysis indicated that most models profited from constraining with an increasing number of data sources. Concluding, constraining models with multiple data sources simultaneously was shown to be valuable for at least four of the five hydrological models to determine model parameters in absence of streamflow.
Abstract
(2017)
-
Iacopo Ceccarini, Luciano Raso, Susan Steele-Dunne, Markus Hrachowitz, Remko Nijzink, Ansoumana Bodian, Pierluigi Claps
The Upper Senegal River, West Africa, is a poorly gauged basin. Nevertheless, discharge predictions are required in this river for the optimal operation of the downstream Manantali reservoir, flood forecasting, development plans
for the entire basin and studies for adaptation to climate change. Despite the need for reliable discharge predictions, currently available rainfall-runoff models for this basin provide only poor performances, particularly during
extreme regimes, both low-flow and high-flow. In this research we develop a rainfall-runoff model that combines remote-sensing input data and a-priori knowledge on catchment physical characteristics. This semi-distributed
model, is based on conceptual numerical descriptions of hydrological processes at the catchment scale. Because of the lack of reliable input data from ground observations, we use the Tropical Rainfall Measuring Mission (TRMM)
remote-sensing data for precipitation and the Global Land Evaporation Amsterdam Model (GLEAM) for the terrestrial potential evaporation. The model parameters are selected by a combination of calibration, by match of observed output and considering a large set of hydrological signatures, as well as a-priori knowledge on the catchment. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to choose the most likely range in which the parameter sets belong. Analysis of different experiments enhances our understanding on the added value of distributed remote-sensing data and a-priori information in rainfall-runoff modelling. Results of this research will be used for decision making at different scales, contributing to a rational use of water resources in this river.
...
The Upper Senegal River, West Africa, is a poorly gauged basin. Nevertheless, discharge predictions are required in this river for the optimal operation of the downstream Manantali reservoir, flood forecasting, development plans
for the entire basin and studies for adaptation to climate change. Despite the need for reliable discharge predictions, currently available rainfall-runoff models for this basin provide only poor performances, particularly during
extreme regimes, both low-flow and high-flow. In this research we develop a rainfall-runoff model that combines remote-sensing input data and a-priori knowledge on catchment physical characteristics. This semi-distributed
model, is based on conceptual numerical descriptions of hydrological processes at the catchment scale. Because of the lack of reliable input data from ground observations, we use the Tropical Rainfall Measuring Mission (TRMM)
remote-sensing data for precipitation and the Global Land Evaporation Amsterdam Model (GLEAM) for the terrestrial potential evaporation. The model parameters are selected by a combination of calibration, by match of observed output and considering a large set of hydrological signatures, as well as a-priori knowledge on the catchment. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to choose the most likely range in which the parameter sets belong. Analysis of different experiments enhances our understanding on the added value of distributed remote-sensing data and a-priori information in rainfall-runoff modelling. Results of this research will be used for decision making at different scales, contributing to a rational use of water resources in this river.
Abstract
(2017)
-
Remko Nijzink, Christopher Hutton, Markus Hrachowitz, Ilias Pechlivanidis, René Capell, Berit Arheimer, Jim Freer, Dawei Han, Thorsten Wagener, Kevin McGuire, Huub Savenije
The moisture storage available to vegetation is a key parameter in the hydrological functioning of ecosystems. This parameter, the root zone storage capacity, determines the partitioning between runoff and transpiration, but is impossible to observe at the catchment scale. In this research, data from the experimental forests of HJ Andrews (Oregon, USA) and Hubbard Brook (New Hampshire, USA) was used to test the hypotheses that: (1) the root zone storage capacity significantly changes after deforestation, (2) changes in the root zone storage capacity can to a large extent explain post-treatment changes to the hydrological regimes and that (3) a time-dynamic formulation of the root zone storage can improve the performance of a hydrological model. At first, root zone storage capacities were estimated based on a simple, water-balance based method. Briefly, the maximum difference between cumulative rainfall and estimated transpiration was determined, which could be considered a proxy for root zone storage capacity. These values were compared with root zone storage capacities obtained from four conceptual models (HYPE, HYMOD, FLEX, TUW), calibrated for consecutive 2-year windows. Both methods showed a sharp decline in root zone storage capacity after deforestation, which was followed by a gradual recovery signal. It was found in a trend analysis that these recovery periods took between 5 and 13 years for the different catchments. Eventually, one of the models was adjusted to allow for a time-dynamic formulation of root zone storage capacity. This adjusted model showed improvements in model performance as evaluated by 28 hydrological signatures, such as rising limb density or peak flows. Thus, this research clearly shows the time-dynamic character of a crucial parameter, which is often considered to remain constant in time. Root zone storage capacities are strongly affected by deforestation, leading to changes in hydrological regimes, and time-dynamic formulations of root zone storage are therefore necessary in systems under change.
...
The moisture storage available to vegetation is a key parameter in the hydrological functioning of ecosystems. This parameter, the root zone storage capacity, determines the partitioning between runoff and transpiration, but is impossible to observe at the catchment scale. In this research, data from the experimental forests of HJ Andrews (Oregon, USA) and Hubbard Brook (New Hampshire, USA) was used to test the hypotheses that: (1) the root zone storage capacity significantly changes after deforestation, (2) changes in the root zone storage capacity can to a large extent explain post-treatment changes to the hydrological regimes and that (3) a time-dynamic formulation of the root zone storage can improve the performance of a hydrological model. At first, root zone storage capacities were estimated based on a simple, water-balance based method. Briefly, the maximum difference between cumulative rainfall and estimated transpiration was determined, which could be considered a proxy for root zone storage capacity. These values were compared with root zone storage capacities obtained from four conceptual models (HYPE, HYMOD, FLEX, TUW), calibrated for consecutive 2-year windows. Both methods showed a sharp decline in root zone storage capacity after deforestation, which was followed by a gradual recovery signal. It was found in a trend analysis that these recovery periods took between 5 and 13 years for the different catchments. Eventually, one of the models was adjusted to allow for a time-dynamic formulation of root zone storage capacity. This adjusted model showed improvements in model performance as evaluated by 28 hydrological signatures, such as rising limb density or peak flows. Thus, this research clearly shows the time-dynamic character of a crucial parameter, which is often considered to remain constant in time. Root zone storage capacities are strongly affected by deforestation, leading to changes in hydrological regimes, and time-dynamic formulations of root zone storage are therefore necessary in systems under change.
Abstract
(2017)
-
S. Susana Almeida, Remko Nijzink, Huub Savenije, Dawei Han, Ilias Pechlivanidis, René Capell, D. Gustafsson, Thorsten Wagener, J Freer, J Parajka, Markus Hrachowitz, Berit Arheimer
Hydrological models are typically calibrated on available streamflow data or, more rarely on other hydrologic variables (i.e. soil moisture, groundwater dynamics, etc.). Whilst the literature is increasingly extensive on the value of different hydrologic variables in constraining model predictions, less attention has been given on how to define plausible parameter prior distributions or how much such priors impact the range of model behaviour before further conditioning. This can be relevant to the uncertainty bounds of any model prediction or in regard to the amount of sensitivity of the model parameters to the chosen model outputs. In this study, we combine four different conceptual hydrological models (HYPE, HYMOD, TUW, FLEX) with Global Sensitivity Analysis techniques to explore what are the most influential parameters and how the parameter priors impact model predictions. Our analysis focuses on 27 catchments across Europe, capturing a wide range of climates, vegetation and landscapes typologies in order to explore the effects of these physical and climatic properties on parameter prior distributions. Our findings provide new insights in the value of different sources of information for constraining hydrological model inputs, and for predicting water resource conditions in catchments worldwide.
...
Hydrological models are typically calibrated on available streamflow data or, more rarely on other hydrologic variables (i.e. soil moisture, groundwater dynamics, etc.). Whilst the literature is increasingly extensive on the value of different hydrologic variables in constraining model predictions, less attention has been given on how to define plausible parameter prior distributions or how much such priors impact the range of model behaviour before further conditioning. This can be relevant to the uncertainty bounds of any model prediction or in regard to the amount of sensitivity of the model parameters to the chosen model outputs. In this study, we combine four different conceptual hydrological models (HYPE, HYMOD, TUW, FLEX) with Global Sensitivity Analysis techniques to explore what are the most influential parameters and how the parameter priors impact model predictions. Our analysis focuses on 27 catchments across Europe, capturing a wide range of climates, vegetation and landscapes typologies in order to explore the effects of these physical and climatic properties on parameter prior distributions. Our findings provide new insights in the value of different sources of information for constraining hydrological model inputs, and for predicting water resource conditions in catchments worldwide.
Abstract
(2017)
-
Remko Nijzink, S. Susana Almeida, J Parajka, Huub Savenije, Markus Hrachowitz, Ilias Pechlivanidis, René Capell, D. Gustafsson, Berit Arheimer, Jim Freer, Dawei Han, Thorsten Wagener, P. Sleziak
The calibration of a hydrological model still depends on the availability of streamflow data, even though more additional sources of information (i.e. remote sensed data products) have become more widely available. In this research, the model parameters of four different conceptual hydrological models (HYPE, HYMOD, TUW, FLEX) were constrained with remotely sensed products. The models were applied over 27 catchments across Europe to cover a wide range of climates, vegetation and landscapes. The fluxes and states of the models were correlated with the relevant products (e.g. MOD10A snow with modelled snow states), after which new a-posteriori parameter distributions were determined based on a weighting procedure using conditional probabilities. Briefly, each parameter was weighted with the coefficient of determination of the relevant regression between modelled states/fluxes and products. In this way, final feasible parameter sets were derived without the use of discharge time series. Initial results show that improvements in model performance, with regard to streamflow simulations, are obtained when the models are constrained with a set of remotely sensed products simultaneously. In addition, we present a more extensive analysis to assess a model’s ability to reproduce a set of hydrological signatures, such as rising limb density or peak distribution. Eventually, this research will enhance our understanding and recommendations in the use of remotely sensed products for constraining conceptual hydrological modelling and improving predictive capability, especially for data sparse regions.
...
The calibration of a hydrological model still depends on the availability of streamflow data, even though more additional sources of information (i.e. remote sensed data products) have become more widely available. In this research, the model parameters of four different conceptual hydrological models (HYPE, HYMOD, TUW, FLEX) were constrained with remotely sensed products. The models were applied over 27 catchments across Europe to cover a wide range of climates, vegetation and landscapes. The fluxes and states of the models were correlated with the relevant products (e.g. MOD10A snow with modelled snow states), after which new a-posteriori parameter distributions were determined based on a weighting procedure using conditional probabilities. Briefly, each parameter was weighted with the coefficient of determination of the relevant regression between modelled states/fluxes and products. In this way, final feasible parameter sets were derived without the use of discharge time series. Initial results show that improvements in model performance, with regard to streamflow simulations, are obtained when the models are constrained with a set of remotely sensed products simultaneously. In addition, we present a more extensive analysis to assess a model’s ability to reproduce a set of hydrological signatures, such as rising limb density or peak distribution. Eventually, this research will enhance our understanding and recommendations in the use of remotely sensed products for constraining conceptual hydrological modelling and improving predictive capability, especially for data sparse regions.
The evolution of root-zone moisture capacities after deforestation
A step towards hydrological predictions under change?
Journal article
(2016)
-
Remko Nijzink, Christopher Hutton, Markus Hrachowitz, Ilias Pechlivanidis, René Capell, Berit Arheimer, Jim Freer, Dawei Han, Thorsten Wagener, Kevin McGuire, Huub Savenije
The core component of many hydrological systems, the moisture storage capacity available to vegetation, is impossible to observe directly at the catchment scale and is typically treated as a calibration parameter or obtained from a priori available soil characteristics combined with estimates of rooting depth. Often this parameter is considered to remain constant in time. Using long-term data (30-40 years) from three experimental catchments that underwent significant land cover change, we tested the hypotheses that: (1) the root-zone storage capacity significantly changes after deforestation, (2) changes in the root-zone storage capacity can to a large extent explain post-treatment changes to the hydrological regimes and that (3) a time-dynamic formulation of the root-zone storage can improve the performance of a hydrological model. A recently introduced method to estimate catchment-scale root-zone storage capacities based on climate data (i.e. observed rainfall and an estimate of transpiration) was used to reproduce the temporal evolution of root-zone storage capacity under change. Briefly, the maximum deficit that arises from the difference between cumulative daily precipitation and transpiration can be considered as a proxy for root-zone storage capacity. This value was compared to the value obtained from four different conceptual hydrological models that were calibrated for consecutive 2-year windows. It was found that water-balance-derived root-zone storage capacities were similar to the values obtained from calibration of the hydrological models. A sharp decline in root-zone storage capacity was observed after deforestation, followed by a gradual recovery, for two of the three catchments. Trend analysis suggested hydrological recovery periods between 5 and 13 years after deforestation. In a proof-of-concept analysis, one of the hydrological models was adapted to allow dynamically changing root-zone storage capacities, following the observed changes due to deforestation. Although the overall performance of the modified model did not considerably change, in 51% of all the evaluated hydrological signatures, considering all three catchments, improvements were observed when adding a time-variant representation of the root-zone storage to the model. In summary, it is shown that root-zone moisture storage capacities can be highly affected by deforestation and climatic influences and that a simple method exclusively based on climate data can not only provide robust, catchment-scale estimates of this critical parameter, but also reflect its timedynamic behaviour after deforestation.
...
The core component of many hydrological systems, the moisture storage capacity available to vegetation, is impossible to observe directly at the catchment scale and is typically treated as a calibration parameter or obtained from a priori available soil characteristics combined with estimates of rooting depth. Often this parameter is considered to remain constant in time. Using long-term data (30-40 years) from three experimental catchments that underwent significant land cover change, we tested the hypotheses that: (1) the root-zone storage capacity significantly changes after deforestation, (2) changes in the root-zone storage capacity can to a large extent explain post-treatment changes to the hydrological regimes and that (3) a time-dynamic formulation of the root-zone storage can improve the performance of a hydrological model. A recently introduced method to estimate catchment-scale root-zone storage capacities based on climate data (i.e. observed rainfall and an estimate of transpiration) was used to reproduce the temporal evolution of root-zone storage capacity under change. Briefly, the maximum deficit that arises from the difference between cumulative daily precipitation and transpiration can be considered as a proxy for root-zone storage capacity. This value was compared to the value obtained from four different conceptual hydrological models that were calibrated for consecutive 2-year windows. It was found that water-balance-derived root-zone storage capacities were similar to the values obtained from calibration of the hydrological models. A sharp decline in root-zone storage capacity was observed after deforestation, followed by a gradual recovery, for two of the three catchments. Trend analysis suggested hydrological recovery periods between 5 and 13 years after deforestation. In a proof-of-concept analysis, one of the hydrological models was adapted to allow dynamically changing root-zone storage capacities, following the observed changes due to deforestation. Although the overall performance of the modified model did not considerably change, in 51% of all the evaluated hydrological signatures, considering all three catchments, improvements were observed when adding a time-variant representation of the root-zone storage to the model. In summary, it is shown that root-zone moisture storage capacities can be highly affected by deforestation and climatic influences and that a simple method exclusively based on climate data can not only provide robust, catchment-scale estimates of this critical parameter, but also reflect its timedynamic behaviour after deforestation.
Constraining a semi-distributed, conceptual hydrological model on evaporation
A case study for the Kulpawn River Basin, Ghana
Hydrological models are typically calibrated on stream flow observations. However, such data are frequently not available. In addition, in many parts of the world not stream flow, but rather evaporation and transpiration are the largest fluxes from hydrological systems. Nevertheless, models trained to evaporation data are rare and typically rely on evaporation estimates that were themselves also derived from models, thereby considerably reducing the robustness of such approaches. In this study, we test the power of alternative approaches to constrain semi-distributed, conceptual models with information on evaporation in the absence of stream flow data. By gradually increasing the constraining information, the analysis is designed in a stepwise way. Both, the models and the relevance of the added information are evaluated for each step. As a first step, a large set of random parameter sets from uniform prior distributions is generated. Subsequently, parameter sets that cannot produce model outputs that satisfy the added constraints are discarded. The information content of these constraints will be gradually increased by making use of the Budyko framework: (1) the model has to reproduce the long-term average actual evaporation of the system, as indicated by the position in the Budyko framework, (2) the model, similarly, has to reproduce the long-term average seasonal variations of actual evaporation, (3) the model has to reproduce the temporal variations of evaporation, e.g. differences between 5-year mean evaporation of different periods, as indicated by different positions in the Budyko framework. As a last step, the model’s temporal dynamics in the root zone moisture content are constrained by comparing it to time series of the NDII (Normalized Difference Infrared Index), which has recently been shown to be a close proxy for plant available water in the root zone and, thus, for transpiration rates ( Sriwongsitanon et al., 2015). The value of the information content of these different constraints to reduce model uncertainty for reproducing the hydrological response of catchments is assessed by comparing the model results of the individual stages to actually observed discharge data in the study catchment.
...
Hydrological models are typically calibrated on stream flow observations. However, such data are frequently not available. In addition, in many parts of the world not stream flow, but rather evaporation and transpiration are the largest fluxes from hydrological systems. Nevertheless, models trained to evaporation data are rare and typically rely on evaporation estimates that were themselves also derived from models, thereby considerably reducing the robustness of such approaches. In this study, we test the power of alternative approaches to constrain semi-distributed, conceptual models with information on evaporation in the absence of stream flow data. By gradually increasing the constraining information, the analysis is designed in a stepwise way. Both, the models and the relevance of the added information are evaluated for each step. As a first step, a large set of random parameter sets from uniform prior distributions is generated. Subsequently, parameter sets that cannot produce model outputs that satisfy the added constraints are discarded. The information content of these constraints will be gradually increased by making use of the Budyko framework: (1) the model has to reproduce the long-term average actual evaporation of the system, as indicated by the position in the Budyko framework, (2) the model, similarly, has to reproduce the long-term average seasonal variations of actual evaporation, (3) the model has to reproduce the temporal variations of evaporation, e.g. differences between 5-year mean evaporation of different periods, as indicated by different positions in the Budyko framework. As a last step, the model’s temporal dynamics in the root zone moisture content are constrained by comparing it to time series of the NDII (Normalized Difference Infrared Index), which has recently been shown to be a close proxy for plant available water in the root zone and, thus, for transpiration rates ( Sriwongsitanon et al., 2015). The value of the information content of these different constraints to reduce model uncertainty for reproducing the hydrological response of catchments is assessed by comparing the model results of the individual stages to actually observed discharge data in the study catchment.
Journal article
(2016)
-
RC Nijzink, L Samaniego, J Mai, R Kumar, S Thober, M Zink, D Schafer, HHG Savenije, M Hrachowitz
Abstract
(2016)
-
Remko Nijzink, C Hutton, I Pechlivanidis, R Capell, B Arheimer, T Wagener, Huub Savenije, Markus Hrachowitz, Keith Smettem
Root zone storage capacity forms a crucial parameter in ecosystem functioning as it is the key parameter that determines the partitioning between runoff and transpiration. There is increasing evidence from several case studies for specific plants that vegetation adapts to the critical situation of droughts. For example, trees will, on the long term, try to improve their internal hydraulic conductivity after droughts, for example by allocating more biomass for roots. In spite of this understanding, the water storage capacity in the root zone is often treated as constant in hydrological models. In this study, it was hypothesized that root zone storage capacities are altered by deforestation and the regrowth of the ecosystem. Three deforested sub catchments as well as not affected, nearby control catchments of the experimental forests of HJ Andrews and Hubbard Brook were selected for this purpose. Root zone storage capacities were on the one hand estimated by a climate-based approach similar to Gao et al. (2014), making use of simple water balance considerations to determine the evaporative demand of the system. In this way, the maximum deficit between evaporative demand and precipitation allows a robust estimation of the root zone storage capacity. On the other hand, three conceptual hydrological models (FLEX, HYPE, HYMOD) were calibrated in a moving window approach for all catchments. The obtained model parameter values representing the root zone storage capacities of the individual catchments for each moving window period were then compared to the estimates derived from climate data for the same periods. Model- and climate-derived estimates of root zone storage capacities both showed a similar evolution. In the deforested catchments, considerable reductions of the root zone storage capacities, compared to the pretreatment situation and control catchments, were observed. In addition, the years after forest clearing were characterized by a gradual recovery of the root zone storage capacities, converging to new equilibrium conditions and linked to forest regrowth. Further trend analysis suggested a relatively quick hydrological recovery between 5 and 15 years in the study catchments. The results lend evidence to the role of both, climate and vegetation dynamics for the development of root zone systems and their controlling influence on hydrological response dynamics.
...
Root zone storage capacity forms a crucial parameter in ecosystem functioning as it is the key parameter that determines the partitioning between runoff and transpiration. There is increasing evidence from several case studies for specific plants that vegetation adapts to the critical situation of droughts. For example, trees will, on the long term, try to improve their internal hydraulic conductivity after droughts, for example by allocating more biomass for roots. In spite of this understanding, the water storage capacity in the root zone is often treated as constant in hydrological models. In this study, it was hypothesized that root zone storage capacities are altered by deforestation and the regrowth of the ecosystem. Three deforested sub catchments as well as not affected, nearby control catchments of the experimental forests of HJ Andrews and Hubbard Brook were selected for this purpose. Root zone storage capacities were on the one hand estimated by a climate-based approach similar to Gao et al. (2014), making use of simple water balance considerations to determine the evaporative demand of the system. In this way, the maximum deficit between evaporative demand and precipitation allows a robust estimation of the root zone storage capacity. On the other hand, three conceptual hydrological models (FLEX, HYPE, HYMOD) were calibrated in a moving window approach for all catchments. The obtained model parameter values representing the root zone storage capacities of the individual catchments for each moving window period were then compared to the estimates derived from climate data for the same periods. Model- and climate-derived estimates of root zone storage capacities both showed a similar evolution. In the deforested catchments, considerable reductions of the root zone storage capacities, compared to the pretreatment situation and control catchments, were observed. In addition, the years after forest clearing were characterized by a gradual recovery of the root zone storage capacities, converging to new equilibrium conditions and linked to forest regrowth. Further trend analysis suggested a relatively quick hydrological recovery between 5 and 15 years in the study catchments. The results lend evidence to the role of both, climate and vegetation dynamics for the development of root zone systems and their controlling influence on hydrological response dynamics.