Review of the manuscript „A framework to regionalize conceptual model parameters for global hydrological modeling“

Abstract. To provide an accurate estimate of global water resources and help to formulate water allocation policies, global hydrological models (GHMs) have been developed. However, it is difficult to obtain parameter values for GHMs, which results in large uncertainty in estimation of the global water balance components. In this study, a framework is developed for building GHMs based on parameter regionalization of catchment scale conceptual hydrological models. That is, using appropriate global scale regionalization scheme (GSRS) and conceptual hydrological models to simulate runoff at the grid scale globally and the Network Response Routing (NRF) method to converge the grid runoff to catchment streamflow. To achieve this, five regionalization methods (i.e. the global mean method, the spatial proximity method, the physical similarity method, the physical similarity method considering distance, and the regression method) are first tested for four conceptual hydrological models over thousands medium-sized catchments (2500–50000 km2) around the world to find the appropriate global scale regionalization scheme. The selected GSRS is then used to regionalize conceptual model parameters for global land grids with 0.5°×0.5° resolution on latitude and longitude. The results show that: (1) Spatial proximity method with the Inverse Distance Weighting (IDW) method and the output average option (SPI-OUT) offers the best regionalization solution, and the greatest gains of the SPI-OUT method were achieved with mean distance between the donor catchments and the target catchment is no more than 1500 km. (2) It was found the Kling-Gupta efficiency (KGE) value of 0.5 is a good threshold value to select donor catchments. And (3) Four different GHMs established based on framework were able to produce reliable streamflow simulations. Overall, the proposal framework can be used with any conceptual hydrological model for estimating global water resources, even though uncertainty exists in terms of using difference conceptual models.

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Incremental model breakdown to assess the multi-hypotheses problem

Hydrology and Earth System Sciences ◽

10.5194/hess-22-4565-2018 ◽

2018 ◽

Vol 22 (8) ◽

pp. 4565-4581 ◽

Cited By ~ 3

Author(s):

Florian U. Jehn ◽

Lutz Breuer ◽

Tobias Houska ◽

Konrad Bestian ◽

Philipp Kraft

Keyword(s):

Hydrological Modeling ◽

Model Performance ◽

Complex Model ◽

Model Parameters ◽

Objective Functions ◽

Final Model ◽

Incremental Model ◽

Model Efficiency ◽

Improved Model ◽

Model Structures

Abstract. The ambiguous representation of hydrological processes has led to the formulation of the multiple hypotheses approach in hydrological modeling, which requires new ways of model construction. However, most recent studies focus only on the comparison of predefined model structures or building a model step by step. This study tackles the problem the other way around: we start with one complex model structure, which includes all processes deemed to be important for the catchment. Next, we create 13 additional simplified models, where some of the processes from the starting structure are disabled. The performance of those models is evaluated using three objective functions (logarithmic Nash–Sutcliffe; percentage bias, PBIAS; and the ratio between the root mean square error and the standard deviation of the measured data). Through this incremental breakdown, we identify the most important processes and detect the restraining ones. This procedure allows constructing a more streamlined, subsequent 15th model with improved model performance, less uncertainty and higher model efficiency. We benchmark the original Model 1 and the final Model 15 with HBV Light. The final model is not able to outperform HBV Light, but we find that the incremental model breakdown leads to a structure with good model performance, fewer but more relevant processes and fewer model parameters.

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The Impact of Error Accounting in a Bayesian Approach to Calibrating Modeled Turbulent Fluxes in an Open-Canopy Forest

Journal of Hydrometeorology ◽

10.1175/jhm-d-17-0030.1 ◽

2017 ◽

Vol 18 (7) ◽

pp. 2029-2042

Author(s):

Tony E. Wong ◽

William Kleiber ◽

David C. Noone

Keyword(s):

Markov Chain ◽

Land Surface ◽

Hydrological Modeling ◽

Soil Layer ◽

Turbulent Fluxes ◽

Surface Model ◽

Model Parameters ◽

Top Soil ◽

The Impact ◽

Calibration Approach

Abstract Land surface models are notorious for containing many parameters that control the exchange of heat and moisture between land and atmosphere. Properly modeling the partitioning of total evapotranspiration (ET) between transpiration and evaporation is critical for accurate hydrological modeling, but depends heavily on the treatment of turbulence within and above canopies. Previous work has constrained estimates of evapotranspiration and its partitioning using statistical approaches that calibrate land surface model parameters by assimilating in situ measurements. These studies, however, are silent on the impacts of the accounting of uncertainty within the statistical calibration framework. The present study calibrates the aerodynamic, leaf boundary layer, and stomatal resistance parameters, which partially control canopy turbulent exchange and thus the evapotranspiration flux partitioning. Using an adaptive Metropolis–Hastings algorithm to construct a Markov chain of draws from the joint posterior distribution of these resistance parameters, an ensemble of model realizations is generated, in which latent and sensible heat fluxes and top soil layer temperature are optimized. A set of five calibration experiments demonstrate that model performance is sensitive to the accounting of various sources of uncertainty in the field observations and model output and that it is critical to account for model structural uncertainty. After calibration, the modeled fluxes and top soil layer temperature are largely free from bias, and this calibration approach successfully informs and characterizes uncertainty in these parameters, which is essential for model improvement and development. The key points of this paper are 1) a Markov chain Monte Carlo calibration approach successfully improves modeled turbulent fluxes; 2) ET partitioning estimates hinge on the representation of uncertainties in the model and data; and 3) despite these inherent uncertainties, constrained posterior estimates of ET partitioning emerge.

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Diagnostic calibration of a hydrological model in an alpine area

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-1253-2014 ◽

2014 ◽

Vol 11 (1) ◽

pp. 1253-1300 ◽

Cited By ~ 4

Author(s):

Z. He ◽

F. Tian ◽

H. C. Hu ◽

H. V. Gupta ◽

H. P. Hu

Keyword(s):

Hydrological Modeling ◽

Hydrological Model ◽

Generation Mechanism ◽

Spatiotemporal Variability ◽

Model Parameters ◽

Distributed Hydrological Model ◽

Runoff Generation ◽

Alpine Area ◽

Long Time ◽

Tailan River Basin

Abstract. Hydrological modeling depends on single- or multiple-objective strategies for parameter calibration using long time sequences of observed streamflow. Here, we demonstrate a diagnostic approach to the calibration of a hydrological model of an alpine area in which we partition the hydrograph based on the dominant runoff generation mechanism (groundwater baseflow, glacier melt, snowmelt, and direct runoff). The partitioning reflects the spatiotemporal variability in snowpack, glaciers, and temperature. Model parameters are grouped by runoff generation mechanism, and each group is calibrated separately via a stepwise approach. This strategy helps to reduce the problem of equifinality and, hence, model uncertainty. We demonstrate the method for the Tailan River basin (1324 km2) in the Tianshan Mountains of China with the help of a semi-distributed hydrological model (THREW).

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Fractional snow-covered area: scale-independent peak of winter parameterization

The Cryosphere ◽

10.5194/tc-15-615-2021 ◽

2021 ◽

Vol 15 (2) ◽

pp. 615-632

Author(s):

Nora Helbig ◽

Yves Bühler ◽

Lucie Eberhard ◽

César Deschamps-Berger ◽

Simon Gascoin ◽

...

Keyword(s):

Snow Depth ◽

Hydrological Modeling ◽

Spatial Scales ◽

Empirical Relationship ◽

Ground Surface ◽

Model Parameters ◽

Data Sets ◽

Snow Covered Area ◽

Covered Area ◽

Geographical Regions

Abstract. The spatial distribution of snow in the mountains is significantly influenced through interactions of topography with wind, precipitation, shortwave and longwave radiation, and avalanches that may relocate the accumulated snow. One of the most crucial model parameters for various applications such as weather forecasts, climate predictions and hydrological modeling is the fraction of the ground surface that is covered by snow, also called fractional snow-covered area (fSCA). While previous subgrid parameterizations for the spatial snow depth distribution and fSCA work well, performances were scale-dependent. Here, we were able to confirm a previously established empirical relationship of peak of winter parameterization for the standard deviation of snow depth σHS by evaluating it with 11 spatial snow depth data sets from 7 different geographic regions and snow climates with resolutions ranging from 0.1 to 3 m. An enhanced performance (mean percentage errors, MPE, decreased by 25 %) across all spatial scales ≥ 200 m was achieved by recalibrating and introducing a scale-dependency in the dominant scaling variables. Scale-dependent MPEs vary between −7 % and 3 % for σHS and between 0 % and 1 % for fSCA. We performed a scale- and region-dependent evaluation of the parameterizations to assess the potential performances with independent data sets. This evaluation revealed that for the majority of the regions, the MPEs mostly lie between ±10 % for σHS and between −1 % and 1.5 % for fSCA. This suggests that the new parameterizations perform similarly well in most geographical regions.

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Comparison between dynamic and static sensitivity analysis approaches for impact assessment of different potential evapotranspiration methods on hydrological models performance

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0192.1 ◽

2021 ◽

Author(s):

Rodric Mérimé Nonki ◽

André Lenouo ◽

Christopher J. Lennard ◽

Raphael M. Tshimanga ◽

Clément Tchawoua

Keyword(s):

Sensitivity Analysis ◽

Impact Assessment ◽

Hydrological Modeling ◽

Potential Evapotranspiration ◽

Model Performance ◽

Estimation Methods ◽

Model Parameters ◽

Hydrological Models ◽

Sensitivity Approach ◽

Static Sensitivity

AbstractPotential Evapotranspiration (PET) plays a crucial role in water management, including irrigation systems design and management. It is an essential input to hydrological models. Direct measurement of PET is difficult, time-consuming and costly, therefore a number of different methods are used to compute this variable. This study compares the two sensitivity analysis approaches generally used for PET impact assessment on hydrological model performance. We conducted the study in the Upper Benue River Basin (UBRB) located in northern Cameroon using two lumped-conceptual rainfall-runoff models and nineteen PET estimation methods. A Monte-Carlo procedure was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. Although there were notable differences between PET estimation methods, the hydrological models performance was satisfactory for each PET input in the calibration and validation periods. The optimized model parameters were significantly affected by the PET-inputs, especially the parameter responsible to transform PET into actual ET. The hydrological models performance was insensitive to the PET input using a dynamic sensitivity approach, while he was significantly affected using a static sensitivity approach. This means that the over-or under-estimation of PET is compensated by the model parameters during the model recalibration. The model performance was insensitive to the rescaling PET input for both dynamic and static sensitivities approaches. These results demonstrate that the effect of PET input to model performance is necessarily dependent on the sensitivity analysis approach used and suggest that the dynamic approach is more effective for hydrological modeling perspectives.

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Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand

Hydrology and Earth System Sciences ◽

10.5194/hess-11-1373-2007 ◽

2007 ◽

Vol 11 (4) ◽

pp. 1373-1390 ◽

Cited By ~ 131

Author(s):

D. Sharma ◽

A. Das Gupta ◽

M. S. Babel

Keyword(s):

River Basin ◽

Bias Correction ◽

Hydrological Modeling ◽

Correction Method ◽

Hydrologic Model ◽

Global Climate Models ◽

Model Parameters ◽

Bias Correction Method ◽

Spatial Disaggregation ◽

Grid Nodes

Abstract. Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.

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Runoff Predicting and Variation Analysis in Upper Ganjiang Basin under Projected Climate Changes

Sustainability ◽

10.3390/su11215885 ◽

2019 ◽

Vol 11 (21) ◽

pp. 5885 ◽

Cited By ~ 3

Author(s):

Chao Deng ◽

Weiguang Wang

Keyword(s):

Resource Management ◽

Water Resource ◽

Water Resource Management ◽

Hydrological Modeling ◽

Hydrologic Model ◽

Global Climate Models ◽

Balance Model ◽

Model Parameters ◽

The Mean ◽

Parameter Approach

Catchment runoff is significantly affected by climate condition changes. Predicting the runoff and analyzing its variations under future climates play a vital role in water security, water resource management, and the sustainable development of the catchment. In traditional hydrological modeling, fixed model parameters are usually used to transfer the global climate models (GCMs) to runoff, while the hydrologic model parameters may be time-varying. It is more appropriate to use the time-variant parameter for runoff modeling. This is achieved by incorporating the time-variant parameter approach into a two-parameter water balance model (TWBM) through the construction of time-variant parameter functions based on the identified catchment climate indicators. Using the Ganjiang Basin with an outlet of the Dongbei Hydrological Station as the study area, we developed time-variant parameter scenarios of the TWBM model and selected the best-performed parameter functions to predict future runoff and analyze its variations under the climate model projection of the BCC-CSM1.1(m). To synthetically assess the model performance improvements using the time-variant parameter approach, an index Δ was developed by combining the Nash–Sutcliffe efficiency, the volume error, the Box–Cox transformed root-mean-square error, and the Kling–Gupta efficiency with equivalent weight. The results show that the TWBM model with time-variant C (evapotranspiration parameter) and SC (water storage capacity of catchment), where growing and non-growing seasons are considered for C, outperformed the model with constant parameters with a Δ value of approximately 5% and 10% for the calibration and validation periods, respectively. The mean annual values of runoff predictions under the four representative concentration pathways (RCPs) exhibited a decreasing trend over the future three decades (2021–2050) when compared to the runoff simulations in the baseline period (1982–2011), where the values were about −9.9%, −19.5%, −16.6%, and −11.4% for the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The decreasing trend of future precipitation exerts impacts on runoff decline. Generally, the mean monthly changes of runoff predictions showed a decreasing trend from January to August for almost all of the RCPs, while an increasing trend existed from September to November, along with fluctuations among different RCPs. This study can provide beneficial references to comprehensively understand the impacts of climate change on runoff prediction and thus improve the regional strategy for future water resource management.

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The Role of the Spatial Distribution of Radar Rainfall on Hydrological Modeling for an Urbanized River Basin in Japan

Water ◽

10.3390/w11081703 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1703 ◽

Cited By ~ 7

Author(s):

Shakti P. C. ◽

Tsuyoshi Nakatani ◽

Ryohei Misumi

Keyword(s):

River Basin ◽

Spatial Resolution ◽

Hydrological Modeling ◽

Distribution Patterns ◽

Rainfall Data ◽

Model Parameters ◽

Radar Rainfall ◽

Rain Events ◽

The Impact ◽

Hydrological Applications

Recently, the use of gridded rainfall data with high spatial resolutions in hydrological applications has greatly increased. Various types of radar rainfall data with varying spatial resolutions are available in different countries worldwide. As a result of the variety in spatial resolutions of available radar rainfall data, the hydrological community faces the challenge of selecting radar rainfall data with an appropriate spatial resolution for hydrological applications. In this study, we consider the impact of the spatial resolution of radar rainfall on simulated river runoff to better understand the impact of radar resolution on hydrological applications. Very high-resolution polarimetric radar rainfall (XRAIN) data are used as input for the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) to simulate runoff from the Tsurumi River Basin, Japan. A total of 20 independent rainfall events from 2012–2015 were selected and categorized into isolated/convective and widespread/stratiform events based on their distribution patterns. First, the hydrological model was established with basin and model parameters that were optimized for each individual rainfall event; then, the XRAIN data were rescaled at various spatial resolutions to be used as input for the model. Finally, we conducted a statistical analysis of the simulated results to determine the optimum spatial resolution for radar rainfall data used in hydrological modeling. Our results suggest that the hydrological response was more sensitive to isolated or convective rainfall data than it was to widespread rain events, which are best simulated at ≤1 km and ≤5 km, respectively; these results are applicable in all sub-basins of the Tsurumi River Basin, except at the river outlet.

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On the astronomical forcing of simple conceptual ice age models

10.5194/egusphere-egu2020-10137 ◽

2020 ◽

Author(s):

Gaëlle Leloup ◽

Didier Paillard

Keyword(s):

Conceptual Model ◽

Climate Model ◽

Ice Age ◽

Early Pleistocene ◽

Model Parameters ◽

Ice Ages ◽

Glacial Cycles ◽

Orbital Parameters ◽

Ice Volume ◽

Astronomical Forcing

Variations of the Earth&#8217;s orbital parameters are known to pace the ice volume variations of the last million year [1], even if the precise mechanisms remain unknown. Several conceptual models have been used to try to better understand the connection between ice-sheet changes and the astronomical forcing. An often overlooked question is to decide which astronomical forcing can best explain the observed cycles.A rather traditional practice was to use the insolation at a some specific day of the year, for instance at mid-july [2] or at the june solstice [3]. But it was also suggested that the integrated forcing above some given threshold could be a better alternative [4]. In a more recent paper, Tzedakis et al. [5] have shown that simple rules, based on the original Milankovitch forcing or caloric seasons, could also be used to explain the timing of ice ages. Here we adapt and simplify the conceptual model of Parrenin and Paillard 2003 [6], to first reduce the set of parameters. Like in the original conceptual model from [6], this simplified conceptual model is based on climate oscillations between two states: glaciation and deglaciation. It switches to one another when crossing a defined threshold. While the triggering of glaciations is only triggered by orbital parameters, the triggering of deglaciations is triggered by a combination of orbital parameters and ice volume. Then, we apply the different possible forcings listed above and we try to adapt the model parameters to reproduce the ice volume record, at least in a qualitative way. This allows us to discuss which kind of astronomical forcing better explains the Quaternary ice ages, in the context of such simple threshold-based models.[1] Variations in the Earth's Orbit: Pacemaker of the Ice Ages, Hays et al., 1976, Science&#8232;[2] Modeling the Climatic Response to Orbital Variations, Imbrie and Imbrie, 1980, Science&#8232;[3] The timing of Pleistocene glaciations from a simple multiple-state climate model, Paillard, 1998, Nature[4] Early Pleistocene Glacial Cycles and the Integrated Summer Insolation Forcing, Huybers et al., 2006, Science[5] A simple rule to determine which insolation cycles lead to interglacials, Tzedakis et al., 2017, Nature[6] Amplitude and phase of glacial cycles from a conceptual model, Parrenin Paillard, 2003, EPSL.

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