scholarly journals Sensitivity and Interdependency Analysis of the HBV Conceptual Model Parameters in a Semi-Arid Mountainous Watershed

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2440
Author(s):  
Hamza Ouatiki ◽  
Abdelghani Boudhar ◽  
Aziz Ouhinou ◽  
Abdelaziz Beljadid ◽  
Marc Leblanc ◽  
...  
Keyword(s):  
Snow Cover ◽  
Conceptual Model ◽  
Water Resource Management ◽  
Optimal Parameter ◽  
Field Capacity ◽  
Temperature Correction ◽  
Model Parameters ◽  
Daily Streamflow ◽  
Model Response ◽  
Semi Arid

Hydrological models, with different levels of complexity, have become inherent tools in water resource management. Conceptual models with low input data requirements are preferred for streamflow modeling, particularly in poorly gauged watersheds. However, the inadequacy of model structures in the hydrologic regime of a given watershed can lead to uncertain parameter estimation. Therefore, an understanding of the model parameters’ behavior with respect to the dominant hydrologic responses is of high necessity. In this study, we aim to investigate the parameterization of the HBV (Hydrologiska Byråns Vattenbalansavedelning) conceptual model and its influence on the model response in a semi-arid context. To this end, the capability of the model to simulate the daily streamflow was evaluated. Then, sensitivity and interdependency analyses were carried out to identify the most influential model parameters and emphasize how these parameters interact to fit the observed streamflow under contrasted hydroclimatic conditions. The results show that the HBV model can fairly reproduce the observed daily streamflow in the watershed of interest. However, the reliability of the model simulations varies from one year to another. The sensitivity analysis showed that each of the model parameters has a certain degree of influence on model behavior. The temperature correction factor (ETF) showed the lowest effect on the model response, while the sensitivity to the degree-day factor (DDF) highly depends on the availability of snow cover. Overall, the changes in hydroclimatic conditions were found to be mostly responsible for the annual variability of the optimal parameter values. Additionally, these changes seem to actuate the interdependency between the parameters of the soil moisture and the response routines, particularly Field Capacity (FC), the recession coefficient K0, the percolation coefficient (KPERC), and the upper reservoir threshold (UZL). The latter combines either to shrink the storage capacity of the model’s reservoirs under extremely high peak flows or to enlarge them under overestimated water supply, mainly provoked by abundant snow cover.

The Suitability of Eight Spatial Rainfall Products to Simulate Daily Streamflow in Semi-Arid Watersheds using the HBV model

2021 ◽  
Author(s):  
Hamza Ouatiki ◽  
Abdelghani Boudhar ◽  
Abdelghani Chehbouni
Keyword(s):  
Hydrologic Modeling ◽  
Pearson Correlation ◽  
Evaluation Process ◽  
False Alarms ◽  
Model Parameters ◽  
Daily Streamflow ◽  
Hbv Model ◽  
Semi Arid ◽  
Streamflow Simulation ◽  
Rainfall Estimates

<p>Accurate rainfall measurements are crucial for hydrologic modeling. They are mainly provided by rain gauges (RGs), which cover only limited areas. Thus, the gauging network density and distribution can be real constraints in water-related studies, particularly in semi-arid regions. This is the case of Ait-Ouchene and Tilouguite, two mountainous sub-watersheds of the Oum-Rr-Rbia river basin, located in Morocco. Several freely available Spatial Rainfall Products (SRP), with quasi-global coverage, provide rainfall estimates that can constitute a potential complement to the RGs. In this context, we intend to investigate the suitability of eight SRPs (ARC2, CHIRPSp25km, CHIRPSp5km, CMORPH-CRT-V1, GPM-IMERG-V6, PERSIANN-CDR, RFE2, and TRMM-3B42-V7) for daily streamflow simulation in Ait-Ouchene and Tillouguite for the period 2001-2010. We proceeded by a pixel-wise and watershed-wise comparison against data of twenty-six RGs in Oum-Rr-Rbia, using the PCC (Pearson Correlation Coefficient), RMSE, Bias, POD (Detection Probability), and FAR (False Alarms Ratio) metrics. Then, the SRPs were used to annually calibrate the HBV conceptual rainfall-runoff model in Ait-Ouchene and Tilouguite. The SRP-driven simulations’ accuracy was assessed against the gauged streamflow using the NSE metric.</p><p>Primarily, the model was tested in Ait-Ouchene through cross-validation, parameter sensitivity, and parameter interdependency analyses, using the RG and MODIS-SCA observations. The results showed that the HBV model can fairly reproduce the observed streamflow, with year-to-year variable reliability. Additionally, the hydroclimatic changes appeared to actuate the model parameters’ interdependency. The latter were found to combine either to shrink the storage capacity of the model’s reservoirs under extremely high streamflow or enlarge them under overestimated water supply, mainly from snow cover. Thus, the snowmelt sub-routine was deactivated, during the evaluation process, to avoid the SWE compensating the bias in the SRP estimates.</p><p>Regarding the SRPs evaluation, the rainfall estimates performed relatively poorly for both direct comparison and hydrologic modeling. Most SRPs yielded PCCs below 0.5, except for IMERG and RFE. They exhibited PCCs between 0.54-0.62 (IMERG) and 0.47-0.71 (RFE) at 50% of the RGs, with IMERG performing the best at eighteen out of the twenty-six RGs. IMERG prevalence was also observed in terms of detection capacity showing the highest PODs alongside PERSIANN. The SRPs detected many rainfall events as false alarms, with median FARs greater than 0.52. However, an analysis, where we considered only the grid-cells encompassing more than one RG, revealed that a portion of the false alarms were rainfalls that fell in the RGs’ vicinity. Moreover, the rainfall estimates were substantially biased, where the large rainfall totals were predominantly underestimated. For streamflow simulation, the SRPs’ performance seemed unsteady and varied depending on years and products. While IMERG and RFE frequently produced the best NSEs, CMORPH consistently showed the weakest results. In addition to the important bias contained in the SRP estimates, the low performance in hydrologic modeling can be related to the abundance of insignificant false alarms. Nevertheless, the SRPs provided better streamflow estimates than the RGs in Tillouguite, which has an unevenly distributed gauging network concentrated near the outlet.</p>

scholarly journals Relative impacts of climate change and land cover change on streamflow using SWAT in the Clackamas River Watershed, USA

2020 ◽  
Author(s):  
Junjie Chen ◽  
Heejun Chang
Keyword(s):  
Climate Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Water Resource Management ◽  
Assessment Tool ◽  
Hydrologic Model ◽  
Low Flow ◽  
Model Parameters ◽  
River Watershed ◽  
Daily Streamflow

Abstract To understand the spatial–temporal pattern of climate and land cover (CLC) change effects on hydrology, we used three land cover change (LCC) coupled scenarios to estimate the changes in streamflow metrics in the Clackamas River Watershed in Oregon for the 2050s (2040–2069) and the 2080s (2070–2099). Coupled scenarios, which were split into individual and combined simulations such as climate change (CC), LCC, CLC change, and daily streamflow were simulated in the Soil and Water Assessment Tool. The interannual variability of streamflow was higher in the lower urbanized area than the upper forested region. The watershed runoff was projected to be more sensitive to CC than LCC. Under the CLC scenario, the top 10% peak flow and the 7-day low flow are expected to increase (2–19%) and decrease (+9 to −20 cm s), respectively, in both future periods. The center timing of runoff in the year is projected to shift 2–3 weeks earlier in response to warming temperature and more winter precipitation falling as rain. High streamflow variability in our findings suggests that uncertainties can stem from both climate models and hydrologic model parameters, calling for more adaptive water resource management in the watershed.

Water Resources Management in the Semi-Arid Central Valley of California

1987 ◽  
Vol 19 (9) ◽  
pp. 97-106
Author(s):  
J. J. Vasconcelos
Keyword(s):  
Resource Management ◽  
Ground Water ◽  
Water Resource ◽  
Water Resource Management ◽  
Arid Regions ◽  
Central Valley ◽  
The United States ◽  
Policy Makers ◽  
Ground Water Resource ◽  
Semi Arid

Hater resource managers in semi-arid regions are faced with some unique problems. The wide variations in precipitation and stream flows in semi-arid regions increase man's dependence on the ground water resource for an ample and reliable supply of water. Proper management of the ground water resource is absolutely essential to the economic well being of semi-arid regions. Historians have discovered the remains of vanished advanced civilizations based on irrigated agriculture which were ignorant of the importance of proper ground water resource management. In the United States a great deal of effort is presently being expended in the study and control of toxic discharges to the ground water resource. What many public policy makers fail to understand is that the potential loss to society resulting from the mineralization of the ground water resource is potentially much greater than the loss caused by toxic wastes discharges, particularly in developing countries. Appropriations for ground water resource management studies in developed countries such as the United States are presently much less than those for toxic wastes management and should be increased. It is the reponsibility of the water resource professional to emphasize to public policy makers the importance of ground water resource management. Applications of ground water resource management models in the semi-arid Central Valley of California are presented. The results demonstrate the need for proper ground water resource management practices in semi-arid regions and the use of ground water management models as a valuable tool for the water resource manager.

scholarly journals A Cybernetic Approach to Modeling Lipid Metabolism in Mammalian Cells

Processes ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 126 ◽  
Author(s):  
Lina Aboulmouna ◽  
Shakti Gupta ◽  
Mano Maurya ◽  
Frank DeVilbiss ◽  
Shankar Subramaniam ◽  
...  
Keyword(s):  
Mammalian Cells ◽  
Metabolic Regulation ◽  
Metabolic Flux ◽  
Optimal Parameter ◽  
Gene Knockout ◽  
Model Parameters ◽  
Mouse Bone Marrow ◽  
Optimization Approach ◽  
Substrate Uptake ◽  
Data Set

The goal-oriented control policies of cybernetic models have been used to predict metabolic phenomena such as the behavior of gene knockout strains, complex substrate uptake patterns, and dynamic metabolic flux distributions. Cybernetic theory builds on the principle that metabolic regulation is driven towards attaining goals that correspond to an organism’s survival or displaying a specific phenotype in response to a stimulus. Here, we have modeled the prostaglandin (PG) metabolism in mouse bone marrow derived macrophage (BMDM) cells stimulated by Kdo2-Lipid A (KLA) and adenosine triphosphate (ATP), using cybernetic control variables. Prostaglandins are a well characterized set of inflammatory lipids derived from arachidonic acid. The transcriptomic and lipidomic data for prostaglandin biosynthesis and conversion were obtained from the LIPID MAPS database. The model parameters were estimated using a two-step hybrid optimization approach. A genetic algorithm was used to determine the population of near optimal parameter values, and a generalized constrained non-linear optimization employing a gradient search method was used to further refine the parameters. We validated our model by predicting an independent data set, the prostaglandin response of KLA primed ATP stimulated BMDM cells. We show that the cybernetic model captures the complex regulation of PG metabolism and provides a reliable description of PG formation.

scholarly journals Regionalizing rainfall–runoff model parameters to predict the daily streamflow of ungauged catchments in the dry tropics

2009 ◽  
Vol 40 (5) ◽  
pp. 433-444 ◽  
Author(s):  
David A. Post
Keyword(s):  
Input Data ◽  
Water Yield ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Wet Season ◽  
Daily Streamflow ◽  
Total Length ◽  
Dry Tropics ◽  
Rainfall Runoff Model ◽  
Runoff Model

A methodology has been derived which allows an estimate to be made of the daily streamflow at any point within the Burdekin catchment in the dry tropics of Australia. The input data requirements are daily rainfall (to drive the rainfall–runoff model) and mean average wet season rainfall, total length of streams, percent cropping and percent forest in the catchment (to regionalize the parameters of the rainfall–runoff model). The method is based on the use of a simple, lumped parameter rainfall–runoff model, IHACRES (Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data). Of the five parameters in the model, three have been set to constants to reflect regional conditions while the other two have been related to physio-climatic attributes of the catchment under consideration. The parameter defining total catchment water yield (c) has been estimated based on the mean average wet season rainfall, while the streamflow recession time constant (τ) has been estimated based on the total length of streams, percent cropping and percent forest in the catchment. These relationships have been shown to be applicable over a range of scales from 68–130,146 km2. However, three separate relationships were required to define c in the three major physiographic regions of the Burdekin: the upper Burdekin, Bowen and Suttor/lower Burdekin. The invariance of the relationships with scale indicates that the dominant processes may be similar across a range of scales. The fact that different relationships were required for each of the three major regions indicates the geographic limitations of this regionalization approach. For most of the 24 gauged catchments within the Burdekin the regionalized rainfall–runoff models were nearly as good as or better than the rainfall–runoff models calibrated to the observed streamflow. In addition, models often performed better over the simulation period than the calibration period. This indicates that future improvements in regionalization should focus on improving the quality of input data and rainfall–runoff model conceptualization rather than on the regionalization procedure per se.

scholarly journals Radar-based Bayesian estimation of ice crystal growth parameters within a microphysical model

2020 ◽  
Author(s):  
Robert S. Schrom ◽  
Marcus van Lier-Walqui ◽  
Matthew R. Kumjian ◽  
Jerry Y. Harrington ◽  
Anders A. Jensen ◽  
...  
Keyword(s):  
Vapor Deposition ◽  
Growth Parameters ◽  
Particle Density ◽  
Optimal Parameter ◽  
Number Concentration ◽  
Department Of Energy ◽  
The Arctic ◽  
Model Parameters ◽  
Growth Ratio ◽  
Radar Measurements

AbstractThe potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model-observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. The combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.

scholarly journals Robust estimation of hydrological model parameters

2008 ◽  
Vol 5 (3) ◽  
pp. 1641-1675 ◽  
Author(s):  
A. Bárdossy ◽  
S. K. Singh
Keyword(s):  
Measurement Errors ◽  
Input Data ◽  
Hydrological Model ◽  
Optimal Parameter ◽  
Model Performance ◽  
Model Parameters ◽  
Geometrical Approach ◽  
State Variables ◽  
Parameter Vector ◽  
Robust Parameter

Abstract. The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives an unique and very best parameter vector. The parameters of hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on the half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study) for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.

scholarly journals Relating surface backscatter response from TRMM Precipitation Radar to soil moisture: results over a semi-arid region

2009 ◽  
Vol 6 (5) ◽  
pp. 6425-6454
Author(s):  
H. Stephen ◽  
S. Ahmad ◽  
T. C. Piechota ◽  
C. Tang
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Arid Regions ◽  
Vegetation Index ◽  
Temporal Trends ◽  
Model Parameters ◽  
Precipitation Radar ◽  
Semi Arid

Abstract. The Tropical Rainfall Measuring Mission (TRMM) carries aboard the Precipitation Radar (TRMMPR) that measures the backscatter (σ°) of the surface. σ° is sensitive to surface soil moisture and vegetation conditions. Due to sparse vegetation in arid and semi-arid regions, TRMMPR σ° primarily depends on the soil water content. In this study we relate TRMMPR σ° measurements to soil water content (ms) in Lower Colorado River Basin (LCRB). σ° dependence on ms is studied for different vegetation greenness values determined through Normalized Difference Vegetation Index (NDVI). A new model of σ° that couples incidence angle, ms, and NDVI is used to derive parameters and retrieve soil water content. The calibration and validation of this model are performed using simulated and measured ms data. Simulated ms is estimated using Variable Infiltration Capacity (VIC) model whereas measured ms is acquired from ground measuring stations in Walnut Gulch Experimental Watershed (WGEW). σ° model is calibrated using VIC and WGEW ms data during 1998 and the calibrated model is used to derive ms during later years. The temporal trends of derived ms are consistent with VIC and WGEW ms data with correlation coefficient (R) of 0.89 and 0.74, respectively. Derived ms is also consistent with the measured precipitation data with R=0.76. The gridded VIC data is used to calibrate the model at each grid point in LCRB and spatial maps of the model parameters are prepared. The model parameters are spatially coherent with the general regional topography in LCRB. TRMMPR σ° derived soil moisture maps during May (dry) and August (wet) 1999 are spatially similar to VIC estimates with correlation 0.67 and 0.76, respectively. This research provides new insights into Ku-band σ° dependence on soil water content in the arid regions.

Snowmelt runoff models

1999 ◽  
Vol 23 (2) ◽  
pp. 205-227 ◽  
Author(s):  
R. I. Ferguson
Keyword(s):  
Snow Cover ◽  
Water Resource Management ◽  
Flood Hazard ◽  
Observation Data ◽  
Internal Validation ◽  
Distributed Models ◽  
Current Trends ◽  
Meltwater Runoff ◽  
Basin Scale ◽  
Earth Observation Data

Models that predict meltwater runoff at a daily timescale are important in water resource management, flood hazard assessment and climate-change impact studies. This article identifies four basic components of such models: meteorological extrapolation, snowmelt estimation at a point, snow-cover depletion and runoff routing. Alternative ways of handling these are discussed, with emphasis on the contrasting treatments in two widely used models: HBV and SRM. Many of the issues in meltwater modelling reflect wider debates in hydrological and environmental modelling, including problems of complexity vs. simplicity, the appropriate level of spatial disaggregation, parameter identification and calibration, and internal validation. In reviewing current trends emphasis is placed on the potential and limitations of fully distributed models, problems in using energy-balance rather than temperature-index melt models at basin scale, ways to deal with spatial variability in snow cover, and the value and limitations of earth observation data.

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