scholarly journals Impact of Catchment Discretization and Imputed Radiation on Model Response: A Case Study from Central Himalayan Catchment

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2339
Author(s):  
Bikas Chandra Bhattarai ◽  
Olga Silantyeva ◽  
Aynom T. Teweldebrhan ◽  
Sigbjørn Helset ◽  
Ola Skavhaug ◽  
...  
Keyword(s):  
Flow Condition ◽  
Hydrological Modeling ◽  
Model Performance ◽  
Low Flow ◽  
Shortwave Radiation ◽  
Snow Cover Area ◽  
Hydrological Simulation ◽  
Model Response ◽  
Simulation Results ◽  
The Impact

Distributed and semi-distributed hydrological modeling approaches commonly involve the discretization of a catchment into several modeling elements. Although some modeling studies were conducted using triangulated irregular networks (TINs) previously, little attention has been given to assess the impact of TINs as compared to the standard catchment discretization techniques. Here, we examine how different catchment discretization approaches and radiation forcings influence hydrological simulation results. Three catchment discretization methods, i.e., elevation zones (Hypsograph) (HYP), regular square grid (SqGrid), and TIN, were evaluated in a highly steep and glacierized Marsyangdi-2 river catchment, central Himalaya, Nepal. To evaluate the impact of radiation on model response, shortwave radiation was converted using two approaches: one with the measured solar radiation assuming a horizontal surface and another with a translation to slopes. The results indicate that the catchment discretization has a great impact on simulation results. Evaluation of the simulated streamflow value using Nash–Sutcliffe efficiency (NSE) and log-transformed Nash–Sutcliffe efficiency (LnNSE) shows that highest model performance was obtained when using TIN followed by HYP (during the high flow condition) and SqGrid (during the low flow condition). Similar order of precedence in relative model performance was obtained both during the calibration and validation periods. Snow simulated from the TIN-based discretized models was validated with Moderate Resolution Imaging Spectroradiometer (MODIS) snow products. Critical Success Indexes (CSI) between TIN-based discretized model snow simulation and MODIS snow were found satisfactory. Bias in catchment average snow cover area from the models with and without using imputed radiation is less than two percent, but implementation of imputed radiation into the Statkraft Hydrological Forecasting Toolbox (Shyft) gives better CSI with MODIS snow.

scholarly journals Responses of hydrological model equifinality, uncertainty, and performance to multi-objective parameter calibration

2018 ◽  
Vol 20 (4) ◽  
pp. 864-885 ◽  
Author(s):  
Younggu Her ◽  
Chounghyun Seong
Keyword(s):  
Model Performance ◽  
Mathematical Representation ◽  
Objective Functions ◽  
Parameter Calibration ◽  
Hydrological Simulation ◽  
Multi Objective ◽  
Adequate Number ◽  
Hydrological System ◽  
And Performance ◽  
The Impact

Abstract Multi-objective calibration can help identify parameter sets that represent a hydrological system and enable further constraining of the parameter space. Multi-objective calibration is expected to be more frequently utilized, along with the advances in optimization algorithms and computing resources. However, the impact of the number of objective functions on modeling outputs is still unclear, and the adequate number of objective functions remains an open question. We investigated the responses of model performance, equifinality, and uncertainty to the number of objective functions incorporated in a hierarchical and sequential manner in parameter calibration. The Hydrological Simulation Program – FORTRAN (HSPF) models that were prepared for bacteria total maximum daily load (TMDL) development served as a mathematical representation to simulate the hydrological processes of three watersheds located in Virginia, and the Expert System for Calibration of HSPF (HSPEXP) statistics were employed as objective functions in parameter calibration experiments. Results showed that the amount of equifinality and output uncertainty overall decreased while the model performance was maintained as the number of objective functions increased sequentially. However, there was no further significant improvement in the equifinality and uncertainty when including more than four objective functions. This study demonstrated that the introduction of an adequate number of objective functions could improve the quality of calibration without requiring additional observations.

scholarly journals Streamflow hydrology in the boreal region under the influences of climate and human interference

2007 ◽  
Vol 363 (1501) ◽  
pp. 2249-2258 ◽  
Author(s):  
Ming-ko Woo ◽  
Robin Thorne ◽  
Kit Szeto ◽  
Daqing Yang
Keyword(s):  
Climate Change ◽  
Low Flow ◽  
Hydrological Simulation ◽  
Boreal Region ◽  
Spring Freshet ◽  
Subarctic Climate ◽  
Future Warming ◽  
Seasonal Streamflow ◽  
The Impact

The boreal region has a subarctic climate that is subject to considerable inter-annual variability and is prone to impacts of future warming. Climate influences the seasonal streamflow regime which typically exhibits winter low flow, terminated by spring freshet, followed by summer flow recession. The effects of climatic variation on streamflow cannot be isolated with confidence but the impact of human regulation of rivers can greatly alter the natural flow rhythm, changing the timing of flow to suit human demands. The effect of scenario climate change on streamflow is explored through hydrological simulation. Example of a Canadian basin under warming scenario suggests that winter flow will increase, spring freshet dates will advance but peak flow will decline, as will summer flow due to enhanced evaporation. While this simulation was site specific, the results are qualitatively applicable to other boreal areas. Future studies should consider the role of human activities as their impacts on streamflow will be more profound than those due to climate change.

scholarly journals Impact of Rain Gauges Distribution on the Runoff Simulation of a Small Mountain Catchment in Southern Ecuador

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1169 ◽  
Author(s):  
Adrián Sucozhañay ◽  
Rolando Célleri
Keyword(s):  
Hydrological Modeling ◽  
Rainfall Variability ◽  
Model Performance ◽  
Rainfall Data ◽  
Model Structure ◽  
Mountain Regions ◽  
Rainfall Estimation ◽  
Runoff Simulation ◽  
Rain Gauges ◽  
The Impact

In places with high spatiotemporal rainfall variability, such as mountain regions, input data could be a large source of uncertainty in hydrological modeling. Here we evaluate the impact of rainfall estimation on runoff modeling in a small páramo catchment located in the Zhurucay Ecohydrological Observatory (7.53 km2) in the Ecuadorian Andes, using a network of 12 rain gauges. First, the HBV-light semidistributed model was analyzed in order to select the best model structure to represent the observed runoff and its subflow components. Then, we developed six rainfall monitoring scenarios to evaluate the impact of spatial rainfall estimation in model performance and parameters. Finally, we explored how a model calibrated with far-from-perfect rainfall estimation would perform using new improved rainfall data. Results show that while all model structures were able to represent the overall runoff, the standard model structure outperformed the others for simulating subflow components. Model performance (NSeff) was improved by increasing the quality of spatial rainfall estimation from 0.31 to 0.80 and from 0.14 to 0.73 for calibration and validation period, respectively. Finally, improved rainfall data enhanced the runoff simulation from a model calibrated with scarce rainfall data (NSeff 0.14) from 0.49 to 0.60. These results confirm that in mountain regions model uncertainty is highly related to spatial rainfall and, therefore, to the number and location of rain gauges.

Better understand mountain hydrology to enhance climate change impact assessment

2020 ◽  
Author(s):  
Youen Grusson ◽  
Manon Dalibard ◽  
Mélanie Raimonet ◽  
Sabine Sauvage ◽  
Gaël Leroux ◽  
...  
Keyword(s):  
Climate Change ◽  
Hydrological Modeling ◽  
Water Flux ◽  
Early Summer ◽  
Low Flow ◽  
Mountainous Area ◽  
Human Society ◽  
Modeling Framework ◽  
Ecological Services ◽  
The Impact

<p>Catchments of European mountains are essential because of their role to provide water to human society. Mountainous area regulate water flux through a complex system of storage and release, playing the role of water tower. Better understand the dynamic functioning of this system at the scale of each compartment and the relationships between the storage and releasing processes are important to understand the impact induced by climate change. In particular, the disappearance of snow during the winter will potentially modify the low flow water level and ecological flow in late spring and early summer, impacting the ecological services provided by e.g. ponds, peat or wetland. The presented study aims to identify the keys factors and their current role in this hydrological system of the Pyrenean Mountains, and identify critical hydrological conditions that will potentially impact the socio-ecological services related to water resources. This goal has been achieved by a development of a high resolution hydrological modeling framework at the scale of the entire Pyrenean massif, together with the study of lower scale representative systems (peatland) and the development of specific future climate scenarios, in order to suggest mitigation actions and adaptability action through water management.</p>

scholarly journals A trading-space-for-time approach to probabilistic continuous streamflow predictions in a changing climate – accounting for changing watershed behavior

2011 ◽  
Vol 15 (11) ◽  
pp. 3591-3603 ◽  
Author(s):  
R. Singh ◽  
T. Wagener ◽  
K. van Werkhoven ◽  
M. E. Mann ◽  
R. Crane
Keyword(s):  
Spatial Relationship ◽  
Model Performance ◽  
Climate Scenario ◽  
Hydrologic Model ◽  
Climatic Conditions ◽  
Low Flow ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Watershed Model ◽  
The Impact

Abstract. Projecting how future climatic change might impact streamflow is an important challenge for hydrologic science. The common approach to solve this problem is by forcing a hydrologic model, calibrated on historical data or using a priori parameter estimates, with future scenarios of precipitation and temperature. However, several recent studies suggest that the climatic regime of the calibration period is reflected in the resulting parameter estimates and model performance can be negatively impacted if the climate for which projections are made is significantly different from that during calibration. So how can we calibrate a hydrologic model for historically unobserved climatic conditions? To address this issue, we propose a new trading-space-for-time framework that utilizes the similarity between the predictions under change (PUC) and predictions in ungauged basins (PUB) problems. In this new framework we first regionalize climate dependent streamflow characteristics using 394 US watersheds. We then assume that this spatial relationship between climate and streamflow characteristics is similar to the one we would observe between climate and streamflow over long time periods at a single location. This assumption is what we refer to as trading-space-for-time. Therefore, we change the limits for extrapolation to future climatic situations from the restricted locally observed historical variability to the variability observed across all watersheds used to derive the regression relationships. A typical watershed model is subsequently calibrated (conditioned) on the predicted signatures for any future climate scenario to account for the impact of climate on model parameters within a Bayesian framework. As a result, we can obtain ensemble predictions of continuous streamflow at both gauged and ungauged locations. The new method is tested in five US watersheds located in historically different climates using synthetic climate scenarios generated by increasing mean temperature by up to 8 °C and changing mean precipitation by −30% to +40% from their historical values. Depending on the aridity of the watershed, streamflow projections using adjusted parameters became significantly different from those using historically calibrated parameters if precipitation change exceeded −10% or +20%. In general, the trading-space-for-time approach resulted in a stronger watershed response to climate change for both high and low flow conditions.

Assessment of potential impact of climate change on streamflow: a case study of the Brahmani River basin, India

2018 ◽  
Vol 10 (3) ◽  
pp. 624-641 ◽  
Author(s):  
Kumari Vandana ◽  
Adlul Islam ◽  
P. Parth Sarthi ◽  
Alok K. Sikka ◽  
Hemlata Kapil
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Low Flow ◽  
Annual Rainfall ◽  
Future Climate Change ◽  
Distributed Parameter ◽  
Climate Change Scenarios ◽  
Model Ensemble ◽  
Simulation Results ◽  
The Impact

Abstract The impact of future climate change on streamflow in the Brahmani River basin, India has been assessed using a distributed parameter hydrological model Precipitation Runoff Modelling System (PRMS) and multi-model ensemble climate change scenarios. The multi-model ensemble climate change scenarios were generated using the Hybrid-Delta ensemble method for A2, A1B, and B1 emission scenarios for three different future periods of the 2020s (2010–2039), 2050s (2040–2069) and 2080s (2070–2099). There is an increase in annual mean temperature in the range of 0.8–1.0, 1.5–2.0 and 2.0–3.3 °C during the 2020s, 2050s, and 2080s, respectively. Annual rainfall is projected to change in the range of −1.6–1.6, 1.6–3.1, and 4.8–8.1% during the 2020s, 2050s and 2080s, respectively. Simulation results indicated changes in annual streamflow in the range of −2.2–2.5, 2.4–4.7, and 7.3–12.6% during the 2020s, 2050s, and 2080s, respectively. Simulation results showed an increase in high flows and reduction in low flows, but the frequency of both high and low flow increases during future periods. The results of this work will be useful in developing a water management adaptation plan in the study basin.

scholarly journals Comparison of hydrological model structures based on recession and low flow simulations

2011 ◽  
Vol 8 (4) ◽  
pp. 6833-6866 ◽  
Author(s):  
M. Staudinger ◽  
K. Stahl ◽  
J. Seibert ◽  
M. P. Clark ◽  
L. M. Tallaksen
Keyword(s):  
Hydrological Model ◽  
Lower Layer ◽  
Model Performance ◽  
Low Flow ◽  
Model Structure ◽  
Hydrological Models ◽  
Flow Simulations ◽  
Low Flows ◽  
The Impact ◽  
Model Structures

Abstract. Low flows are often poorly reproduced by commonly used hydrological models, which are traditionally designed to meet peak flow situations. Hence, there is a need to improve hydrological models for low flow prediction. This study assessed the impact of model structure on low flow simulations and recession behaviour using the Framework for Understanding Structural Errors (FUSE). FUSE identifies the set of subjective decisions made when building a hydrological model, and provides multiple options for each modeling decision. Altogether 79 models were created and applied to simulate stream flows in the snow dominated headwater catchment Narsjø in Norway (119 km2). All models were calibrated using an automatic optimisation method. The results showed that simulations of summer low flows were poorer than simulations of winter low flows, reflecting the importance of different hydrological processes. The model structure influencing winter low flow simulations is the lower layer architecture, whereas various model structures were identified to influence model performance during summer.

scholarly journals Sensitivity analysis of hydrological models: review and way forward

2017 ◽  
Vol 8 (4) ◽  
pp. 557-575 ◽  
Author(s):  
Manjula Devak ◽  
C. T. Dhanya
Keyword(s):  
Sensitivity Analysis ◽  
Hydrological Modeling ◽  
Perturbation Technique ◽  
Model Complexity ◽  
Future Research ◽  
Model Parameters ◽  
Strong Impact ◽  
Hydrological Models ◽  
Model Response ◽  
The Impact

Abstract Different hydrological models provide diverse perspectives of the system being modeled, and inevitably, are imperfect representations of reality. Irrespective of the choice of models, the major source of error in any hydrological modeling is the uncertainty in the determination of model parameters, owing to the mismatch between model complexity and available data. Sensitivity analysis (SA) methods help to identify the parameters that have a strong impact on the model outputs and hence influence the model response. In addition, SA assists in analyzing the interaction between parameters, its preferable range and its spatial variability, which in turn influence the model outcomes. Various methods are available to perform SA and the perturbation technique varies widely. This study attempts to categorize the SA methods depending on the assumptions and methodologies involved in various methods. The pros and cons associated with each SA method are discussed. The sensitivity pertaining to the impact of space and time resolutions on model results is highlighted. The applicability of different SA approaches for various purposes is understood. This study further elaborates the objectives behind selection and application of SA approaches in hydrological modeling, hence providing valuable insights on the limitations, knowledge gaps, and future research directions.

scholarly journals Comparison of hydrological model structures based on recession and low flow simulations

2011 ◽  
Vol 15 (11) ◽  
pp. 3447-3459 ◽  
Author(s):  
M. Staudinger ◽  
K. Stahl ◽  
J. Seibert ◽  
M. P. Clark ◽  
L. M. Tallaksen
Keyword(s):  
Hydrological Model ◽  
Lower Layer ◽  
Model Performance ◽  
Low Flow ◽  
Model Structure ◽  
Hydrological Models ◽  
Flow Simulations ◽  
Low Flows ◽  
The Impact ◽  
Model Structures

Abstract. Low flows are often poorly reproduced by commonly used hydrological models, which are traditionally designed to meet peak flow situations. Hence, there is a need to improve hydrological models for low flow prediction. This study assessed the impact of model structure on low flow simulations and recession behaviour using the Framework for Understanding Structural Errors (FUSE). FUSE identifies the set of subjective decisions made when building a hydrological model and provides multiple options for each modeling decision. Altogether 79 models were created and applied to simulate stream flows in the snow dominated headwater catchment Narsjø in Norway (119 km2). All models were calibrated using an automatic optimisation method. The results showed that simulations of summer low flows were poorer than simulations of winter low flows, reflecting the importance of different hydrological processes. The model structure influencing winter low flow simulations is the lower layer architecture, whereas various model structures were identified to influence model performance during summer.

scholarly journals Effect of Watershed Subdivision and Antecedent Moisture Condition on HEC-HMS Model Performance in the Maha Oya Basin, Sri Lanka

2018 ◽  
Vol 5 (2) ◽  
pp. 22-37
Author(s):  
M. Kamran ◽  
RL.H.L. Rajapakse
Keyword(s):  
Sri Lanka ◽  
Hydrological Modeling ◽  
Soil Layer ◽  
Model Performance ◽  
Base Flow ◽  
Low Flow ◽  
Moisture Condition ◽  
Flow Estimation ◽  
Antecedent Moisture ◽  
Antecedent Moisture Condition

In large scale watersheds, the accuracy level of medium and low flow simulation could decrease due to uncertainty of the watershed parameters. In hydrological modeling, sub division of watershed would help to better implement decision-making related to water resources management, which relies heavily on hydrologic simulations. However, an important concern will be raised over problems associated with lumped hydrologic models with watershed subdivision broadly applied in so called semi-distributed hydrological models since scale issues would significantly affect model performance, and thus, lead to dramatic variations in simulation results. It is important to achieve the appropriate level of sub divisions (discretization). Further at times, the resulting flood level can be much higher than expected, due to storm events. This is unprecedented and the reason may be due to saturated moisture level in the soil layer. Therefore, the Antecedent Moisture Condition (AMC) is an important parameter to be investigated to check the accuracy and possibility of further improvement of the model. In this paper, Hydrologic Modeling System (HEC-HMS) was used for continuous simulation to investigate the effect of watershed subdivision on the model performance. Further, the antecedent moisture condition (AMC) events were used to study the impacts of AMC on the model performance. Badalgama watershed is selected as study area in Maha Oya Basin in Sri Lanka. Spatial extents of Maha Oya Basin and Badalgama watershed are 1553 km² and 1272 km², respectively. Four rainfall stations and one river gauging station were selected in Badalgama watershed. Nash–Sutcliffe (NASH) coefficient and Mean Ratio of Absolute Error (MRAE) were selected as objective functions for modeling. The main focus was on MRAE, as the objective function, but Nash coefficient was also estimated and checked for comparison. In particular, results show that generally the accuracy of the model decreased from six to sixteen sub divisions, which shows that variation in the total number of sub watersheds had very little effect on runoff hydrographs and improvements generally disappear when the number of subdivisions reaches a relatively small number, approximately between six and sixteen sub-watersheds. The accuracy of the model with AMC-III increased by 12.04% when compared to AMC-II hence showing more reliable results as compared with AMC-II condition. In this research, recession method was used for base flow estimation, which led to mass balance error exceeding 20%. Therefore it is recommended that for improving the accuracy, linear reservoir method for base flow estimation should be used in order to conserve the water balance and AMC-III should be used for fully saturated soil instead of AMC-II.

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