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Provided for Non-Commercial Research and Educational Use Only. Not for Reproduction Or Distribution Or Commercial Use Provided for non-commercial research and educational use only. Not for reproduction or distribution or commercial use. This article was originally published by IWA Publishing. IWA Publishing recognizes the retention of the right by the author(s) to photocopy or make single electronic copies of the paper for their own personal use, including for their own classroom use, or the personal use of colleagues, provided the copies are not offered for sale and are not distributed in a systematic way outside of their employing institution. Please note that you are not permitted to post the IWA Publishing PDF version of your paper on your own website or your institution’s website or repository. Please direct any queries regarding use or permissions to [email protected] 36 © IWA Publishing 2014 Journal of Water and Climate Change | 05.1 | 2014 Development of HydroClimatic Conceptual Streamflow (HCCS) model for tropical river basin Parag P. Bhagwat and Rajib Maity ABSTRACT Combined processes of land-surface hydrology and hydroclimatology influence the response of a Parag P. Bhagwat Rajib Maity (corresponding author) watershed to different hydroclimatic variables. In this paper, streamflow response of a watershed to Department of Civil Engineering, Indian Institute of Technology Kharagpur, hydrometeorological variables is investigated over a part of two tropical Indian rivers – Narmada and Kharagpur 721302, fl West Bengal, Mahanadi. The proposed HydroClimatic Conceptual Stream ow (HCCS) model is able to consider the India time-varying basin characteristics and major hydrologic processes to model basin-scale streamflow E-mail: [email protected]; [email protected] using climate inputs at a daily scale. In addition, the proposed model is able to provide additional overall estimates of ground water recharge component and evapotranspiration component from the entire basin. Moreover, ability to consider the time-varying watershed characteristics and hydroclimatic inputs renders the proposed model usable for assessment of future streamflow variation. This application is also investigated for both the study basins. In general, the methodological approach of the proposed model can be applied to other tropical basins for daily streamflow modelling as well as future streamflow assessment. Key words | climate, conceptual model, hydroclimatology, prediction, streamflow INTRODUCTION In the context of a changing climate, identification of stream- better or at least comparable performance to existing flow response to other hydroclimatological variables is a approaches. research challenge (Kumar & Maity ; Maity & Kashid ). Most of the existing approaches attempt to consider Background and literature review and conceptualize different hydrological processes. How- ever, time varying watershed characteristics are not A brief review (with respect to the huge amount of literature considered. As a consequence, consistency (decadal to cli- available) of existing approaches reveals that the hydrologi- matic scale) of the model performance is affected under a cal models for streamflow estimation can be grouped into changing climate and changing watershed characteristics three broad categories viz: (a) physically based models, (b) that are not accounted for. Here lies the importance of this conceptual models, and (c) artificial intelligence (AI)-based study since recent observation of climate change has added models. In physically based models, existing knowledge of a new dimension towards this overall research direction in all possible hydrological processes is represented through hydrologic modelling. The question examined was, is it poss- a set of mathematical equations. Examples of some popular ible to model watershed response as streamflow with the physically based models include System Hydrologique Euro- simultaneous consideration of changing climate and time pean (Abbott et al. a,b), Better Assessment Science varying watershed characteristics? As such, it was found Integrating point and Non-point Sources (EPA ), Soil that there is a need to develop a streamflow model having and Water Assessment Tool (SWAT) (Eckhardt & Arnold few parameters, which will be able to consider time varying ; Grizzetti et al. ), and Community Land Model watershed characteristics and climatic inputs, and provide (Lawrence et al. ). However, an inability for accurate doi: 10.2166/wcc.2013.015 37 P. P. Bhagwat & R. Maity | HydroClimatic Conceptual Streamflow model Journal of Water and Climate Change | 05.1 | 2014 mathematical representation of hydrological processes con- properties of the watershed. As a consequence, the outcome sidering the huge spatial variability, computational demands of such models sometimes becomes crude. Sometimes, even and overparameterization effects, and non-availability of the major components, such as evapotranspiration, ground required comprehensive data sets make the results of phys- water recharge component and streamflow, are not separ- ically based models poor in many cases (Piotrowski & ated from each other. Moreover, typical absence of short- Napiorkowski ; Zeng et al. ). term (i.e., fortnight to seasonal) and/or long-term (i.e., deca- Conceptual models are also derived using (relatively dal to centennial) dynamic characteristics raise questions simpler) mathematical equations, which mainly consider against its applicability to capture the dynamic, time-varying the major hydrological process of the complex hydrological response of watershed to long-term climate change impact cycle, which is difficult to understand fully. However, in the analysis studies, which are particularly essential in a chan- absence of complete knowledge, they may be represented in ging climate. a simplified way by means of the system concept. A system is AI-based models are much simpler with respect to the a set of connected parts that form the whole. By using the understanding of underlying physical processes. These system concept, effort is directed to the construction of a models are developed based on interrelationships between model relating inputs and outputs rather than to the extre- input and output values of the concerned variables. In the mely difficult task of exact representation of the system last two decades, use of AI-based models has increased due details, which may not be significant from a practical point to their lower dependence on physical understanding of the of view or may not be known. Nevertheless, knowledge of underlying process(es) by the users, and provide quick as the physical system helps in developing a conceptually well as reasonably impressive results compared with the clear model. The major hydrological processes considered other two types of models discussed before. Some of the are mostly rainfall, evapotranspiration, infiltration, surface most popular AI-based models, which are applied to hydrolo- runoff, etc., which have significant importance towards the gical modelling, are based on artificial neural network (Kumar watershed output. As mentioned before, the physically et al. ), genetic programming (Maity & Kashid ), and based models require a large number of data and still pro- support vector machines (SVMs). However, apart from the cri- duce poor results due to a variety of reasons. Hence, ticism of the ‘black-box’ nature, the performance of such conceptual models are used as alternative models for water- models is often found to be excellent and difficult to replicate sheds. Some of the generally used conceptual models are the using other conceptual/physically based approaches. This Stanford watershed model, which is an early finding in con- said, the same criticism which was mentioned in the case of ceptual hydrological modelling by Crawford & Linsley conceptual models earlier, exists for such modelling (), the Institute Royal Meteorology Belgium (IRMB) approaches as well. That is, due to their static nature, studies model (Bultot & Dupriez ), HYDROLOG model related to long-term climate change impact analysis may not which was later modified by Chiew & McMahon () to be always possible with this modelling approach. the MODifed HYDROLOG (MODHYDROLOG) model, Hydrologiska Byråns Vattenbalansavdelning (HBV) model Motivation and objectives which was developed at the Swedish Meteorological and Hydrological Institute (SMHI) in 1972 (Bergström ), Specifically, this study attempts to develop a basin-scale and the Hydrologic Simulation Program-FORTRAN model daily hydroclimatic streamflow model (named as HydroCli- which is a modified version of Stanford model (Johnson matic Conceptual Streamflow (HCCS) model), considering et al. ), and so on. These models are designed to the time-varying watershed characteristics and major hydro- approximate the general hydrological processes, which dic- logic processes to model basin-scale streamflow using tate the hydrological cycle. However, most of the climate inputs. The developed modelling approach is con- conceptual models consider all hydrological processes to ceptual in nature. Its performance is investigated for two be lumped together, greatly simplify the hydrological pro- Indian tropical river basins. Performances of AI-based cesses involved, and do not consider time-varying machine learning approaches are found to overshadow the 38 P. P. Bhagwat & R. Maity | HydroClimatic Conceptual Streamflow model Journal of Water and Climate Change | 05.1 | 2014 performance of other modelling approaches. Thus, the per- dependent. The proposed methodology is motivated by formance of the proposed model is compared with the SACramento
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