Attempting Flow Forecasts of the Indus River, Pakistan Using Remotely Sensed Snow Cover Data

Makhdoom, M.T.A.; Solomon, S. I.

doi:10.2166/nh.1986.0011

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…Snow covered area has also been used directly in simple regression equations to derive seasonal runoff in areas such as Pakistan (Rango et al, 1977), India (Ramamoorthi, 1987) and Norway (0strem et al, 1981). Similar work was done by Makhdoom & Solomon (1986) for the Indus River in Pakistan. More recently, Donald et al (1992) used airborne SAR data to detect snow cover extent in a discontinuous ripe snow pack for an area in southern Ontario, Canada.…”

Section: Snow Coyer Mappingsupporting

confidence: 52%

Remote sensing applications in hydrological modelling

Kite¹,

Pietroniro²

1996

Hydrological Sciences Journal

116

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Previous studies have suggested that remotely sensed data should provide major benefits to hydrology and water resources and yet there are few case studies that show practical benefits. One of the reasons for this is the lack of tools to convert remotely sensed data to the type of information useful to water resource systems operators. Hydrological models can play an important role in this translation of data to information. This paper reviews some of the techniques presently used in hydrological models to make use of remotely sensed data and provides a comprehensive reference list. Applications de la télédétection à la modélisation hydrologique Résumé Si de précédentes études on suggéré que les données télédétec-tées pouvaient rendre de grands services à l'hydrologie et à l'étude des ressources en eau, il n'existe que peu d'études de cas mettant en évidence leur intérêt pratique. L'une des causes de cet état de choses est le manque d'outils permettant de traduire les données télédétectées en informations directement utilisables par les gestionnaires des ressources en eau. Les modèles hydrologiques peuvent jouer un grand rôle dans ce processus de transformation de données en informations. Le présent article passe en revue quelques unes des techniques actuellement utilisées en modélisation hydrologique permettant d'utiliser des données télé-détectées et fournit une bibliographie détaillée.

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Section: Snow Coyer Mappingsupporting

confidence: 52%

Remote sensing applications in hydrological modelling

Kite¹,

Pietroniro²

1996

Hydrological Sciences Journal

116

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“…While these studies have reported better results with time, these conceptual rainfall-runoff models are not useful for longer-term (seasonal) forecasting since they are based on near-real-time daily weather data. In addition, the relationship between snow-covered areas versus streamflow can be complicated by variations of snow depth (Makhdoom and Solomon, 1986). However, snow water equivalent or snow measurement information has not been considered for Satluj Basin flow prediction because such data has not been previously available.…”

Section: Pal Et Al: Predictability Of Western Himalayan River Flowmentioning

confidence: 99%

Predictability of Western Himalayan river flow: melt seasonal inflow into Bhakra Reservoir in northern India

Pal

Lall

Robertson

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. Snowmelt-dominated streamflow of the Western Himalayan rivers is an important water resource during the dry pre-monsoon spring months to meet the irrigation and hydropower needs in northern India. Here we study the seasonal prediction of melt-dominated total inflow into the Bhakra Dam in northern India based on statistical relationships with meteorological variables during the preceding winter. Total inflow into the Bhakra Dam includes the Satluj River flow together with a flow diversion from its tributary, the Beas River. Both are tributaries of the Indus River that originate from the Western Himalayas, which is an understudied region. Average measured winter snow volume at the upper-elevation stations and corresponding lower-elevation rainfall and temperature of the Satluj River basin were considered as empirical predictors. Akaike information criteria (AIC) and Bayesian information criteria (BIC) were used to select the best subset of inputs from all the possible combinations of predictors for a multiple linear regression framework. To test for potential issues arising due to multicollinearity of the predictor variables, cross-validated prediction skills of the best subset were also compared with the prediction skills of principal component regression (PCR) and partial least squares regression (PLSR) techniques, which yielded broadly similar results. As a whole, the forecasts of the melt season at the end of winter and as the melt season commences were shown to have potential skill for guiding the development of stochastic optimization models to manage the trade-off between irrigation and hydropower releases versus flood control during the annual fill cycle of the Bhakra Reservoir, a major energy and irrigation source in the region.

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“…The snowpack parameter most frequently estimated using remote sensing is the amount of snow-covered area as a proportion of a basin under snow cover (Makhdoom and Solomon, 1986). The nature of the snow cover must be characterized adequately if distributed models are to improve hydrological modelling through improved representation of the state variables (Beven, 1989).…”

Section: Satellite Data Processingmentioning

confidence: 99%

Modelling runoff using optical satellite remote sensing data in a high mountainous alpine catchment of Italy

Swamy

Brivio

1997

Hydrol. Process.

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Development of hydrological models for seasonal and real-time runo forecast in rivers of high alpine catchments is useful for management of water resources. The conceptual models for this purpose are based on a temperature index and/ or energy budget and can be either lumped or distributed over the catchment area. Remote sensing satellite data are most useful to acquire near real-time geophysical parameters in order to input to the distributed forecasting models. In the present study, integration of optical satellite remote sensing-derived information was made with ground meteorological and hydrological data, and predetermined catchment morphological parameters, to study the feasibility of application of a distributed temperature index snowmelt runo model to one of the high mountainous catchments in the Italian Alps, known as Cordevole River Basin. Five sets of Landsat Multispectral Scanning System (MSS) and Thematic Mapper (TM) computer-compatible tapes (CCTs) were processed using digital image processing techniques in order to evaluate the snow cover variation quantitatively. Digital elevation model, slope and aspect parameters were developed and used during satellite data processing. The satellite scenes were classi®ed as snow, snow under transition and snow free areas. A second-order polynomial ®t has been attempted to approximate the snow depletion and to estimate daily snow cover areal extent for three elevation zones of the catchment separately. Model performance evaluation based on correlation coecient, Nash±Sutclie coecient and percentage volume deviation indicated very good simulation between measured and computed discharges for the entire snowmelt period. The use of average temperature values computed from the maximum and minimum temperatures into the model was studied and a suitable algorithm was proposed.

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Attempting Flow Forecasts of the Indus River, Pakistan Using Remotely Sensed Snow Cover Data

Cited by 10 publications

References 3 publications

Remote sensing applications in hydrological modelling

Remote sensing applications in hydrological modelling

Predictability of Western Himalayan river flow: melt seasonal inflow into Bhakra Reservoir in northern India

Modelling runoff using optical satellite remote sensing data in a high mountainous alpine catchment of Italy

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