Height–Area–Storage Functional Models for Evaporation-Loss Inclusion in Reservoir-Planning Analysis

Adebayo J. Adeloye, Ibrahim Y. Wuni , Quan V. Dau, B.-S. Soundharajan, K.-S. Kasiviswanathan

Research output: Contribution to journalArticle

Abstract

Reservoir planning without the explicit accommodation of evaporation loss leads to errors in capacity estimates. However, whenever evaporation loss is considered, its quantification uses linear approximations of the intrinsically nonlinear height–area–storage (H–A–S) relationship to estimate the reservoir area, leading to bias in capacity estimates. In this work, biases resulting from using various H–A–S models are evaluated. These models include linear and nonlinear functions, either specifically developed for the case-study sites or available in the Global Reservoir and Dam (GRanD) database. All empirically derived approximations used data for two dams in India: the Bhakra on Sutlej River and the Pong on the Beas River, both tributaries of the Indus River. The results showed that linear H–A–S models underestimate the exposed surface area of the Pong reservoir by up to 11.19%; the bias at Bhakra was much less. The GRanD H–A–S model performed very poorly at both reservoirs, producing overprediction in exposed reservoir area of up to 100% and 415% at the Pong and Bhakra reservoirs, respectively. Analyses also showed that up to 29% increase in reservoir capacity is required to compensate for the effect of net evaporation loss at low demand levels. As demand increases, the required evaporation-correction capacity decreases in proportional terms and is indistinguishable for all H–A–S models. Finally, recommendations are made on using the results for evaporation adjustment at nongauged sites in the region.
Original languageEnglish
Article number1413
JournalWater
Volume11
Issue number7
Early online date10 Jul 2019
DOIs
Publication statusPublished - Jul 2019

Fingerprint

Rivers
evaporation
Evaporation
planning
dams (hydrology)
inclusion
Planning
Linear Models
river
Dams
linear model
trend
Indus River
linear models
India
demand
dam
quantification
rivers
Databases

Keywords

  • Evaporation loss
  • Height-area-storage functions
  • Reservoir
  • SPA
  • Storage capacity
  • WEAP

ASJC Scopus subject areas

  • Biochemistry
  • Geography, Planning and Development
  • Aquatic Science
  • Water Science and Technology

Cite this

Adeloye, Adebayo J. ; Wuni , Ibrahim Y. ; Dau, Quan V. ; Soundharajan, B.-S. ; Kasiviswanathan, K.-S. / Height–Area–Storage Functional Models for Evaporation-Loss Inclusion in Reservoir-Planning Analysis. In: Water. 2019 ; Vol. 11, No. 7.
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abstract = "Reservoir planning without the explicit accommodation of evaporation loss leads to errors in capacity estimates. However, whenever evaporation loss is considered, its quantification uses linear approximations of the intrinsically nonlinear height–area–storage (H–A–S) relationship to estimate the reservoir area, leading to bias in capacity estimates. In this work, biases resulting from using various H–A–S models are evaluated. These models include linear and nonlinear functions, either specifically developed for the case-study sites or available in the Global Reservoir and Dam (GRanD) database. All empirically derived approximations used data for two dams in India: the Bhakra on Sutlej River and the Pong on the Beas River, both tributaries of the Indus River. The results showed that linear H–A–S models underestimate the exposed surface area of the Pong reservoir by up to 11.19{\%}; the bias at Bhakra was much less. The GRanD H–A–S model performed very poorly at both reservoirs, producing overprediction in exposed reservoir area of up to 100{\%} and 415{\%} at the Pong and Bhakra reservoirs, respectively. Analyses also showed that up to 29{\%} increase in reservoir capacity is required to compensate for the effect of net evaporation loss at low demand levels. As demand increases, the required evaporation-correction capacity decreases in proportional terms and is indistinguishable for all H–A–S models. Finally, recommendations are made on using the results for evaporation adjustment at nongauged sites in the region.",
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Height–Area–Storage Functional Models for Evaporation-Loss Inclusion in Reservoir-Planning Analysis. / Adeloye, Adebayo J.; Wuni , Ibrahim Y.; Dau, Quan V.; Soundharajan, B.-S.; Kasiviswanathan, K.-S.

In: Water, Vol. 11, No. 7, 1413, 07.2019.

Research output: Contribution to journalArticle

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AU - Adeloye, Adebayo J.

AU - Wuni , Ibrahim Y.

AU - Dau, Quan V.

AU - Soundharajan, B.-S.

AU - Kasiviswanathan, K.-S.

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AB - Reservoir planning without the explicit accommodation of evaporation loss leads to errors in capacity estimates. However, whenever evaporation loss is considered, its quantification uses linear approximations of the intrinsically nonlinear height–area–storage (H–A–S) relationship to estimate the reservoir area, leading to bias in capacity estimates. In this work, biases resulting from using various H–A–S models are evaluated. These models include linear and nonlinear functions, either specifically developed for the case-study sites or available in the Global Reservoir and Dam (GRanD) database. All empirically derived approximations used data for two dams in India: the Bhakra on Sutlej River and the Pong on the Beas River, both tributaries of the Indus River. The results showed that linear H–A–S models underestimate the exposed surface area of the Pong reservoir by up to 11.19%; the bias at Bhakra was much less. The GRanD H–A–S model performed very poorly at both reservoirs, producing overprediction in exposed reservoir area of up to 100% and 415% at the Pong and Bhakra reservoirs, respectively. Analyses also showed that up to 29% increase in reservoir capacity is required to compensate for the effect of net evaporation loss at low demand levels. As demand increases, the required evaporation-correction capacity decreases in proportional terms and is indistinguishable for all H–A–S models. Finally, recommendations are made on using the results for evaporation adjustment at nongauged sites in the region.

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