Evaluation of evaporation estimation methods for a covered reservoir in a semi-arid climate (south-eastern Spain)

Gallego-Elvira, Belén; Baille, Alain; Martín-Górriz, B.; Valero, José Francisco Maestre; Álvarez, Victoriano Martínez

doi:10.1016/j.jhydrol.2012.06.035

Cited by 27 publications

(10 citation statements)

References 26 publications

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“…Ideally, the Q t should be computed based on the water temperature profile data as detailed in e.g. Gianniou and Antonopoulos (2007) and Gallego-Elvira et al (2012). This requirement was met for all lakes except Lake Tahoe and Ross Barnett Reservoir.…”

Section: Testing Sites and Datasetsmentioning

confidence: 99%

Evaluation of three energy balance-based evaporation models for estimating monthly evaporation for five lakes using derived heat storage changes from a hysteresis model

Duan

Bastiaanssen

2017

Environ. Res. Lett.

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Section: Testing Sites and Datasetsmentioning

confidence: 99%

Evaluation of three energy balance-based evaporation models for estimating monthly evaporation for five lakes using derived heat storage changes from a hysteresis model

Duan

Bastiaanssen

2017

Environ. Res. Lett.

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“…6 uses net irradiance (R n, in W m -2 ), ground heat flux (G, in W m -2 ), dry air density (ρ a , in kg m -3 ), the specific heat capacity of air (c p , in J kg -1 K -1 ), the vapor pressure deficit of air (e s -e a, in Pa), the rate of change of saturation specific humidity with air temperature (Δ, in Pa K -1 ), the psychrometric constant (γ, in Pa K -1 ), and surface and aerodynamic resistances (r s and r a , in s m -1 ) to calculate the energy rate of water evaporation (E, in g s -1 m -2 ) multiplied with the latent heat of vaporization (λ, in J g -1 ). Due to complications and the need for several parameters, this equation has been simplified in many different ways (Linacre 1977;Salama et al 2015;Gallego-Elvira et al 2012). Linacre (1977) This includes R as a daily range of temperature and R ann as the difference between the mean temperatures of the coldest and hottest months (values in °C).…”

Section: Drying Modelsmentioning

confidence: 99%

Feedstock availability and moisture content data processing for multi-year simulation of forest biomass supply in energy production

Aalto¹,

Korpinen²,

Ranta³

2019

Silva Fenn.

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Simulation and modeling have become more common in forest biomass studies. Dynamic simulation has been used to study the supply chain of forest biomass with numerous different models. A robust predictive multi-year model requires biomass availability data, where annual variation is included spatially and temporally. This can be done by using data from enterprises, but in some cases relevant data is not accessible. Another option is to use forest inventory data to estimate biomass availability, but this data must be processed in the correct form to be utilized in the model. This study developed a method for preparing forest inventory data for a multi-year simulation supply model using the theoretical availability of feedstock. Methods for estimating quality changes during roadside storage are also presented, including a possible parameter estimation to decrease the amount of data needed. The methods were tested case by case using the inventory database âBiomass Atlasâ and weather data from a weather station in Mikkeli, Finland. The data processing method for biomass allocation produced a reasonable quantity of stands and feedstock, having a realistic annual supply with variation for the demand point. The results of the study indicate that it is possible to estimate moisture content changes using weather data. The estimations decreased the accuracy of the model and, therefore, estimations should be kept minimal. The presented data preparation method can generate a supply of forest biomass for the simulation model, but the validity of the data must be ensured for correct model behavior.

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“…3.3 with annual evaporative losses for uncovered surface E = 1.6 m yr −1 (see Table 3) and estimated cover efficiency ε = 0.8. Considering water price w = 1 USD m −3 (e.g., seawater reverse osmosis costs are in the range of 0.5 to 3 USD m −3 Gilau and Small, 2008;Guler et al, 2015) floating cover cost P c = 5 USD m −2 (based on commercially available HDPE floating balls) and cover maintenance cost P m = 0.1 USD m −2 yr −1 , the economic efficiency of such floating elements for a period of 5 years is ∼ 1 USD m −2 . Hence, for a reservoir with 100 × 100 m 2 surface area, water costs savings equivalent to USD 10 000 are feasible for a 5-years operation (along with 64 000 m 3 of water protected from evaporation).…”

Section: Costs and Water Savingmentioning

confidence: 99%

Evaporation suppression and energy balance of water reservoirs covered with self-assembling floating elements

Aminzadeh

Lehmann

2018

Hydrol. Earth Syst. Sci.

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Abstract. The growing pressure on natural freshwater resources and the projected climate variability are expected to increase the need for water storage during rainy periods. Evaporative losses present a challenge for the efficiency of water storage in reservoirs, especially in arid regions with chronic water shortages. Among the available methods for suppressing evaporative losses, self-assembling floating elements offer a simple and scalable solution, especially for small reservoirs. The use of floating elements has often been empirically based; we thus seek a framework for systematic consideration of floating element properties, local climate and reservoir conditions to better predict evaporative loss, energy balance and heat fluxes from covered water reservoirs. We linked the energy balance of the water column with energy considerations of the floating elements. Results suggest significant suppression of evaporative losses from covered reservoirs in which incoming radiative energy is partitioned to sensible and long wave fluxes that reduce latent heat flux and thus increase the Bowen ratio over covered water reservoirs. Model findings were consistent with laboratory-scale observations using an uncovered and covered small basin. The study offers a physically based framework for testing design scenarios in terms of evaporation suppression efficiency for various climatic conditions; it hence strengthens the science in the basis of this important water resource conservation strategy.

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Evaluation of evaporation estimation methods for a covered reservoir in a semi-arid climate (south-eastern Spain)

Cited by 27 publications

References 26 publications

Evaluation of three energy balance-based evaporation models for estimating monthly evaporation for five lakes using derived heat storage changes from a hysteresis model

Evaluation of three energy balance-based evaporation models for estimating monthly evaporation for five lakes using derived heat storage changes from a hysteresis model

Feedstock availability and moisture content data processing for multi-year simulation of forest biomass supply in energy production

Evaporation suppression and energy balance of water reservoirs covered with self-assembling floating elements

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