Unraveling the contribution of potential evaporation formulation to uncertainty under climate change

Lemaitre-Basset, Thibault; Oudin, Ludovic; Thirel, Guillaume; Collet, Lila

doi:10.5194/hess-26-2147-2022

Cited by 19 publications

(21 citation statements)

References 51 publications

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“…The most parsimonious use just one (eg Thornthwaite, 1948;Oudin et al, 2005) or two (eg Blaney and Criddle, 1950;Priestley and Taylor, 1972) meteorological variables, but rely on empirical factors which are calibrated to historical or present-day climate. Different formulations of PE can show different trends under changing climate, adding to the uncertainty of climate change impacts on hydrology (Lemaitre-Basset et al, 2022). The more complex Penman-Monteith formulation (Monteith, 1965), while requiring a larger number of observed inputs, is derived from fundamental physics, so is able to represent temporal changes in drivers.…”

Section: Introductionmentioning

confidence: 99%

Hydro-PE: gridded datasets of historical and future Penman-Monteith potential evaporation for the United Kingdom

Robinson

Brown

Kay

et al. 2022

Preprint

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Abstract. We present two new potential evaporation datasets for the United Kingdom: a historical dataset, Hydro-PE HadUKGrid, which is derived from the HadUK-Grid gridded observed meteorology (1969–2021); and a future dataset, Hydro-PE UKCP18 RCM, which is derived from UKCP18 regional climate projections (1980–2080). Both datasets are suitable for hydrological modelling, and provide Penman-Monteith potential evapotranspiration parameterised for short grass, with and without a correction for interception on days with rainfall. The potential evapotranspiration calculations have been formulated to closely follow the methodology of the existing Meteorological Office Rainfall and Evaporation Calculation System (MORECS) potential evapotranspiration, which has historically been widely used by hydrological modellers in the United Kingdom. The two datasets have been created using the same methodology, to allow seamless modelling from past to future. Hydro-PE HadUK-Grid shows good agreement with MORECS in much of the United Kingdom, although Hydro-PE HadUK Grid is higher in the mountainous regions of Scotland and Wales. This is due to differences in the underlying meteorology, in particular the wind speed, which are themselves due to the different spatial scales of the data. Hydro-PE HadUK-Grid can be downloaded from https://doi.org/10.5285/9275ab7e-6e93-42bc-8e72-59c98d409deb (Brown et al., 2022) and Hydro-PE UKCP18 RCM can be downloaded from https://doi.org/10.5285/eb5d9dc4-13bb-44c7-9bf8-c5980fcf52a4 (Robinson et al., 2021).

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Section: Introductionmentioning

confidence: 99%

Hydro-PE: gridded datasets of historical and future Penman-Monteith potential evaporation for the United Kingdom

Robinson

Brown

Kay

et al. 2022

Preprint

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“…In this study, climate projection data were provided by six GCMs coupled with nine regional circulation models (RCMs) in order to spatially disaggregate data from GCMs. Due to the computation time limitations in generating the climate projections (CPs), not all GCM-RCM couples are available for every RCP (Table 1, Lemaitre-Basset et al, 2022). In our study, we considered 12 CPs (Soubeyroux et al, 2021) from the Euro-Cordex project (Jacob et al, 2014) selected by Météo-France to ensure the representativeness of future conditions in France.…”

Section: Input Datamentioning

confidence: 99%

Effects of Climate Change on Hydrological Indicators of Subsurface Drainage for a Representative French Drainage Site

Jeantet

Thirel

Jeliazkov

et al. 2022

Front. Environ. Sci.

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The aim of this study is to evaluate from a hydrological perspective and in the context of climate change the future of subsurface drainage of the La Jaillière site (western France), which is representative of the pedology of the majority of French subsurface drainage. We used a uniquely large and comprehensive range of 17 hydrological indicators (HIs), describing the temporal dynamics of drainage season, soil saturation, drained water balance and flood events. The HI values are calculated from simulated discharges provided by a subsurface drainage model, the SIDRA-RU model, fed by 12 climate projections from 1975 to 2100 (CMIP5 Euro-Cordex project), with three climate change scenarios: Representative Concentration Pathways (RCP) 2.6, RCP4.5 and RCP8.5. We first verified that the HIs simulated using climate projections in the SIDRA-RU model over the historical period were not critically biased compared to the HIs obtained from the reference climatic reanalysis (SAFRAN). Second, we analysed and compared the HI evolution over different periods and under different scenarios. Our results showed that the number of significant changes in HI values increased under climate change by 2100, depending on the RCP: 2 HIs out of the 17 changed under RCP2.6; 6 HIs under RCP4.5; 10 HIs under RCP8.5. The intensity of drainage peak flows linked to flood events and the annual maximal discharge changed significantly under all RCPs. The temporality of the drainage season was substantially affected according to how pessimistic the RCP was. The worst changes were observed under RCP8.5, which exacerbated extreme events: The wet period was shorter while the dry period was longer by about 67%; the drought index increased by 100%; the summer drained water balance decreased by 9%. On the contrary, in winter, the duration of the wet period decreased while maintaining the same drained water balance, thus inducing stronger flood events leading to an earlier saturation of the drainage networks. The sustainability of the drainage system design at La Jaillière is therefore threatened, with the risk of fulfilling its function less effectively by 2100, exposing current crops to more important runoff and affecting water quality by increasing the leaching of agrochemical inputs.

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“…Many methods of ET o rate determination are recommended. Most important are Blaney Criddle, Penman Montieth method, and Pan Evaporation method [7].…”

Section: Introductionmentioning

confidence: 99%

Leaching Fraction (LF) of Irrigation Water for Saline Soils Using Machine Learning

Bashir¹,

Bajwa²,

Iqbal³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Soil salinity is a serious land degradation issue in agriculture. It is a major threat to agriculture productivity. Extra irrigation water is applied to leach down the salts from the root zone of the plants in the form of a Leaching fraction (LF) of irrigation water. For the leaching process to be effective, the LF of irrigation water needs to be adjusted according to the environmental conditions and soil salinity level in the form of Evapotranspiration (ET) rate. The relationship between environmental conditions and ET rate is hard to be defined by a linear relationship and data-driven Machine learning (ML) based decisions are required to determine the calibrated Evapotranspiration (ET c ) rate. ML-assisted ET c is proposed to adjust the LF according to the ET c and soil salinity level. A regression model is proposed to determine the ET c rate according to the prevailing temperature, humidity, and sunshine, which would be used to determine the smart LF according to the ET c and soil salinity level. The proposed model is trained and tested against the Blaney Criddle method of Reference evapotranspiration (ET o ) determination. The validation of the model from the test dataset reveals the accuracy of the ML model in terms of Root mean squared errors (RMSE) are 0.41, Mean absolute errors (MAE) are 0.34, and Mean squared errors (MSE) are 0.28 mm day −1 . The applications of the proposed solution in a real-time environment show that the LF by the proposed solution is more effective in reducing the soil salinity as compared to the traditional process of leaching.

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Unraveling the contribution of potential evaporation formulation to uncertainty under climate change

Cited by 19 publications

References 51 publications

Hydro-PE: gridded datasets of historical and future Penman-Monteith potential evaporation for the United Kingdom

Hydro-PE: gridded datasets of historical and future Penman-Monteith potential evaporation for the United Kingdom

Effects of Climate Change on Hydrological Indicators of Subsurface Drainage for a Representative French Drainage Site

Leaching Fraction (LF) of Irrigation Water for Saline Soils Using Machine Learning

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