Multilevel and multiscale drought reanalysis over France with the Safran-Isba-Modcou hydrometeorological suite

Abstract. Physically-based droughts can be defined as a water deficit in at least one component of the land surface hydrological cycle. The reliance of different activity domains (water supply, irrigation, hydropower, etc.) on specific components of this cycle requires drought monitoring to be based on indices related to meteorological, agricultural, and hydrological droughts. This paper describes a high-resolution retrospective analysis of such droughts in France over the last fifty years, based on the Safran-Isba-Modcou (SIM) hydrometeorological suite. The high-resolution 1958–2008 Safran atmospheric reanalysis was used to force the Isba land surface scheme and the hydrogeological model Modcou. Meteorological droughts are characterized with the Standardized Precipitation Index (SPI) at time scales varying from 1 to 24 months. Similar standardizing methods were applied to soil moisture and streamflow for identifying multiscale agricultural droughts – through the Standardized Soil Wetness Index (SSWI) – and multiscale hydrological droughts, through the Standardized Flow Index (SFI). Based on a common threshold level for all indices, drought event statistics over the 50-yr period – number of events, duration, severity and magnitude – have been derived locally in order to highlight regional differences at multiple time scales and at multiple levels of the hydrological cycle. Independent spatio-temporal drought events have then been identified and described by combining local characteristics with the evolution of area under drought. Summary statistics have finally been used to compare past severe drought events, from multi-year precipitation deficits (1989–1990) to short hot and dry periods (2003). This multilevel and multiscale drought climatology will serve as a basis for assessing the impacts of climate change on droughts in France.

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Multilevel and multiscale drought reanalysis over France with the Safran-Isba-Modcou hydrometeorological suite

Hydrology and Earth System Sciences ◽

10.5194/hess-14-459-2010 ◽

2010 ◽

Vol 14 (3) ◽

pp. 459-478 ◽

Cited By ~ 138

Author(s):

J.-P. Vidal ◽

E. Martin ◽

L. Franchistéguy ◽

F. Habets ◽

J.-M. Soubeyroux ◽

...

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Time Scales ◽

Time Scale ◽

Land Surface ◽

Hydrological Cycle ◽

Multiple Time Scales ◽

Flow Index ◽

Severe Drought ◽

Hydrological Droughts

Abstract. Physically-based droughts can be defined as a water deficit in at least one component of the land surface hydrological cycle. The reliance of different activity domains (water supply, irrigation, hydropower, etc.) on specific components of this cycle requires drought monitoring to be based on indices related to meteorological, agricultural, and hydrological droughts. This paper describes a high-resolution retrospective analysis of such droughts in France over the last fifty years, based on the Safran-Isba-Modcou (SIM) hydrometeorological suite. The high-resolution 1958–2008 Safran atmospheric reanalysis was used to force the Isba land surface scheme and the hydrogeological model Modcou. Meteorological droughts are characterized with the Standardized Precipitation Index (SPI) at time scales varying from 1 to 24 months. Similar standardizing methods were applied to soil moisture and streamflow for identifying multiscale agricultural droughts – through the Standardized Soil Wetness Index (SSWI) – and multiscale hydrological droughts, through the Standardized Flow Index (SFI). Based on a common threshold level for all indices, drought event statistics over the 50-yr period – number of events, duration, severity and magnitude – have been derived locally in order to highlight regional differences at multiple time scales and at multiple levels of the hydrological cycle (precipitation, soil moisture, streamflow). Results show a substantial variety of temporal drought patterns over the country that are highly dependent on both the variable and time scale considered. Independent spatio-temporal drought events have then been identified and described by combining local characteristics with the evolution of area under drought. Summary statistics have finally been used to compare past severe drought events, from multi-year precipitation deficits (1989–1990) to short hot and dry periods (2003). Results show that the ranking of drought events depends highly on both the time scale and the variable considered. This multilevel and multiscale drought climatology will serve as a basis for assessing the impacts of climate change on droughts in France.

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Application of an optimality-based model to operate at half-hourly timestep to implement plant acclimation within a land-surface modelling framework

10.5194/egusphere-egu21-2801 ◽

2021 ◽

Author(s):

Giulia Mengoli ◽

Anna Agustí-Panareda ◽

Souhail Boussetta ◽

Sandy P. Harrison ◽

Carlo Trotta ◽

...

Keyword(s):

Time Scales ◽

Land Surface ◽

Multiple Time Scales ◽

Multiple Time ◽

Extended Model ◽

Test Model ◽

Area Index ◽

Surface Modelling ◽

Plant Acclimation ◽

Land Surface Modelling

<p>Vegetation and atmosphere are linked through the perpetual exchange of water, carbon and energy. An accurate representation of the processes involved in these exchanges is crucial in forecasting Earth system states. Although vegetation has become an undisputed key component in land-surface modelling (LSMs), the current generation of models differ in terms of how key processes are formulated. Plant processes react to environmental changes on multiple time scales. Here we differentiate a fast (minutes) and a slower (acclimated &#8211; weeks to months) response. Some current LSMs include plant acclimation, even though they require additional parameters to represent this response, but the majority of them represent only the fast response and assume that this also applies at longer time scales. Ignoring acclimation in this way could be the cause of inconsistent future projections. Our proposition is to include plant acclimation in a LSM schema, without having to include new plant-functional-type-dependent parameters. This is possible by using an alternative model development strategy based on eco-evolutionary theory, which explicitly predicts the acclimation of photosynthetic capacities and stomatal behaviour to environmental variations. So far, this theory has been tested only at weekly to monthly timescales. Here we develop and test an approach to apply an existing optimality-based model of gross primary production (GPP), the P model, at the sub-daily timestep necessary for use in an LSM, making an explicit differentiation between the fast and slow responses of photosynthesis and stomatal conductance. We test model performance in reproducing the diurnal cycle of GPP as recorded by flux tower measurements across different biomes, including boreal and tropical forests. The extended model requires only a few meteorological inputs, and a satellite-derived product for leaf area index or green vegetation cover. It is able to manage both timescales of acclimation without PFT-dependent photosynthetic parameters and has shown to operate with very good performance at all sites so far investigated. The model structure avoids the need to store past climate and vegetation states. These findings therefore suggest a simple way to include both instantaneous and acclimated responses within a LSM framework, and to do so in a robust way that does not require the specification of multiple parameters for different plant functional types.</p>

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On the convergence of high resolution methods with multiple time scales for hyperbolic conservation laws

Mathematics of Computation ◽

10.1090/s0025-5718-02-01469-2 ◽

2002 ◽

Vol 72 (243) ◽

pp. 1239-1251 ◽

Cited By ~ 16

Author(s):

Robert Kirby

Keyword(s):

High Resolution ◽

Conservation Laws ◽

Time Scales ◽

Hyperbolic Conservation Laws ◽

Multiple Time Scales ◽

Multiple Time ◽

Hyperbolic Conservation

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Drought Risk Assessment in Yunnan Province of China Based on Wavelet Analysis

Advances in Meteorology ◽

10.1155/2016/1579415 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 7

Author(s):

HuiCong Jia ◽

DongHua Pan

Keyword(s):

Time Scales ◽

Yunnan Province ◽

Linear Trend ◽

Annual Precipitation ◽

Multiple Time Scales ◽

Drought Risk ◽

Severe Drought ◽

Multiple Time ◽

Large Area ◽

Spring Precipitation

A wavelet transform technique was used to analyze the precipitation data for nearly 60 years (1954–2012) in Yunnan Province of China. The wavelet coefficients and the variance yield of wavelet were calculated. The results showed that, in nearly 60 years, the spring precipitation increased slightly; however, the linear trend of other seasonal and annual precipitations showed a reducing trend. Seasonal and annual precipitation had the characteristics of multiple time scales. Different time scales showed the different cyclic alternating patterns. Overall, in the next period of time, different seasons and the annual precipitation will be in the periods of precipitation-reduced oscillation; high drought disaster risks may occur in Yunnan province. Particularly, by analyzing large area of severe drought of Yunnan province in 2009–2012, the predicted results of wavelet were verified. The results may provide a scientific basis for guiding agricultural production and the drought prevention work for Yunnan Province and other places of China.

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Multi-time scale data assimilation for atmosphere–ocean state estimates

Climate of the Past Discussions ◽

10.5194/cpd-11-3729-2015 ◽

2015 ◽

Vol 11 (4) ◽

pp. 3729-3757 ◽

Cited By ~ 1

Author(s):

N. Steiger ◽

G. Hakim

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Time Scales ◽

Time Scale ◽

Low Frequency ◽

Climate System ◽

Multiple Time Scales ◽

Multiple Time ◽

Proxy Data ◽

Frequency Components

Abstract. Paleoclimate proxy data span seasonal to millennial time scales, and Earth's climate system has both high- and low-frequency components. Yet it is currently unclear how best to incorporate multiple time scales of proxy data into a single reconstruction framework and to also capture both high- and low-frequency components of reconstructed variables. Here we present a data assimilation algorithm that can explicitly incorporate proxy data at arbitrary time scales. Through a series of pseudoproxy experiments, we find that atmosphere–ocean states are most skilfully reconstructed by incorporating proxies across multiple time scales compared to using proxies at short (annual) or long (~ decadal) time scales alone. Additionally, reconstructions that incorporate long time-scale pseudoproxies improve the low-frequency components of the reconstructions relative to using only high-resolution pseudoproxies. We argue that this is because time averaging high-resolution observations improves their covariance relationship with the slowly-varying components of the coupled-climate system, which the data assimilation algorithm can exploit. These results are insensitive to the choice of climate model, despite the model variables having very different spectral characteristics. Our results also suggest that it may be possible to reconstruct features of the oceanic meridional overturning circulation based solely on atmospheric surface temperature proxies.

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Multiscale Land–Atmosphere Coupling and Its Application in Assessing Subseasonal Forecasts over East Asia

Journal of Hydrometeorology ◽

10.1175/jhm-d-17-0215.1 ◽

2018 ◽

Vol 19 (5) ◽

pp. 745-760 ◽

Cited By ~ 7

Author(s):

Dingwen Zeng ◽

Xing Yuan

Keyword(s):

Soil Moisture ◽

East Asia ◽

Time Scales ◽

Land Surface ◽

Precipitation Forecast ◽

Multiple Time Scales ◽

Strength Analysis ◽

Multiple Time ◽

Seasonal Forecasts ◽

Rain Belt

Abstract The land surface, with a memory longer than the atmosphere in nature, has been recognized as an important source for Subseasonal to Seasonal (S2S) predictability through land–atmosphere coupling at multiple time scales. Understanding of the land–atmosphere coupling is important for improving subseasonal forecasting that is expected to fill the gap between medium-range weather forecasts and seasonal forecasts. Based on reanalysis and S2S reforecast datasets, land–atmosphere coupling is investigated over East Asia from daily to monthly time scales during summertime. Reanalysis results show that soil moisture–evapotranspiration (ET) coupling is closely related to the monsoonal rain belt shift. The coupling can be significant over humid regions (e.g., south China) during postmonsoon periods, where soil is usually drier, but insignificant over semiarid regions (e.g., north China) after the arrival of a monsoon, where soil is wetter. The dependence of soil moisture–ET coupling on soil wetness conditions decreases as the time scale increases, indicating more significant coupling at longer time scales. Similar sensitivities to time scales are found between ET and lifting condensation level (LCL), and between ET and precipitation, especially over land–atmosphere coupling hotspots. Monthly coupling strength analysis shows that ET–LCL coupling is a key process that determines the soil moisture–precipitation coupling, and the response of convective instability to ET is stronger at longer time scales. Subseasonal forecasting models also show more significant land–atmosphere coupling at monthly than daily time scales, where the ECMWF and NCEP models that best reproduce the coupling and its changes with monsoonal rain belt shifts have the best precipitation forecast skill among S2S models.

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