scholarly journals Influence of multidecadal hydroclimate variations on hydrological extremes: the case of the Seine basin

2019 ◽  
Author(s):  
Rémy Bonnet ◽  
Julien Boé ◽  
Florence Habets
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
North Atlantic Ocean ◽  
North Pacific Ocean ◽  
Seine River ◽  
River Flows ◽  
The North ◽  
Hydroclimate Variability ◽  
Extreme Hydrological Events

Abstract. The multidecadal hydroclimate variations of the Seine basin since the 1850s are investigated. Given the scarcity of long term observations of hydrological variables, a hydrometeorological reconstruction is developed based on an method that combines the results of a downscaled long-term atmospheric reanalysis and local observations of precipitation and temperature. This method improves the representation of daily flows as well as at longer time steps. This reconstruction provide therefore an interesting tool to study the multidecadal hydroclimate variability of the Seine basin, as well as its possible influence on extreme hydrological events. Based on this reconstruction, it is shown that the Seine river flows, groundwater and soil moisture, have been influenced by multidecadal variations from the 1850s. Spring precipitations play a central role by directly influencing the multidecadal variability of spring flows, but also soil moisture and groundwater recharge, which then modulate summer river flows. Groundwater controls a large part of the multidecadal variations in river flows, particularly in summer and fall. These hydroclimate variations seem to influence extreme hydrological events. The positive multidecadal phases indeed appear to be more conducive to flooding, with twice as many flood days as in the negative phases while the negative multidecadal phases seems to influenced the droughts intensity. These hydroclimate variations over the Seine basin are driven by anomalies in large scale atmospheric circulations, which themselves appear to be influenced by sea surface temperature anomalies over of the North Atlantic Ocean and the North Pacific Ocean.

scholarly journals Influence of multidecadal variability on high and low flows: the case of the Seine basin

2020 ◽  
Vol 24 (4) ◽  
pp. 1611-1631
Author(s):  
Rémy Bonnet ◽  
Julien Boé ◽  
Florence Habets
Keyword(s):  
Large Scale ◽  
Hydrological Modeling ◽  
Spring Precipitation ◽  
Seine River ◽  
River Flows ◽  
Multidecadal Variability ◽  
Low Flows ◽  
The North ◽  
Hydrological Variability

Abstract. The multidecadal hydroclimate variations of the Seine basin since the 1850s are investigated. Given the scarcity of long-term hydrological observations, a hydrometeorological reconstruction is developed based on hydrological modeling and a method that combines the results of a downscaled long-term atmospheric reanalysis and local observations of precipitation and temperature. This method improves previous attempts and provides a realistic representation of daily and monthly river flows. This new hydrometeorological reconstruction, available over more than 150 years while maintaining fine spatial and temporal resolutions, provides a tool to improve our understanding of the multidecadal hydrological variability in the Seine basin, as well as its influence on high and low flows. This long-term reconstruction allows analysis of the strong multidecadal variations of the Seine river flows. The main hydrological mechanisms at the origin of these variations are highlighted. Spring precipitation plays a central role by directly influencing not only the multidecadal variability in spring flows but also soil moisture and groundwater recharge, which then regulate summer river flows. These multidecadal hydroclimate variations in the Seine basin are driven by anomalies in large-scale atmospheric circulation, which themselves appear to be influenced by sea surface temperature anomalies over the North Atlantic and the North Pacific. The multidecadal hydroclimate variations also influence high and low flows over the last 150 years. The analysis of two particularly severe historical droughts, the 1921 and the 1949 events, illustrates how long-term hydroclimate variations may impact short-term drought events, particularly through groundwater–river exchanges. The multidecadal hydroclimate variations described in this study, probably of internal origin, could play an important role in the evolution of water resources in the Seine basin in the coming decades. It is therefore essential to take the associated uncertainties into account in future projections.

DESIGN AND IMPLEMENTATION OF THE OCEANOGRAPHIC MODELING AND OBSERVATION NETWORK (REMO) FOR PHYSICAL OCEANOGRAPHY STUDIES AND OCEAN FORECASTING

2014 ◽  
Vol 31 (2) ◽  
Author(s):  
Jose Antonio Moreira Lima
Keyword(s):  
Atlantic Ocean ◽  
Large Scale ◽  
North Pacific Ocean ◽  
Integrated Approach ◽  
Sea Surface ◽  
Global Ocean ◽  
Height Anomaly ◽  
The North ◽  
Observation Network ◽  
Ocean Forecasting

This paper is concerned with the planning, implementation and some results of the Oceanographic Modeling and Observation Network, named REMO, for Brazilian regional waters. Ocean forecasting has been an important scientific issue over the last decade due to studies related to climate change as well as applications related to short-range oceanic forecasts. The South Atlantic Ocean has a deficit of oceanographic measurements when compared to other ocean basins such as the North Atlantic Ocean and the North Pacific Ocean. It is a challenge to design an ocean forecasting system for a region with poor observational coverage of in-situ data. Fortunately, most ocean forecasting systems heavily rely on the assimilation of surface fields such as sea surface height anomaly (SSHA) or sea surface temperature (SST), acquired by environmental satellites, that can accurately provide information that constrain major surface current systems and their mesoscale activity. An integrated approach is proposed here in which the large scale circulation in the Atlantic Ocean is modeled in a first step, and gradually nested into higher resolution regional models that are able to resolve important processes such as the Brazil Current and associated mesoscale variability, continental shelf waves, local and remote wind forcing, and others. This article presents the overall strategy to develop the models using a network of Brazilian institutions and their related expertise along with international collaboration. This work has some similarity with goals of the international project Global Ocean Data Assimilation Experiment OceanView (GODAE OceanView).

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals Past megadroughts in central Europe were longer, more severe and less warm than modern droughts

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
M. Ionita ◽  
M. Dima ◽  
V. Nagavciuc ◽  
P. Scholz ◽  
G. Lohmann
Keyword(s):  
Central Europe ◽  
North Atlantic Ocean ◽  
Explosive Volcanism ◽  
American Southwest ◽  
The North ◽  
European Climate ◽  
Blocking Activity ◽  
Meteorological Observations ◽  
Dalton Minimum

AbstractMegadroughts are notable manifestations of the American Southwest, but not so much of the European climate. By using long-term hydrological and meteorological observations, as well as paleoclimate reconstructions, here we show that central Europe has experienced much longer and severe droughts during the Spörer Minimum (~AD 1400–1480) and Dalton Minimum (~AD 1770–1840), than the ones observed during the 21st century. These two megadroughts appear to be linked with a cold state of the North Atlantic Ocean and enhanced winter atmospheric blocking activity over the British Isles and western part of Europe, concurrent with reduced solar forcing and explosive volcanism. Moreover, we show that the recent drought events (e.g., 2003, 2015, and 2018), are within the range of natural variability and they are not unprecedented over the last millennium.

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

Landscape development in western and central Dronning Maud Land, East Antarctica

2001 ◽  
Vol 13 (3) ◽  
pp. 302-311 ◽  
Author(s):  
Jens-Ove Näslund
Keyword(s):  
Large Scale ◽  
High Elevation ◽  
Continental Margins ◽  
Landscape Development ◽  
Ice Streams ◽  
The North ◽  
Ice Stream ◽  
Dronning Maud Land ◽  
Break Up

Large-scale bedrock morphology and relief of two key areas, the Jutulsessen Nunatak and the Jutulstraumen ice stream are used to discuss glascial history and landscape development in western and central Dronning Maud Land, Antarctica. Two main landform components were identified: well-defined summit plateau surfaces and a typical alpine glacial landscape. The flat, high-elevation plateau surfaces previously were part of one or several continuous regional planation surfaces. In western Dronning Maud Land, overlying cover rocks of late Palaeozoic age show that the planation surface(s) existed in the early Permian, prior to the break-up of Gondwana. A well-develoment escarpment, a mega landform typical for passive continental margins, bounds the palaeosurface remnants to the north for a distance of at least 700 km. The Cenozoic glacial landscape, incised in the palaeosurface and escarpment, is exemplified by Jutulsessen Nunatak, where a c. 1.2 km deep glacial valley system is developed. However, the prominent Penck-Jutul Trough represents some of the deepest dissection of the palaeosurface. This originally tectonic feature is today occupied by the Jutulstraumen ice stream. New topographic data show that the bed of the Penck-Jutul Trough is situated 1.9±1.1 km below sea level, and that the total landscape relief is at least 4.2 km. Today's relief is a result of several processes, including tectonic faulting, subaerial weathering, fluvial erosion, and glacial erosion. It is probable that erosion by ice streams has deepened the tectonic troughs of Dronning Maud Land since the onset of ice sheet glaciation in the Oligocene, and continues today. An attempt is made to identify major events in the long-term landscape development of Dronning Maud Land, since the break-up of the Gondwana continent.

scholarly journals Climate impacts on multidecadal <i>p</i>CO<sub>2</sub> variability in the North Atlantic: 1948–2009

2015 ◽  
Vol 12 (17) ◽  
pp. 15223-15244
Author(s):  
M. L. Breeden ◽  
G. A. McKinley
Keyword(s):  
North Atlantic ◽  
Large Scale ◽  
Dissolved Inorganic Carbon ◽  
Atlantic Multidecadal Oscillation ◽  
Climate Impacts ◽  
Co2 Uptake ◽  
Subpolar Gyre ◽  
The North ◽  
The North Atlantic

Abstract. The North Atlantic is the most intense region of ocean CO2 uptake. Here, we investigate multidecadal timescale variability of the partial pressure CO2 (pCO2) that is due to the natural carbon cycle using a regional model forced with realistic climate and pre-industrial atmospheric pCO2 for 1948–2009. Large-scale patterns of natural pCO2 variability are primarily associated with basin-averaged sea surface temperature (SST) that, in turn, is composed of two parts: the Atlantic Multidecadal Oscillation (AMO) and a long-term positive SST trend. The North Atlantic Oscillation (NAO) drives a secondary mode of variability. For the primary mode, positive AMO and the SST trend modify pCO2 with different mechanisms and spatial patterns. Warming with the positive AMO increases subpolar gyre pCO2, but there is also a significant reduction of dissolved inorganic carbon (DIC) due primarily to reduced vertical mixing. The net impact of positive AMO is to reduce pCO2 in the subpolar gyre. Through direct impacts on SST, the net impacts of positive AMO is to increase pCO2 in the subtropical gyre. From 1980 to present, long-term SST warming has amplified AMO impacts on pCO2.

scholarly journals Drought Relief and Reversal over North America from 1500 to 2016

2021 ◽  
Vol 25 (1) ◽  
pp. 94-107
Author(s):  
M. C. A. Torbenson ◽  
D. W. Stahle ◽  
I. M. Howard ◽  
D. J. Burnette ◽  
D. Griffin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Land Surface ◽  
Large Scale ◽  
Temporal Stability ◽  
Warm Season ◽  
Cool Season ◽  
Continental Scale ◽  
The North ◽  
Drought Relief

Abstract Season-to-season persistence of soil moisture drought varies across North America. Such interseasonal autocorrelation can have modest skill in forecasting future conditions several months in advance. Because robust instrumental observations of precipitation span less than 100 years, the temporal stability of the relationship between seasonal moisture anomalies is uncertain. The North American Seasonal Precipitation Atlas (NASPA) is a gridded network of separately reconstructed cool-season (December–April) and warm-season (May–July) precipitation series and offers new insights on the intra-annual changes in drought for up to 2000 years. Here, the NASPA precipitation reconstructions are rescaled to represent the long-term soil moisture balance during the cool season and 3-month-long atmospheric moisture during the warm season. These rescaled seasonal reconstructions are then used to quantify the frequency, magnitude, and spatial extent of cool-season drought that was relieved or reversed during the following summer months. The adjusted seasonal reconstructions reproduce the general patterns of large-scale drought amelioration and termination in the instrumental record during the twentieth century and are used to estimate relief and reversals for the most skillfully reconstructed past 500 years. Subcontinental-to-continental-scale reversals of cool-season drought in the following warm season have been rare, but the reconstructions display periods prior to the instrumental data of increased reversal probabilities for the mid-Atlantic region and the U.S. Southwest. Drought relief at the continental scale may arise in part from macroscale ocean–atmosphere processes, whereas the smaller-scale regional reversals may reflect land surface feedbacks and stochastic variability.

Sign in / Sign up

Export Citation Format

Share Document