Simon Munier

Estimating ENSO Influence on the Global Mean Sea Level, 1993–2010

Interannual global mean sea level (GMSL) variations and El Nino-Southern Oscillation (ENSO) are highly correlated, with positive/negative GMSL anomalies during El Nino/La Nina events. In a previous study, we showed that interannual GMSL and total land water storage variations are inversely correlated, with lower-than-average total water storage on land and higher-than-average GMSL during El Nino. This result is in agreement with the observed rainfall deficit/excess over land/oceans during El Nino (and vice versa during La Nina). It suggests that the positive GMSL anomaly observed during El Nino is likely due to an ocean mass rather than thermal expansion increase. Here, we analyze the respective contribution of the Atlantic, Indian, and Pacific oceans to the interannual (ENSO-related) GMSL anomalies observed during the altimetry era (i.e., since 1993) with an emphasis on the 1997/1998 El Nino event. For each oceanic region, we compute the steric contribution, and remove it from the altimetry-based mean sea level to estimate the ocean mass component. We find that mass changes of the tropical Pacific Ocean, mainly in the region within 0–25°N, are mostly responsible for the observed 1997/1998 ENSO-related GMSL anomaly. The ocean mass excess of this region almost perfectly compensates the total land water deficit during the 1997/1998 El Nino. An estimate of the ocean-atmosphere water balance of this region shows that the time derivative of the ocean mass component is well correlated with net P-E (precipitation minus evaporation) over most of the study period, except during the 1997/1998 ENSO event, where there is a temporary ocean mass increase, not compensated by the net P-E. We thus propose that the 1997/1998 ocean mass increase of this north tropical Pacific area be linked to an imbalance between the inflow/outflow entering/leaving the north tropical Pacific. A preliminary qualitative analysis indicates that a significant reduction of the Makassar Strait transport, (about 80% of the total Indonesian throughflow), as previously reported in the literature during the strong 1997/1998 El Nino event, could explain the north tropical Pacific Ocean mass excess reported in this study, hence the observed positive GMSL anomaly.

Combining data sets of satellite‐retrieved products for basin‐scale water balance study: 2. Evaluation on the Mississippi Basin and closure correction model

In this study, we applied the integration methodology developed in the companion paper by Aires (2014) by using real satellite observations over the Mississippi Basin. The methodology provides basin-scale estimates of the four water budget components (precipitation P, evapotranspiration E, water storage change Delta S, and runoff R) in a two-step process: the Simple Weighting (SW) integration and a Postprocessing Filtering (PF) that imposes the water budget closure. A comparison with in situ observations of P and E demonstrated that PF improved the estimation of both components. A Closure Correction Model (CCM) has been derived from the integrated product (SW+PF) that allows to correct each observation data set independently, unlike the SW+PF method which requires simultaneous estimates of the four components. The CCM allows to standardize the various data sets for each component and highly decrease the budget residual (P - E - Delta S - R). As a direct application, the CCM was combined with the water budget equation to reconstruct missing values in any component. Results of a Monte Carlo experiment with synthetic gaps demonstrated the good performances of the method, except for the runoff data that has a variability of the same order of magnitude as the budget residual. Similarly, we proposed a reconstruction of Delta S between 1990 and 2002 where no Gravity Recovery and Climate Experiment data are available. Unlike most of the studies dealing with the water budget closure at the basin scale, only satellite observations and in situ runoff measurements are used. Consequently, the integrated data sets are model independent and can be used for model calibration or validation.

ASSIMILATION OF DISCHARGE DATA INTO SEMI-DISTRIBUTED CATCHMENT MODELS FOR SHORT TERM FLOW FORECASTING: CASE STUDY OF THE SEINE RIVER BASIN

This study addresses the sensitivity of short-term flow forecasting in the Seine River basin (43,800 km2, France) to the spatial distribution using a semi-distributed model (Transfer with GR, TGR). The basin was decomposed into intermediate basins depending on the gauging stations selected for this study. A lumped hydrological model was applied on each intermediate basin and a routing model was used to propagate the discharge through the river network. Discharge data at the gauging stations were assimilated using a Kalman filter and tests for flow forecasting were performed with a lead time up to 72 h. Several spatial configurations, defined by a selection of one or several gauging stations, were tested and the performances were compared to a reference lumped model currently used operationally by the regional flood forecasting centre. Results showed that the forecasting performance improves with an increase in the degree of spatialization. Nevertheless this improvement was not systematic and the integration of some particular gauging stations degraded the model performance. In addition, it was shown that integrating some other stations (generally the most upstream) led to a negligible improvement. This suggests that in an operational context, where the model has to be robust and computationally efficient, some efforts should focus on finding the optimal spatial distribution, which is not necessarily the one using all the available stations.

Closed-form Expression of the Response-Time of an Open-Channel

Computing accurately the response time of an open channel is of extreme importance for management operations on canal networks, such as feed-forward control problems. The methods proposed in the literature to approximate the response time do not always account for the influence of a cross structure at the downstream end of a canal pool, which may have a significant impact on the response time. This paper proposes a new approach to compute the response time, accounting explicitly for the backwater and the feedback effects due to the downstream cross structure. The method provides a distributed analytical expression of the response time as a function of the characteristics of the canal (geometry, roughness) and of the downstream cross structure. A test canal with a weir or a gate at the downstream end is used to compare the new method with some of the others. Results show that the proposed expression accurately reproduces the response time for various backwater and downstream boundary conditions.

Parameter identification for the shallow water equation using modal decomposition

A parameter identification problem for systems governed by first-order, linear hyperbolic partial differential equations subjected to periodic forcing is investigated. The problem is posed as a PDE constrained optimization problem with data of the problem given by the measured input and output variables at the boundary of the domain. By using the governing equations in the frequency domain, a spatially dependent transfer matrix relating the input variables to the output variables is obtained. It is shown that by considering a finite number of dominant oscillatory modes of the input, an accurate representation of the output can be obtained. This converts the original PDE constrained optimization problem to one without any constraints. The optimal parameters can be identified using standard nonlinear programming. The utility of the proposed approach is illustrated by considering a river reach in the Sacramento-San-Joaquin Delta, California, that is subjected to tidal forcing. The dynamics of the reach are modeled by linearized Saint-Venant equations. The available data is the flow variables measured upstream and downstream of the reach. The parameter identification problem is to estimate the average free-surface width, the bed slope, the friction coefficient and the steady-state boundary conditions. It is shown that the estimated model gives an accurate prediction of the flow variables at an intermediate location within the reach.

Satellite Leaf Area Index: Global Scale Analysis of the Tendencies Per Vegetation Type Over the Last 17 Years

The main objective of this study is to detect and quantify changes in the vegetation dynamics of each vegetation type at the global scale over the last 17 years. With recent advances in remote sensing techniques, it is now possible to study the Leaf Area Index (LAI) seasonal and interannual variability at the global scale and in a consistent way over the last decades. However, the coarse spatial resolution of these satellite-derived products does not permit distinguishing vegetation types within mixed pixels. Considering only the dominant type per pixel has two main drawbacks: the LAI of the dominant vegetation type is contaminated by spurious signal from other vegetation types and at the global scale, significant areas of individual vegetation types are neglected. In this study, we first developed a Kalman Filtering (KF) approach to disaggregate the satellite-derived LAI from GEOV1 over nine main vegetation types, including grasslands and crops as well as evergreen, broadleaf and coniferous forests. The KF approach permits the separation of distinct LAI values for individual vegetation types that coexist within a pixel. The disaggregated LAI product, called LAI-MC (Multi-Cover), consists of world-wide LAI maps provided every 10 days for each vegetation type over the 1999–2015 period. A trend analysis of the original GEOV1 LAI product and of the disaggregated LAI time series was conducted using the Mann-Kendall test. Resulting trends of the GEOV1 LAI (which accounts for all vegetation types) compare well with previous regional or global studies, showing a greening over a large part of the globe. When considering each vegetation type individually, the largest global trend from LAI-MC is found for coniferous forests (0.0419 m 2 m − 2 yr − 1 ) followed by summer crops (0.0394 m 2 m − 2 yr − 1 ), while winter crops and grasslands show the smallest global trends (0.0261 m 2 m − 2 yr − 1 and 0.0279 m 2 m − 2 yr − 1 , respectively). The LAI-MC presents contrasting trends among the various vegetation types within the same pixel. For instance, coniferous and broadleaf forests experience a marked greening in the North-East of Europe while crops and grasslands show a browning. In addition, trends from LAI-MC can significantly differ (by up to 50%) from trends obtained with GEOV1 by considering only the dominant vegetation type over each pixel. These results demonstrate the usefulness of the disaggregation method compared to simple ones. LAI-MC may provide a new tool to monitor and quantify tendencies of LAI per vegetation type all over the globe.

Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area

Within a global Land Data Assimilation System (LDAS-Monde), satellite-derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) products are jointly assimilated with a focus on the Euro-Mediterranean region at 0.5 • resolution between 2007 and 2015 to improve the monitoring quality of land surface variables. These products are assimilated in the CO 2 responsive version of ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model, which is able to represent the vegetation processes including the functional relationship between stomatal aperture and photosynthesis, plant growth and mortality (ISBA-A-gs). This study shows the positive impact on SSM and LAI simulations through assimilating their satellite-derived counterparts into the model. Using independent flux estimates related to vegetation dynamics (evapotranspiration, Sun-Induced Fluorescence (SIF) and Gross Primary Productivity (GPP)), it is also shown that simulated water and CO 2 fluxes are improved with the assimilation. These vegetation products tend to have higher root-mean-square deviations in summer when their values are also at their highest, representing 20-35% of their absolute values. Moreover, the connection between SIF and GPP is investigated, showing a linear relationship depending on the vegetation type with correlation coefficient values larger than 0.8, which is further improved by the assimilation.

Assessment of Precipitation Error Propagation in Multi-Model Global WaterResources Reanalysis

This study focuses on the Iberian Peninsula and investigates the propagation of precipitation uncertainty, and its interaction with hydrologic modeling, in global water resource reanalysis. Analysis is based on ensemble hydrologic simulations for a period spanning 11 years (2000–2010). To simulate the hydrological variables of surface runoff, subsurface runoff, and evapotranspiration, we used four land surface models (LSMs) – JULES (Joint UK Land Environment Simulator), ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems), SURFEX (Surface Externalisée), and HTESSEL (Hydrology – Tiled European Centre for Medium-Range Weather Forecasts – ECMWF – Scheme for Surface Exchanges over Land) – and one global hydrological model, WaterGAP3 (Water – a Global Assessment and Prognosis). Simulations were carried out for five precipitation products – CMORPH (the Climate Prediction Center Morphing technique of the National Oceanic and Atmospheric Administration, or NOAA), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), 3B42V(7), ECMWF reanalysis, and a machine-learning-based blended product. As a reference, we used a ground-based observation-driven precipitation dataset, named SAFRAN, available at 5 km, 1 h resolution. We present relative performances of hydrologic variables for the different multi-model and multi-forcing scenarios. Overall, results reveal the complexity of the interaction between precipitation characteristics and different modeling schemes and show that uncertainties in the model simulations are attributed to both uncertainty in precipitation forcing and the model structure. Surface runoff is strongly sensitive to precipitation uncertainty, and the degree of sensitivity depends significantly on the runoff generation scheme of each model examined. Evapotranspiration fluxes are comparatively less sensitive for this study region. Finally, our results suggest that there is no single model–forcing combination that can outperform all others consistently for all variables examined and thus reinforce the fact that there are significant benefits to exploring different model structures as part of the overall modeling approaches used for water resource applications.

L'influence d'El Niño et de La Niña sur le niveau de la mer

The detrended global mean sea level displays positive/negative anomalies of a few millimetres amplitude during El Nino/La Nina events that are inversely correlated to total terrestrial water storage variations.This result is in agreement with the observed rainfall def icit/excess over land/oceans during El Nino (and vice versa during La Nina). It suggests that the positive anomaly observed during El Nino in the global mean sea level is likely due to the ocean mass rather than thermal expansion. A detailed analysis over each oceanic region shows that the global mean sea level anomaly observed during the strong 1997-1998 El Nino resulted from an excess of mass of the north tropical Pacific Ocean with almost perfect compensation with the total terrestrial water deficit during this El Nino.