Revealing the spatial pattern of subsurface soil salinity over the Argentinean Dry Chaco

2021 ◽  
Author(s):  
Michiel Maertens ◽  
Veerle Vanacker ◽  
Gabriëlle De Lannoy ◽  
Frederike Vincent ◽  
Raul Giménez ◽  
...  
Keyword(s):  
Soil Salinity ◽  
Land Surface ◽  
Large Scale ◽  
Hydrological Cycle ◽  
Regional Scale ◽  
Shallow Groundwater ◽  
Soil Salinization ◽  
Dry Forest ◽  
Surface Model ◽  
Subsurface Soil

<p>The South-American Dry Chaco is a unique ecoregion as it is one of the largest sedimentary plains in the world hosting the planet’s largest dry forest. The 787.000 km² region covers parts of Argentina, Paraguay, and Bolivia and is characterized by a negative climatic water balance as a consequence of limited rainfall inputs (800 mm/year) and high temperatures (21°C). In combination with the region’s extreme flat topography (slopes < 0.1%) and shallow groundwater tables, saline soils are expected in substantial parts of the region. In addition, it is expected that large-scale deforestation processes disrupt the hydrological cycle resulting in rising groundwater tables and further increase the risk for soil salinization.</p><p>In this study, we identified the regional-scale patterns of subsurface soil salinity in the Dry Chaco.  Field data were obtained during a two-month field campaign in the dry season of 2019. A total of 492 surface- and 142 subsurface-samples were collected along East-West transects to determine soil electric conductivity, pH, bulk density and humidity. Spatial regression techniques were used to reveal the topographic and ecohydrological variables that are associated with subsurface soil salinity over the Dry Chaco. The hydrological information was obtained from a state-of-the-art land surface model with an improved set of satellite-derived vegetation and land cover parameters.</p><p>In the presentation, we will present a subsurface soil salinity map for a part of the Argentinean Dry Chaco and provide relevant insights into the driving mechanisms behind it.</p>

scholarly journals Simulating carbon exchange using a regional atmospheric model coupled to an advanced land-surface model

2008 ◽  
Vol 5 (5) ◽  
pp. 4161-4207 ◽  
Author(s):  
H. W. Ter Maat ◽  
R. W. A. Hutjes
Keyword(s):  
Carbon Dioxide ◽  
Land Surface ◽  
Large Scale ◽  
Regional Scale ◽  
Atmospheric Model ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Surface Model ◽  
Carbon Exchange ◽  
Regional Atmospheric Modelling System

Abstract. A large scale mismatch exists between our understanding and quantification of ecosystem atmosphere exchange of carbon dioxide at local scale and continental scales. This paper will focus on the carbon exchange on the regional scale to address the following question: What are the main controlling factors determining atmospheric carbon dioxide content at a regional scale? We use the Regional Atmospheric Modelling System (RAMS), coupled with a land surface scheme simulating carbon, heat and momentum fluxes (SWAPS-C), and including also sub models for urban and marine fluxes, which in principle include the main controlling mechanisms and capture the relevant dynamics of the system. To validate the model, observations are used which were taken during an intensive observational campaign in the central Netherlands in summer 2002. These included flux-site observations, vertical profiles at tall towers and spatial fluxes of various variables taken by aircraft. The coupled regional model (RAMS-SWAPS-C) generally does a good job in simulating results close to reality. The validation of the model demonstrates that surface fluxes of heat, water and CO2 are reasonably well simulated. The comparison against aircraft data shows that the regional meteorology is captured by the model. Comparing spatially explicit simulated and observed fluxes we conclude that in general simulated latent heat fluxes are underestimated by the model to the observations which exhibit large standard deviation for all flights. Sensitivity experiments demonstrated the relevance of the urban emissions of carbon dioxide for the carbon balance in this particular region. The same test also show the relation between uncertainties in surface fluxes and those in atmospheric concentrations.

scholarly journals Modelling sun-induced fluorescence and photosynthesis with a land surface model at local and regional scales in northern Europe

2017 ◽  
Vol 14 (7) ◽  
pp. 1969-1987 ◽  
Author(s):  
Tea Thum ◽  
Sönke Zaehle ◽  
Philipp Köhler ◽  
Tuula Aalto ◽  
Mika Aurela ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Land Surface ◽  
Large Scale ◽  
Model Simulation ◽  
Gross Primary Production ◽  
Regional Scale ◽  
Coniferous Forest ◽  
Flux Measurement ◽  
Surface Model ◽  
Fluorescence Measurements

Abstract. Recent satellite observations of sun-induced chlorophyll fluorescence (SIF) are thought to provide a large-scale proxy for gross primary production (GPP), thus providing a new way to assess the performance of land surface models (LSMs). In this study, we assessed how well SIF is able to predict GPP in the Fenno-Scandinavian region and what potential limitations for its application exist. We implemented a SIF model into the JSBACH LSM and used active leaf-level chlorophyll fluorescence measurements (Chl F) to evaluate the performance of the SIF module at a coniferous forest at Hyytiälä, Finland. We also compared simulated GPP and SIF at four Finnish micrometeorological flux measurement sites to observed GPP as well as to satellite-observed SIF. Finally, we conducted a regional model simulation for the Fenno-Scandinavian region with JSBACH and compared the results to SIF retrievals from the GOME-2 (Global Ozone Monitoring Experiment-2) space-borne spectrometer and to observation-based regional GPP estimates. Both observations and simulations revealed that SIF can be used to estimate GPP at both site and regional scales. At regional scale the model was able to simulate observed SIF averaged over 5 years with r2 of 0.86. The GOME-2-based SIF was a better proxy for GPP than the remotely sensed fAPAR (fraction of absorbed photosynthetic active radiation by vegetation). The observed SIF captured the seasonality of the photosynthesis at site scale and showed feasibility for use in improving of model seasonality at site and regional scale.

scholarly journals Averaging over spatiotemporal heterogeneity substantially biases evapotranspiration rates in a mechanistic large-scale land evaporation model

2020 ◽  
Author(s):  
Elham Rouholahnejad Freund ◽  
Massimiliano Zappa ◽  
James W. Kirchner
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Hydrological Cycle ◽  
Land Surface Model ◽  
Aggregation Bias ◽  
Surface Model ◽  
Land Surface Models ◽  
Evaporation Model ◽  
Surface Models ◽  
Large Scale Land

Abstract. Evapotranspiration (ET) influences land-climate interactions, regulates the hydrological cycle, and contributes to the Earth's energy balance. Due to its feedbacks to large-scale hydrological processes and its impact on atmospheric dynamics, ET is a key driver of droughts and heatwaves. Existing land surface models differ substantially, both in their estimates of current ET fluxes and in their projections of how ET will evolve in the future. Any bias in estimated ET fluxes will affect the partitioning between sensible and latent heat, and thus alter model predictions of temperature and precipitation. One potential source of bias is the so-called aggregation bias that arises whenever nonlinear processes, such as those that regulate ET fluxes, are modeled using averages of heterogeneous inputs. Here we demonstrate a general mathematical approach to quantifying and correcting for this aggregation bias, using the GLEAM land evaporation model as a relatively simple example. We demonstrate that this aggregation bias can lead to substantial overestimates in ET fluxes in a typical large-scale land surface model when sub-grid heterogeneities in land surface properties are averaged out. Using Switzerland as a test case, we examine the scale-dependence of this aggregation bias and show that it can lead to overestimation of daily ET fluxes by as much as 21 % averaged over the whole country. We show how our approach can be used to identify the dominant drivers of aggregation bias, and to estimate sub-grid closure relationships that can correct for aggregation biases in ET estimates, without explicitly representing sub-grid heterogeneities in large-scale land surface models.

scholarly journals Deriving global flood hazard maps of fluvial floods through a physical model cascade

2012 ◽  
Vol 16 (11) ◽  
pp. 4143-4156 ◽  
Author(s):  
F. Pappenberger ◽  
E. Dutra ◽  
F. Wetterhall ◽  
H. L. Cloke
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Flood Hazard ◽  
Regional Scale ◽  
Surface Model ◽  
Return Periods ◽  
Hazard Maps ◽  
Data Set ◽  
Flood Hazard Maps ◽  
River Routing

Abstract. Global flood hazard maps can be used in the assessment of flood risk in a number of different applications, including (re)insurance and large scale flood preparedness. Such global hazard maps can be generated using large scale physically based models of rainfall-runoff and river routing, when used in conjunction with a number of post-processing methods. In this study, the European Centre for Medium Range Weather Forecasts (ECMWF) land surface model is coupled to ERA-Interim reanalysis meteorological forcing data, and resultant runoff is passed to a river routing algorithm which simulates floodplains and flood flow across the global land area. The global hazard map is based on a 30 yr (1979–2010) simulation period. A Gumbel distribution is fitted to the annual maxima flows to derive a number of flood return periods. The return periods are calculated initially for a 25 × 25 km grid, which is then reprojected onto a 1 × 1 km grid to derive maps of higher resolution and estimate flooded fractional area for the individual 25 × 25 km cells. Several global and regional maps of flood return periods ranging from 2 to 500 yr are presented. The results compare reasonably to a benchmark data set of global flood hazard. The developed methodology can be applied to other datasets on a global or regional scale.

scholarly journals Averaging over spatiotemporal heterogeneity substantially biases evapotranspiration rates in a mechanistic large-scale land evaporation model

2020 ◽  
Vol 24 (10) ◽  
pp. 5015-5025
Author(s):  
Elham Rouholahnejad Freund ◽  
Massimiliano Zappa ◽  
James W. Kirchner
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Hydrological Cycle ◽  
Land Surface Model ◽  
Aggregation Bias ◽  
Surface Model ◽  
Land Surface Models ◽  
Evaporation Model ◽  
Surface Models ◽  
Large Scale Land

Abstract. Evapotranspiration (ET) influences land–climate interactions, regulates the hydrological cycle, and contributes to the Earth's energy balance. Due to its feedback to large-scale hydrological processes and its impact on atmospheric dynamics, ET is one of the drivers of droughts and heatwaves. Existing land surface models differ substantially, both in their estimates of current ET fluxes and in their projections of how ET will evolve in the future. Any bias in estimated ET fluxes will affect the partitioning between sensible and latent heat and thus alter model predictions of temperature and precipitation. One potential source of bias is the so-called “aggregation bias” that arises whenever nonlinear processes, such as those that regulate ET fluxes, are modeled using averages of heterogeneous inputs. Here we demonstrate a general mathematical approach to quantifying and correcting for this aggregation bias, using the GLEAM land evaporation model as a relatively simple example. We demonstrate that this aggregation bias can lead to substantial overestimates in ET fluxes in a typical large-scale land surface model when sub-grid heterogeneities in land surface properties are averaged out. Using Switzerland as a test case, we examine the scale dependence of this aggregation bias and show that it can lead to an average overestimation of daily ET fluxes by as much as 10 % across the whole country (calculated as the median of the daily bias over the growing season). We show how our approach can be used to identify the dominant drivers of aggregation bias and to estimate sub-grid closure relationships that can correct for aggregation biases in ET estimates, without explicitly representing sub-grid heterogeneities in large-scale land surface models.

Climate change impact on terrestrial water balance components at continental and global scales

2020 ◽  
Author(s):  
Olga Nasonova ◽  
Yeugeniy Gusev ◽  
Evgeny Kovalev
Keyword(s):  
Climate Change ◽  
Water Balance ◽  
Land Surface ◽  
Large Scale ◽  
Regional Scale ◽  
Global Climate Models ◽  
Surface Model ◽  
Water Balance Components ◽  
Rcp Scenarios ◽  
Terrestrial Water

<p>This work is a continuation of our previous investigations performed within the framework of the International Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) on a regional scale when hydrological projections and their uncertainties were obtained for 11 large-scale river basins using the physically based land surface model Soil Water – Atmosphere – Plants (SWAP) driven by meteorological projections from five Global Climate Models (GCMs). In the present work, we decided to spread our investigations to continental and global scales. The main goals are as follows: (i) projecting changes in terrestrial water balance components in the 21<sup>st</sup> century due to possible climate change for different continents and for the whole globe, (ii) evaluation of uncertainties in the obtained projections sourced from application of different GCMs and different climatic scenarios, (iii) studying the patterns of spatial distribution of changes in the water balance components and their uncertainties.</p><p>Simulations of the water balance components (evapotranspiration and runoff) for the entire land surface of the globe (with the exception of Antarctica) were performed by the SWAP model with a spatial resolution of 0.5<sup>o</sup>×0.5<sup>o</sup> for the period of 1961-2099. The model was driven by daily meteorological outputs from five GCMs (including GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M) obtained for each of four Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). As a result, 20 variants of daily values of evapotranspiration, runoff, and precipitation were obtained for each calculational grid cell. Then, the climatic annual values of the water balance components for four periods (historical and three prognostic ones: 2006-2036, 2037-2067, 2068-2099) were obtained and their changes for different prognostic periods compared to historical values were calculated. Besides, uncertainties in the projected changes of the water balance components resulted from application of different GCMs and RCP scenarios were estimated. The obtained results were mapped and averaged over the continents, latitudinal zones, and the globe that allowed us to identify spatio-temporal patterns of changes in the water balance components and their uncertainties due to possible climate changes.</p>

scholarly journals Weak sensitivity of the terrestrial water budget to global soil texture maps in the ORCHIDEE land surface model

2020 ◽  
Vol 24 (7) ◽  
pp. 3753-3774
Author(s):  
Salma Tafasca ◽  
Agnès Ducharne ◽  
Christian Valentin
Keyword(s):  
Soil Water ◽  
Soil Texture ◽  
Land Surface ◽  
Large Scale ◽  
Regional Scale ◽  
Land Surface Model ◽  
Added Value ◽  
Surface Model ◽  
Total Runoff ◽  
The Impact

Abstract. Soil physical properties play an important role in estimating soil water and energy fluxes. Many hydrological and land surface models (LSMs) use soil texture maps to infer these properties. Here, we investigate the impact of soil texture on soil water fluxes and storage at different scales using the ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms) LSM, forced by several complex or globally uniform soil texture maps. At the point scale, the model shows a realistic sensitivity of runoff processes and soil moisture to soil texture and reveals that loamy textures give the highest evapotranspiration and lowest total runoff rates. The three tested complex soil texture maps result in similar water budgets at all scales, compared to the uncertainties of observation-based products and meteorological forcing datasets, although important differences can be found at the regional scale, particularly in areas where the different maps disagree on the prevalence of clay soils. The three tested soil texture maps are also found to be similar by construction, with a shared prevalence of loamy textures, and have a spatial overlap over 40 % between each pair of maps, which explains the overall weak impact of soil texture map change. A useful outcome is that the choice of the input soil texture map is not crucial for large-scale modelling, but the added value of more detailed soil information (horizontal and vertical resolution, soil composition) deserves further studies.

scholarly journals Deriving global flood hazard maps of fluvial floods through a physical model cascade

2012 ◽  
Vol 9 (5) ◽  
pp. 6615-6647 ◽  
Author(s):  
F. Pappenberger ◽  
E. Dutra ◽  
F. Wetterhall ◽  
H. Cloke
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Flood Hazard ◽  
Regional Scale ◽  
Surface Model ◽  
Return Periods ◽  
Hazard Maps ◽  
Data Set ◽  
Flood Hazard Maps ◽  
River Routing

Abstract. Global flood hazard maps can be used in the assessment of flood risk in a number of different applications, including (re)insurance and large scale flood preparedness. Such global hazard maps can be generated using large scale physically based models of rainfall-runoff and river routing, when used in conjunction with a number of post-processing methods. In this study, the European Centre for Medium Range Weather Forecasts (ECMWF) land surface model is coupled to ERA-Interim reanalysis meteorological forcing data, and resultant runoff is passed to a river routing algorithm which simulates floodplains and flood flow across the global land area. The global hazard map is based on a 30 yr (1979–2010) simulation period. A Gumbel distribution is fitted to the annual maxima flows to derive a number of flood return periods. The return periods are calculated initially for a 25 × 25 km grid, which is then reprojected onto a 1 × 1 km grid to derive maps of higher resolution and estimate flooded fractional area for the individual 25 × 25 km cells. Several global and regional maps of flood return periods ranging from 2 to 500 yr are presented. The results compare reasonably to a benchmark data set of global flood hazard. The developed methodology can be applied to other datasets on a global or regional scale.

scholarly journals Validation of a new SAFRAN-based gridded precipitation product for Spain and comparisons to Spain02 and ERA-Interim

2017 ◽  
Vol 21 (4) ◽  
pp. 2187-2201 ◽  
Author(s):  
Pere Quintana-Seguí ◽  
Marco Turco ◽  
Sixto Herrera ◽  
Gonzalo Miguez-Macho
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Interpolation Method ◽  
Rain Gauge ◽  
Surface Model ◽  
Low Resolution ◽  
Precipitation Events

Abstract. Offline land surface model (LSM) simulations are useful for studying the continental hydrological cycle. Because of the nonlinearities in the models, the results are very sensitive to the quality of the meteorological forcing; thus, high-quality gridded datasets of screen-level meteorological variables are needed. Precipitation datasets are particularly difficult to produce due to the inherent spatial and temporal heterogeneity of that variable. They do, however, have a large impact on the simulations, and it is thus necessary to carefully evaluate their quality in great detail. This paper reports the quality of two high-resolution precipitation datasets for Spain at the daily time scale: the new SAFRAN-based dataset and Spain02. SAFRAN is a meteorological analysis system that was designed to force LSMs and has recently been extended to the entirety of Spain for a long period of time (1979/1980–2013/2014). Spain02 is a daily precipitation dataset for Spain and was created mainly to validate regional climate models. In addition, ERA-Interim is included in the comparison to show the differences between local high-resolution and global low-resolution products. The study compares the different precipitation analyses with rain gauge data and assesses their temporal and spatial similarities to the observations. The validation of SAFRAN with independent data shows that this is a robust product. SAFRAN and Spain02 have very similar scores, although the latter slightly surpasses the former. The scores are robust with altitude and throughout the year, save perhaps in summer when a diminished skill is observed. As expected, SAFRAN and Spain02 perform better than ERA-Interim, which has difficulty capturing the effects of the relief on precipitation due to its low resolution. However, ERA-Interim reproduces spells remarkably well in contrast to the low skill shown by the high-resolution products. The high-resolution gridded products overestimate the number of precipitation days, which is a problem that affects SAFRAN more than Spain02 and is likely caused by the interpolation method. Both SAFRAN and Spain02 underestimate high precipitation events, but SAFRAN does so more than Spain02. The overestimation of low precipitation events and the underestimation of intense episodes will probably have hydrological consequences once the data are used to force a land surface or hydrological model.

scholarly journals Continuous high-resolution midlatitude-belt simulations for July–August 2013 with WRF

2017 ◽  
Vol 10 (5) ◽  
pp. 2031-2055 ◽  
Author(s):  
Thomas Schwitalla ◽  
Hans-Stefan Bauer ◽  
Volker Wulfmeyer ◽  
Kirsten Warrach-Sagi
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Temperature Fields ◽  
Surface Model ◽  
Limited Area ◽  
Data Set ◽  
Latitude Belt ◽  
Simulated Precipitation ◽  
The Impact

Abstract. Increasing computational resources and the demands of impact modelers, stake holders, and society envision seasonal and climate simulations with the convection-permitting resolution. So far such a resolution is only achieved with a limited-area model whose results are impacted by zonal and meridional boundaries. Here, we present the setup of a latitude-belt domain that reduces disturbances originating from the western and eastern boundaries and therefore allows for studying the impact of model resolution and physical parameterization. The Weather Research and Forecasting (WRF) model coupled to the NOAH land–surface model was operated during July and August 2013 at two different horizontal resolutions, namely 0.03 (HIRES) and 0.12° (LOWRES). Both simulations were forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis data at the northern and southern domain boundaries, and the high-resolution Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) data at the sea surface.The simulations are compared to the operational ECMWF analysis for the representation of large-scale features. To analyze the simulated precipitation, the operational ECMWF forecast, the CPC MORPHing (CMORPH), and the ENSEMBLES gridded observation precipitation data set (E-OBS) were used as references.Analyzing pressure, geopotential height, wind, and temperature fields as well as precipitation revealed (1) a benefit from the higher resolution concerning the reduction of monthly biases, root mean square error, and an improved Pearson skill score, and (2) deficiencies in the physical parameterizations leading to notable biases in distinct regions like the polar Atlantic for the LOWRES simulation, the North Pacific, and Inner Mongolia for both resolutions.In summary, the application of a latitude belt on a convection-permitting resolution shows promising results that are beneficial for future seasonal forecasting.

Sign in / Sign up

Export Citation Format

Share Document