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With an increase in the number of natural processes represented, global land surface models (LSMs) have become more and more accurate in representing natural terrestrial ecosystems. However, they are still limited with respect to the impact of agriculture on land surface variables. This is particularly true for agro-hydrological processes related to a strong human control on freshwater. While many LSMs consider natural processes only, the development of human-related processes, e.g. crop phenology and irrigation in LSMs, is key. In this study, we present the implementation of a new crop phenology and irrigation scheme in the ISBA (interactions between soil–biosphere–atmosphere) LSM. This highly flexible scheme is designed to account for various configurations and can be applied at different spatial scales. For each vegetation type within a model grid cell, three irrigation systems can be used at the same time. A limited number of parameters are used to control (1) the amount of water used for irrigation, (2) irrigation triggering (based on the soil moisture stress), and (3) crop seasonality (emergence and harvesting). A case study is presented over Nebraska (USA). This region is chosen for its high irrigation density and because independent observations of irrigation practices can be used to verify the simulated irrigation amounts. The ISBA simulations with and without the new crop phenology and irrigation scheme are compared to different satellite-based observations. The comparison shows that the irrigation scheme improves the simulated vegetation variables such as leaf area index, gross primary productivity, and land surface temperature. In addition to a better representation of land surface processes, the results point to potential applications of this new version of the ISBA model for water resource monitoring and climate change impact studies.

The land data assimilation system, LDAS-Monde, developed by the research department of the French meteorological service (Centre National de Recherches Météorologiques – CNRM) is capable of well representing land surface variables (LSVs) from regional to global scales. It jointly assimilates satellite-derived observations of leaf area index (LAI) and surface soil moisture (SSM) into the interactions between soil–biosphere–atmosphere (ISBA) land surface model (LSM), increasing the accuracy of the model simulations of the LSVs. The assimilation of vegetation variables directly impacts root zone soil moisture (RZSM) through seven control variables consisting in soil moisture of seven soil layers from the soil surface to 1 m depth. This positive impact is particularly useful in dry conditions, where SSM and RZSM are decoupled to a large extent. However, this positive impact does not reach its full potential due to the low temporal availability of optical-based LAI observations, which is, at best, every 10 d, and can suffer from months of missing data over regions and seasons with heavy cloud cover such as winter or in monsoon conditions. In that context, this study investigates the assimilation of low-frequency passive microwave vegetation optical depth (VOD), available in almost all weather conditions, as a proxy for LAI. The Vegetation Optical Depth Climate Archive (VODCA) dataset provides near-daily observations of vegetation conditions, which is far more frequent than optical-based products such as LAI. This study's goal is to convert the more frequent X-band VOD observations into proxy-LAI observations through linear seasonal re-scaling and to assimilate them in place of direct LAI observations. Seven assimilation experiments are run from 2003 to 2018 over the contiguous United States (CONUS), with (1) no assimilation and the assimilation of (2) SSM, (3) LAI, (4) re-scaled X-band VOD (VODX), (5) re-scaled VODX only when LAI observations are available, (6) LAI + SSM, and (7) re-scaled VODX + SSM. This study analyzes these assimilation experiments by comparing them to satellite-derived observations and in situ measurements and is focused on the variables of LAI, SSM, gross primary production (GPP), and evapotranspiration (ET). Each experiment is driven by atmospheric forcing reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5. Results show improved representation of GPP and ET by assimilating re-scaled VOD in place of LAI. Additionally, the joint assimilation of vegetation-related variables (i.e., LAI or re-scaled VOD) and SSM demonstrates a small improvement in the representation of soil moisture over the assimilation of any dataset by itself.

With an increase in the number of natural processes represented, global land surface models (LSMs) have become more and more accurate in representing natural terrestrial ecosystems. However, they are still limited, especially in the representation of the impact of agriculture on land surface variables. This is particularly true for agro-hydrological processes related to a strong human control on freshwater. While most LSMs consider natural processes only, the development of human-related processes, e.g. crop phenology and irrigation in LSMs, is key. In this study we present the implementation of a new irrigation scheme in the ISBA (Interaction between Soil, Biosphere, and Atmosphere) LSM. This highly flexible scheme is designed to account for various configurations and can be applied at different spatial scales. For each vegetation type within a model grid cell, three irrigation systems can be used at the same time. A limited number of parameters are used to control (1) the amount of water used for irrigation, (2) irrigation triggering (based on the soil moisture stress) and (3) crop seasonality (emergence, harvesting). After a presentation of the simulations of the new scheme at a plot scale, an evaluation is proposed over Nebraska (USA). This region is chosen for its high irrigation density and because independent observations of irrigation practices can be used to verify the simulated irrigation amounts. The ISBA simulations with and without the irrigation scheme are compared to different satellite-based observations. The comparison shows that the irrigation scheme improves the simulated vegetation variables such as leaf area index and gross primary productivity and other variables largely impacted by irrigation such as evapotranspiration and land surface temperature. In addition to a better representation of land surface processes, the results point to potential applications of this new version of the ISBA model for water resource monitoring and climate change impact studies.

A global land data assimilation system (LDAS-Monde) was developed by CNRM. It uses a version of the interactions between soil, biosphere, and atmosphere (ISBA) land surface model able to simulate photosynthesis and plant growth. Vegetation variables such as leaf area index (LAI) and surface soil moisture can be jointly assimilated in the model. Sequential assimilation of LAI is possible thanks to the fully photosynthesis-driven phenology. The simulated LAI is flexible and can be analyzed at a given date. Also, the assimilation of LAI alone can be used to analyze the root-zone soil moisture. The assimilation of level 1 microwave observations is being investigated. Recent results and potential applications of LDAS-Monde are presented.

LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches M&eacute, t&eacute, orologiques (CNRM) to monitor land surface variables (LSV) at various scales, from regional to global. With LDAS-Monde, it is possible to jointly assimilate satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the interactions between soil biosphere and atmosphere (ISBA) land surface model (LSM) in order to analyze the soil moisture profile together with vegetation biomass. In this study, we investigate LDAS-Monde&rsquo, s ability to predict LSV states up to two weeks in the future using atmospheric forecasts. In particular, the impact of the initialization, and the evolution of the forecasted variables in the LSM are addressed. LDAS-Monde is an offline system normally driven by atmospheric reanalysis, but in this study is forced by atmospheric forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the 2017&ndash, 2018 period over the contiguous United States (CONUS) at a 0.2&deg, &times, 0.2&deg, spatial resolution. These LSV forecasts are initialized either by the model alone (LDAS-Monde open-loop, without assimilation) or by the analysis (assimilation of SSM and LAI). These two forecasts are then evaluated using satellite-derived observations of SSM and LAI, evapotranspiration (ET) estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and ET), LDAS-Monde provides reasonably accurate and consistent predictions two weeks in advance. Additionally, the initial conditions after assimilation are shown to make a positive impact with respect to LAI and ET. This impact persists in time for these two vegetation-related variables. Many model variables, such as SSM, root zone soil moisture (RZSM), LAI, ET, and drainage, remain relatively consistent as the forecast lead time increases, while runoff is highly variable.

LDAS-Monde is a global Land Data Assimilation System developed in the research department of Météo-France (CNRM) to monitor Land Surface Variables (LSVs) at various scales, from regional to global. With LDAS-Monde, it is possible to assimilate satellite derived observations of Surface Soil Moisture (SSM) and Leaf Area Index (LAI) e.g. from the Copernicus Global Land Service (CGLS). It is an offline system normally driven by atmospheric reanalyses such as ECMWF ERA5.In this study we investigate LDAS-Monde ability to use atmospheric forecasts to predict LSV states up to weeks in advance. In addition to the accuracy of the forecast predictions, the impact of the initialization on the LSVs forecast is addressed. To perform this study, LDAS-Monde is forced by a fifteen-day forecast from ECMWF for the 2017-2018 period over the Contiguous United States (CONUS) at 0.2o x 0.2o spatial resolution. These LSVs forecasts are initialized either by the model alone (LDAS-Monde open-loop, no assimilation, Fc_ol) or by the analysis (assimilation of SSM and LAI, Fc_an). These two sets of forecast are then assessed using satellite derived observations of SSM and LAI, evapotranspiration estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and evapotranspiration), LDAS-Monde provides reasonably accurate predictions two weeks in advance. Additionally, the initial conditions are shown to make a positive impact with respect to LAI, evapotranspiration, and deeper layers of soil moisture when using Fc_an. Moreover, this impact persists in time, particularly for vegetation related variables. Other model variables (such as runoff and drainage) are also affected by the initial conditions. Future work will focus on the transfer of this predictive information from a research to stakeholder tool.

Rural towns are especially susceptible to the effects of drought because their economies are dependent on natural resources. However, they are also resilient in many ways to natural hazards because they are rich in civic engagement and social capital. Because of the diverse nature of drought’s impacts, understanding its complex dynamics and its effects requires a multidisciplinary approach. To study these dynamics, this research combines appreciative inquiry, the Community Capitals Framework, and a range of climatological monitoring data to assess the 2012–14 Great Plains drought’s effect on McCook, Nebraska. Community coping measures, such as water-use reduction and public health programs, were designed to address the immediate effects of heat and scant rainfall during the initial summer and the subsequent years. Residents generally reported the community was better prepared than in previous droughts, including the persistent multiyear early-2000s drought. However, the results highlight wide variation in community perspectives about the drought’s severity and impacts, as well as divergent experiences and coping responses. Despite these factors, we find evidence of the transformative potential of moving from drought coping to drought mitigation. We attribute the city’s resilience to the ability to draw upon prior experience with droughts, having a formal municipal plan, and strong human and social capital to coordinate individual knowledge and expertise across agencies. We suggest that droughts have served a catalytic function, prompting the community to transform land-use practices, water conservation planning, and built infrastructure in lasting ways.

The land data assimilation system, LDAS-Monde, developed by the Research Department of the French Meteorological service (Centre National de Recherches Météorologiques – CNRM) is capable of well representing Land Surface Variables (LSVs) from regional to global scales. It jointly assimilates satellite-derived observations of leaf area index (LAI) and surface soil moisture (SSM) into the Interactions between Soil Biosphere and Atmosphere (ISBA) land surface model (LSM), increasing the accuracy of the model simulations and forecasts of the LSVs. The assimilation of vegetation variables directly impacts RZSM through seven control variables consisting in soil moisture of seven soil layers from the soil surface to 1 m depth. This capability is particularly useful in dry conditions, where SSM and RZSM are decoupled to a large extent. However, this positive impact does not reach its full potential due to the low temporal availability of optical-based LAI observations, at best, every ten days, and can suffer from months of no data over regions and seasons with heavy cloud cover such as winter or monsoon conditions. In that context, this study investigates the assimilation of low frequency passive microwave vegetation optical depth (VOD), available in almost all weather conditions, as a proxy of LAI. The Vegetation Optical Depth Climate Archive (VODCA) dataset provides near-daily observations of vegetation conditions, far more frequently than optical based product such as LAI. This study's goal is to convert the more frequent X-band VOD observations into proxy-LAI observations through linear re-scaling and to assimilate them in place of direct LAI observations. Seven assimilation experiments were run from 2003 to 2018 over the contiguous United States (CONUS), with 1) no assimilation, the assimilation of 2) SSM, 3) LAI, 4) re-scaled VODX, 5) re-scaled VODX only when LAI observations available, 6) LAI + SSM, and 7) re-scaled VODX + SSM. This study analyzes these assimilation experiments by comparing to satellite derived observations and in situ measurements and is focused on the variables of LAI, SSM, gross primary production (GPP), and evapotranspiration (ET). Each experiment is driven by atmospheric forcing reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5. Results showed improved representation of GPP and ET by assimilating re-scaled VOD in place of LAI. Additionally, the joint assimilation of vegetation related variables (i.e. LAI or re-scaled VOD) and SSM demonstrates a small improvement in the representation of soil moisture over the assimilation of any dataset by itself.