Implementation and validation of a new irrigation scheme in the ISBA land surface model

Abstract

Abstract. 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.

Evaluating the impact of land surface on medium‐range weather forecasts using screen‐level analyses

“…The scale of the effort and investment given by national centres to develop and improve their land-surface modelling component is well illustrated by Boussetta et al (2021), with their description of ECLand, the land-surface modelling system at the European Centre for Medium-Range Weather Forecasts (ECMWF). Other recent studies to be noted include those of Druel et al (2021), Li et al (2021), and Takata and Hanasaki (2021).…”

Challenges and benefits of quantifying irrigation through the assimilation of Sentinel-1 backscatter observations into Noah-MP

“…The authors realized a study in India, where irrigation provides a large contribution to winter crop production, and they found that data assimilation (DA) of total water storage (TWS) RS observations into the catchment land surface model (CLSM) introduces a negative trend in groundwater due to pumping for irrigation, along with an associated erroneous negative trend in modelled evapotranspiration when irrigation is unmodelled. Other studies have attempted to incorporate irrigation schemes into global LSMs, including the Interaction between Soil, Biosphere, and Atmosphere (ISBA) LSM (Druel et al, 2021), the Community Land Model (CLM; Pokhrel et al, 2012), and the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) model (De Rosnay et al, 2003), demonstrating the regional impact of irrigation on different water storages and on energy partitioning between sensible and latent heat fluxes (Lawston et al, 2015). Different types of irrigation systems were also implemented into the NASA Land Information System (LIS; Kumar et al, 2006;Peters-Lidard et al, 2007) framework and coupled with several LSMs, such as the Noah LSM (Chen et al, 1996;Chen and Dudhia, 2001;Mahrt and Pan, 1984) and the Noah-MP LSM (Niu et al, 2011).…”

Challenges and benefits of quantifying irrigation through the assimilation of Sentinel-1 backscatter observations into Noah-MP

“…The authors realized a study in India, where irrigation provides a large contribution to winter crop production, and they found that data assimilation (DA) of total water storage (TWS) RS observations into the Catchment land surface model (CLSM) introduces a negative trend in groundwater due to pumping for irrigation along with an associated erroneous negative trend in modelled evapotranspiration when irrigation is unmodelled. Other studies have attempted to incorporate irrigation schemes into global LSMs, including the Interaction between Soil, Biosphere, and Atmosphere (ISBA) LSM (Druel et al, 2021), the Community Land Model (CLM; Pokhrel et al, 2012) and the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) model (De Rosnay et al, 2003), demonstrating the regional impact of irrigation on different water storages and on energy partitioning between sensible and latent heat fluxes (Lawston et al, 2015). Different types of irrigation systems were also implemented into the NASA Land Information System (LIS) framework and coupled with several LSMs, such as the Noah LSM (Chen et al, 1996;Chen and Dudhia, 2001;Mahrt and Pan, 1984) and the Noah-MP LSM (Niu et al, 2011).…”