Irrigation in JULES Land-Only Simulations over South and East Asia

2021 ◽  
Author(s):  
Markus Todt ◽  
Pier Luigi Vidale ◽  
Patrick C. McGuire ◽  
Omar V. Müller
Keyword(s):  
Soil Moisture ◽  
East Asia ◽  
Land Surface ◽  
River Flow ◽  
Heat Fluxes ◽  
Global Climate Models ◽  
Irrigated Agriculture ◽  
Irrigation Season ◽  
Irrigation Scheme ◽  
The Impact

<p>Capturing soil moisture-atmosphere feedbacks in a weather or climate model requires realistic simulation of various land surface processes. However, irrigation and other water management methods are still missing in most global climate models today, despite irrigated agriculture being the dominant land use in parts of Asia. In this study, we test the irrigation scheme available in the land model JULES (Joint UK Land Environment Simulator) by running land-only simulations over South and East Asia driven by WFDEI (WATCH Forcing Data ERA-Interim) forcing data. Irrigation in JULES is applied on a daily basis by replenishing soil moisture in the upper soil layers to field capacity, and we use a version of the irrigation scheme that extracts water for irrigation from groundwater and rivers, which physically limits the amount of irrigation that can be applied. We prescribe irrigation for C3 grasses in order to simulate the effects of agriculture, albeit retaining the simpler, widely used 5-PFT (plant functional type) configuration in JULES. Irrigation generally increases soil moisture and evapotranspiration, which results in increasing latent heat fluxes and decreasing sensible heat fluxes. Comparison with combined observational/machine-learning products for turbulent fluxes shows that while irrigation can reduce biases, other biases in JULES, unrelated to irrigation, are larger than improvements due to the inclusion of irrigation. Irrigation also affects water fluxes within the soil, e.g. runoff and drainage into the groundwater level, as well as soil moisture outside of the irrigation season. We find that the irrigation scheme, at least in the uncoupled land-atmosphere setting, can rapidly deplete groundwater to the point that river flow becomes the main source of irrigation (over the North China Plain and the Indus region) and can have the counterintuitive effect of decreasing annual average soil moisture (over the Ganges plain). Subsequently, we will explore the impact of irrigation on regional climate by conducting coupled land-atmosphere simulations.</p>

scholarly journals Influence of Land Cover and Soil Moisture on the Horizontal Distribution of Sensible and Latent Heat Fluxes in Southeast Kansas during IHOP_2002 and CASES-97

2007 ◽  
Vol 8 (1) ◽  
pp. 68-87 ◽  
Author(s):  
Margaret A. LeMone ◽  
Fei Chen ◽  
Joseph G. Alfieri ◽  
Mukul Tewari ◽  
Bart Geerts ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Latent Heat ◽  
Land Surface ◽  
Field Experiments ◽  
Time Of Day ◽  
Horizontal Distribution ◽  
Heat Fluxes ◽  
Surface Model ◽  
Green Vegetation ◽  
The Impact

Abstract Analyses of daytime fair-weather aircraft and surface-flux tower data from the May–June 2002 International H2O Project (IHOP_2002) and the April–May 1997 Cooperative Atmosphere Surface Exchange Study (CASES-97) are used to document the role of vegetation, soil moisture, and terrain in determining the horizontal variability of latent heat LE and sensible heat H along a 46-km flight track in southeast Kansas. Combining the two field experiments clearly reveals the strong influence of vegetation cover, with H maxima over sparse/dormant vegetation, and H minima over green vegetation; and, to a lesser extent, LE maxima over green vegetation, and LE minima over sparse/dormant vegetation. If the small number of cases is producing the correct trend, other effects of vegetation and the impact of soil moisture emerge through examining the slope ΔxyLE/ΔxyH for the best-fit straight line for plots of time-averaged LE as a function of time-averaged H over the area. Based on the surface energy balance, H + LE = Rnet − Gsfc, where Rnet is the net radiation and Gsfc is the flux into the soil; Rnet − Gsfc ∼ constant over the area implies an approximately −1 slope. Right after rainfall, H and LE vary too little horizontally to define a slope. After sufficient drying to produce enough horizontal variation to define a slope, a steep (∼−2) slope emerges. The slope becomes shallower and better defined with time as H and LE horizontal variability increases. Similarly, the slope becomes more negative with moister soils. In addition, the slope can change with time of day due to phase differences in H and LE. These trends are based on land surface model (LSM) runs and observations collected under nearly clear skies; the vegetation is unstressed for the days examined. LSM runs suggest terrain may also play a role, but observational support is weak.

scholarly journals A Numerical Sensitivity Analysis of Soil Moisture Feedback on Convective Precipitation

2019 ◽  
Vol 20 (1) ◽  
pp. 23-44 ◽  
Author(s):  
Marika Koukoula ◽  
Efthymios I. Nikolopoulos ◽  
Jonilda Kushta ◽  
Nikolaos S. Bartsotas ◽  
George Kallos ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Spatial Resolution ◽  
Land Surface ◽  
Atmospheric Model ◽  
Atmospheric Modeling ◽  
Heat Fluxes ◽  
Convective Precipitation ◽  
Limited Area ◽  
The Impact

Abstract Of the boundary conditions that affect the simulation of convective precipitation, soil moisture is one of the most important. In this study, we explore the impact of the soil moisture on convective precipitation, and factors affecting it, through an extensive numerical experiment based on four convective precipitation events that caused moderate to severe flooding in the Gard region of southern France. High-spatial-resolution (1 km) weather simulations were performed using the integrated atmospheric model Regional Atmospheric Modeling System/Integrated Community Limited Area Modeling System (RAMS/ICLAMS). The experimental framework included comparative analysis of five simulation scenarios for each event, in which we varied the magnitude and spatial distribution of the initial volumetric water content using realistic soil moisture fields with different spatial resolution. We used precipitation and surface soil moisture from radar and satellite sensors as references for the comparison of the sensitivity tests. Our results elucidate the complexity of the relationship between soil moisture and convective precipitation, showing that the control of soil water content on partitioning land surface heat fluxes has significant impacts on convective precipitation. Additionally, it is shown how different soil moisture conditions affect the modeled microphysical structure of the clouds, which translates into further changes in the magnitude and distribution of precipitation.

scholarly journals Estimates of Global Surface Hydrology and Heat Fluxes from the Community Land Model (CLM4.5) with Four Atmospheric Forcing Datasets

2016 ◽  
Vol 17 (9) ◽  
pp. 2493-2510 ◽  
Author(s):  
Aihui Wang ◽  
Xubin Zeng ◽  
Donglin Guo
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Heat Fluxes ◽  
Surface Model ◽  
Atmospheric Forcing ◽  
Surface Hydrology ◽  
Community Land Model ◽  
Global Land ◽  
Ensemble Mean ◽  
The Impact

Abstract Global land surface hydrology and heat fluxes can be estimated by running a land surface model (LSM) driven by the atmospheric forcing dataset. Previous multimodel studies focused on the impact of different LSMs on model results. Here the sensitivity of the Community Land Model, version 4.5 (CLM4.5), results to the atmospheric forcing dataset is documented. Together with the model default global forcing dataset (CRU–NCEP, hereafter CRUNCEP), three newly developed, reanalysis-based, near-surface meteorological datasets (i.e., MERRA, CFSR, and ERA-Interim) with the precipitation adjusted by the Global Precipitation Climatology Project monthly product were used to drive CLM4.5. All four simulations were run at 0.5° × 0.5° grids from 1979 to 2009 with the identical initialization. The simulated monthly surface hydrology variables, fluxes, and the forcing datasets were then evaluated against various observation-based datasets (soil moisture, runoff, snow depth and water equivalent, and flux tower measurements). To partially avoid the mismatch between model gridbox values and point measurements, three approaches were taken. The model simulations based on three newly constructed forcing datasets are overall better than the simulation from CRUNCEP, in particular for soil moisture and snow quantities. The ensemble mean from the CLM4.5 simulations using the four forcing datasets is generally superior to individual simulations, and the ensemble mean latent and sensible heat fluxes over global land (60°S–90°N) are 42.8 and 40.3 W m−2, respectively. The differences in both precipitation and other atmospheric forcing variables (e.g., air temperature and downward solar radiation) contribute to the differences in simulated results. The datasets are available from the authors for further evaluation and for various applications.

scholarly journals Land surface controls on afternoon precipitation diagnosed from observational data: uncertainties, confounding factors and the possible role of vegetation interception

2013 ◽  
Vol 13 (11) ◽  
pp. 29137-29201 ◽  
Author(s):  
B. P. Guillod ◽  
B. Orlowsky ◽  
D. Miralles ◽  
A. J. Teuling ◽  
P. Blanken ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Strong Relationship ◽  
Heat Fluxes ◽  
Turbulent Heat ◽  
Seasonal Forecasts ◽  
The Impact

Abstract. The feedback between soil moisture and precipitation has long been a topic of interest due to its potential for improving weather and seasonal forecasts. The generally proposed mechanism assumes a control of soil moisture on precipitation via the partitioning of the surface turbulent heat fluxes, as assessed via the Evaporative Fraction, EF, i.e. the ratio of latent heat to the sum of latent and sensible heat, in particular under convective conditions. Our study investigates the poorly understood link between EF and precipitation by investigating the impact of before-noon EF on the frequency of afternoon precipitation over the contiguous US, using a statistical analysis of the relationship between multiple datasets of EF and precipitation. We analyze remote sensing data products (EF from GLEAM, Global Land Evaporation: the Amsterdam Methodology, based on satellite observations; and radar precipitation from NEXRAD, the NEXt generation weather RADar system), FLUXNET station data, and the North American Regional Reanalysis (NARR). While most datasets agree on the existence of regions of positive relationship between between EF and precipitation in the Eastern and Southwestern US, observation-based estimates (GLEAM, NEXRAD and to some extent FLUXNET) also indicate a strong relationship in the Central US which is not found in NARR. Investigating these differences, we find that much of these relationships can be explained by precipitation persistence alone, with ambiguous results on the additional role of EF in causing afternoon precipitation. Regional analyses reveal contrasting mechanisms over different regions. Over the Eastern US, our analyses suggest that the apparent EF-precipitation coupling takes place on a short day-to-day time scale and is either atmospherically controlled (from precipitation persistence and potential evaporation) or driven by vegetation interception and subsequent re-evaporation (rather than soil moisture and related plant transpiration/bare soil evaporation), in line with the high forest cover and the wet regime of that region. Over the Central and Southwestern US, the impact of EF on convection triggering is additionally linked to soil moisture variations, owing to the soil moisture–limited climate regime.

scholarly journals On the ability of Sentinel-1 backscatter to detect soil moisture and vegetation changes caused by irrigation fluxes over the Po River Valley (Italy)

2021 ◽  
Author(s):  
Sara Modanesi ◽  
Christian Massari ◽  
Alexander Gruber ◽  
Luca Brocca ◽  
Hans Lievens ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
River Valley ◽  
System Level ◽  
Surface Model ◽  
Irrigation Scheme ◽  
Po River ◽  
Area Index ◽  
The Impact

<p>Worldwide, the amount of water used for agricultural purposes is rising because of an increasing food demand. In this context, the detection and quantification of irrigation is crucial, but the availability of ground observations is limited. Therefore, an increasing number of studies are focusing on the use of models and satellite data to detect and quantify irrigation. For instance, the parameterization of irrigation in large scale Land Surface Models (LSM) is improving, but it is still characterized by simplifying assumptions, such as the lack of dynamic crop information, the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. Remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining models and satellite information through data assimilation can offer a viable way to quantify the water used for irrigation.</p><p>The aim of this study is to test how well modelled soil moisture and vegetation estimates from the Noah-MP LSM, with or without irrigation parameterization in the NASA Land Information System (LIS), are able to mimic in situ observations or to capture the signal of high-resolution Sentinel-1 backscatter observations in an irrigated area. The experiments were carried out over select sites in the Po river Valley, an important agricultural area in Northern Italy. To prepare for a data assimilation system, Level-1 Sentinel-1 backscatter observations, aggregated and sampled onto the 1 km EASE-v2 grid, were used to calibrate a Water Cloud Model (WCM) using simulated soil moisture and Leaf Area Index estimates. The WCM was calibrated with and without activating an irrigation scheme in Noah-MP. Results demonstrate that the use of the irrigation scheme provides the optimal calibration of the WCM, confirming the ability of Sentinel-1 to track the impact of human activities on the water cycle. Additionally, a first data assimilation experiment demonstrates the potential of Sentinel-1 backscatter observations to correct errors in Land Surface Model (LSM) simulations that are caused by unmodelled or wrongly modelled irrigation.</p>

scholarly journals Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

2021 ◽  
Vol 25 (12) ◽  
pp. 6283-6307
Author(s):  
Sara Modanesi ◽  
Christian Massari ◽  
Alexander Gruber ◽  
Hans Lievens ◽  
Angelica Tarpanelli ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Bayesian Optimization ◽  
Irrigation Scheme ◽  
Area Index ◽  
Observation Operator ◽  
Future Data ◽  
The Impact

Abstract. Worldwide, the amount of water used for agricultural purposes is rising, and the quantification of irrigation is becoming a crucial topic. Because of the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large-scale land surface models (LSMs) is improving, but it is still hampered by the lack of information about dynamic crop rotations, or the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. On the other hand, remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation can offer the optimal way to quantify the water used for irrigation. This work represents the first and necessary step towards building a reliable LSM data assimilation system which, in future analysis, will investigate the potential of high-resolution radar backscatter observations from Sentinel-1 to improve irrigation quantification. Specifically, the aim of this study is to couple the Noah-MP LSM running within the NASA Land Information System (LIS), with a backscatter observation operator for simulating unbiased backscatter predictions over irrigated lands. In this context, we first tested how well modelled surface soil moisture (SSM) and vegetation estimates, with or without irrigation simulation, are able to capture the signal of aggregated 1 km Sentinel-1 backscatter observations over the Po Valley, an important agricultural area in northern Italy. Next, Sentinel-1 backscatter observations, together with simulated SSM and leaf area index (LAI), were used to optimize a Water Cloud Model (WCM), which will represent the observation operator in future data assimilation experiments. The WCM was calibrated with and without an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that using an irrigation scheme provides a better calibration of the WCM, even if the simulated irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chances of having error cross-correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture, and vegetation estimates via future data assimilation.

Development and Testing of a Surface Flux and Planetary Boundary Layer Model for Application in Mesoscale Models

1995 ◽  
Vol 34 (1) ◽  
pp. 16-32 ◽  
Author(s):  
Jonathan E. Pleim ◽  
Aijun Xiu
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Planetary Boundary Layer ◽  
Land Surface ◽  
Field Experiments ◽  
Weather Prediction ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Global Climate Models ◽  
Pbl Model

Abstract Although the development of soil, vegetation, and atmosphere interaction models has been driven primarily by the need for accurate simulations of long-term energy and moisture budgets in global climate models, the importance of these processes at smaller scales for short-term numerical weather prediction and air quality studies is becoming more appreciated. Planetary boundary layer (PBL) development is highly dependent on the partitioning of the available net radiation into sensible and latent heat fluxes. Therefore, adequate treatmentof surface properties such as soil moisture and vegetation characteristics is essential for accurate simulation of PBL development, convective and low-level cloud processes, and the temperature and humidity of boundary layer air. In this paper, the development ofa simple coupled surface and PBL model, which is planned for incorporation into the Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM4/5), is described. The soil-vegetation model is based on a simple force-restore algorithm with explicit soil moisture and evapotranspiration. The PBL model is a hybrid of nonlocal closure for convective conditions and eddy diffusion for all other conditions. A one-dimensional version of the model has been applied to several case studies from field experiments in both dry desert-like conditions (Wangara) and moist vegetated conditions(First International Satellite Land Surface Climatology Project Field Experiment) to demonstrate the model's ability to realistically simulate surface fluxes as well as PBL development. This new surface-PBL model is currently being incorporated into the MM4-MM5 system.

scholarly journals Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

2021 ◽  
Author(s):  
Sara Modanesi ◽  
Christian Massari ◽  
Alexander Gruber ◽  
Hans Lievens ◽  
Angelica Tarpanelli ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Cloud Model ◽  
Bayesian Optimization ◽  
Irrigation Scheme ◽  
Area Index ◽  
Observation Operator ◽  
The Impact

Abstract. Worldwide, the amount of water used for agricultural purposes is rising and the quantification of irrigation is becoming a crucial topic. Because of the the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large scale Land Surface Models (LSM) is improving, but it is still hampered by the lack of information about dynamic crop rotations or the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. On the other hand, remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation can offer the optimal way to quantify the water used for irrigation. The aim of this study is to optimize a land modeling system, consisting of the Noah-MP LSM, coupled with a backscatter observation operator, over irrigated land in order to simulate backscatter predictions. This is a first step towards building a reliable data assimilation system to ingest level-1 Sentinel-1 observations. In this context, we tested how well modeled soil moisture and vegetation estimates from the Noah-MP LSM running within the NASA Land Information System (LIS), with or without irrigation simulation, are able to capture the signal of high-resolution Sentinel-1 backscatter observations over the Po river Valley, an important agricultural area in Northern Italy. Next, aggregated 1-km Sentinel-1 backscatter observations were used to calibrate a Water Cloud Model (WCM) as observation operator using simulated soil moisture and Leaf Area Index estimates. The WCM was calibrated with and without activating an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that activating an irrigation scheme provides the optimal calibration of the WCM, even if the irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chance of having error cross correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture and vegetation estimates via future data assimilation.

scholarly journals The incorporation of an organic soil layer in the Noah-MP land surface model and its evaluation over a boreal aspen forest

2016 ◽  
Vol 16 (13) ◽  
pp. 8375-8387 ◽  
Author(s):  
Liang Chen ◽  
Yanping Li ◽  
Fei Chen ◽  
Alan Barr ◽  
Michael Barlage ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Latent Heat ◽  
Land Surface ◽  
Soil Layer ◽  
Land Surface Model ◽  
Organic Soil ◽  
Heat Fluxes ◽  
Surface Model ◽  
The Impact

Abstract. A thick top layer of organic matter is a dominant feature in boreal forests and can impact land–atmosphere interactions. In this study, the multi-parameterization version of the Noah land surface model (Noah-MP) was used to investigate the impact of incorporating a forest-floor organic soil layer on the simulated surface energy and water cycle components at the BERMS Old Aspen site (OAS) field station in central Saskatchewan, Canada. Compared to a simulation without an organic soil parameterization (CTL), the Noah-MP simulation with an organic soil (OGN) improved Noah-MP-simulated soil temperature profiles and soil moisture at 40–100 cm, especially the phase and amplitude (Seasonal cycle) of soil temperature below 10 cm. OGN also enhanced the simulation of sensible and latent heat fluxes in spring, especially in wet years, which is mostly related to the timing of spring soil thaw and warming. Simulated top-layer soil moisture is better in OGN than that in CTL. The effects of including an organic soil layer on soil temperature are not uniform throughout the soil depth and are more prominent in summer. For drought years, the OGN simulation substantially modified the partitioning of water between direct soil evaporation and vegetation transpiration. For wet years, the OGN-simulated latent heat fluxes are similar to CTL except for the spring season when OGN produced less evaporation, which was closer to observations. Including organic soil produced more subsurface runoff and resulted in much higher runoff throughout the freezing periods in wet years.

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