Joint modeling of crop and irrigation in the Central United States using the Noah-MP land surface model

ABSTRACTWarm-season rainfall associated with mesoscale convective systems (MCSs) in the central United States is characterized by higher intensity and nocturnal timing compared to rainfall from non-MCS systems, suggesting their potentially different footprints on the land surface. To differentiate the impacts of MCS and non-MCS rainfall on the surface water balance, a water tracer tool embedded in the Noah land surface model with multiparameterization options (WT-Noah-MP) is used to numerically “tag” water from MCS and non-MCS rainfall separately during April–August (1997–2018) and track their transit in the terrestrial system. From the water-tagging results, over 50% of warm-season rainfall leaves the surface–subsurface system through evapotranspiration by the end of August, but non-MCS rainfall contributes a larger fraction. However, MCS rainfall plays a more important role in generating surface runoff. These differences are mostly attributed to the rainfall intensity differences. The higher-intensity MCS rainfall tends to produce more surface runoff through infiltration excess flow and drives a deeper penetration of the rainwater into the soil. Over 70% of the top 10th percentile runoff is contributed by MCS rainfall, demonstrating its important contribution to local flooding. In contrast, lower-intensity non-MCS rainfall resides mostly in the top layer and contributes more to evapotranspiration through soil evaporation. Diurnal timing of rainfall has negligible effects on the flux partitioning for both MCS and non-MCS rainfall. Differences in soil moisture profiles for MCS and non-MCS rainfall and the resultant evapotranspiration suggest differences in their roles in soil moisture–precipitation feedbacks and ecohydrology.

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Assimilation of Remotely Sensed Leaf Area Index into the Noah-MP Land Surface Model: Impacts on Water and Carbon Fluxes and States over the Continental United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0237.1 ◽

2019 ◽

Vol 20 (7) ◽

pp. 1359-1377 ◽

Cited By ~ 14

Author(s):

Sujay V. Kumar ◽

David M. Mocko ◽

Shugong Wang ◽

Christa D. Peters-Lidard ◽

Jordan Borak

Keyword(s):

United States ◽

Leaf Area Index ◽

Leaf Area ◽

Land Surface ◽

Land Surface Model ◽

Carbon Fluxes ◽

Surface Model ◽

Vegetation Changes ◽

Area Index ◽

Terrestrial Water

Abstract Accurate representation of vegetation states is required for the modeling of terrestrial water–energy–carbon exchanges and the characterization of the impacts of natural and anthropogenic vegetation changes on the land surface. This study presents a comprehensive evaluation of the impact of assimilating remote sensing–based leaf area index (LAI) retrievals over the continental United States in the Noah-MP land surface model, during a time period of 2000–17. The results demonstrate that the assimilation has a beneficial impact on the simulation of key water budget terms, such as soil moisture, evapotranspiration, snow depth, terrestrial water storage, and streamflow, when compared with a large suite of reference datasets. In addition, the assimilation of LAI is also found to improve the carbon fluxes of gross primary production (GPP) and net ecosystem exchange (NEE). Most prominent improvements in the water and carbon variables are observed over the agricultural areas of the United States, where assimilation improves the representation of vegetation seasonality impacted by cropping schedules. The systematic, added improvements from assimilation in a configuration that employs high-quality boundary conditions highlight the significant utility of LAI data assimilation in capturing the impacts of vegetation changes.

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Simulating the Effects of Irrigation over the United States in a Land Surface Model Based on Satellite-Derived Agricultural Data

Journal of Hydrometeorology ◽

10.1175/2009jhm1116.1 ◽

2010 ◽

Vol 11 (1) ◽

pp. 171-184 ◽

Cited By ~ 149

Author(s):

Mutlu Ozdogan ◽

Matthew Rodell ◽

Hiroko Kato Beaudoing ◽

David L. Toll

Keyword(s):

United States ◽

Heat Flux ◽

Land Surface ◽

Agricultural Land ◽

Weather Prediction ◽

Land Surface Model ◽

The United States ◽

Surface Model ◽

Irrigation Scheme ◽

Ground Heat Flux

Abstract A novel method is introduced for integrating satellite-derived irrigation data and high-resolution crop-type information into a land surface model (LSM). The objective is to improve the simulation of land surface states and fluxes through better representation of agricultural land use. Ultimately, this scheme could enable numerical weather prediction (NWP) models to capture land–atmosphere feedbacks in managed lands more accurately and thus improve forecast skill. Here, it is shown that the application of the new irrigation scheme over the continental United States significantly influences the surface water and energy balances by modulating the partitioning of water between the surface and the atmosphere. In this experiment, irrigation caused a 12% increase in evapotranspiration (QLE) and an equivalent reduction in the sensible heat flux (QH) averaged over all irrigated areas in the continental United States during the 2003 growing season. Local effects were more extreme: irrigation shifted more than 100 W m−2 from QH to QLE in many locations in California, eastern Idaho, southern Washington, and southern Colorado during peak crop growth. In these cases, the changes in ground heat flux (QG), net radiation (RNET), evapotranspiration (ET), runoff (R), and soil moisture (SM) were more than 3 W m−2, 20 W m−2, 5 mm day−1, 0.3 mm day−1, and 100 mm, respectively. These results are highly relevant to continental-to-global-scale water and energy cycle studies that, to date, have struggled to quantify the effects of agricultural management practices such as irrigation. On the basis of the results presented here, it is expected that better representation of managed lands will lead to improved weather and climate forecasting skill when the new irrigation scheme is incorporated into NWP models such as NOAA’s Global Forecast System (GFS).

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Contribution of Meteorological Downscaling to Skill and Precision of Seasonal Drought Forecasts

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0259.1 ◽

2021 ◽

Author(s):

Ryan A. Zamora ◽

Benjamin F. Zaitchik ◽

Matthew Rodell ◽

Augusto Getirana ◽

Sujay Kumar ◽

...

Keyword(s):

United States ◽

Land Surface ◽

Land Surface Model ◽

Surface Model ◽

Drought Forecasting ◽

Surface Hydrology ◽

Seasonal Drought ◽

Earth Observing System ◽

The North ◽

Land Data Assimilation System

AbstractResearch in meteorological prediction on sub-seasonal to seasonal (S2S) timescales has seen growth in recent years. Concurrent with this, demand for seasonal drought forecasting has risen. While there is obvious synergy between these fields, S2S meteorological forecasting has typically focused on low resolution global models, while the development of drought can be sensitive to the local expression of weather anomalies and their interaction with local surface properties and processes. This suggests that downscaling might play an important role in the application of meteorological S2S forecasts to skillful forecasting of drought. Here, we apply the Generalized Analog Regression Downscaling (GARD) algorithm to downscale meteorological hindcasts from the NASA Goddard Earth Observing System (GEOS) global S2S forecast system. Downscaled meteorological fields are then applied to drive offline simulations with the Catchment Land Surface Model (CLSM) to forecast United States Drought Monitor (USDM) style drought indicators derived from simulated surface hydrology variables. We compare the representation of drought in these downscaled hindcasts to hindcasts that are not downscaled, using the North American Land Data Assimilation System Phase 2 (NLDAS-2) dataset as an observational reference. We find that downscaling using GARD improves hindcasts of temperature and temperature anomalies, but the results for precipitation are mixed and generally small. Overall, GARD downscaling led to improved hindcast skill for total drought across the Contiguous United States (CONUS), and improvements were greatest for extreme (D3) and exceptional (D4) drought categories.

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GRACE Improves Seasonal Groundwater Forecast Initialization over the United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0096.1 ◽

2020 ◽

Vol 21 (1) ◽

pp. 59-71 ◽

Cited By ~ 6

Author(s):

Augusto Getirana ◽

Matthew Rodell ◽

Sujay Kumar ◽

Hiroko Kato Beaudoing ◽

Kristi Arsenault ◽

...

Keyword(s):

United States ◽

Data Assimilation ◽

Land Surface ◽

Land Surface Model ◽

The United States ◽

High Plains ◽

Surface Model ◽

Forecast Performance ◽

Terrestrial Water ◽

The Impact

AbstractWe evaluate the impact of Gravity Recovery and Climate Experiment data assimilation (GRACE-DA) on seasonal hydrological forecast initialization over the United States, focusing on groundwater storage. GRACE-based terrestrial water storage (TWS) estimates are assimilated into a land surface model for the 2003–16 period. Three-month hindcast (i.e., forecast of past events) simulations are initialized using states from the reference (no data assimilation) and GRACE-DA runs. Differences between the two initial hydrological condition (IHC) sets are evaluated for two forecast techniques at 305 wells where depth to water table measurements are available. Results show that using GRACE-DA-based IHC improves seasonal groundwater forecast performance in terms of both RMSE and correlation. While most regions show improvement, degradation is common in the High Plains, where withdrawals for irrigation practices affect groundwater variability more strongly than the weather variability, which demonstrates the need for simulating such activities. These findings contribute to recent efforts toward an improved U.S. drought monitoring and forecast system.

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Drought Variability over the Conterminous United States for the Past Century

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0158.1 ◽

2021 ◽

Author(s):

Lu Su ◽

Qian Cao ◽

Mu Xiao ◽

David M. Mocko ◽

Michael Barlage ◽

...

Keyword(s):

United States ◽

Land Surface ◽

Land Surface Model ◽

Area Coverage ◽

Surface Model ◽

Past Century ◽

Drought Duration ◽

Drought Variability ◽

Dead Vegetation ◽

Reconstruction Period

AbstractWe examine the drought variability over the Conterminous United States (CONUS) for 1915-2018 using the Noah-MP land-surface model. We examine different model options on drought reconstruction including optional representation of groundwater and dynamic vegetation phenology. Over our 104-year reconstruction period, we identify 12 great droughts that each covered at least 36% of CONUS and lasted for at least 5 months. The great droughts tend to have smaller areas when groundwater and/or dynamic vegetation are included in the model configuration. We detect a small decreasing trend in dry area coverage over CONUS in all configurations. We identify 45 major droughts in the baseline (with a dry area coverage greater than 23.6% of CONUS) that are, on average, somewhat less severe than great droughts. We find that representation of groundwater tends to increase drought duration for both great and major droughts, primarily by leading to earlier drought onset (some due to short-lived recovery from a previous drought) or later demise (groundwater anomalies lag precipitation anomalies). In contrast, representation of dynamic vegetation tends to shorten major droughts duration, primarily due to earlier drought demise ( closed stoma or dead vegetation reduces ET loss during droughts). On a regional basis, the U.S. Southwest (Southeast) has the longest (shortest) major drought durations. Consistent with earlier work, dry area coverage in all subregions except the Southwest has decreased. The effects of groundwater and dynamic vegetation vary regionally due to differences in groundwater depths (hence connectivity with the surface) and vegetation types.

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Using scientific workflows to calibrate an Australian land surface model (AWRA-L)

Piantadosi, J., Anderssen, R.S. and Boland J. (eds) MODSIM2013, 20th International Congress on Modelling and Simulation ◽

10.36334/modsim.2013.j9.vleeshouwer ◽

2013 ◽

Keyword(s):

Land Surface ◽

Land Surface Model ◽

Scientific Workflows ◽

Surface Model

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L'apport de la télédétection spatiale à la modélisation des surfaces continentales

La Météorologie ◽

10.37053/lameteorologie-2020-0016 ◽

2020 ◽

pp. 052

Author(s):

Jean-Christophe Calvet ◽

Jean-Louis Champeaux

Keyword(s):

Satellite Data ◽

Land Surface ◽

Land Surface Model ◽

Static Model ◽

Geographic Information ◽

Surface Model ◽

Model Parameters

Cet article présente les différentes étapes des développements réalisés au CNRM des années 1990 à nos jours pour spatialiser à diverses échelles les simulations du modèle Isba des surfaces terrestres. Une attention particulière est portée sur l'intégration, dans le modèle, de données satellitaires permettant de caractériser la végétation. Deux façons complémentaires d'introduire de l'information géographique dans Isba sont présentées : cartographie de paramètres statiques et intégration au fil de l'eau dans le modèle de variables observables depuis l'espace. This paper presents successive steps in developments made at CNRM from the 1990s to the present-day in order to spatialize the simulations of the Isba land surface model at various scales. The focus is on the integration in the model of satellite data informative about vegetation. Two complementary ways to integrate geographic information in Isba are presented: mapping of static model parameters and sequential assimilation of variables observable from space.

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