Global change in the root zone: lessons from soil moisture dynamics in a multifactor climate manipulation experiment

Assessing the future of water resources in terrestrial biomes is contingent on observations from climate-manipulation experiments. Global change in the Anthropocene could produce various permutations of warming, atmospheric carbon levels, and moisture availability; however the impact on ecosystem hydrology is largely studied individually (e.g., elevated CO2 or temperature) rather than interactively. We sought to specify how various combinations of &#160;drought, elevated CO2 (+150 ppm, +300 ppm) and warming (+1.5&#176;C and + 3&#176;C) may alter the partitioning of soil moisture in the root zone of mountain grassland. Using spectral techniques, we transformed these high resolution data (i.e., 4 soil depths and every 15 min) into the frequency domain to study the interactive effects of climate change on sub-hourly to seasonal soil moisture signals. Diurnal moisture signals in heated plots (+3&#176;C in air temperature) were up to 3x stronger (in amplitude) during summer drawdown compared to plots receiving heat and elevated CO2 (+300 ppm). This preliminary analysis suggests that elevated atmospheric carbon may buffer heat-driven soil moisture losses in grassland root zones by reducing transpiration fluxes during seasonal dry periods.

Download Full-text

The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

Hydrology and Earth System Sciences ◽

10.5194/hess-19-4831-2015 ◽

2015 ◽

Vol 19 (12) ◽

pp. 4831-4844 ◽

Cited By ~ 19

Author(s):

C. Draper ◽

R. Reichle

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Surface Soil ◽

Root Zone ◽

Surface Model ◽

Surface Soil Moisture ◽

Near Surface ◽

In Situ Observations ◽

The Impact

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

Download Full-text

Area-representative validation of remotely sensed high resolution soil moisture using a cosmic-ray neutron sensor

10.5194/egusphere-egu2020-16222 ◽

2020 ◽

Author(s):

Dragana Panic ◽

Isabella Pfeil ◽

Andreas Salentinig ◽

Mariette Vreugdenhil ◽

Wolfgang Wagner ◽

...

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Root Zone ◽

Spatial Scales ◽

Temporal Frequency ◽

Cosmic Ray ◽

Irrigation Scheduling ◽

Water Index ◽

The Impact

Reliable measurements of soil moisture (SM) are required for many applications worldwide, e.g., for flood and drought forecasting, and for improving the agricultural water use efficiency (e.g., irrigation scheduling). For the retrieval of large-scale SM datasets with a high temporal frequency, remote sensing methods have proven to be a valuable data source. (Sub-)daily SM is derived, for example, from observations of the Advanced Scatterometer (ASCAT) since 2007. These measurements are available on spatial scales of several square kilometers and are in particular useful for applications that do not require fine spatial resolutions but long and continuous time series. Since the launch of the first Sentinel-1 satellite in 2015, the derivation of SM at a spatial scale of 1 km has become possible for every 1.5-4 days over Europe (SSM1km) [1]. Recently, efforts have been made to combine ASCAT and Sentinel-1 to a Soil Water Index (SWI) product, in order to obtain a SM dataset with daily 1 km resolution (SWI1km) [2]. Both datasets are available over Europe from the Copernicus Global Land Service (CGLS, https://land.copernicus.eu/global/). As the quality of such a dataset is typically best over grassland and agricultural areas, and degrades with increasing vegetation density, validation is of high importance for the further development of the dataset and for its subsequent use by stakeholders.Traditionally, validation studies have been carried out using in situ SM sensors from ground networks. Those are however often not representative of the area-wide satellite footprints. In this context, cosmic-ray neutron sensors (CRNS) have been found to be valuable, as they provide integrated SM estimates over a much larger area (about 20 hectares), which comes close to the spatial support area of the satellite SM product. In a previous study, we used CRNS measurements to validate ASCAT and S1 SM over an agricultural catchment, the Hydrological Open Air Laboratory (HOAL), in Petzenkirchen, Austria. The datasets were found to agree, but uncertainties regarding the impact of vegetation were identified.In this study, we validated the SSM1km, SWI1km and a new S1-ASCAT SM product, which is currently developed at TU Wien, using CRNS. The new S1-ASCAT-combined dataset includes an improved vegetation parameterization, trend correction and snow masking. The validation has been carried out in the HOAL and on a second site in Marchfeld, Austria&#8217;s main crop producing area. As microwaves only penetrate the upper few centimeters of the soil, we applied the soil water index concept [3] to obtain soil moisture estimates of the root zone (approximately 0-40 cm) and thus roughly corresponding to the depth of the CRNS measurements. In the HOAL, we also incorporated in-situ SM from a network of point-scale time-domain-transmissivity sensors distributed within the CRNS footprint. The datasets were compared to each other by calculating correlation metrics. Furthermore, we investigated the effect of vegetation on both the satellite and the CRNS data by analyzing detailed information on crop type distribution and crop water content.[1] Bauer-Marschallinger et al., 2018a: https://doi.org/10.1109/TGRS.2018.2858004 [2] Bauer-Marschallinger et al., 2018b: https://doi.org/10.3390/rs10071030 [3] Wagner et al., 1999: https://doi.org/10.1016/S0034-4257(99)00036-X

Download Full-text

The Impact of Rainfall Error Characterization on the Estimation of Soil Moisture Fields in a Land Data Assimilation System

Journal of Hydrometeorology ◽

10.1175/jhm-d-11-0115.1 ◽

2012 ◽

Vol 13 (3) ◽

pp. 1107-1118 ◽

Cited By ~ 7

Author(s):

Viviana Maggioni ◽

Rolf H. Reichle ◽

Emmanouil N. Anagnostou

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Root Zone ◽

Error Model ◽

Near Surface ◽

Land Data Assimilation ◽

Land Data Assimilation System ◽

The Impact ◽

Data Assimilation System ◽

Assimilation System

Abstract This study presents a numerical experiment to assess the impact of satellite rainfall error structure on the efficiency of assimilating near-surface soil moisture observations. Specifically, the study contrasts a multidimensional satellite rainfall error model (SREM2D) to a simpler rainfall error model (CTRL) currently used to generate rainfall ensembles as part of the ensemble-based land data assimilation system developed at the NASA Global Modeling and Assimilation Office. The study is conducted in the Oklahoma region using rainfall data from a NOAA multisatellite global rainfall product [the Climate Prediction Center (CPC) morphing technique (CMORPH)] and the National Weather Service rain gauge–calibrated radar rainfall product [Weather Surveillance Radar-1988 Doppler (WSR-88D)] representing the “uncertain” and “reference” model rainfall forcing, respectively. Soil moisture simulations using the Catchment land surface model (CLSM), obtained by forcing the model with reference rainfall, are randomly perturbed to represent satellite retrieval uncertainty, and assimilated into CLSM as synthetic near-surface soil moisture observations. The assimilation estimates show improved performance metrics, exhibiting higher anomaly correlation coefficients (e.g., ~0.79 and ~0.90 in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively) and lower root-mean-square errors (e.g., ~0.034 m3 m−3 and ~0.024 m3 m−3 in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively). The more elaborate rainfall error model in the assimilation system leads to slightly improved assimilation estimates. In particular, the relative enhancement due to SREM2D over CTRL is larger for root zone soil moisture and in wetter rainfall conditions.

Download Full-text

Streamflow data assimilation for soil moisture analysis

Geoscientific Model Development ◽

10.5194/gmd-3-1-2010 ◽

2010 ◽

Vol 3 (1) ◽

pp. 1-12 ◽

Cited By ~ 7

Author(s):

K. Warrach-Sagi ◽

V. Wulfmeyer

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Climate Models ◽

Root Zone ◽

Weather Forecast ◽

Surface Model ◽

River Catchment ◽

Streamflow Data ◽

The Impact

Abstract. Streamflow depends on the soil moisture of a river catchment and can be measured with relatively high accuracy. The soil moisture in the root zone influences the latent heat flux and, hence, the quantity and spatial distribution of atmospheric water vapour and precipitation. As numerical weather forecast and climate models require a proper soil moisture initialization for their land surface models, we enhanced an Ensemble Kalman Filter to assimilate streamflow time series into the multi-layer land surface model TERRA-ML of the regional weather forecast model COSMO. The impact of streamflow assimilation was studied by an observing system simulation experiment in the Enz River catchment (located at the downwind side of the northern Black Forest in Germany). The results demonstrate a clear improvement of the soil moisture field in the catchment. We illustrate the potential of streamflow data assimilation for weather forecasting and discuss its spatial and temporal requirements for a corresponding, automated river gauging network.

Download Full-text

Soil Water Measurement as Basis to Optimize Irrigation Scheduling: A Review

HortScience ◽

10.21273/hortsci.33.3.553f ◽

1998 ◽

Vol 33 (3) ◽

pp. 553f-554

Author(s):

A.K. Alva ◽

A. Fares

Keyword(s):

Soil Moisture ◽

Water Content ◽

Soil Water ◽

Real Time ◽

Soil Water Content ◽

Root Zone ◽

Soil Water Potential ◽

Irrigation Scheduling ◽

Rooting Depth ◽

The Impact

Supplemental irrigation is often necessary for high economic returns for most cropping conditions even in humid areas. As irrigation costs continue to increase more efforts should be exerted to minimize these costs. Real time estimation and/or measurement of available soil water content in the crop root zone is one of the several methods used to help growers in making the right decision regarding timing and quantity of irrigation. The gravimetric method of soil water content determination is laborious and doesn't suite for frequent sampling from the same location because it requires destructive soil sampling. Tensiometers, which measure soil water potential that can be converted into soil water content using soil moisture release curves, have been used for irrigation scheduling. However, in extreme sandy soils the working interval of tensiometer is reduced, hence it may be difficult to detect small changes in soil moisture content. Capacitance probes which operate on the principle of apparent dielectric constant of the soil-water-air mixture are extremely sensitive to small changes in the soil water content at short time intervals. These probes can be placed at various depths within and below the effective rooting depth for a real time monitoring of the water content. Based on this continuous monitoring of the soil water content, irrigation is scheduled to replenish the water deficit within the rooting depth while leaching below the root zone is minimized. These are important management practices aimed to increase irrigation efficiency, and nutrient uptake efficiency for optimal crop production, while minimizing the impact of agricultural non-point source pollutants on the groundwater quality.

Download Full-text

Increasing the depth of a Land Surface Model. Part II: Temperature sensitivity to improved subsurface thermodynamics and associated permafrost response

Journal of Hydrometeorology ◽

10.1175/jhm-d-21-0023.1 ◽

2021 ◽

Cited By ~ 1

Author(s):

N. J. Steinert ◽

J. F. González-Rouco ◽

P. de Vrese ◽

E. García-Bustamante ◽

S. Hagemann ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Root Zone ◽

Land Surface Model ◽

Phase Changes ◽

Surface Model ◽

Climate Projections ◽

Stable States ◽

Soil Parameter ◽

The Impact

AbstractThe impact of various modifications of the JSBACH Land Surface Model to represent soil temperature and cold-region hydro-thermodynamic processes in climate projections of the 21st century is examined. We explore the sensitivity of JSBACH to changes in the soil thermodynamics, energy balance and storage, and the effect of including freezing and thawing processes. The changes involve 1) the net effect of an improved soil physical representation and 2) the sensitivity of our results to changed soil parameter values and their contribution to the simulation of soil temperatures and soil moisture, both aspects being presented in the frame of an increased bottom boundary depth from 9.83 m to 1418.84 m. The implementation of water phase changes and supercooled water in the ground creates a coupling between the soil thermal and hydrological regimes through latent heat exchange. Momentous effects on subsurface temperature of up to ±3 K, together with soil drying in the high northern latitudes, can be found at regional scales when applying improved hydro-thermodynamic soil physics. The sensitivity of the model to different soil parameter datasets occurs to be low but shows important implications for the root zone soil moisture content. The evolution of permafrost under pre-industrial forcing conditions emerges in simulated trajectories of stable states that differ by 4 – 6 • 106 km2 and shows large differences in the spatial extent of 105 –106 km2 by 2100, depending on the model configuration.

Download Full-text

A Prior Estimation of the Spatial Distribution Parameter of Soil Moisture Storage Capacity Using Satellite-Based Root-Zone Soil Moisture Data

Remote Sensing ◽

10.3390/rs11212580 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2580 ◽

Cited By ~ 1

Author(s):

Yifei Tian ◽

Lihua Xiong ◽

Bin Xiong ◽

Ruodan Zhuang

Keyword(s):

Spatial Distribution ◽

Soil Moisture ◽

Storage Capacity ◽

Spatial Information ◽

Root Zone ◽

River Catchment ◽

Soil Moisture Storage ◽

Soil Moisture Data ◽

Moisture Storage ◽

The Impact

Integration of satellite-based data with hydrological modelling was generally conducted via data assimilation or model calibration, and both approaches can enhance streamflow predictions. In this study, we assessed the feasibility of another approach that uses satellite-based soil moisture data to directly estimate the parameter β to represent the degree of the spatial distribution of soil moisture storage capacity in the semi-distributed Hymod model. The impact of using historical root-zone soil moisture data from the Soil Moisture Active Passive (SMAP) mission on the prior estimation of the parameter β was explored. Two different ways to incorporate the root-zone soil moisture data to estimate the parameter β are proposed, i.e., one is to derive a priori distribution of β , and the other is to derive a fixed value for β . The simulations of the Hymod models employing the two ways to estimate β are compared with the results produced by the original model, i.e., the one without employing satellite-based data to estimate the parameter β , at three study catchments (the Upper Hanjiang River catchment, the Xiangjiang River catchment, and the Ganjiang River catchment). The results illustrate that the two ways to incorporate the SMAP root-zone soil moisture data in order to predetermine the parameter β of the semi-distributed Hymod model both perform well in simulating streamflow during the calibration period, and a slight improvement was found during the validation period. Notably, deriving a fixed β value from satellite soil moisture data can provide better performance for ungauged catchments despite reducing the model freedom degrees due to fixing the β value. It is concluded that the robustness of the Hymod model in predicting the streamflow can be improved when the spatial information of satellite-based soil moisture data is utilized to estimate the parameter β .

Download Full-text

Streamflow data assimilation for soil moisture analysis

Geoscientific Model Development Discussions ◽

10.5194/gmdd-2-551-2009 ◽

2009 ◽

Vol 2 (1) ◽

pp. 551-579 ◽

Cited By ~ 1

Author(s):

K. Warrach-Sagi ◽

V. Wulfmeyer

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Climate Models ◽

Root Zone ◽

Weather Forecast ◽

Surface Model ◽

River Catchment ◽

Streamflow Data ◽

The Impact

Abstract. Streamflow depends on the soil moisture of a river catchment and can be measured with relatively high accuracy. The soil moisture in the root zone influences the latent heat flux and hence the quantity and spatial distribution of atmospheric water vapour and precipitation. As numerical weather forecast and climate models require a proper soil moisture initialization for their land surface models, we enhanced an Ensemble Kalman Filter to assimilate streamflow timeseries into the multi-layer land surface model TERRA-ML of the regional weather forecast model COSMO. The impact of streamflow assimilation was studied by an observing system simulation experiment in the Enz River catchment (located at the downwind side of the northern Black Forest in Germany). The results demonstrate a clear improvement of the soil moisture field in the catchment. We illustrate the potential of streamflow data assimilation for weather forecasting and discuss its spatial and temporal requirements for a corresponding, automated river gauging network.

Download Full-text

Interactive Effect of Meteorological Drought and Vegetation Types on Root Zone Soil Moisture and Runoff in Rangeland Watersheds

Water ◽

10.3390/w11112357 ◽

2019 ◽

Vol 11 (11) ◽

pp. 2357 ◽

Cited By ~ 2

Author(s):

Yonghong Hao ◽

Qi Liu ◽

Chongwei Li ◽

Gehendra Kharel ◽

Lixing An ◽

...

Keyword(s):

Soil Moisture ◽

Root Zone ◽

Interactive Effect ◽

Woody Species ◽

Meteorological Drought ◽

Peak Frequency ◽

Juniperus Virginiana ◽

Delayed Response ◽

Antecedent Soil Moisture ◽

The Impact

The meteorological droughts in the climate transition zone of the Great Plains of the USA are projected to intensify, potentially leading to major shifts in water provisioning services in rangelands. To understand how meteorological drought interacts with vegetation to regulate runoff response, we collected precipitation, root zone soil moisture, and runoff data from experimental grassland and juniper (Juniperus virginiana L., redcedar) woodland watersheds for five years encompassing a drought year to pluvial year cycle. We contrasted the frequency distribution of precipitation intensities and applied wavelet analysis to reveal the coherence between precipitation and root zone soil moisture patterns. Compared with grassland, the root zone soil moisture in woodland had a narrower range, with the peak frequency skewed to a lower soil moisture content. The conversion of herbaceous vegetation to evergreen juniper woodland results in a delayed response of runoff to precipitation due to reduced antecedent soil moisture. The reduction of streamflow from the woodland watershed was greater in the normal and pluvial years than in the drought year. Thus, conversion from grassland to evergreen woody vegetation prolongs the impact of meteorological drought on soil moisture and streamflow. Restoring prairie that is heavily encroached by woody species may serve as an adaptive measure to mitigate the climate change impact on water resources and other ecosystem services provided by rangeland.

Download Full-text

Assimilation of Remotely Sensed Soil Moisture and Snow Depth Retrievals for Drought Estimation

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0132.1 ◽

2014 ◽

Vol 15 (6) ◽

pp. 2446-2469 ◽

Cited By ~ 98

Author(s):

Sujay V. Kumar ◽

Christa D. Peters-Lidard ◽

David Mocko ◽

Rolf Reichle ◽

Yuqiong Liu ◽

...

Keyword(s):

United States ◽

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Snow Depth ◽

Root Zone ◽

Remotely Sensed ◽

Open Loop ◽

Surface Model ◽

The Impact

Abstract The accurate knowledge of soil moisture and snow conditions is important for the skillful characterization of agricultural and hydrologic droughts, which are defined as deficits of soil moisture and streamflow, respectively. This article examines the influence of remotely sensed soil moisture and snow depth retrievals toward improving estimates of drought through data assimilation. Soil moisture and snow depth retrievals from a variety of sensors (primarily passive microwave based) are assimilated separately into the Noah land surface model for the period of 1979–2011 over the continental United States, in the North American Land Data Assimilation System (NLDAS) configuration. Overall, the assimilation of soil moisture and snow datasets was found to provide marginal improvements over the open-loop configuration. Though the improvements in soil moisture fields through soil moisture data assimilation were barely at the statistically significant levels, these small improvements were found to translate into subsequent small improvements in simulated streamflow. The assimilation of snow depth datasets were found to generally improve the snow fields, but these improvements did not always translate to corresponding improvements in streamflow, including some notable degradations observed in the western United States. A quantitative examination of the percentage drought area from root-zone soil moisture and streamflow percentiles was conducted against the U.S. Drought Monitor data. The results suggest that soil moisture assimilation provides improvements at short time scales, both in the magnitude and representation of the spatial patterns of drought estimates, whereas the impact of snow data assimilation was marginal and often disadvantageous.

Download Full-text