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

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.

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Impacts of Antecedent Soil moisture on the Rainfall–Runoff Transformation Process Based on High-Resolution Observations in Soil Tank Experiments

Water ◽

10.3390/w11020296 ◽

2019 ◽

Vol 11 (2) ◽

pp. 296 ◽

Cited By ~ 5

Author(s):

Shuang Song ◽

Wen Wang

Keyword(s):

Soil Moisture ◽

Overland Flow ◽

Response Times ◽

Hysteretic Behavior ◽

Rainfall Runoff ◽

Moisture Index ◽

Antecedent Soil Moisture ◽

Non Linear ◽

Subsurface Runoff ◽

The Impact

An experimental soil tank (12 m long × 1.5 m wide × 1.5m deep) equipped with a spatially distributed instrument network was designed to conduct the artificial rainfall–runoff experiments. Soil moisture (SM), precipitation, surface runoff (SR) and subsurface runoff (SSR) were continuously monitored. A total of 32 rainfall–runoff events were analyzed to investigate the non-linear patterns of rainfall–runoff response and estimate the impact of antecedent soil moisture (ASM) on runoff formation. Results suggested that ASM had a significant impact on runoff at this plot scale, and a moisture threshold-like value which was close to field capacity existed in the relationship between soil water content and event-based runoff coefficient (φe), SSR and SSR/SR. A non-linear relationship between antecedent soil moisture index (ASI) that represented the initial storage capacity of the soil tank and total runoff was also observed. Response times of SR and SM to rainfall showed a marked variability under different conditions. Under wet conditions, SM at 10 cm started to increase prior to SR on average, whereas it responds slower than SR under dry conditions due to the effect of water repellency. The predominant contributor to SR generation for all events is the Hortonian overland flow (HOF). There is a hysteretic behavior between subsurface runoff flow and soil moisture with a switch in the hysteretic loop direction based on the wetness conditions prior to the event.

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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.

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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

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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.

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Meteorological drought in the Miño-Limia-Sil hydrographic demarcation: The role of atmospheric drivers

10.5194/nhess-2019-314 ◽

2019 ◽

Author(s):

Rogert Sorí ◽

Marta Vázquez ◽

Milica Stojanovic ◽

Raquel Nieto ◽

Margarida Liberato ◽

...

Keyword(s):

Soil Moisture ◽

Iberian Peninsula ◽

Atmospheric Circulation ◽

Root Zone ◽

Meteorological Drought ◽

The Arctic ◽

Regional Patterns ◽

Runoff Change ◽

Drought Conditions ◽

Dry And Wet Conditions

Abstract. Drought is one of the main natural hazards because of its environmental, economic, and social impacts. Therefore, its study, monitoring and prediction for small regions, countries, or whole continents are challenging. In this work, the meteorological droughts affecting the Miño-Limia-Sil Hydrographic Demarcation (MLSHD) in the northwestern Iberian Peninsula during the period of 1980–2017 were identified. For this purpose, and to assess the combined effects of temperature and precipitation on drought conditions, the Standardised Precipitation-Evapotranspiration Index (SPEI) was utilised. During the study period there was no trend in the series of SPEI at the temporal scale of 1 mo (SPEI1); however, the number of drought episodes and their severity have been increasing historically, but this metric was not statistically significant. Particular emphasis was given to investigating atmospheric circulation as a driver of different drought conditions. To this aim, a daily weather type classification was utilised for the entire Iberian Peninsula. The results showed that atmospheric circulation from the southwest, west, and northwest were directly related to dry and wet conditions in the MLSHD during the entire climatological year. Contrastingly, weather types imposing atmospheric circulation from the northeast, east, and southeast and pure anticyclonic circulation were negatively correlated with the SPEI1. In this sense, the major teleconnection atmospheric patterns related to dry/wet conditions were the Arctic Oscillation, Scandinavian Pattern, and North Atlantic Oscillation. Dry and wet conditions according to the SPEI at shorter temporal scales were closely related to the soil moisture in the root zone, and also strongly influenced the streamflow of the Miño and Limia rivers, especially during the rainy season. However, a direct relationship between soil moisture and streamflow was also observed when dry/wet conditions accumulated for more than 1 y. We concluded that regional patterns of land-use change and moisture recycling are important to consider in explaining runoff change, integrating land and water management, and informing water governance.

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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.

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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.

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Subseasonal Meteorological Drought Development over the Central United States during Spring

Journal of Climate ◽

10.1175/jcli-d-21-0435.1 ◽

2021 ◽

pp. 1-64

Keyword(s):

Soil Moisture ◽

Root Zone ◽

Meteorological Drought ◽

Individual Case ◽

Reanalysis Data ◽

Late Spring ◽

Evaporative Demand ◽

Near Surface ◽

Divergent Flow ◽

Central United States

Abstract Diagnosis of rapidly developing springtime droughts in the central U.S. has mostly been made via numerous individual case studies rather than in an aggregate sense. This study investigates common aspects of subseasonal “meteorological drought” evolution, here defined as persistent precipitation minus evapotranspiration (P-ET) deficits, revealed in early (April 1-May 15) and late (May 16-June 30) spring composites of 5-day running mean JRA-55 reanalysis data for three different central U.S. regions during 1958-2018. On average, these droughts are initiated by a quasi-stationary Rossby wave packet (RWP), propagating from the western North Pacific, which arises about a week prior to drought onset. The RWP is related to a persistent ridge west of the incipient drought region and strong subsidence over it. This subsidence is associated with low-level divergent flow that dries the atmosphere and suppresses precipitation for roughly 1-2 weeks, and generally has a greater impact on the moisture budget than does reduced poleward moisture transport. The resulting “dynamically driven” evaporative demand corresponds to a rapid drying of the root-zone soil moisture, which decreases ∼40 percentiles within ∼10 days. Anomalous near-surface warmth develops only after P-ET deficit onset, as does anomalously low soil moisture that then lingers a month or more, especially in late spring. The horizontal scale of the RWPs, and of the related drought anomalies, decreases from early to late spring, consistent with the climatological change in the Pacific Rossby waveguide. Finally, while this composite analysis is based upon strong, persistent P-ET deficits, it still appears to capture much of the springtime development of so-called “flash droughts” as well.

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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.

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Hydro-meteorological thresholds based on synthetic dataset for improved prediction of rainfall-induced shallow landslides.

10.5194/egusphere-egu2020-10479 ◽

2020 ◽

Author(s):

Pasquale Marino ◽

Roberto Greco ◽

David James Peres ◽

Thom A. Bogaard

Keyword(s):

Soil Moisture ◽

Rainfall Intensity ◽

Root Zone ◽

Water Pressure ◽

Shallow Landslides ◽

Root Penetration ◽

Landslide Prediction ◽

Antecedent Soil Moisture ◽

Near Surface ◽

Physically Based

Prediction of rainfall-induced landslides is often entrusted to the definition of empirical thresholds (usually expressed in terms of rainfall intensity and duration), linking the precipitation to the triggering of landslides. However, rainfall intensity-duration thresholds do not exploit the knowledge of the hydrological processes developing in the slope, so they tend to generate false and missed alarms. Rainfall-induced shallow landslides usually occur in initially unsaturated soil covers following an increase of pore water pressure, due to the increase of soil moisture, caused by large and persistent rainfall. Clearly, it should be possible to use soil moisture for landslide prediction. Recently, Bogaard & Greco (2018) proposed the cause-trigger conceptual framework to develop hydro-meteorological thresholds that combine the antecedent causal factors and the actual trigger connected with landslide initiation. In fact, in some regions where rainfall-induced shallow landslides are particularly dangerous and pose a serious risk to people and infrastructures, the antecedent saturation is the predisposing factor, while the actual landslide triggering is associated with the hydrologic response to the recent and incoming precipitation. In fact, numerous studies already tried to introduce, directly or with models, the effects of antecedent soil moisture content in the empirical thresholds for improving landslide forecasting. Soil moisture can be measured locally, by a range of on-site measurement techniques, or remotely, from satellites or airborne. On-site measurements have proved promising in improving the performance of thresholds for landslide early warning. On-site data are accurate but sparse, so there is an increasing interest on the possible use of remotely sensed data. And in fact, recent research has shown that they can provide useful information for landslide prediction at regional scale, despite their coarse resolution and inherent uncertainty.However, while remote sensing techniques provide near-surface (5cm depth) soil moisture estimate, the depth involved in shallow landslide is typically 1-2m below the surface. This depth, overlapping with the root penetration zone, is influenced by antecedent precipitation, soil texture, vegetation and, so, it is very difficult to find a clear relationship with near-surface soil moisture. Many studies have been conducted to provide root-zone soil moisture through physically-based approaches and data driven methods, data assimilation schemes, and satellite information.In this study, the question if soil moisture information derived from current or future satellite products can improve landslide hazard prediction, and to what extent, is investigated. Hereto, real-world landslide and hydrology information, from two sites of Southern Italy characterized by frequent shallow landslides (Peloritani mountains, in Sicily, and Partenio mountains, in Campania), is analyzed. To get datasets long enough to carry out statistical analyses, synthetic time series of rainfall and soil cover response have been generated, with the application of a stochastic rainfall model and a physically based infiltration model, for both the sites. Near-surface and root-zone soil moisture have been tested, accounting also for effects of uncertainty and of coarse spatial and temporal resolution of measurements. The obtained results show that, in all cases, soil moisture information allows building hydro-meteorological thresholds for landslide prediction, significantly outperforming the currently adopted purely meteorological thresholds.&#160;&#160;

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