scholarly journals Observed Soil Moisture–Precipitation Feedback in Illinois: A Systematic Analysis over Different Scales

2016 ◽  
Vol 17 (6) ◽  
pp. 1645-1660 ◽  
Author(s):  
H. M. Duerinck ◽  
R. J. van der Ent ◽  
N. C. van de Giesen ◽  
G. Schoups ◽  
V. Babovic ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Time Scale ◽  
Regional Analysis ◽  
Precipitation Variability ◽  
Climate Indices ◽  
Back Trajectory ◽  
Strong Positive Correlation ◽  
Coupled Models ◽  
Continental Scale ◽  
State Average

Abstract The lack of understanding on the soil moisture–precipitation feedback mechanisms remains a large source of uncertainty for land–atmosphere coupled models. Previous observation-based studies on the soil moisture–precipitation feedback in Illinois have shown contradictory results. This paper extends earlier research by providing a more holistic analysis considering different scales based on an 11-yr (2003–13) hourly soil moisture dataset, which makes it possible to revisit the disputed hypothesis on the correlation between warm-season soil moisture and subsequent precipitation. This study finds a strong positive correlation between late spring/early summer state-average soil moisture at the root-zone depths and subsequent state-average summer precipitation. On the daily to weekly time scale, however, no relation is found. Moreover, regional analysis suggests that precipitation variability over central Illinois can be best explained by the soil moisture variability in northwest Illinois. Using a back-trajectory method [Water Accounting Model-2 layers (WAM-2layers)] from May to July, the evaporative sources of precipitation in Illinois are identified. The pattern of the source regions shows little interannual variability, while the strength of the sources changes significantly and the Gulf of Mexico contributes more during wet years. However, strong influences (teleconnections) of sea surface temperatures on the subsequent precipitation variability in Illinois are not found on a seasonal scale. The long time scale of the soil moisture–precipitation correlation and the weak influences of SSTs and climate indices may suggest that precipitation variability in spring/summer in Illinois is mostly related to continental-scale soil moisture–precipitation feedback.

scholarly journals Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data

2021 ◽  
Vol 13 (4) ◽  
pp. 554
Author(s):  
A. A. Masrur Ahmed ◽  
Ravinesh C Deo ◽  
Nawin Raj ◽  
Afshin Ghahramani ◽  
Qi Feng ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Climate Index ◽  
Climate Indices ◽  
High Temporal Resolution ◽  
Synoptic Scale ◽  
Time Step ◽  
Gated Recurrent Unit

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management.

scholarly journals Has erosion globally increased? Long-term erosion rates as a function of climate derived from the impact crater inventory

2018 ◽  
Author(s):  
Stefan Hergarten ◽  
Thomas Kenkmann
Keyword(s):  
Time Scale ◽  
Impact Crater ◽  
Regional Effect ◽  
Tropical Zone ◽  
Erosion Rates ◽  
Continental Scale ◽  
River Loads ◽  
The Impact

Abstract. Worldwide erosion rates seem to have increased strongly since the beginning of the Quaternary, but there is still discussion about the role of glaciation as a potential driver and even whether the increase is real at all or an artefact due to losses in the long-term sedimentary record. In this study we derive estimates of average erosion rates on the time scale of some tens of million years from the terrestrial impact crater inventory. This approach is completely independent from all other methods to infer erosion rates such as river loads, preserved sediments, cosmogenic nuclides and thermochronometry. Our approach yields average erosion rates as a function of present-day topography and climate. The results confirm that topography accounts for the main part of the huge variation of erosion on Earth, but also identifies a significant systematic dependence on climate in contrast to several previous studies. We found a fivefold increase in erosional efficacy from the cold regimes to the tropical zone and that temperate and arid climates are very similar in this context. Combining our results to a worldwide mean erosion rate we found that erosion rates on the time scale of some tens of million years are at least as high as present-day rates and suggest that glaciation has a rather regional effect with a limited impact at the continental scale.

scholarly journals Effects of Climate Variability on Water Storage in the Colorado River Basin

2009 ◽  
Vol 10 (5) ◽  
pp. 1257-1270 ◽  
Author(s):  
Ruud Hurkmans ◽  
Peter A. Troch ◽  
Remko Uijlenhoet ◽  
Paul Torfs ◽  
Matej Durcik
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Colorado River ◽  
Decadal Variability ◽  
Southern Oscillation ◽  
Water Storage ◽  
Low Frequency ◽  
Colorado River Basin ◽  
Climate Indices ◽  
Deep Soil

Abstract Understanding the long-term (interannual–decadal) variability of water availability in river basins is paramount for water resources management. Here, the authors analyze time series of simulated terrestrial water storage components, observed precipitation, and discharge spanning 74 yr in the Colorado River basin and relate them to climate indices that describe variability of sea surface temperature and sea level pressure in the tropical and extratropical Pacific. El Niño–Southern Oscillation (ENSO) indices in winter [January–March (JFM)] are related to winter precipitation as well as to soil moisture and discharge in the lower Colorado River basin. The low-frequency mode of the Pacific decadal oscillation (PDO) appears to be strongly correlated with deep soil moisture. During the negative PDO phase, saturated storage anomalies tend to be negative and the “amplitudes” (mean absolute anomalies) of shallow soil moisture, snow, and discharge are slightly lower compared to periods of positive PDO phases. Predicting interannual variability, therefore, strongly depends on the capability of predicting PDO regime shifts. If indeed a shift to a cool PDO phase occurred in the mid-1990s, as data suggest, the current dry conditions in the Colorado River basin may persist.

scholarly journals Drought Relief and Reversal over North America from 1500 to 2016

2021 ◽  
Vol 25 (1) ◽  
pp. 94-107
Author(s):  
M. C. A. Torbenson ◽  
D. W. Stahle ◽  
I. M. Howard ◽  
D. J. Burnette ◽  
D. Griffin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Land Surface ◽  
Large Scale ◽  
Temporal Stability ◽  
Warm Season ◽  
Cool Season ◽  
Continental Scale ◽  
The North ◽  
Drought Relief

Abstract Season-to-season persistence of soil moisture drought varies across North America. Such interseasonal autocorrelation can have modest skill in forecasting future conditions several months in advance. Because robust instrumental observations of precipitation span less than 100 years, the temporal stability of the relationship between seasonal moisture anomalies is uncertain. The North American Seasonal Precipitation Atlas (NASPA) is a gridded network of separately reconstructed cool-season (December–April) and warm-season (May–July) precipitation series and offers new insights on the intra-annual changes in drought for up to 2000 years. Here, the NASPA precipitation reconstructions are rescaled to represent the long-term soil moisture balance during the cool season and 3-month-long atmospheric moisture during the warm season. These rescaled seasonal reconstructions are then used to quantify the frequency, magnitude, and spatial extent of cool-season drought that was relieved or reversed during the following summer months. The adjusted seasonal reconstructions reproduce the general patterns of large-scale drought amelioration and termination in the instrumental record during the twentieth century and are used to estimate relief and reversals for the most skillfully reconstructed past 500 years. Subcontinental-to-continental-scale reversals of cool-season drought in the following warm season have been rare, but the reconstructions display periods prior to the instrumental data of increased reversal probabilities for the mid-Atlantic region and the U.S. Southwest. Drought relief at the continental scale may arise in part from macroscale ocean–atmosphere processes, whereas the smaller-scale regional reversals may reflect land surface feedbacks and stochastic variability.

scholarly journals An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Yueling Ma ◽  
Carsten Montzka ◽  
Bagher Bayat ◽  
Stefan Kollet
Keyword(s):  
Soil Moisture ◽  
Indirect Method ◽  
Short Term Memory ◽  
Groundwater Table ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Continental Scale ◽  
Groundwater Table Depth ◽  
Long Short Term Memory

The lack of high-quality continental-scale groundwater table depth observations necessitates developing an indirect method to produce reliable estimation for water table depth anomalies (wtda) over Europe to facilitate European groundwater management under drought conditions. Long Short-Term Memory (LSTM) networks are a deep learning technology to exploit long-short-term dependencies in the input-output relationship, which have been observed in the response of groundwater dynamics to atmospheric and land surface processes. Here, we introduced different input variables including precipitation anomalies (pra), which is the most common proxy of wtda, for the networks to arrive at improved wtda estimates at individual pixels over Europe in various experiments. All input and target data involved in this study were obtained from the simulated TSMP-G2A data set. We performed wavelet coherence analysis to gain a comprehensive understanding of the contributions of different input variable combinations to wtda estimates. Based on the different experiments, we derived an indirect method utilizing LSTM networks with pra and soil moisture anomaly (θa) as input, which achieved the optimal network performance. The regional medians of test R2 scores and RMSEs obtained by the method in the areas with wtd ≤ 3.0 m were 76–95% and 0.17–0.30, respectively, constituting a 20–66% increase in median R2 and a 0.19–0.30 decrease in median RMSEs compared to the LSTM networks only with pra as input. Our results show that introducing θa significantly improved the performance of the trained networks to predict wtda, indicating the substantial contribution of θa to explain groundwater anomalies. Also, the European wtda map reproduced by the method had good agreement with that derived from the TSMP-G2A data set with respect to drought severity, successfully detecting ~41% of strong drought events (wtda ≥ 1.5) and ~29% of extreme drought events (wtda ≥ 2) in August 2015. The study emphasizes the importance to combine soil moisture information with precipitation information in quantifying or predicting groundwater anomalies. In the future, the indirect method derived in this study can be transferred to real-time monitoring of groundwater drought at the continental scale using remotely sensed soil moisture and precipitation observations or respective information from weather prediction models.

scholarly journals Evaluation of soil moisture in CMIP5 simulations over contiguous United States using in situ and satellite observations

2016 ◽  
Author(s):  
Shanshui Yuan ◽  
Steven M. Quiring
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Soil Column ◽  
Regional Analysis ◽  
Soil Layer ◽  
Model Performance ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Near Surface

Abstract. This study provides a comprehensive evaluation of soil moisture simulations in the Coupled Model Intercomparison Project Phase 5 (CMIP5) extended historical experiment (2003 to 2012). Soil moisture from in situ and satellite sources are used to evaluate CMIP5 simulations in the contiguous United States (CONUS). Both near-surface (0–10 cm) and soil column (0–100 cm) simulations from more than 14 CMIP5 models are evaluated during the warm season (April–September). Multi-model ensemble means and the performance of individual models are assessed at a monthly time scale. Our results indicate that CMIP5 models can reproduce the seasonal variability in soil moisture over CONUS. However, the models tend to overestimate the magnitude of both near-surface and soil-column soil moisture in the western U.S. and underestimate it in the eastern U.S. There are large variations in model performance, especially in the near-surface. There are significant regional and inter-model variations in performance. Results of a regional analysis show that in deeper soil layer, the CMIP5 soil moisture simulations tend to be most skillful in the southern U.S. Based on both the satellite-derived and in situ soil moisture, CESM1, CCSM4 and GFDL-ESM2M perform best in the 0–10 cm soil layer and CESM1, CCSM4, GFDL-ESM2M and HadGEM2-ES perform best in the 0–100 cm soil layer.

scholarly journals Analysis of aggregation and disaggregation effects for grid-based hydrological models and the development of improved precipitation disaggregation procedures for GCMs

1999 ◽  
Vol 3 (1) ◽  
pp. 95-108 ◽  
Author(s):  
H. S. Wheater ◽  
T. J. Jolley ◽  
C. Onof ◽  
N. Mackay ◽  
R. E. Chandler
Keyword(s):  
Soil Moisture ◽  
Time Scale ◽  
Scale Dependence ◽  
Moisture Distribution ◽  
Image Processing Technique ◽  
Simple Extension ◽  
Hydrological Models ◽  
Radar Rainfall ◽  
Spatial Coverage ◽  
Grid Based

Abstract. Appropriate representation of hydrological processes within atmospheric General Circulation Models (GCMs) is important with respect to internal model dynamics (e.g. surface feedback effects on atmospheric fluxes, continental runoff production) and to simulation of terrestrial impacts of climate change. However, at the scale of a GCM grid-square, several methodological problems arise. Spatial disaggregation of grid-square average climatological parameters is required in particular to produce appropriate point intensities from average precipitation. Conversely, aggregation of land surface heterogeneity is necessary for grid-scale or catchment scale application. The performance of grid-based hydrological models is evaluated for two large (104km2) UK catchments. Simple schemes, using sub-grid average of individual land use at 40 km scale and with no calibration, perform well at the annual time-scale and, with the addition of a (calibrated) routing component, at the daily and monthly time-scale. Decoupling of hillslope and channel routing does not necessarily improve performance or identifiability. Scale dependence is investigated through application of distribution functions for rainfall and soil moisture at 100 km scale. The results depend on climate, but show interdependence of the representation of sub-grid rainfall and soil moisture distribution. Rainfall distribution is analysed directly using radar rainfall data from the UK and the Arkansas Red River, USA. Among other properties, the scale dependence of spatial coverage upon radar pixel resolution and GCM grid-scale, as well as the serial correlation of coverages are investigated. This leads to a revised methodology for GCM application, as a simple extension of current procedures. A new location-based approach using an image processing technique is then presented, to allow for the preservation of the spatial memory of the process.

scholarly journals Mechanisms of Seasonal Soil Moisture Drought Onset and Termination in the Southern Great Plains

2019 ◽  
Vol 20 (4) ◽  
pp. 751-771 ◽  
Author(s):  
Richard Seager ◽  
Jennifer Nakamura ◽  
Mingfang Ting
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Great Plains ◽  
Land Surface ◽  
Tropical Pacific ◽  
Moisture Change ◽  
Southern Plains ◽  
Southern Great Plains ◽  
Continental Scale ◽  
Over Time

AbstractMechanisms of drought onset and termination are examined across North America with a focus on the southern Plains using data from land surface models and regional and global reanalyses for 1979–2017. Continental-scale analysis of covarying patterns reveals a tight coupling between soil moisture change over time and intervening precipitation anomalies. The southern Great Plains are a geographic center of patterns of hydrologic change. Drying is induced by atmospheric wave trains that span the Pacific and North America and place northerly flow anomalies above the southern Plains. In the southern Plains winter is least likely, and fall most likely, for drought onset and spring is least likely, and fall or summer most likely, for drought termination. Southern Plains soil moisture itself, which integrates precipitation over time, has a clear relationship to tropical Pacific sea surface temperature (SST) anomalies with cold conditions favoring dry soils. Soil moisture change, however, though clearly driven by precipitation, has a weaker relation to SSTs and a strong relation to internal atmospheric variability. Little evidence is found of connection of drought onset and termination to driving by temperature anomalies. An analysis of particular drought onsets and terminations on the seasonal time scale reveals commonalities in terms of circulation and moisture transport anomalies over the southern Plains but a variety of ways in which these are connected into the large-scale atmosphere and ocean state. Some onsets are likely to be quite predictable due to forcing by cold tropical Pacific SSTs (e.g., fall 2010). Other onsets and all terminations are likely not predictable in terms of ocean conditions.

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