Column Soil Moisture Research Articles

Application of ESACCI SM product-assimilated to a statistical model to assess the drought propagation for different Agro-Climatic zones of India using copula

Meteorological drought precedes the agricultural drought and studying the propagation time from meteorological to agricultural drought can substantially reduce agricultural losses. To find this propagation time between the meteorological and agricultural drought, this study analyzed the copula-based conditional probability between the Standardized Precipitation Index at 12 timescales (SPI-1 to12, meteorological drought) and Standardized Soil Moisture Index at 1 month timescale (SSI-1, agricultural drought), over the fifteen Agro-Climatic Zones (ACZs) of India. SSI is computed using a total column Soil Moisture (SM) derived from ESACCI SM using the Statistical Soil Moisture Profile (SSMP) model. The SSMP-based ESACCI SM is positively correlated (Correlation Coefficient of 0.871) with ERA5 Land SM. To compute the conditional probability, three copulas namely Frank, Clayton, and Gumbel copulas are fitted between SPI-1 to 12 and SSI-1. The Goodness of Fit analysis showed the dominance of the Gumbel copula among the Clayton, Frank, and Gumbel copulas. Gumbel copula completely dominated the SPI timescales of 1–3 and 11–12 whereas Clayton copula was relatively favored for SPI timescales of 4–10. The propagation time results revealed that the monsoon season (JAS) consistently exhibits the highest propagation time, averaging around 10 months followed by summer (AMJ, 7 months), spring (JFM, 4 months), and winter (OND, 2–3 months) seasons. The Mann-Kendall trend test revealed that the same season can have different trends in different ACZs and vice versa, underlining the localized and season-specific strategies for drought management, considering each ACZ's unique environmental, agricultural, and socio-economic conditions in each zone. Overall, this study showcases the capability of remote sensing and copula-based statistical models, in understanding meteorological to agricultural drought propagation. It also highlights the efficiency of statistical models in deriving total column SM from surface SM.

Modeling management of continuous dryland cotton with an intervening winter wheat cover crop in a semiarid climate

Although winter cover crop residue can mitigate the stresses of dryland production in semi-arid regions, cover crops can also reduce soil moisture and cash crop yields. In some field studies of dryland cotton grown after terminated winter wheat in the U.S. Southern High Plains (SHP) and Texas Rolling Plains cotton yields were increased relative to continuous cotton, while others had no significant yield or soil water effect. These uncertain outcomes may be due to the trial's limited sampling of seasonal rainfall conditions. To estimate the probabilities of cover crop effects under more representative SHP climate conditions, 294 station-years of crop model simulations of terminated winter wheat followed by dryland cotton were conducted. These simulations were driven by weather data from 21 SHP weather stations during 2005–2019. Each station-year's simulations were repeated under 54 combinations of wheat planting, termination, and cotton planting dates, 2 soil series with different water capacities, and 10 initial soil moisture conditions. When simulations begin with fall soil moisture at field capacity optimal management options for both soils plant wheat early and cotton late, but have different wheat termination dates. Before cotton planting winter cover crop effects are dominated by reduced surface evaporation and increased transpiration, with greater transpiration effects producing decreased column soil moisture (CSM) at wheat termination. Some soil moisture recharge occurs between termination and cotton planting, but cover crops reduce CSM at cotton planting in both soils in ~75% of simulations. Reduced soil evaporation and soil moisture recovery continues after cotton planting, resulting in positive effects on seed cotton yield in 50% of the silty clay loam simulations and in 67% of the fine sandy loam simulations. Gradually reducing initial fall soil moisture in the silty clay loam reduces wheat biomass but increases the incidence of positive effects on seed cotton yields and CSM at cotton planting and harvest. By contrast, drier initial soil moisture in the fine sandy loam had relatively minor yield and CSM effects. In both soils terminated wheat residue led to increased CSM at cotton harvest in at least 70% of the simulations regardless of soil moisture at wheat planting.

Drought Cascade in the Terrestrial Water Cycle: Evidence From Remote Sensing

AbstractDroughts are complex phenomena that typically evolve slowly over time. They originate through interactions between the atmosphere and the land surface, but show delayed evolution as they cascade through soil moisture, surface, and groundwater supplies, sometimes resulting in prolonged and insidious impacts on regional hydrological systems. While previous studies have observed this cascade phenomenon at select in situ locations, the temporal characteristics of drought cascades have never been quantitatively estimated at large scales using primarily remote sensing techniques. Here, we use satellite observations of vapor pressure deficit, Precipitation, and terrestrial water storage, and reanalysis‐based estimates of column soil moisture to characterize cascade phenomena for four major US drought case studies. By mapping the time evolution through this suite of variables, we observe large‐scale behavior generally consistent with theory and are able to quantify emergent patterns in the evolution of drought signals that may support improved future predictability.

Annual Cycle of Planetary Boundary Layer Heights Estimated From Wind Profiler Network Data

AbstractThe planetary boundary layer (PBL) height over the U.S. Great and Central Plains during 1992–2012 is examined here using a combination of wind profiler‐derived PBL height estimates and reanalysis fields from the Modern Era Retrospective Reanalysis Version 2. The combined analysis allows for a process level study of the reasons behind the monthly mean behavior of the observed PBL heights under clear‐sky conditions as well as the PBL height variability. Wind profiler PBL height monthly mean annual cycles were grouped into general categories of behavior, each analyzed using Modern Era Retrospective Reanalysis Version 2 fields of sensible and latent heat flux, surface temperature, net radiation, and soil moisture. In the “canonical” category the latent heat plays little role in the determination of the monthly mean PBL height, and it follows the annual cycle of the surface temperature. In the other categories, precipitation and latent heat flux had more influence in setting the annual cycle of the PBL height. An analysis of variance revealed that the role of latent heat in determining the PBL height variations is large (explaining up to 40% of PBL height variability) even in the canonical category for which latent heat played no role in setting the monthly mean. In other categories the latent heat explained up to 80% of the PBL height variations. The amount of that influence is shown to be related to the variability of column soil moisture.

Assessing the Evolution of Soil Moisture and Vegetation Conditions during a Flash Drought–Flash Recovery Sequence over the South-Central United States

AbstractThis study examines the evolution of soil moisture, evapotranspiration, vegetation, and atmospheric conditions during an unusual flash drought–flash recovery sequence that occurred across the south-central United States during 2015. This event was characterized by a period of rapid drought intensification (flash drought) during late summer that was terminated by heavy rainfall at the end of October that eliminated the extreme drought conditions over a 2-week period (flash recovery). A detailed analysis was performed using time series of environmental variables derived from meteorological, remote sensing, and land surface modeling datasets. Though the analysis revealed a similar progression of cascading effects in each region, characteristics of the flash drought such as its onset time, rate of intensification, and vegetation impacts differed between regions due to variations in the antecedent conditions and the atmospheric anomalies during its growth. Overall, flash drought signals initially appeared in the near-surface soil moisture, followed closely by reductions in evapotranspiration. Total column soil moisture deficits took longer to develop, especially in the western part of the region where heavy rainfall during the spring and early summer led to large moisture surpluses. Large differences were noted in how land surface models in the North American Land Data Assimilation System depicted soil moisture evolution during the flash drought; however, the models were more similar in their assessment of conditions during the flash recovery period. This study illustrates the need to use multiple datasets to track the evolution and impacts of rapidly evolving flash drought and flash recovery events.

An Assessment of CAMS-CSM in Simulating Land–Atmosphere Heat and Water Exchanges

The Chinese Academy of Meteorological Sciences (CAMS) has been devoted to developing a climate system model (CSM) to meet demand for climate simulation and prediction for the East Asian region. In this study, we evaluated the performance of CAMS-CSM in regard to sensible heat flux (H), latent heat flux (LE), surface temperature, soil moisture, and snow depth, focusing on the Atmospheric Model Intercomparison Project experiment, with the aim of participating in the Coupled Model Intercomparison Project phase 6. We systematically assessed the simulation results achieved by CAMS-CSM for these variables against various reference products and ground observations, including the FLUXNET model tree ensembles H and LE data, Climate Prediction Center soil moisture data, snow depth climatology data, and Chinese ground observations of snow depth and winter surface temperature. We compared these results with data from the ECMWF Interim reanalysis (ERA-Interim) and Global Land Data Assimilation System (GLDAS). Our results indicated that CAMS-CSM simulations were better than or comparable to ERAInterim reanalysis for snow depth and winter surface temperature at regional scales, but slightly worse when simulating total column soil moisture. The root-mean-square differences of H in CAMS-CSM were all greater than those from the ERA-Interim reanalysis, but less than or comparable to those from GLDAS. The spatial correlations for H in CAMS-CSM were the lowest in nearly all regions, except for North America. CAMS-CSM LE produced the lowest bias in Siberia, North America, and South America, but with the lowest spatial correlation coefficients. Therefore, there are still scopes for improving H and LE simulations in CAMS-CSM, particularly for LE.

Hydrologic controls of controlled and free draining subsurface drainage systems

One of the strategies proposed for reducing nitrate losses from subsurface tile drain systems in Midwestern agricultural lands, controlled drainage, is known to reduce drain flow on an annual basis, but is not well understood for individual events. Understanding hydrologic controls that regulate outflow from free and controlled drainage systems during drainage events can offer improved insight on the overall functioning of these systems so that they can be better managed or retrofitted to increase their environmental benefits. This study used data from a monitoring site in east central Indiana to investigate the hydrology of 22 drainage events in free and controlled subsurface drainage systems. Relationships between event drainage volume, drain flow hydrograph metrics, column soil moisture, water table depth, and precipitation time series for each event were explored to determine the effect of precipitation characteristics and antecedent conditions on these metrics. Controlled drainage reduced event drainage volume and peak flows by 22% ± 12% and 29% ± 16% respectively, and increased the time to peak of drainage by 98% ± 52%. Higher total precipitation and precipitation time spread increased infiltration, while the average precipitation intensity did not correlate with drainage volumes in either system. Peak flow rate in free draining quadrants were positively affected by higher precipitation and average precipitation intensity, while in managed quadrants, the antecedent soil moisture appeared to be more influential in affecting peak flow rate than precipitation characteristics. Surface runoff potential increased with the increase in average precipitation intensity in all quadrants. Saturation excess ponding or possibly overland flow occurred in events that have a low average precipitation intensity, and a high precipitation time spread. Field observations indicate that saturation excess overland flow was more pronounced in controlled quadrants because water table levels rose higher than the water table levels of their free draining counterpart.

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

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 is 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). Multimodel ensemble means and the performance of individual models are assessed at a monthly timescale. Our results indicate that CMIP5 models can reproduce the seasonal variability in soil moisture over CONUS. However, the models tend to overestimate the amount of both near-surface and soil column soil moisture in the western US and underestimate it in the eastern US. There are large variations across models, especially for the near-surface soil moisture. There are significant regional variations in performance as well. Results of a regional analysis show that in the deeper soil layers, the CMIP5 soil moisture simulations tend to be most skillful in the southern US. 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.

Comparison and Assessment of Three Advanced Land Surface Models in Simulating Terrestrial Water Storage Components over the United States

Abstract To prepare for the next-generation North American Land Data Assimilation System (NLDAS), three advanced land surface models [LSMs; i.e., Community Land Model, version 4.0 (CLM4.0); Noah LSM with multiphysics options (Noah-MP); and Catchment LSM-Fortuna 2.5 (CLSM-F2.5)] were run for the 1979–2014 period within the NLDAS-based framework. Unlike the LSMs currently executing in the operational NLDAS, these three advanced LSMs each include a groundwater component. In this study, the model simulations of monthly terrestrial water storage anomaly (TWSA) and its individual water storage components are evaluated against satellite-based and in situ observations, as well as against reference reanalysis products, at basinwide and statewide scales. The quality of these TWSA simulations will contribute to determining the suitability of these models for the next phase of the NLDAS. Overall, it is found that all three models are able to reasonably capture the monthly and interannual variability and magnitudes of TWSA. However, the relative contributions of the individual water storage components to TWSA are very dependent on the model and basin. A major contributor to the TWSA is the anomaly of total column soil moisture content for CLM4.0 and Noah-MP, while the groundwater storage anomaly is the major contributor for CLSM-F2.5. Other water storage components such as the anomaly of snow water equivalent also play a role in all three models. For each individual water storage component, the models are able to capture broad features such as monthly and interannual variability. However, there are large intermodel differences and quantitative uncertainties, which are motivating follow-on investigations in the NLDAS Science Testbed developed by the NASA and NCEP NLDAS teams.

Why Do Different Drought Indices Show Distinct Future Drought Risk Outcomes in the U.S. Great Plains?

Vigorous discussions and disagreements about the future changes in drought intensity in the U.S. Great Plains have been taking place recently within the literature. These discussions have involved widely varying estimates based on drought indices and model-based projections of the future. To investigate and understand the causes for such a disparity between these previous estimates, the authors analyzed the soil moisture at the near-surface soil layer and the entire soil column, as well as the Palmer drought severity index, the Palmer Z index, and the standardized precipitation and evaporation index using the output from the Community Climate System Model, version 4 (CCSM4), and 25 state-of-the-art climate models. These drought indices were computed using potential evapotranspiration estimated by the physically based Penman–Monteith method (PE_pm) and the empirically based Thornthwaite method (PE_th). The results showed that the short-term drought indices are similar to modeled surface soil moisture and show a small but consistent drying trend in the future. The long-term drought indices and the total column soil moisture, however, are consistent in projecting more intense future drought. When normalized, the drought indices with PE_th all show unprecedented future drying, while the drought indices with PE_pm show comparable dryness with the modeled soil moisture. Additionally, the drought indices with PE_pm are closely related to soil moisture during both the twentieth and twenty-first centuries. Overall, the drought indices with PE_pm, as well as the modeled total column soil moisture, suggest a widespread and very significant drying in the Great Plains toward the end of the century. The results suggest that the sharp contrasts about future drought risk in the Great Plains discussed in previous studies are caused by 1) comparing the projected changes in short-term droughts with that of the long-term droughts and/or 2) computing the atmospheric evaporative demand using an empirically based method (e.g., PE_th). The analysis here may be applied for drought projections in other regions across the globe.

Uncertainties, Correlations, and Optimal Blends of Drought Indices from the NLDAS Multiple Land Surface Model Ensemble

AbstractThis study analyzed uncertainties and correlations over the United States among four ensemble-mean North American Land Data Assimilation System (NLDAS) percentile-based drought indices derived from monthly mean evapotranspiration ET, total runoff Q, top 1-m soil moisture SM1, and total column soil moisture SMT. The results show that the uncertainty is smallest for SM1, largest for SMT, and moderate for ET and Q. The strongest correlation is between SM1 and SMT, and the weakest correlation is between ET and Q. The correlation between ET and SM1 (SMT) is strongest in arid–semiarid regions, and the correlation between Q and SM1 (SMT) is strongest in more humid regions in the Pacific Northwest and the Southeast. Drought frequency analysis shows that SM1 has the most frequent drought occurrence, followed by SMT, Q, and ET. The study compared the NLDAS drought indices (a research product) with the U.S. Drought Monitor (USDM; an operational product) in terms of drought area percentage derived from each product. It proposes an optimal blend of NLDAS drought indices by searching for weights for each index that minimizes the RMSE between NLDAS and USDM drought area percentage for a 10-yr period (2000–09) with a cross validation. It reconstructed a 30-yr (1980–2009) Objective Blended NLDAS Drought Index (OBNDI) and monthly drought percentage. Overall, the OBNDI performs the best with the smallest RMSE, followed by SM1 and SMT. It should be noted that the contribution to OBNDI from different variables varies with region. So a single formula is probably not the best representation of a blended index. The representation of a blended index using the multiple formulas will be addressed in a future study.

Soil Moisture Drought in China, 1950–2006

Abstract Four physically based land surface hydrology models driven by a common observation-based 3-hourly meteorological dataset were used to simulate soil moisture over China for the period 1950–2006. Monthly values of total column soil moisture from the simulations were converted to percentiles and an ensemble method was applied to combine all model simulations into a multimodel ensemble from which agricultural drought severities and durations were estimated. A cluster analysis method and severity–area–duration (SAD) algorithm were applied to the soil moisture data to characterize drought spatial and temporal variability. For drought areas greater than 150 000 km2 and durations longer than 3 months, a total of 76 droughts were identified during the 1950–2006 period. The duration of 50 of these droughts was less than 6 months. The five most prominent droughts, in terms of spatial extent and then duration, were identified. Of these, the drought of 1997–2003 was the most severe, accounting for the majority of the severity–area–duration envelope of events with areas smaller than 5 million km2. The 1997–2003 drought was also pervasive in terms of both severity and spatial extent. It was also found that soil moisture in north central and northeastern China had significant downward trends, whereas most of Xinjiang, the Tibetan Plateau, and small areas of Yunnan province had significant upward trends. Regions with downward trends were larger than those with upward trends (37% versus 26% of the land area), implying that over the period of analysis, the country has become slightly drier in terms of soil moisture. Trends in drought severity, duration, and frequency suggest that soil moisture droughts have become more severe, prolonged, and frequent during the past 57 yr, especially for northeastern and central China, suggesting an increasing susceptibility to agricultural drought.

Assessment of Drought due to Historic Climate Variability and Projected Future Climate Change in the Midwestern United States

Abstract Understanding the occurrence and variability of drought events in historic and projected future climate is essential to managing natural resources and setting policy. The Midwest region is a key contributor in corn and soybean production, and the occurrence of droughts may affect both quantity and quality of these crops. Soil moisture observations play an essential role in understanding the severity and persistence of drought. Considering the scarcity of the long-term soil moisture datasets, soil moisture observations in Illinois have been one of the best datasets for studies of soil moisture. In the present study, the authors use the existing observational dataset and then reconstruct long-term historic time series (1916–2007) of soil moisture data using a land surface model to study the effects of historic climate variability and projected future climate change on regional-scale (Illinois and Indiana) drought. The objectives of this study are to (i) estimate changes and trends associated with climate variables in historic climate variability (1916–2007) and in projected future climate change (2009–99) and (ii) identify regional-scale droughts and associated severity, areal extent, and temporal extent under historic and projected future climate using reconstructed soil moisture data and gridded climatology for the period 1916–2007 using the Variable Infiltration Capacity (VIC) model. The authors reconstructed the soil moisture for a long-term (1916–2007) historic time series using the VIC model, which was calibrated for monthly streamflow and soil moisture at eight U.S. Geological Survey (USGS) gauge stations and Illinois Climate Network’s (ICN) soil moisture stations, respectively, and then it was evaluated for soil moisture, persistence of soil moisture, and soil temperature and heat fluxes. After calibration and evaluation, the VIC model was implemented for historic (1916–2007) and projected future climate (2009–99) periods across the study domain. The nonparametric Mann–Kendall test was used to estimate trends using the gridded climatology of precipitation and air temperature variables. Trends were also estimated for annual anomalies of soil moisture variables, snow water equivalent, and total runoff using a long-term time series of the historic period. Results indicate that precipitation, minimum air temperature, total column soil moisture, and runoff have experienced upward trends, whereas maximum air temperature, frozen soil moisture, and snow water equivalent experienced downward trends. Furthermore, the decreasing trends were significant for the frozen soil moisture in the study domain. The results demonstrate that retrospective drought periods and their severity were reconstructed using model-simulated data. Results also indicate that the study region is experiencing reduced extreme and exceptional droughts with lesser areal extent in recent decades.