Root Zone Soil Research Articles

Drought cascades across multiple systems in Central Asia identified based on the dynamic space–time motion approach

Abstract. Drought is typically induced by the extreme water deficit stress that cascades through the atmosphere, hydrosphere, and biosphere. Cascading drought events could cause severe damage in multiple systems. However, identifying cascading drought connections considering the dynamic space–time progression remains challenging, which hinders further exploring the emergent patterns of drought cascades. This study proposes a novel framework for tracking drought cascades across multiple systems by utilizing dynamic space–time motion similarities. Our investigation focuses on the four primary drought types in Central Asia from 1980 to 2007, namely precipitation (PCP), evapotranspiration (ET), runoff, and root zone soil moisture (SM), representing the four systems of atmosphere, hydrosphere, biosphere, and soil layer respectively. A total of 503 cascading drought events are identified in this study, including the 261 four-system cascading drought events. Our results show a significant prevalence of the four-system cascading drought pattern in Central Asia with high systematic drought risk, mainly when seasonal PCP droughts with high severity/intensity and sizeable spatial extent are observed. As for the temporal order in the cascading drought events, ET droughts are likely to occur earlier than runoff droughts after PCP droughts, and SM droughts are more likely to occur at last, implying the integrated driven effect of the energy-limited and water-limited phases on the drought progression in Central Asia. Our proposed framework could attain precise internal spatial trajectories within each cascading drought event and enable the capture of space–time cascading connections across diverse drought systems and associated hazards. The identification of cascading drought patterns could provide a systematic understanding of the drought evolution across multiple systems under exacerbated global warming.

Root zone soil moisture in over 25 % of global land permanently beyond pre-industrial variability as early as 2050 without climate policy

Abstract. Root zone soil moisture is a key variable representing water cycle dynamics that strongly interact with ecohydrological, atmospheric, and biogeochemical processes. Recently, it was proposed as the control variable for the green water planetary boundary, suggesting that widespread and considerable deviations from baseline variability now predispose Earth system functions critical to an agriculture-based civilization to destabilization. However, the global extent and severity of root zone soil moisture changes under future scenarios remain to be scrutinized. Here, we analysed root zone soil moisture departures from the pre-industrial climate variability for a multi-model ensemble of 14 Earth system models (ESMs) in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in four climate scenarios as defined by the shared socioeconomic pathways (SSPs) SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5 between 2021 and 2100. The analyses were done for 43 ice-free climate reference regions used by the Intergovernmental Panel on Climate Change (IPCC). We defined “permanent departures” when a region's soil moisture exits the regional variability envelope of the pre-industrial climate and does not fall back into the range covered by the baseline envelope until 2100. Permanent dry departures (i.e. lower soil moisture than pre-industrial variability) were found to be most pronounced in Central America, southern Africa, the Mediterranean region, and most of South America, whereas permanent wet departures are most pronounced in south-eastern South America, northern Africa, and southern Asia. In the Mediterranean region, dry permanent departure may have already happened according to some models. By 2100, there are dry permanent departures in the Mediterranean in 70 % of the ESMs in SSP1–2.6, the most mitigated situation, and more than 90 % in SSP3–7.0 and SSP5–8.5, the medium–high and worst-case scenarios. North-eastern Africa is projected to experience wet permanent departures in 64 % of the ESMs under SSP1–2.6 and 93 % under SSP5–8.5. The percentage of ice-free land area with departures increases in all SSP scenarios as time goes by. Wet departures are more widespread than dry departures throughout the studied time frame, except in SSP1–2.6. In most regions, the severity of the departures increases with the severity of global warming. In 2050, permanent departures (ensemble median) occur in about 10 % of global ice-free land areas in SSP1–2.6 and in 25 % in SSP3–7.0. By the end of the 21st century, the occurrence of permanent departures in SSP1–2.6 increases to 34 % and, in SSP3–7.0, to 45 %. Our findings underscore the importance of mitigation to avoid further degrading the Earth system functions upheld by soil moisture.

Response of N2O emission and denitrifying genes to iron (II) supplement in root zone and bulk region during wetting-drying alternation in paddy soil

Ferrous (Fe (II)) plays a key role in the production or consumption of N2O through biochemical pathways in paddy soil. Drying-wetting alternations can affect Fe cycling and the associated N2O emissions, however, the underlying microbial mechanisms remain largely unknown, particularly in plant systems with the presence of roots. In this study, a laboratory experiment with four Fe (II) levels (0, 50, 150, and 300 mg Fe (II) kg−1 dry soil) was conducted to investigate the response of N2O emission and denitrifying genes to iron (II) supplement in root zone and bulk region during wetting-drying cycles in paddy soils. The results indicated that supplemented Fe (II) led to a reduction in soil N2O emissions, predominantly observed during the drying phase. The bulk soil exhibited higher N2O fluxes compared to root zone. However, the decrease in cumulative N2O (cumul-N2O) by Fe (II) was significantly greater in bulk region than in root-affected soil (P < 0.001), registering 85–91 % reduction for soils amended with low to medium rates (50 and 150 mg Fe (II) kg−1 dry soil) throughout the experiment. This might be attributed to the more sensitive response of N2O reductase (nosZ gene) to low or medium Fe (II) supplementation in bulk soil than in root zone. The highest Fe (II) addition (300 mg Fe (II) kg−1 dry soil) showed a negative impact on nosZ-containing gene during initial flooding phase, resulting in the lower reduction rate (40 %) in cumulative N2O emission from bulk soil. Cross-correlation analysis showed that the changes of nosZ-containing genes in root zone occurred before variations of cumulative N2O data, indicating that compared to bulk soil, root zone soil exhibited a more pronounced of N2O consumption capability, especially after addition of ferrous. In summary, judicious Fe (II) application into soil promotes N2O reduction during wetting-drying alternation. The response of nosZ abundance to Fe (II) supplementation is likely the pivotal microbial factor influencing N2O emission reduction during wetting-drying alternation in paddy soil.

A copula-based parametric composite drought index for drought monitoring and applicability in arid Central Asia

Due to the complexity of meteorological and hydrological conditions in a changing environment, previous drought indices for monitoring a specific drought type do not reflect the overall regional situation of water scarcity. Therefore, in order to obtain accurate and reliable drought monitoring, a more integrated drought index should be developed to identify drought events comprehensively. In this paper, a non-linear trivariate drought index (NTDI) was constructed based on the joint probability distribution of parametric copulas, combining precipitation (P), potential evapotranspiration (PET), and root zone soil moisture (SM) variables. Subsequently, it was respectively compared with four drought indices, SPEI, SSMI, SC-PDSI and TVDI, and cross-validated with actual recorded drought events and annual crop yield to evaluate its applicability in arid Central Asia (ACA). The results indicated that: (1) Frank copula (1-,3-month scale) and Gumbel copula (6-,12-month scale) were considered to be the best-fitted copula functions for constructing joint probability distributions in the ACA. (2) The NTDI integrated the P-PET and SM drought signals to sensitively capture drought onset and duration, reflecting the combined characteristics of meteorological and agricultural drought. (3) The drought information expressed by NTDI was generally consistent with recorded drought events, and the monitoring results are accurate. (4)The NTDI performed better in agricultural drought monitoring than other drought indices. This study provides a reliable multivariate composite indicator which is significant for drought monitoring, prevention and risk assessment in ACA.

SNOOPI: Demonstrating Earth remote sensing using P-band signals of opportunity (SoOp) on a CubeSat

SigNals Of Opportunity: P-band Investigation (SNOOPI) will be the first in-space demonstration of P-band (240–380 MHz) signals of opportunity (SoOp) remote sensing and validation of a prototype P-band SoOp instrument. This technique has the potential to enable remote sensing of root-zone soil moisture (RZSM) and snow water equivalent (SWE). SNOOPI technology validation goals will be met by targeting observations within 9 km of SMAP calibration/validation sites in the continental United States. A second priority is collection of continuous phase data over snow-covered regions. These goals are evaluated under constraints of a limited data budget and mission lifetime, with a launch expected in early 2024. SNOOPI will capture the autocorrelation of a signal formed by the interference between the direct and reflected signals. The complex surface reflection coefficient (phase and magnitude) can be extracted from this observation. Definition of critical instrument and mission requirements for SNOOPI are described. Analyses and simulations used to justify these requirements are presented, including orbit coverage, uncertainty in the open-loop delay-Doppler prediction, observations error, and mission planning operations.

Potentilla parvifolia strongly influenced soil microbial community and environmental effect along an altitudinal gradient in central Qilian Mountains in western China.

The Qilian Mountains (QLMs) form an important ecological security barrier in western China and a priority area for biodiversity conservation. Potentilla parvifolia is a widespread species in the mid-high altitudes of the QLMs and has continuously migrated to higher altitudes in recent years. Understanding the effects of P. parvifolia on microbial community characteristics is important for exploring future changes in soil biogeochemical processes in the QLMs. This study found that P. parvifolia has profound effects on the community structure and ecological functions of soil microorganisms. The stability and complexity of the root zone microbial co-occurrence network were significantly higher than those of bare soils. There was a distinct altitudinal gradient in the effect of P. parvifolia on soil microbial community characteristics. At an elevation of 3204 m, P. parvifolia promoted the accumulation of carbon, nitrogen, and phosphorus and increased sucrase activity and soil C/N while significantly improving the community richness index of fungi (p < .05) compared with that of bacteria and the relative abundance of Ascomycota. The alpha diversity of fungi in the root zone soil of P. parvifolia was also significantly increased at 3550 m altitude. Furthermore, the community similarity distance matrix of fungi showed an evident separation at 3204 m. However, at an altitude of 3750 m, P. parvifolia mainly affected the bacterial community. Potentilla parvifolia increased the bacterial community richness. This is in agreement with the findings based on the functional prediction that P. parvifolia favors the growth and enrichment of denitrifying communities at 3550 and 3750 m. The results provide a scientific basis for predicting the evolutionary trends of the effects of P. parvifolia on soil microbial communities and functions and have important implications for ecological governance in the QLMs.

On the Physics of High CAPE

Abstract Large values of convective available potential energy (CAPE) are an important ingredient for many severe convective storms, yet there has been comparatively little research on how, physically, such large values arise or why they take on the observed values and climatology. Here we build on recently published observational and theoretical work to construct a simple, one-dimensional coupled soil–atmosphere model of preconvective boundary layer growth, driven by a single diurnal cycle of prescribed net surface radiation. Based on this model and previously published research, we suggest that high CAPE (&gt;∼1000 J kg−1) results when air masses that have been significantly modified by passage over dry, lightly vegetated soils are advected over moist and/or moderately vegetated soils and then exposed to surface solar heating. Several diurnal cycles may be needed to raise the moist static energy of the boundary layer to levels consistent with high CAPE. The production of CAPE and erosion of convective inhibition (CIN) are strongly affected by the potential temperature of the desert-modified air mass, the level of near-surface soil moisture (and root-zone soil moisture if significant vegetation is present), the type of soil, and the characteristics of the vegetation. Consequently, CAPE production and severe convective weather may be significantly affected by regional-scale land-use changes and by climate change. Significance Statement The energy available for severe convective storms depends strongly on the properties of the underlying soil and vegetation and the temperature of air masses formed over dry terrain upstream. This implies that the severity of convective storms can be strongly affected by changes in land use and by climate change.

Grass-legume mixtures maintain forage biomass under microbial diversity loss via gathering Pseudomonas in root zone soil.

Grass-legume mixtures are a common practice for establishing artificial grasslands, directly or indirectly contributing to the improvement of yield. In addition, this method helps maintain soil and plant health by reducing the use of chemical fertilizers. The impact of grass-legume mixtures on yield and its underlying microbial mechanisms have been a focus of scientific investigation. However, the benefits of mixtures in the context of soil microbial diversity loss remain a problem worthy of exploration. In this study, we examined different aboveground and belowground diversity combinations to elucidate the mechanisms by which grass-legume mixtures help maintain stable yields in the face of diversity loss. We identified the significantly enriched Pseudomonas genus microbial ASV53, which was gathered through homogeneous selection and served as a keystone in the co-occurrence network. ASV53 showed a strong positive correlation with biomass and the abundance of nitrogen-fixing genes. These findings provide a new theoretical foundation for utilizing grass-legume mixtures to enhance grass yields and address the challenges posed by diversity loss.

Effect of Irrigation, Phosphorous and Potassium Application on Root Traits, Soil Microbial Growth and Physiology of Green Gram

Aims: To manage the implication of irrigation water at different growth stages coupled with basal phosphorus and foliar potassium application targeting to reduce water stress.&#x0D; Study Design: Split split-plot design.&#x0D; Place and Duration of Study: Agricultural Research Station, Chatabar, Odisha. India, during 2022-2023 cropping season.&#x0D; Methodology: Three irrigation treatments in main plots (I1, I2 and I3) - I1 included three irrigations at vegetative, flowering and pod formation periods, I2 skipped one irrigation at pod formation and I3 skipped at flowering. Sub-plot consisted of phosphorus fertilizer treatments (P1, P2 and P3) providing 100, 85 and 115% recommended dose of P. In sub sub-plots- 2% foliar spray of KCl at vegetative and flower initiation periods. The parameters recorded soil moisture depletion, root traits, microbiological parameters, plant physiological parameters and grain yield.&#x0D; Results: The depletion of soil moisture increased with crop growth; I3 depleted at a maximum rate (4.5 mm d-1) followed by I2 (4.1 mm d-1). The enhanced availability of root zone soil moisture improved microbial biomass carbon (MBC) and dehydrogenase activity (DHA). The I1 improved the maximum root tips, forks and crossings; while I3 reduced an overall of 131% of the root traits than I1. The crop growth rate (CGR) and leaf area index (LAI) were also improved according to soil moisture availability; however, I2 merely decreased 8 and 13% in CGR and LAI as compared to I1. P3 improved the maximum of the soil microbiology (MBC and DHA), root improvement and plant physiology. I1P3 produced the highest grain yield of 9.06 q ha-1, although, I2P3 produced was just 7% lower than I1P1.&#x0D; Conclusion: The application of three irrigation produced maximum grain yield; while the skipping of one irrigation at pod formation stage with 15% higher P application could provide satisfactory yield where availability of irrigation water is limited.

Machine Learning Downscaling of SoilMERGE in the United States Southern Great Plains

SoilMERGE (SMERGE) is a root-zone soil moisture (RZSM) product that covers the entire continental United States and spans 1978 to 2019. Machine learning techniques, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Gradient Boost (GBoost) downscaled SMERGE to spatial resolutions straddling the field scale domain (100 to 3000 m). Study area was northern Oklahoma and southern Kansas. The coarse resolution of SMERGE (0.125 degree) limits this product’s utility. To validate downscaled results in situ data from four sources were used that included: United States Department of Energy Atmospheric Radiation Measurement (ARM) observatory, United States Climate Reference Network (USCRN), Soil Climate Analysis Network (SCAN), and Soil moisture Sensing Controller and oPtimal Estimator (SoilSCAPE). In addition, RZSM retrievals from NASA’s Airborne Microwave Observatory of Subcanopy and Surface (AirMOSS) campaign provided a nearly spatially continuous comparison. Three periods were examined: era 1 (2016 to 2019), era 2 (2012 to 2015), and era 3 (2003 to 2007). During eras 1 and 2, RF outperformed XGBoost and GBoost, whereas during era 3 no model dominated. Performance was better during eras 1 and 2 as opposed to the pre-L band era 3. Improvements across all eras, regions, and models realized from downscaling included an increase in correlation from 0.03 to 0.42 and a decrease in ubRMSE from −0.0005 to −0.0118 m3/m3. This study demonstrates the feasibility of SMERGE downscaling opening the prospect for the development of a long-term RZSM dataset at a more desirable field-scale resolution with the potential to support diverse hydrometeorological and agricultural applications.

Flash drought fades away under the effect of accumulated water deficits: the persistence and transition to conventional drought

Flash drought typically refers to the rapid intensification process that would only persist for a certain amount of time. In spite of short duration, flash drought may cause destructive impacts on agricultural and ecological sectors due to the sustained drought severity during or after the rapid intensification period. Understanding the persistence of flash drought, its regional patterns, and to what extent a transition from rapid intensification to conventional drought occurs is beneficial for drought monitoring and drought management. Employing surface and root-zone soil moisture reanalysis datasets, the notion of accumulated water deficits over varying time scales (can be detected by the moving averages of daily soil moisture series from 1 to 300 d) was introduced to explore how the signal of flash drought fades away over China during the period of 1950–2021. Results show that the flash drought signal gradually attenuates under the increased time scales of water deficits. With significant spatial differences, more than 50% of flash drought on average would be lost at a 10 d time scale, and the attenuation ratio may reach 90% when the time scale increases to 100 d. Under the effects of accumulated water deficits, the majority of flash drought events may evolve into conventional drought before dissipating completely. Soil moisture memory has a finite effect on the attenuation of flash drought signal. Flash drought signal dissipates slowly in areas with strong soil moisture memory. As time scale increases, both flash drought signal and the memory of soil moisture decrease, and their correlation also weakens.

Ridge-furrow planting with black film mulching increases rainfed summer maize production by improving resources utilization on the Loess Plateau of China

Soil mulching has been widely used to improve crop productivity around the world, but how various mulching practices affect soil hydrothermal conditions, water-temperature-radiation utilization and finally grain yield of rainfed maize has been poorly understood. A two-season field experiment was conducted on rainfed summer maize in 2021 and 2022 on the Loess Plateau of China, with six soil mulching practices: flat planting with non-mulching (FPNM), flat planting with full straw mulching (FPSM), flat planting with full black film mulching (FPBF), flat planting with full transparent film mulching (FPTF), ridge-furrow planting with black film mulching on the ridge (RFBF) and ridge-furrow planting with transparent film mulching on the ridge (RFTF). The results showed that compared with the traditional FPNM, the five mulching treatments (FPSM, FPBF, FPTF, RFBF and RFTF) greatly regulated root-zone soil hydrothermal conditions and resulted in obvious soil drying-wetting alternation, which significantly changed the daytime average root-zone soil temperature in the 0 – 25 cm soil layer (by − 1.1 ℃, − 1.9 ℃, + 2.7 ℃, − 1.6℃ and + 0.8 ℃, respectively) and significantly reduced crop evapotranspiration. The five mulching treatments promoted the root growth, leaf area index and canopy light interception rate due to the improved soil water and/or temperature conditions. The structural equation modeling indicated that soil hydrothermal condition and water-temperature-radiation use efficiency could directly or indirectly explain 98% of the grain yield variation under various soil mulching practices; radiation use efficiency (RUE) determined maize production in the water-sufficient year (2021), whereas maize production was mainly determined by crop water productivity (WP) in the water-limited year (2022). The RFBF increased maize production most, followed by RFTF. Compared with FPNM, RFBF increased leaf area index by 45.4%, canopy light interception rate by 19.1%, aboveground biomass by 68.8%, 1000-grain weight by 15.1%, grain yield by 58.6%, WP by 71.8%, thermal time use efficiency by 64.6% and RUE by 30.5%. In conclusion, RFBF optimized the root-zone soil hydrothermal conditions for root growth, which in turn promoted aboveground growth and water-temperature-radiation use efficiency, and finally improved the gain yield of rainfed summer maize on the Loess Plateau of China. Therefore, RFBF was considered as a promising agricultural practice to improve rain-fed summer maize production and resource utilization efficiency on the Loess Plateau of China.

Untangling the Causal Links between Satellite Vegetation Products and Environmental Drivers on a Global Scale by the Granger Causality Method

The Granger Causality (GC) statistical test explores the causal relationships between different time series variables. By employing the GC method, the underlying causal links between environmental drivers and global vegetation properties can be untangled, which opens possibilities to forecast the increasing strain on ecosystems by droughts, global warming, and climate change. This study aimed to quantify the spatial distribution of four distinct satellite vegetation products’ (VPs) sensitivities to four environmental land variables (ELVs) at the global scale given the GC method. The GC analysis assessed the spatially explicit response of the VPs: (i) the fraction of absorbed photosynthetically active radiation (FAPAR), (ii) the leaf area index (LAI), (iii) solar-induced fluorescence (SIF), and, finally, (iv) the normalized difference vegetation index (NDVI) to the ELVs. These ELVs can be categorized as water availability assessing root zone soil moisture (SM) and accumulated precipitation (P), as well as, energy availability considering the effect of air temperature (T) and solar shortwave (R) radiation. The results indicate SM and P are key drivers, particularly causing changes in the LAI. SM alone accounts for 43%, while P accounts for 41%, of the explicitly caused areas over arid biomes. SM further significantly influences the LAI at northern latitudes, covering 44% of cold and 50% of polar biome areas. These areas exhibit a predominant response to R, which is a possible trigger for snowmelt, showing more than 40% caused by both cold and polar biomes for all VPs. Finally, T’s causality is evenly distributed amongst all biomes with fractional covers between ∼10 and 20%. By using the GC method, the analysis presents a novel way to monitor the planet’s ecosystem, based on solely two years as input data, with four VPs acquired by the synergy of Sentinel-3 (S3) and 5P (S5P) satellite data streams. The findings indicated unique, biome-specific responses of vegetation to distinct environmental drivers.

Spatio-temporal changes in global root zone soil moisture from 1981 to 2017

Root zone soil moisture (RZSM) has a direct impact on ecosystem function, vegetation growth and food security, and plays a vital role in global climate system, water and carbon cycles. However, large variations and uncertainties still exist in RZSM across the globe under the warming climate. In this study, we applied comparison map profile (CMP), Theil-Sen regression and partial correlation analysis to investigate the spatial and temporal changes of RZMS and its driving factors from 1981 to 2017 by using three soil moisture products–ERA5, GLDAS and MERRA-2. Results showed that RZSM derived from three products presented a similar spatial pattern that the highest RZSM values occurred in tropical forest and cold areas, followed by subtropical, while the relatively low RZSM values were observed in arid and semiarid regions. Globally, RZSM decreased in all of three datasets with a rate of −0.14 × 10-3 m3 m−3 yr−1 on average (p < 0.001), which was largely correlated with temperature anomalies. Spatially, the RZSM trends greatly varied, with 21–31% of global land areas experiencing a significant decreasing trend and 7–24% for an increasing trend, respectively, confirming their different sensitivities to climate change. Temperature-driven RZSM dominated 19–29% of global land areas and was primarily distributed in northern high-latitude areas. The areas dominated by evapotranspiration were mainly in arid and semiarid areas, accounting for 29–44% of global land areas. Precipitation dominates the remaining 36–45% of global land areas mainly in eastern America and Europe, suggesting variations in the dominance of environmental factors on the spatial patterns of RZSM trend. Our findings will deepen our understanding of the impacts of climate change on the long-term trend of global soil moisture, and will be greatly critical to global soil water resource protection and management under the warming climate.

Remotely sensed estimation of root-zone salinity in salinized farmland based on soil-crop water relations

Accurate monitoring and evaluation of root-zone soil salt content (SSC) are critical for sustainable development of irrigated agriculture in arid and semi-arid areas. Based on soil-crop water relations and farmland evapotranspiration (ET) fused through remote sensing data, this study developed an inversion method to estimate root-zone SSC using a case study from cotton fields under film mulched drip irrigation (CFFMDI) in the Manas River Basin (MRB) over 21 years (2000–2020). Two hypotheses were set as: (1) relative transpiration can be approximated by relative ET; and (2) the soil water stress response function is linearly proportional to the ratio of relative water supply. Measured data from a field experiment and collected data from regional survey and literature retrieval were used to optimize parameters and verify the hypotheses and method. The method was then applied to analyze the spatial and temporal distribution characteristics and cumulative effects of root-zone SSC. Results showed that the hypotheses and the method were reasonable and reliable in estimating root-zone SSC (with coefficient of determination R2 > 0.50). Along with the popularization of film-mulched drip irrigation and the expansion of CFFMDI over the past 21 years, regional-scale root-zone SSC declined significantly with an annual attenuation rate of about 0.09 g kg−1. Due to the gradual reduction of irrigation amount per unit area, the decline was more rapid before 2011 (0.18 g kg−1), but slightly slowed down or even reversed at the end of the second decade (2015–2020). By 2020, the mean regional root-zone SSC reached 3.93 g kg−1. At the beginning of this century, MRB was mainly composed of mildly- (59.8%) and moderately-salinized CFFMDI (39.9%). However, by 2020, non- (69.7%) and mildly-salinized cotton field (28.2%) dominated the basin. The inversion method of root-zone SSC fully considers the water consumption mechanism of soil-crop system, thus shows great potential in effective planning and management of soil and water resources in arid salinized areas such as MRB.

Simulation of root zone soil water dynamics under cotton-silverleaf nightshade interactions in drip-irrigated cotton

Simulation of root zone soil water dynamics under cotton-silverleaf nightshade interactions in drip-irrigated cotton

Drip irrigation impacts on the root zone soil environment and enrichment characteristics of heavy metals in soybean

Drip irrigation impacts on the root zone soil environment and enrichment characteristics of heavy metals in soybean

IMERG Precipitation Improves the SMAP Level-4 Soil Moisture Product

Abstract The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6. Significance Statement Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia.

Sensitivities of rainfed maize production to root zone soil water, air temperature and shortwave radiation in the Sanjiang Plain under sub-humid cool-temperate climates

Development of rainfed maize (Zea mays L.) is sensitive to fluctuations of environmental conditions, while whether the sensitivity varies across the growth stages is still unclear. Based on a 5-year dataset collected from consecutive observations, this study examined the sensitivities of biomass and yield production of rainfed maize to root zone soil water, air temperature and shortwave radiation at four growth stages in the Sanjiang Plain of Northeastern China under sub-humid cool-temperate climates. The multiple linear regression model was employed to establish functional relations between biomass and yield production and significant explanatory variables. A Monte-Carlo simulation was used to test sensitivities of biomass and yield production to perturbation of a single significant explanatory variable or co-perturbation of multiple significant explanatory variables. Results showed that root zone soil water prevailed over air temperature and shortwave radiation in affecting rainfed maize development for the most time of the growing period. Biomass production was most sensitive to root zone soil water which had positive variance contributions of 70 – 100% at the early and late vegetative stages and a negative variance contribution of −99.4% at the early reproductive stage. Yield production was also sensitive to root zone soil water at the early reproductive stage with a 100% positive variance contribution. Biomass and yield production were most sensitive to air temperature at the late reproductive stage and the positive variance contributions of air temperature were 97.7 – 100%. Shortwave radiation negatively contributed to biomass production by −28.6% at the late vegetative stage. The findings of this study suggest that more attention could be paid to the most sensitive factor at different growth stages of rainfed maize for great biomass accumulation and high grain yield.

Comprehensive validation of seven root zone soil moisture products at 1153 ground sites across China

ABSTRACTRoot zone soil moisture (RZSM) is a critical parameter for drought monitoring, agriculture, and vegetation growth. In situ measurements of RZSM are scarce, and therefore, RZSM products are derived mainly through modeling approaches. This study evaluated seven RZSM products, including ERA5, MERRA2, CFSR, SMAP level 4, GLDAS-NOAH2.1, GLDAS-Catchment2.2, and SMOS CATDS Level 4, using 1153 ground sites across China. The evaluations aimed to address which products perform better, how well they capture the seasonal variations, and how they perform under different land surface and climate conditions. Results showed that MERRA2 and NOAH had the best performance, with MERRA2 having a better ability to capture surplus and deficit soil moisture conditions. The study also revealed that precipitation forcing plays a vital role in improving RZSM simulations. Conversely, ERA5 overestimates RZSM, and SMOS exhibits a dry bias. From the time series variability, all datasets except SMOS can capture the temporal dynamics of RZSM well. Moreover, the accuracy of the RZSM products varies significantly in tropical climate zones. This study provides crucial insights for improving land surface and hydrological models in estimating RZSM, which will help in drought management and agriculture production.