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Drought poses large risks to ecosystem services and agricultural production because of their devastating impacts on the environment, economy, and society (Zhang, Keenan & Zhou, 2021). Generally, drought classied into four types including meteorological drought, soil drought, hydrological drought, and social-economic drought (Wilhite & Pulwarty, 2017), which are strongly-correlated with sustained precipitation deficit but various elements of hydrosphere response to drought in different degrees (AghaKouchak et al., 2015). The combination of interacting physical processes across several related droughts is referred to as the compound droughts

Publisher Copyright: Author A lightning strike is considered one of the most risky natural phenomena that can lead to human harmful and the surrounding soil layers. To tackle this issue, this article investigates the influence of direct lightning characteristics in terms of human body safety. Specifically, such investigation is carried out on the effect of resistivities of two-layer soils on human safety when lightning stroke hits the towers of the high voltage transmission lines (HVTLs). The merit of the proposed study is that the soil ionization phenomenon is taken into consideration. Further, the study focuses on the current passing through the human heart, when step and touch (contact) voltages are generated by grounding potential rise, caused by direct lightning strikes transmission tower and the produced potential rise that a person could be exposed. Also, studying the effects of peak current and time of lightning strokes are investigated. Additionally, the paper presents the effect of different reflection factors on human safety.For validation purposes, the ATP program is used in the simulation of the grounding system as well as the human body model. Numerous simulations were accomplished in order to examine the behavior of the current passing through with the human heart. Based on the simulation results, it was concluded that the soil characteristics have superior influences on the contact and step potentials and, accordingly, the survival threshold.

The study was conducted to investigate the effect of Black Plastic Mulch (BPM) and different rates of NPK (15:15:15) fertilizer on the growth and yield of three chili cultivars under field and protected cultivation. Two levels of mulch (BPM and no mulch), three levels of NPK (0, 200 and 300 kg/ha), and three cultivars “Demond F1” (DF1), “Legon 18” (L18), and “Shito Adope”, (SA) were used. Split application of half of each of the 200 and 300 kg/ha rates were applied two and four weeks after transplanting. Vegetative growth indices, soil moisture, and soil temperature were measured at 2, 4, 6, 8, and 10 weeks after transplanting. The yield was calculated using AVRDC (1990) formula for chili pepper. The study revealed an increase in soil temperature and moisture under BPM for both studies. Also, 200 kg/ha NPK and BPM led to an increase in vegetative growth and yield for DF1 and SA. However, 300 kg/ha NPK decreased vegetative growth. In addition, there was no significant influence of BPM on the number of fruits harvested. However, for the pot study, BPM led to an increase in the number of fruits for “Legon 18” as compared to the other cultivars. The study, therefore, concludes that 200 kg/ha NPK was the best when cultivating DF1 and SA, for chili growth and development in the savannah ecological zone.

Refereed/Peer-reviewed When choosing sites for monitoring of soil moisture for hydrological purposes, a suitable process that considers the factors influencing soil moisture level should be followed. In this study, two multi-criteria decision-making (MCDM) methods, the multi-influencing factor (MIF) method and the analytical hierarchy process (AHP) method, were used to identify the optimal soil moisture monitoring (SMM) sites in the Dry Creek Catchment in South Australia. The most representative areas for nine SMM sites were obtained using the MIF method, considering the factors of rainfall, soil type, land use, catchment slope, elevation, and upslope accumulated area (UAA). The AHP method was used to select the optimal sites using the site-specific criteria. 30.3% of the catchment area in the Australian Water Resources Assessment Landscape (AWRA-L) Grid_DC2 can be considered acceptable as representative area with the MIF method. Four potential sites were evaluated for each AWRA-L grid using the relative weights of the site-specific criteria with the AHP method. The Grid_DC2 required two sites that had the highest overall weight chosen with the AHP analysis. The procedure was repeated for the remaining four AWRA-L grids within the study area to select the required SMM sites.

Plants need water to grow well, so watering must follow the plant's needs. Parameters that affect the process of watering plants are air temperature and soil moisture, thus, we need a control system that can make decisions in the form of watering time to meet plant water needs according to their needs, namely an automatic watering system. Fuzzy method can be used for human-like decision making in a control system. This study uses the Sugeno fuzzy method which is integrated into the Microcontroller as the controller. Testing of this system is carried out on eggplant plants which are regulated based on how long the watering time is so that the air temperature and soil moisture are following the plant's needs. The test resulted that the effective watering time interval so that the eggplant soil moisture was maintained at 40-60% was 21 seconds. It can be concluded that the fuzzy inference embedded in the design has been able to overcome the problem of watering eggplant plants, where the test shows that after the watering process, the soil moisture value can be maintained at an average value of 55% so that the soil moisture is following the needs of the plant.

Agriculture is the most water-demanding economic activity. Nevertheless, the monitoring of agricultural production systems can improve the soil water condition and contribute to soil conservation, as well as increase irrigation efficiency through quick and assertive decision-making. Thus, the objective of this work was to carry out a real-time evaluation of the wetting front (WF), the water infiltration rate in the soil, and to verify whether the system performance can affect infiltration test data in a Red Latosol with clayey and very clayey textures. The public domain prototype system consisted of a permeameter, and 10 soil moisture sensors that were calibrated by the oven drying method and inserted into a PVC pipe from 10 cm to 100 cm depth. The equipment allowed the evaluation of the wetting front and calculation of the infiltration rate and water retention and variations along the soil profile. Data were analyzed using descriptive statistics using RStudio and Excel. The results showed that the prototype system is effective to simulate the water infiltration rate in the two soil textures analyzed at low cost. Agriculture is the most water-demanding economic activity. Nevertheless, the monitoring of agricultural production systems can improve the soil water condition and contribute to soil conservation, as well as increase irrigation efficiency through quick and assertive decision-making. Thus, the objective of this work was to carry out a real-time evaluation of the wetting front (WF), the water infiltration rate in the soil, and to verify whether the system performance can affect infiltration test data in a Red Latosol with clayey and very clayey textures. The public domain prototype system consisted of a permeameter, and 10 soil moisture sensors that were calibrated by the oven drying method and inserted into a PVC pipe from 10 cm to 100 cm depth. The equipment allowed the evaluation of the wetting front and calculation of the infiltration rate and water retention and variations along the soil profile. Data were analyzed using descriptive statistics using RStudio and Excel. The results showed that the prototype system is effective to simulate the water infiltration rate in the two soil textures analyzed at low cost.

Dirre Sheikh Hussein is a religious complex in Ethiopia that dates from the 12th century or earlier. An important focus for pilgrimage, it lies in an isolated part of Oromia State on poorly drained semi-arid lowlands. Significant architecturally and culturally, recent expansion and refurbishment of the major buildings led to structural issues, though these are now largely overcome through consolidation of the prayer hall roof, laying a stone walkway, digging drainage ditches etc. Current deterioration of the buildings can be seen as: (i) fading of colours, (ii) cracks in walls, roofs, and festive places, (iii) deposits of sand around walls and (iv) staining and drainage marks on the white painted surfaces. Heavy falls of rain overwhelm drainage and overfill storage pools. Meteorological observations are infrequent locally, so those made some 70–100 km away were used, along with regridded historical data, reanalysis and CMIP6 projections. These revealed increases in temperature, precipitation and humidity, and provided indications of long-term climate pressures at the site. Changing patterns of future precipitation, particularly heavy rain, may threaten the site. The relative humidity changes are small, though an increasing Scheffer index suggests potentially enhanced wood decay. Changes in soil moisture have the potential to disrupt the foundations. Very hot days may become a problem for the two annual celebrations at the site.

It is a priority develop intelligent irrigation systems to save water. Using optimal control formulations and techniques, the water consumption can be made to follow more closely the hydrological needs of the crop, taking into account current weather conditions. Here, the mathematical model presented by the authors in previous publications is improved. This new model incorporates new features like the slope of the soil, the possibility to include a percentage of water losses due to runoff, and a percentage of water losses if the soil is on the field capacity. A new and efficient replan strategy is applied tacking into account the data measured from moisture sensors, to ensure that hydric needs of the crop is fulfilled. A new approach to deal with multiple irrigation points is also proposed. It allows to redistribute the available water in the case an irrigation point is not able to provide the water needed.

In preparation for active microwave-based data assimilation into a crop modeling system, the mapping of daily 1-km AquaCrop model (v6.1) biomass and surface soil moisture to backscatter was optimised, using two forward operators, i.e. the Water Cloud Model (WCM) and the Support Vector Regression (SVR). Both forward operators were calibrated (2014–2018) with 1-km Sentinel-1 backscatter (ϒ°) observations in VV and VH polarisation, for three different study domains in Europe. For the validation period (2019–2021), the ϒ° simulations showed reasonable performances around Czech Republic and the Iberian Peninsula, to good performances over Belgium, but with strong variations within each domain. The domain-averaged root mean square difference between the model and Sentinel-1 ϒ° remained below 2 dB for both forward operators and all three study domains, and the mean bias for VV remained close to 0 dB, and close 0.5 dB for the VH polarisation. The WCM and SVR performed better in VV than VH and overall the SVR performed slightly better in mapping the AquaCrop soil moisture and vegetation to backscatter than the WCM. Additionally, the assumed linear relationship in the WCM between soil moisture and soil ϒ° holds better for VV than for VH. The remaining differences between WCM or SVR simulations and Sentinel-1 observations are mainly caused by AquaCrop model errors. ispartof: Remote Sensing Of Environment vol:294 status: published

Aim: Widespread greening and an increasing global terrestrial carbon sink over recent decades have been reported. However, the spatio-temporal relationships between vegetation greenness and productivity and the factors influencing this relationship remain unclear. We define a new metric of ecosystem-scale photosynthetic efficiency (EPE) to analyse its spatio-temporal pattern and investigate how potential drivers regulate the greenness–productivity relationship. Location: Global. Time period: From 2001 to 2016. Major taxa studied: Global terrestrial ecosystems. Methods: This study used global datasets of leaf area index (LAI) and solar-induced fluorescence (SIF) as proxies of vegetation greenness and ecosystem productivity, respectively, to propose a new metric of SIF/LAI, representing ecosystem-scale photosynthetic efficiency (EPE). We identified the spatial pattern and dynamics of EPE and examined factors influencing EPE. Results: The results showed a weaker increase in productivity compared with the global greening rate from 2001 to 2016, suggesting a decline in EPE at the global scale. This decline in EPE indicates a disproportionate increase in terrestrial productivity against the widespread greening. When stratified into areas following an aridity gradient, we found that EPE overall showed upward trends in arid and semi-arid areas, and downward trends in dry sub-humid and humid regions. The EPE was controlled primarily by soil moisture, which promoted or constrained the EPE in xeric and mesic ecosystems, respectively. Moreover, the increase in short vegetation cover and atmospheric water demand contributed positively or negatively to EPE changes in xeric and mesic ecosystems, respectively. Main conclusions: Our study shows that greening of the Earth is associated with decreasing EPE, revealing that current rates of carbon sequestration do not increase proportionally to greening of the Earth and highlighting that soil moisture is a key controller of EPE. These results help to reduce the uncertainties in future climate change impacts on vegetation dynamics, thus having implications for sustainable ecosystem management and climate change mitigation.

We analyzed changes in the long-term vegetation cover and in fire activity over the past 5000 years in the Ecuadorian páramo using a sediment core from Papallacta (Ecuador). The chronology is constrained by three tephra layers and 32 AMS 14C ages, and 168 samples yielded a high-resolution record of environmental changes. We estimated the upslope wind convectivity as the ratio between pollen transported from the Andean cloud forest and Poaceae pollen to distinguish changes in atmospheric moisture from changes in soil moisture. The record showed that the two sources of moisture, either from year-round adiabatic cloud dripping linked to SASM activity or to ENSO variability at decadal-scale, influenced vegetation-cover changes. Between 5000 and 2450 cal yr BP, both soil moisture and biomass burning were higher than after 2450 cal yr BP. The shift between the two states matches the zonal increase in summer insolation that drove the ITCZ to its southernmost position. Our results underline resilience to volcanic activity, the importance of the upslope convective dripping with the lowest convective index observed at ~4500 cal yr BP, the anomalous last century with the highest convective activity and the driest soil conditions recorded in the last 5000 years, the recent increase in fire activity and the link between soil moisture and the position of the ITCZ.

Automated soil moisture systems are commonly used in precision agriculture and environmental monitoring. Using low-cost sensors, the spatial extension can be maximized, but the accuracy might be reduced. In this paper, we address the trade-off between cost and accuracy comparing low-cost and commercial soil moisture sensors. The analysis is based on the capacitive sensor SKU:SEN0193 under lab and field conditions. The laboratory tests aimed at evaluating the response speed, best supply voltage, temperature dependency, calibration, and applicability for controlled infiltration column tests (one meter high). Laboratory tests indicated that the sensor is temperature and voltage-sensitive. The use of 5.5 V as supply voltage for the sensors drastically reduced the correlation between output and degree of soil saturation, thus we suggest the use of 3.3V. Soil temperature had a negligible impact on the sensor output: 0.27% of soil saturation degree per degree Celsius. For field implementation, a low-cost monitoring station was built using Arduino as a microcontroller and tested during three months. The sensors could represent daily and seasonal oscillation in soil moisture resulting from solar heating and precipitation. In addition to individual calibration, two simplified calibration techniques are proposed: universal calibration, based on all 63 sensors, and a single-point calibration using the sensor's response in dry soil. The monitored wetting front was compared to the one estimated by Hydrus model and had a high correlation. The low-cost sensor performance was later compared to commercial sensors based on five variables: (1) cost, (2) accuracy, (3) qualified labor demand, (4) sample volume, and (5) life expectancy. Commercial sensors promote soil moisture measurements with high accuracy at a high acquisition cost. On the other hand, low-cost sensors, such as the SKU:SEN0193, provide data with medium accuracy at a very low acquisition cost, enabling spatial monitoring through multiple-point measurements. Thus, the use of the SKU:SEN0193 sensor is suggested in projects with budget limitations with short duration where there is a medium requirement accuracy or when the spatial variability of soil water content is considerable. Despite the physical fragility of the hardware used (sensors and monitoring station) and the lower accuracy when compared to other commercial sensors, this work demonstrates through the case study of the SKU:SEN0193 sensor the possibility of using low-cost technologies for monitoring environmental variables.

Nighttime irrigation scheduling is a longstanding water conservation approach to reduce evaporative losses in Utah's semi-arid, urban landscapes. However, residential demand now exceeds system capacity, and the efficiency of the practice has come under question. Therefore, the objectives of this study include: 1) quantify whether a water savings exists between day and night irrigation with a water balance and energy balance approach, and 2) analyze microclimate and crop quality effects to determine any changes to evaporative demand or plant stress by irrigation timing. In 2019, we established 12 field plots in North Logan, UT (41.77° N, -111.81° W, 1380 m elevation) to test two irrigation timings (2:00 - 4:00 am and 2:00 - 4:00 pm) with two urban crops (turfgrass and zinnia cut flowers) on a sprinkler system in triplicate. Field instrumentation included a central weather station, flow meters on sprinkler sets, soil moisture sensors at 0.08 m and 0.18 m depths, canopy air temperature and vapor pressure, surface temperature, and catch cups for wind drift and canopy interception calculations. Daytime irrigation resulted in greater wind drift and evaporative losses (WDEL) by 7% compared to nighttime in both crops. A decrease in canopy and surface temperature, as well as the vapor pressure deficit was greater with daytime irrigation than nighttime, decreasing the evaporative demand at maximum air temperature (Tmax). Turf and zinnia yield and quality were not affected by irrigation timing, though the percentage cover of turf was significantly greater with daytime irrigation compared to nighttime. Thus, landscape irrigation scheduling should avoid hours with peak wind speed to reduce the WDEL and irrigate the field at or after Tmax to reduce the evaporative or heat stress to the crop.

Phenology and productivity are important functional indicators of grassland ecosystems. However, our understanding of how intra-annual precipitation patterns affect plant phenology and productivity in grasslands is still limited. Here, we conducted a two-year precipitation manipulation experiment to explore the responses of plant phenology and productivity to intra-annual precipitation patterns at the community and dominant species levels in a temperate grassland. We found that increased early growing season precipitation enhanced the above-ground biomass of the dominant rhizome grass, Leymus chinensis, by advancing its flowering date, while increased late growing season precipitation increased the above-ground biomass of the dominant bunchgrass, Stipa grandis, by delaying senescence. The complementary effects in phenology and biomass of the dominant species, L. chinensis and S. grandis, maintained stable dynamics of the community above-ground biomass under intra-annual precipitation pattern variations. Our results highlight the critical role that intra-annual precipitation and soil moisture patterns play in the phenology of temperate grasslands. By understanding the response of phenology to intra-annual precipitation patterns, we can more accurately predict the productivity of temperate grasslands under future climate change.

Climate-driven permafrost thaw alters the strongly coupled carbon and nitrogen cycles within the Arctic tundra, influencing the availability of limiting nutrients including nitrate (NO3−). Researchers have identified two primary mechanisms that increase nitrogen and NO3− availability within permafrost soils: (1) the ‘frozen feast’, where previously frozen organic material becomes available as it thaws, and (2) ‘shrubification’, where expansion of nitrogen-fixing shrubs promotes increased soil nitrogen. Through the synthesis of original and previously published observational data, and the application of multiple geospatial approaches, this study investigates and highlights a third mechanism that increases NO3− availability: the hydrogeomorphic evolution of polygonal permafrost landscapes. Permafrost thaw drives changes in microtopography, increasing the drainage of topographic highs, thus increasing oxic conditions that promote NO3− production and accumulation. We extrapolate relationships between NO3− and soil moisture in elevated topographic features within our study area and the broader Alaskan Coastal Plain and investigate potential changes in NO3− availability in response to possible hydrogeomorphic evolution scenarios of permafrost landscapes. These approximations indicate that such changes could increase Arctic tundra NO3− availability by ~250–1000%. Thus, hydrogeomorphic changes that accompany continued permafrost degradation in polygonal permafrost landscapes will substantially increase soil pore water NO3− availability and boost future fertilization and productivity in the Arctic.

Earth's ecosystems are increasingly threatened by "hot drought," which occurs when hot air temperatures coincide with precipitation deficits, intensifying the hydrological, physiological, and ecological effects of drought by enhancing evaporative losses of soil moisture (SM) and increasing plant stress due to higher vapor pressure deficit (VPD). Drought-induced reductions in gross primary production (GPP) exert a major influence on the terrestrial carbon sink, but the extent to which hotter and atmospherically drier conditions will amplify the effects of precipitation deficits on Earth's carbon cycle remains largely unknown. During summer and autumn 2020, the U.S. Southwest experienced one of the most intense hot droughts on record, with record-low precipitation and record-high air temperature and VPD across the region. Here, we use this natural experiment to evaluate the effects of hot drought on GPP and further decompose those negative GPP anomalies into their constituent meteorological and hydrological drivers. We found a 122 Tg C (>25%) reduction in GPP below the 2015-2019 mean, by far the lowest regional GPP over the Soil Moisture Active Passive satellite record. Roughly half of the estimated GPP loss was attributable to low SM (likely a combination of record-low precipitation and warming-enhanced evaporative depletion), but record-breaking VPD amplified the reduction of GPP, contributing roughly 40% of the GPP anomaly. Both air temperature and VPD are very likely to continue increasing over the next century, likely leading to more frequent and intense hot droughts and substantially enhancing drought-induced GPP reductions.

Aims Plant investment in sexual reproduction is affected by nitrogen (N): phosphorus (P) stoichiometry. It has been suggested that an important adaptation to strong P limitation is reduced investment in sexual reproduction. We aim to investigate the specific influence of N:P on sexual reproduction performance within and between grassland species. Methods Eleven grassland species were selected in ten plots covering N limitation, co-limitation and P limitation. Nutrients in soil and above-ground biomass were determined, plus soil pH and soil moisture. A range of sexual reproduction traits were measured as a proxy for investment in sexual reproduction. Results At the intraspecific level: compared with N-limited plots, in P-limited/co-limited plots, flowering time was later, flowering period in individuals was shorter, and number of flowers (inflorescences) per individual was smaller. At the interspecific level, in P-limited/co-limited plots, species had a significantly earlier flowering time and a longer seed stalk and seed panicle, than those in N-limited plots. However, flowering period was shorter and number of flowers (inflorescences) per individual was smaller under P limitation/co-limitation. Moreover, significant correlations between soil pH and soil moisture, and sexual reproduction performance of the selected grassland species were also found. Conclusions P limitation/co-limitation restrict the sexual reproduction of grassland species, which may hamper their dispersal capacity. We recommend future studies further analyze the relationship between soil pH and N:P stoichiometry and the influence of soil pH, as well as soil moisture on sexual reproduction performance of grassland species in addition to analyzing N:P stoichiometry.

Wildfires are a major concern around the globe because of the immediate impact they have on people's lives, local ecosystems, and the environment. Soil moisture is one of the most important factors that influence wildfire occurrences and spread. However, it is also one of the most challenging hydrological variables to measure routinely and accurately. Therefore, soil moisture is significantly underutilized in operational wildfire risk applications. Thus, the aim here is to use a well-established operational soil moisture product to isolate the soil moisture-fire relationship and assess the utility of using soil moisture as a leading indicator of potential fire risk. We evaluated the value of remotely-sensed soil moisture observations from the soil moisture active passive sensor for monitoring and predicting fire risk in Australia and California. We quantified the relationship between observed fire activity and soil moisture conditions and analyzed the soil moisture conditions for two extreme fire events. Our findings show that fire activity is strongly associated with soil moisture anomalies. Lagged correlation analysis demonstrated that a remote-sensing based soil moisture product could predict fire activity with a 1–2 month lead-time. Soil moisture anomalies consistently decreased in the months preceding fire occurrence, often from normal to drier conditions, according to a spatiotemporal analysis of soil moisture in two extreme fire events. Overall, our findings indicate that soil moisture conditions prior to large wildfires can aid in their prediction and operational satellite-based soil moisture products such as the one used here have real value for supporting wildfire susceptibility and impacts.

Wheat is one of the most important crops worldwide, and thus the use of remote sensing data for wheat monitoring has attracted much interest. Synthetic Aperture Radar (SAR) observations show that, at C-band and VV polarization, wheat canopy attenuates the surface scattering component from the underlying soil during a significant part of its growth cycle. This behavior needs to be accounted for or corrected before soil moisture retrieval is attempted. The objective of this paper is to develop a new method for wheat attenuation correction (WATCOR) applicable to Sentinel-1 VV time series and based solely on the information contained in the time series itself. The hypothesis of WATCOR is that without attenuation, VV backscatter would follow a stable long-term trend during the agricultural season, with short-term variations caused by soil moisture dynamics. The method relies on time series smoothing and changing point detection, and its implementation follows a series of simple steps. The performance of the method was compared by evaluating the correlation between backscatter and soil moisture content in six wheat fields with available soil moisture data. The Water Cloud Model (WCM) was also applied as a benchmark. The results showed that WATCOR successfully removed the attenuation in the time series, and achieved the highest correlation with soil moisture, improving markedly the correlation of the original backscatter. WATCOR can be easily implemented, as it does not require parameterization or any external data, only an approximate indication of the period where attenuation is likely to occur. This work was supported by the Spanish Ministry of Science and Innovation through project PID2019-107386RB-I00 / AEI / 10.13039/501100011033 and doctoral grant (BES-2017-080560).

The Dry Chaco in South America is a semi-arid ecoregion prone to dryland salinization. In this region, we investigated coarse-scale surface soil moisture (), soil temperature, soil salinity and vegetation, using L-band microwave brightness temperature (B) observations and retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite missions, Catchment Land Surface Model (CLSM) simulations, and in situ measurements within 26 sampled satellite pixels. Across these 26 sampled pixels, the satellite-based outperformed CLSM when evaluated against field data, and forward L-band B simulations derived from in situ and temperature performed better than those derived from CLSM estimates when evaluated against SMOS B observations. The surface salinity for the sampled pixels was on average only 4 mg/g and only locally influenced the B simulations, when including salinity in the dielectric mixing model of the forward radiative transfer model (RTM) simulations. To explore the potential of retrieving salinity together with other RTM parameters to optimize B simulations over the entire Dry Chaco, the RTM was inverted using 10 years of multi-angular SMOS B data and constraints of CLSM and temperature. However, the latter modeled was not sufficiently accurate and factors such as open surface water were missing in the background constraints, so that the salinity retrievals effectively represented a bulk correction of the dielectric constant, rather than salinity per se. However, the retrieval of vegetation, scattering albedo and surface roughness resulted in realistic values.

Groundwater is the most significant freshwater source and plays a critical role in the earth's water and energy balance. The lack of groundwater observations with a high spatiotemporal resolution at a global scale hinders our ability to study and model the environment when shallow groundwater has a direct impact on surface soil moisture. This study aims to estimate the spatial and temporal distributions of shallow groundwater-influenced areas at a global scale. We trained an ensemble machine learning algorithm, using outputs from a variably saturated soil moisture flux model, to identify the shallow groundwater occurrence. Model simulations spanned various climate zones and soil types across the globe. The overall accuracy of the algorithm in reproducing the soil moisture flux model results was 95.5%. We applied the algorithm to spaceborne soil moisture observations retrieved by NASA's SMAP satellite and present a global-scale shallow groundwater map derived from the SMAP observations. The derived global distribution of shallow groundwater identifies wetlands, large riparian corridors, and seasonally inundated lowlands. The results showed that 19% of terrestrial land cover had been influenced by shallow groundwater at some point in time during the period of interest (2015–2018). Temporally, shallow groundwater follows an annual cyclic pattern with 2% to 6% of the land surface being influenced globally. This study shows that SMAP observations could be used in estimating shallow groundwater in high spatiotemporal resolution at a global scale, potentially providing invaluable inputs for modeling and environmental monitoring studies.

Identifying the thresholds of drought that, if crossed, suppress vegetation functioning is vital for accurate quantification of how land ecosystems respond to climate variability and change. We present a globally applicable framework to identify drought thresholds for vegetation responses to different levels of known soil-moisture deficits using four remotely sensed vegetation proxies spanning 2001–2018. The thresholds identified represent critical inflection points for changing vegetation responses from highly resistant to highly vulnerable in response to drought stress, and as a warning signal for substantial vegetation impacts. Drought thresholds varied geographically, with much lower percentiles of soil-moisture anomalies in vegetated areas covered by more forests, corresponding to a comparably stronger capacity to mitigate soil water deficit stress in forested ecosystems. Generally, those lower thresholds are detected in more humid climates. State-of-the-art land models, however, overestimated thresholds of soil moisture (i.e. overestimating drought impacts), especially in more humid areas with higher forest covers and arid areas with few forest covers. Based on climate model projections, we predict that the risk of vegetation damage will increase by the end of the twenty-first century in some hotspots like East Asia, Europe, Amazon, southern Australia and eastern and southern Africa. Our data-based results will inform projections on future drought impacts on terrestrial ecosystems and provide an effective tool for drought management.

For many areas, satellite-based precipitation products or reanalysis model data represent the only available precipitation information. Unfortunately, the resolution of these datasets is generally too coarse for many applications. A very promising downscaling approach is to use soil moisture due to its clear physical connection to precipitation. We investigate the feasibility of using soil moisture derived from land surface temperature in this context. These data are more widely available in the required resolution compared to other soil moisture data. Rain gauge-adjusted radar data from Namoi serves as a spatial reference dataset for two objectives: to identify the most suitable globally available precipitation dataset and to explore the precipitation information contained in the soil moisture data. The results show that these soil moisture data cannot be used to downscale satellite-based precipitation data to a high resolution because of cloud cover interference. Therefore, the Integrated Multi-satellitE Retrievals for GPM (IMERG) late data represents the best precipitation dataset for many areas in Australia that require timely precipitation information, according to this study.

Context and research question Suppose that we raise the groundwater levels in certain places, thus increasing the soil moisture content. What impact would that presumably have on the yield of common agricultural crops in Flanders? This research question is addressed in the PEILIMPACT project. In order to mitigate the effects of climate change, the Flemish coalition agree- ment 2019-2024 strongly emphasizes increased resilience to drought, including through the active deployment of a resilient space with (additional) nature. Agricultural activit- ies can experience positive effects through the water being buffered in the landscape. Yet, there are also possible negative effects: if the water level is too high, this could compromise the ability to work the land, could negatively affect crop growth and in- crease disease pressure on crops, as well as the availability and leaching of nutrients to surface and groundwater. Research methodology Through targeted dialogue moments with individual farmers from different agricultural regions in Flanders, we obtain experiential knowledge about the effect of too high or too low groundwater levels on certain crops. We detect possible obstacles to their agricultural activity and important effects on yield, both positive and negative, and their causes. The model must help to determine “sufficiently favorable” groundwater levels for agriculture given a number of parameters. Simple guide values are too gener- alistic, because suitable groundwater levels for agriculture depend on the type of soil, the crop and the depth of the roots, the time of year, and so on. To determine feasible water level increases for a specific situation, model calculations for a range of different weather scenarios and for the crops grown in a specific location, is needed. In this study we determine the effect of groundwater levels in crop yield based on open data layers in Flanders. Relevance An evaluation framework for the impact of groundwater level increases can be used to calculate the effect of water management decisions and to link these to compensation for affected landowners as well as to discuss sustainable solutions with farmers and nature managers. The framework can also assist farmers in crop selection etc. on a particular field with its specific soil and meteorological context. This project was carried out by the Flemish Institute for Agricultural, Fisheries and Food Research (ILVO) [Diana Estrella, Sarah Garré, Tom De Swaef] i.s.m. KWR Water Research Institute [Ruud Bartholomeus] and Wageningen University & Research (WUR) [Martin Mulder, Mirjam Hack-ten Broeke] &nbsp

This study investigated the moisture distribution characteristics of a soil wetting body under different influencing factors to inform the design and management of a moistube irrigation system. A mathematical model of soil moisture movement under moistube irrigation was established based on Hydrus-2D software. The suitability of the Hydrus-2D simulation model was verified by laboratory experiments. Numerical simulations were carried out with Hydrus-2D to investigate the influence of soil texture, initial moisture content, moistube specific discharge and irrigation time on the moisture distribution of a soil wetting body. The soil moisture content is highest at the moistube, and its value is related to the moistube-specific discharge and soil texture. The soil moisture content at any point in the wetting body decreased linearly with increasing distance from the wetting front to the moistube in the five set directions (vertical downward, 45° downward, horizontal, 45° upward and vertical upward). This trend is applicable to fine-textured and coarse-textured soil. An estimation model of soil moisture content including soil saturated hydraulic conductivity, initial soil moisture, the specific flow rate of the moistube and the maximum value of the wetting front distance in all directions is proposed. The model estimation is good (root mean square error = 0.008–0.018 cm3·cm−3, close to 0; Nash-Sutcliffe efficiency coefficient = 0.987, close to 1), and it can provide a practical tool for moistube irrigation design and agricultural water management.

Irrigation is critical to meeting global food and fiber demands. Optimizing agricultural irrigation may help sustain production levels, while reducing its demand for water. This research evaluated precision sprinklers and drip irrigation for pivots, five pivot track mitigation tools, three scientific irrigation scheduling (SIS) methods, sensors for surface irrigation cutoff, and automating surface systems to implement surge irrigation. With pivots and surface irrigation being the most common methods for irrigation in the West, small improvements from these tools could result in significant water savings. Low energy precision application (LEPA) sprinklers and mobile drip irrigation (MDI) were tested on two pivots. LEPA did not often maintain yield, even with similar application amounts to the mid elevation sprinkler application (MESA) control. MDI reduced yield by 6 – 25% in 2018, while applying half as much water as MESA. In following years, MDI rarely maintained yield, even when applying more than MESA. For LEPA and MDI to maintain yield with less water, the correct situations and proper adaptations must be carefully chosen. With the intent to improve pivot wheel tracks, installing LEPA around the pivot tower was the best of five tested methods, reducing track depths by 47 – 63% in one year. Adapting the correct method to field conditions and position on the pivot can result in shallower wheel tracks without sacrificing yield. The use of soil moisture sensors, a commercial irrigation scheduler, and a free irrigation scheduler to determine irrigation amounts, were compared with the rates chosen by farmers. At some farms, the SIS methods maintained yield with 10-15% less irrigation. These benefits were usually when precipitation was high, which the SIS methods accounted for well. Surface irrigators found that sensors helped reduce irrigation application by correctly timing the cutoff, saving them time, water, and money. Automation to implement surge irrigation increased irrigation use efficiency by 43% the first year, but had less drastic results in the final year, likely due to the severity of an ongoing drought. These collective results demonstrate that simple water optimization techniques could reduce irrigation diversions by 15 and possibly up to 25% or more in some cases.

В статье представлены результаты изменения плотности сложения и водного режима чернозема обыкновенного в зависимости от обработки почвы при возделывании однолетних трав. Показано, что приемы основной обработки почвы существенно не влияли на изменение плотности сложения, но повышали эффективность использования влаги на производство одной тонны зеленой массы горохо-овсяной смеси. При возделывании культур однолетних трав без применения основной обработки (прямой посев) водопотребление было значительно выше, а урожайность резко снижалась., The article presents the results of changes in the bulk soil density and water regime of ordinary chernozem depending on tillage in the cultivation of annual grasses. It is shown that the methods of basic tillage did not significantly affect the change in bulk soil density, but increased the efficiency of moisture use for the production of 1 ton of green mass of pea-oat mixture. When cultivating crops of annual grasses without the use of the main treatment (direct sowing), water consumption was significantly higher, and the yield decreased sharply.

The increase in population and the megadrought that the American west has been experiencing for the past two decades and for the foreseeable future are the cause of great stress on the region’s water resources. In an effort to mitigate the worst effects of water shortages many public and private agencies have been pursuing ways to reduce water use while maintaining the current status quo of living standards, industrial productivity, and agricultural yield. This project aims to study water use in irrigated lands dedicated to agriculture with the objective of quantifying crop water use, often referred to as evapotranspiration (ET), as this is among the most important metrics when it comes to increasing water use efficiency, i.e., increasing the amount of agricultural output per amount of water input. This study aims to develop a methodology that allows land managers to estimate evapotranspiration using familiar tools and data i.e., soil moisture sensors. The first study site in Vernal, Utah, was used to develop the proposed methodology because alongside the soil moisture data, accurate evapotranspiration measurements were available which allowed for the calibration and verification of the model. The second study site in Modena, Utah, in which accurate evapotranspiration measurements were also available, was used as a testing ground for the estimates provided by the proposed methodology allowing to assess whether local calibration was required or not. The third study site in West Weber, Utah, where two irrigation methods (drip and flood irrigation) were employed, was used as a practical example of how this methodology could be implemented in a real farm scenario and what insight could be gained from it. The proposed Soil Moisture based EvapoTranspiration (SMET) estimation model reliably provided accurate results (within ±7%) in the first two study sites where they could be compared with results from more sophisticated methods. In the third study site the SMET model offered previously hard to obtain insight on how the water use is influenced by the two irrigation techniques employed and on how water use can differ even within the same field. The encouraging results from this study prove that this user-friendly and low-cost crop water use estimation method can provide managers, producers, and policy makers with an accurate number, allowing for better-informed decisions regarding water conservation and maximizing the beneficial use of this limited resource.

Studies around the world have shown that increases in extreme precipitation are not directly resulting in reported instances of flooding. Despite the evidence that antecedent soil moisture conditions play a significant role in regulating the runoff response during extreme storm events, fewer works have tried to understand the spatial and temporal dynamics of such coupled interactions. In the present work, we aim to improve our understanding of the dominant spatiotemporal patterns of soil moisture (SM) – precipitation (P) dependence over Europe. Using an event-based, non-parametric Event Coincidence Analysis (ECA) approach, we first quantified the probability of SM preceding P by the precursor coincidence rate. We also examined the statistical precursor relationship between SM-P joint episodes and reported annual flood events using the conditional precursor coincidence rate. The results show that seasonalityis a major driving force in the spatiotemporal dynamics of SM-P coupling over Europe. The conditional precursor coincidence rates are in line with previous flood hazard studies over Europe and show the importance of considering such diverse interdependencies in flood risk mitigation. Our work has implications for understanding the occurrence of preconditioned compound events worldwide and provides a vital starting point for characterising the major processes that need to be accounted for in comprehensive state of the art flood risk assessment studies.

This paper focuses on the environment of Ethiopia, a country highly sensitive to droughts severely affecting vegetation. Vegetation monitoring of Ethiopian Highlands requires visualization of environmental parameters to assess droughts negatively influencing agricultural sustainable management of crops. Therefore, this study presented mapping of several climate and environmental variables including Palmer Drought Severity Index (PDSI). The data were visualized and interpreted alongside the topographic data to evaluate the environmental conditions for vegetation. The datasets included WorldClim and GEBCO and Digital Chart of the World (DCW). Research has threefold objectives: i) environmental mapping; ii) technical cartographic scripting; iii) data processing. Following variables were visualized on seven new maps: 1) topography; 2) soil moisture; 3) T °C minimum; 4) T °C maximum; 5) Wind speed; 6) Precipitation; 7) Palmer Drought Severity Index (PDSI). New high-resolution thematic environmental maps are presented and the utility of GMT for mapping multi-source datasets is described. With varying degrees of soil moisture (mean value of 15.0), min T°C (−1.8°C to 24°C), max T°C (14.4°C to 40.2°C) and wind speed (0.1 to 6.1 m/s), the maps demonstrate the variability of the PDSI fields over the country area (from −11.7 to 2.3) induced by the complex sum of these variables and intensified by the topographic effects notable over the Ethiopian Highlands which can be used for vegetation analysis. The paper presents seven new maps and contributes to the environmental studies of Ethiopia.

The growth and physiology of soil microorganisms, which play vital roles in biogeochemical cycling, are shaped by both current and historical soil environmental conditions. Here, we developed and applied a genome-resolved metagenomic implementation of quantitative stable isotope probing (qSIP) with an H218O labeling experiment to identify actively growing soil microorganisms and their genomic capacities. qSIP enabled measurement of taxon-specific growth because isotopic incorporation into microbial DNA requires production of new genome copies. We studied three Mediterranean grassland soils across a rainfall gradient to evaluate the hypothesis that historic precipitation levels are an important factor controlling trait selection. We used qSIP-informed genome-resolved metagenomics to resolve the active subset of soil community members and identify their characteristic ecophysiological traits. Higher year-round precipitation levels correlated with higher activity and growth rates of flagellar motile microorganisms. In addition to heavily isotopically labeled bacteria, we identified abundant isotope-labeled phages, suggesting phage-induced cell lysis likely contributed to necromass production at all three sites. Further, there was a positive correlation between phage activity and the activity of putative phage hosts. Contrary to our expectations, the capacity to decompose the diverse complex carbohydrates common in soil organic matter or oxidize methanol and carbon monoxide were broadly distributed across active and inactive bacteria in all three soils, implying that these traits are not highly selected for by historical precipitation. IMPORTANCE Soil moisture is a critical factor that strongly shapes the lifestyle of soil organisms by changing access to nutrients, controlling oxygen diffusion, and regulating the potential for mobility. We identified active microorganisms in three grassland soils with similar mineral contexts, yet different historic rainfall inputs, by adding water labeled with a stable isotope and tracking that isotope in DNA of growing microbes. By examining the genomes of active and inactive microorganisms, we identified functions that are enriched in growing organisms, and showed that different functions were selected for in different soils. Wetter soil had higher activity of motile organisms, but activity of pathways for degradation of soil organic carbon compounds, including simple carbon substrates, were comparable for all three soils. We identified many labeled, and thus active bacteriophages (viruses that infect bacteria), implying that the cells they killed contributed to soil organic matter. The activity of these bacteriophages was significantly correlated with activity of their hosts.

As increasing drought events limit water resources available for irrigation, farmers and other water users are looking for ways to monitor how much water crops use over a growing season. The amount of water used by crops over time is the evapotranspiration (ET) rate. This study compares different methods for ET estimation to recommend methods to water users based on their accuracy, efficiency, and accessibility. Each method was used to estimate ET for sprinkler-irrigated corn and alfalfa fields in Modena, UT over the 2021 growing season. The Soil Moisture based ET (SMET) method was used to estimate ET based on daily changes in the soil water content. The OpenET web platform [www.openetdata.org] and the OpenET Application Programming Interface (API) were used to retrieve ET estimates based on imagery from the Landsat satellite. ET estimates for the area were also retrieved from a Utah Department of Natural Resources (DNR) report. The eddy covariance (EC) method is accepted as the standard for estimating ET and served as the standard for the comparison of all other methods. Results indicate that while all methods underestimate ET, the SMET method agrees most closely to the EC method. Further analysis of the OpenET and Utah DNR methods is required to fully explain the reasons for the apparent ET estimation discrepancies. This study also highlights the advantages and limitations of each method.

Functional traits play a key role in driving plant community effects on ecosystem function. We examined nine functional traits in various palm (Arecaceae) species and their relationships with moisture, tree-fall gaps, slope, and forest type at 29 transects (500×5 m) in the northeastern region of the Colombian Amazon. Redundancy analysis of mean trait values of species within a plot weighted by their abundance and Pearson correlations were used to evaluate the relationships between traits and environmental factors. The community trait composition was correlated with local environmental factors, which explained 23% of the trait variance. We detected functional dominance of the tallest palms in soils with high moisture and in floodplain forests (p ≤0.05). Palms with relatively long leaves were dominant in the flooded forests. Acaulescent and small palms were dominant on high slopes, and interra firmeforests, long-petioled palms were dominant in forest gaps. The number of seeds per fruit was not correlated with any environmental variable. Thus, hydrology is one of the main drivers of the functional composition of neotropical palm communities at the local scale, segregating tall palms with competitive and evasive strategies from small understory palms, which are mainly stress tolerant.

Soil respiration (SR) in forests contributes significant carbon dioxide emissions from terrestrial ecosystems and is highly sensitive to environmental changes, including soil temperature, soil moisture, microbial community, surface litter, and vegetation type. Indeed, a small change in SR may have large impacts on the global carbon balance, further influencing feedbacks to climate change. Thus, detailed characterization of SR responses to changes in environmental conditions is needed to accurately estimate carbon dioxide emissions from forest ecosystems. However, data for such analyses are still limited, especially in tropical forests of Southeast Asia where various stages of forest succession exist due to previous land‐use changes. In this study, we measured SR and some environmental factors including soil temperature (ST), soil moisture (SM), and organic matter content (OM) in three successional tropical forests in both wet and dry periods. We also analyzed the relationships between SR and these environmental variables. Results showed that SR was higher in the wet period and in older forests. Although no response of SR to ST was found in younger forest stages, SR of the old‐growth forest significantly responded to ST, plausibly due to the nonuniform forest structure, including gaps, that resulted in a wide range of ST. Across forest stages, SM was the limiting factor for SR in the wet period, whereas SR significantly varied with OM in the dry period. Overall, our results indicated that the responses of SR to environmental factors varied temporally and across forest succession. Nevertheless, these findings are still preliminary and call for detailed investigations on SR and its variations with environmental factors in Southeast Asian tropical forests where patches of successional stages dominate., We measured and analyzed soil respiration (SR) with environmental factors in three successional forests in Thailand. Results showed that SR was generally higher in the wet season and in older forests. Across all forest stages, soil moisture was the main factor contributing to variation in SR in the wet season while organic matter content played significant role in the dry season.

In polar deserts, depth hoar (hereinafter: DH) growth is not systematic unlike on tundra and this is critical for snowpack properties. Here, we address the spatio-temporal variability of the DH layer in the polar desert at two sites in the Canadian High Arctic: Ward Hunt Island (83° N) and Resolute Bay (75° N). Our data show that, over humid areas, DH represented a larger fraction of the snowpack and was characterized by lower density and coarser crystals than over dry gravelly areas. Increased soil moisture extends the zero-curtain period during freeze-up, leading to stronger temperature gradients in the snowpack and greater kinetic metamorphism. Our results also demonstrate that the large inter-annual variability in DH is primarily driven by wind conditions in the fall since this key variable controls the initial snow density and snow onset date. These strong controls exerted by soil moisture and meteorological conditions on DH growth in polar deserts highlight the possibility of major changes in polar snowpacks physical properties in response to the rapid climate and environmental changes currently affecting these regions.

To better understand the characteristics and mechanisms of droughts at different drought stages, this study selected the Xiangjiang River Basin in China as the study area, and evaluated soil moisture (SM) at different depths for drought monitoring, through SM data simulated with the variable infiltration capacity (VIC) model. To solve the problem of unreasonable drought/wetness classifications based on the soil moisture anomaly percentage index (SMAPI), an improved soil moisture anomaly percentage index (ISMAPI) was developed by introducing the Box–Cox transformation. The drought/wetness frequency generated by ISMAPI demonstrated preferable spatial comparability in comparison with those from SMAPI. The lag time of ISMAPI relative to the standardized precipitation evapotranspiration index was closely related to soil depth, and was characterized by a fast response in shallow soil layers and a relatively slow response in deep soil layers. SM in shallow soil layers provided a measure for monitoring short-term droughts, whereas SM in deep soil layers provided a better measure for long-term persistent drought events. Furthermore, the occurrence and mitigation time of drought events identified by SM in deep soil layers usually lagged behind that identified by SM in shallow soil layers. Compared with deep SM, SM in shallow soil layers responded faster to meteorological anomalies, thereby resulting in shorter periods of SM persistence in shallow soil layers than in deep soil layers. This can explain the differences of SM at different depths in drought monitoring.

Soil permittivity estimation using Polarimetric Synthetic Aperture Radar (PolSAR) data has been an extensively researched area. Nonetheless, it provides ample scope for further improvements. The vegetation cover over the soil surface leads to a complex interaction of the incident polarized wave with the canopy and subsequently with the underlying soil surface. This paper introduces a novel methodology to estimate soil permittivity over croplands with vegetation cover using the full and compact polarimetric modes. The proposed method utilizes the full and compact polarimetric scattering-type parameters, θ FP and θ CP , respectively. These scattering type parameters are a function of the soil permittivity and the Barakat degree of polarization. The method considers the X-Bragg scattering model for the soil surface. In particular, these scattering-type parameters explicitly account for the depolarizing structure of the scattered wave while characterizing targets. Thus, the depolarization information in terms of surface roughness in the X-Bragg model gets inherent importance while using θ FP and θ CP , unlike existing scattering-type parameters. Therefore, the proposed technique enhances the expected value of the inversion accuracies. This study validated the major phenology stages of four crops using the UAVSAR full-pol and simulated compact pol SAR data and the ground truth data collected during the SMAPVEX12 campaign over Manitoba, Canada. The proposed method estimated permittivity with an RMSE of 2.2 to 4.69 for FP and 3.28 to 5.45 for CP SAR data along with a Pearson coefficient, r ≥ 0.62.

Terrestrial ecosystems are fully coupled with the climate. The planet has been greening owing to the increased vegetation growth in response to the changing atmosphere, which in turn has feedback on the climate. Greening has slowed down the rise in global land-surface air temperature mainly through a coincident increase of evapotranspiration and precipitation in wet regions. In dry regions, greening intensifies the decrease in soil moisture induced by greening-enhanced transpiration. Uncertain, however, is how the climate effects of greening in semi-arid lands might differ for variable wet and dry conditions. Here, we focus on the biosphere–atmosphere interactions in Australia by modeling the perturbation of vegetation changes under various states of sea surface temperature (SST), including the climatology mean, El Niño, and La Niña conditions. For the dry conditions of El Niño, greening exacerbates water stress and largely depletes the soil moisture, while for the wet conditions of La Niña, greening-enhanced evapotranspiration and precipitation resupply the soil moisture. For the normal conditions using the climatology mean SST, a small decrease in soil moisture occurs but with large spatial contrast because of heterogeneous changes of evapotranspiration and precipitation induced by greening. We emphasize that the alternating dry and wet conditions modulated by the large-scale climate variability are vital to understanding the response of climate to greening. Furthermore, vegetation-based warming mitigation policies need to be cautious when inferring distinct climate effects associated with greening.

International audience; A heatwave in Siberia starting in January 2020, initiated by a wave 5 pattern in the jet stream, caused the surface air temperature to reach 38°C in June with important impacts on ecosystems and water resources. Here we show that this dynamical setup started a chain of events leading to this long-lasting and unusual event: positive temperature anomalies over Siberia caused early snowmelt, leading to substantial earlier vegetation greening accompanied by decreased soil moisture and browning in the summer. This soil moisture depletion and vegetation browning, in turn, increased the impact of the heatwave on the atmosphere through a land-atmosphere feedback. This line of evidence suggests that large-scale dynamics and land-atmosphere interactions both contributed to the magnitude and persistence of this record-breaking heatwave, in addition to the background global warming impact on mean temperature. Here, we describe a carry-over effect in Siberia from a spring positive temperature anomaly into summer dryness and browning, with retroaction into the atmosphere. With the Arctic warming twice as fast as the global average, this event foreshadows the future of northern latitude continents and emphasizes the importance of both atmospheric dynamics and land-atmosphere interactions in the future as the climate changes. More frequent similar events could have major consequences on the carbon cycle in these carbon-rich northern latitude regions.

Soil hydraulic properties are central for soil quality and affect forest productivity and impacts of climate-change on forests. The water retention characteristics (WRC) of mineral forest soils in Finland are not well known and practical tools to predict them are lacking. We statistically analyzed mineral forest soils WRC from over 130 sites in Finland, focusing on the humus layer and main root zone (0-19 cm depth). We showed that mineral forest soils can be grouped to five WRC classes that are well predictable from soil bulk density, organic matter content and clay fraction. However, majority of the forest soils are hydrologically rather similar. We found that neither topsoil maps nor any combination of open geospatial data was able to predict WRC. Thus, in absence of site-specific soil data, parameterizing WRC as a function of forest site type was proposed. We demonstrated the approach in soil moisture modeling at a small forest headwater catchment and showed soil moisture response to weather conditions is jointly affected by WRC, stand attributes and topography. We showed drought risks are highest on dense mature forests at nutrient poor sites, and lower in young stands on peatlands and lowland herb-rich sites with groundwater influence. The results improve hydrological predictions for Finnish forests, and the open dataset can contribute to larger synthesis and development of boreal forest soil pedo-transfer functions., The soil hydrological properties were measured in EU BioSoil -programme and as part of ICP forest health monitoring (EU/Life+ FutMon Programme). The work was funded by Ministry of Agricultural and Forestry of Finland (project Maaperätiedon kehittäminen, 2021-2022). SL acknowledges financial support from the Academy of Finland (no. 296116 and 348102) and Formas (no. 2018-01820)

Organic soil amendments such as manure, biochar and compost are among the most efficient and widely used methods to increase soil carbon sequestration in agricultural soils. Even though their benefits are well known, many wood-derived materials are not yet utilized in Nordic agriculture due to a lack of incentives and knowledge of their effects in the local climate. We studied greenhouse gas exchange, plant growth and soil properties of a clay soil cultivated with oat in southern Finland in an extremely dry year. Two years earlier, the field was treated with three ligneous soil amendments—lime-stabilized fiber from the pulp industry, willow biochar and spruce biochar—which we compared against fertilized and non-fertilized controls. We found that the soil amendments increased porosity and the mean soil water holding capacity, which was most noticeable in plots amended with spruce biochar. There was a trend indicating that the mean yield and overall biomass production were larger in plots with soil amendments; however, the difference to unamended control was seldom significant due to the high variance among replicates. Manual chamber measurements revealed that carbon dioxide and methane exchange rates were reduced most probably by the exceptionally hot and dry weather conditions, but no differences could be found between the amended and unamended treatments. The nitrous oxide emissions were significantly smaller from the vegetated soil amended with willow biochar compared with the unamended control. Emissions from non-vegetated soil, representing heterotrophic respiration, were similar but without significant differences between treatments. Overall, the studied soil amendments indicated positive climatic impact two years after their application, but further research is needed to conclusively characterize the specific effects of organic soil amendments on processes affecting greenhouse gas exchange and plant growth.

The beginning of the 21st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems., This research is supported by Belspo EODAHR (SR/00/376), the European Commission, Horizon 2020 SHui (773903), FWO CONSOLIDATION (G0A7320N), ESA 4D-MED (4000136272/21/I-EF) and KU Leuven C1 (C14/21/057).

Soil moisture has been considered a key variable in governing the terrestrial ecosystem. However, it is challenging to preserve indigenous soil characteristics using conventional soil moisture monitoring methods that require maximum soil contacts. To overcome this issue, we developed a non-destructive method of evaluating soil moisture using a contactless ultrasonic system. This system was designed to measure leaky Rayleigh waves at the air–soil joint-half space. The influences of soil moisture on leaky Rayleigh waves were explored under sand, silt, and clay in a controlled experimental design. Our results showed that there were strong relationships between the energy and amplitude of leaky Rayleigh waves and soil moisture for all three soil cases. These results can be explained by reduced soil strengths during evaporation processes for coarse soil particles as opposed to fine soil particles. To evaluate soil moisture based on the dynamic parameters and wave properties obtained from the observed leaky Rayleigh waves, we used the random forest model. The accuracy of predicted soil moisture was exceptional for test data sets under all soil types (R2 ≥ 0.98, RMSE ≤ 0.0089 m3 m−3). That is, our study demonstrated that the leaky Rayleigh waves had great potential to continuously assess soil moisture variations without soil disturbances.

Sustainable irrigation expansion is pivotal to guarantee global food security. Fertigation techniques with surface or subsurface drip lines are among the most promising irrigation practices. Yet, the effect of surface versus subsurface drip fertigation on ecosystem respiration (CO2) fluxes remains largely unknown while there is not available information regarding CH4 emissions, even though this knowledge is essential to obtain a comprehensive overview of the carbon (C) footprint of these practices. To address this knowledge gap, a field experiment was set up in a maize (Zea mays L.) crop comparing surface and subsurface drip (buried at 30 cm depth) in combination with four fertilization treatments (unfertilized control, calcium nitrate and ammonium sulphate with or without the nitrification inhibitor DMPP). Our results showed that all fertilized treatments (except the unfertilized control) were net CH4 sources (135–208 g C ha−1). Subsurface irrigation reduced cumulative CH4 emissions compared to surface irrigation by 80% (P < 0.05). Moreover, subsurface irrigation also abated ecosystem respiration fluxes (by 41%, P < 0.05) relative to surface irrigation. Emission peaks of both C fluxes were observed after fertigation events. Our results demonstrate that subsurface irrigation is a water-saving strategy that can contribute to decreasing the net greenhouse gas emissions of irrigated croplands through the abatement of CH4 and respiration fluxes.

[EN] Large, severe wildfires can cause unpredictable changes in Mediterranean plant communities that undermine ecosystem resilience. Understanding the geophysical drivers shaping vegetation dynamics after fire is vital to provide integrated knowledge into plant community recovery feedbacks in the context of global change. We investigated the role of fire severity and a comprehensive set of geophysical drivers in the post-fire vegetation recovery of Mediterranean landscapes. We used physical-based remote sensing techniques to estimate vegetation recovery and examined whether this recovery is plant-community dependent in the short-term (one year) after the fire. The fractional vegetation cover (FCOVER) recovery was selected as a resilience indicator for Pinus sylvestris L. (Scots pine) forests, Quercus ilex L. (holm oak) woodlands, shrublands dominated by Cytisus oromediterraneus Rivas Mart. et al. (black broom) and grasslands. To obtain the pre- and post-fire FCOVER, we used the Gaussian processes regression algorithm trained with the PROSAIL-D radiative transfer model. The algorithm was parameterized to account for the variability of biophysical conditions in the plant communities considered and was applied to the Sentinel-2 multispectral imagery. Validation of remotely-sensed FCOVER with field data in burned and control plots was successful (R2 = 0.84 and RMSE = 10.83%), without significant under or overestimation. We selected 31 variables pertaining to fire severity, climate, post-fire soil moisture, pre-fire soil condition, and landscape configuration as putative predictors of FCOVER recovery percentage (FCOVER%rec), calculated as the ratio of post-fire to pre-fire FCOVER. Random Forest regression (RFR) was used to disentangle the influences of fire severity and geophysical drivers on community-specific FCOVER%rec. Fire severity and post-fire surface soil moisture variables were relevant predictors in RFR models in all plant communities. However, their importance was largely community-dependent. In grasslands, surface soil moisture in the early summer season one year after fire was the major variable explaining variability in FCOVER%rec. In holm oak woodlands, the contribution of fire severity and surface soil moisture was balanced. Conversely, fire severity largely outweighed other variables in driving FCOVER%rec in black broom shrublands and Scots pine forests. Pre-fire climate, soil condition and landscape configuration variables were not meaningful predictors in general. Our findings offer novel insights into the processes that underlie resilience in fire-prone plant communities located in the western Mediterranean Basin. This establishes a baseline to help prioritize adaptive management strategies for the most vulnerable areas in the region. SI AEI British Ecological Society Portuguese Foundation for Science and Technology

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG [Abstract:] Soil water content (SWC) and temperature (ST) are main parameters in agriculture, but are difficult to predict due to the numerous processes involved. To find stability patterns, this study evaluated the soil hydro-thermic response in a rainfed organic vineyard with humid climate, a permanent grass cover and under no-tillage and homogeneous soil and topographic conditions. The differences between the rows (R) and inter-row areas (IR) and two cultivars (Agudelo –Ag– and Blanco Legítimo –BL–) were assessed. SWC and ST were measured with 12 probes every 15 min at 5, 15 and 25 cm depth over the crop cycle (242 days). On average, wetter (+2.2%) and cooler (−1.4%) values appeared in Ag than in BL that may be associated with differences in vine water demand. IR had wetter (+5.9%) and cooler (−0.7%) conditions than R due to higher water consumption by vines. Significant differences appeared when time-series analysis was split into three periods: drying and warming (spring), dry and warm (summer), and wetting and cooling (autumn). SWC and ST correlated well in R, but moderate correlations appeared in IR, showing a more complex pattern in this zone. In general, the most stable conditions appeared at 15 cm depth, with drier and warmer conditions in the deepest layer in spring and autumn. This vertical pattern did not vary over time for ST, but IR had the most stable and moistest conditions in summer at 25 cm. The relative differences among zones and cultivars revealed that Ag had moister, but less representative, conditions than BL at the three soil layers in spring, and this pattern kept in summer at 15 and 25 cm, but only in autumn at 15 cm. Regarding ST, the pattern was very stable, and cooler and representative conditions prevailed in Ag. BL has been rarely cited in the literature, and these results contributed with new insights about the SWC and ST dynamic on this cultivar. Cooler conditions always appeared in IR, compared with R; and R always had more representative values of SWC than IR. The pattern of ST was more variable and R only had more representative values than IR at 15 cm in summer and at 25 cm in spring and summer. This article represents the first study that calculated the index of temporal stability (ITS) for SWC and ST in any type of woody crop. Our findings allowed to identify the most representative areas of the hydro-thermic response of the soil in the vineyard, which is of interest to save time and resources during long-term monitoring tasks in commercial vineyards. Xunta de Galicia; ED431H 2020/18

Water availability to plants a significant role in agricultural areas, especially in arid and semi-arid areas. This research aimed to evaluate the potential of Water Cloud Model (WCM) for retrieving surface soil moisture, which is associated to water availability, in a semi-arid areas based on the combination between Sentinel-1B SAR (Synthetic Aperture Radar) and optical Sentinel-2B data. The performance of the applied model was assessed using ground observed soil moisture (0-5 cm). Accuracy evaluation was performed by the cross-validation method (k-fold), it showed a coefficients of determination (R2) of 0.65 and RMSE of 1.45%. The obtained results show a good concordance between retrieved model and ground observed surface soil moisture. In addition, this model was used for the mapping spatio-temporal variation of soil moisture at high spatial resolution in the study areas. This approach could be used by environmentalists and decision-makers as a practical tool for monitoring and estimating the change of surface moisture content.

Modeling crop water use and soil moisture availability is becoming increasingly critical, particularly in light of recent drought events. Our study focuses on the spatial application of the AquaCrop model, using a raster-based approach in an R-based environment. The formulated methodology was initially applied and tested on two point-based examples in the Central region of Hungary, followed by the spatial application of the model at the Rákos Stream catchment in the same region. For evaluation purposes, we also utilized satellite-based NDVI data. The results showed that there is a strong correlation between NDVI values and the model-based biomass estimation. We also found that the model simulated the soil moisture content fairly well, with a correlation coefficient of 0.82. While our results support the validity of the applied methodology, it is also clear that input data availability and quality are still critical issues in spatial application of the AquaCrop model.

Data set name: Hydrological data from Ressi experimental catchment (Italy) Ressi is a small forested catchment located in the Italian pre-Alps (45°47′11.79″ N; 11°15′54.12″ E). The Ressi experimental catchment was established in the summer 2011 by the hydrology group at the Department of Land, Environment, Agriculture and Forestry of the University of Padova (Italy). Since 2012, the research carried out in the catchment is focused on the investigation of the water fluxes in the soil-plant-atmosphere continuum and the impact of vegetation on runoff generation. This page contains information about the experimental catchment and data collected since August 2012. Further details about the catchment and the data set can be found in the Wiki section (please see below).