Mean Soil Moisture Research Articles

Dry season Irrigation Regime Effects on Water Use, Rootzone Moisture and Yield of Cacao (Theobroma cacao L.) in a Rainforest Zone of Nigeria

The humid tropics is characterized by wet-dry seasonal transitions, irrigation has the potential for building adaptation and resilience to climate stress for enhancing crop performance. A field trial was conducted to investigate the effects of dry season irrigation regimes on water use, root zone moisture dynamics and yield of cacao in a rainforest zone of Nigeria. Irrigation amounts computed as EPan x Pan coefficients were based on cumulative class A Pan evaporation. Irrigation treatments were coded as IrT1 (EPan x 1,0), IrT2 (EPan x 0.7) and IrT3 (EPan x 0.5). Irrigation water was applied 5-days interval and discharged via point source emitters (2.8 l/h discharge rate ) on drip lines laterally installed per row of trees. Mean irrigation requirements were 4.49 mm (9.81:9.6 l/tree/day), 3.14 mm (7.06:6.8 l/tree/day) and 2.44 mm (5.49: 4.8 l/tree/day) while total seasonal water applied were 121.19, 84.83 and 60.59 l/tree for IrT1, IrT2 and IrT3 respectively. Mean soil moisture contents and cacao evapotranspiration (ETc) were 52, 45 and 28 % and 4.54, 3.19 and 2.32 mm/day while evaporation from soil area wetted by emitters (EWz) were 5.65, 2.82 and 0.19 mm/day for respective IrT1, IrT2 and IrT3. The deficit irrigation strategies (IrT2 and 31 IrT3) imposed soil moisture deficit stress on cacao and produced lower pod and bean yields, it however enhanced water use efficiencies (25 and 44 %) and 30 and 50 % water savings. The study established suitable Pan coefficients for scheduling irrigation for cacao yield enhancement and drought (climate stress) amelioration.

Assessing hydrological sensitivity to future climate change over the Canadian southern boreal forest

This study develops a physically based hydrological process model using the Cold Region Hydrological Modelling (CRHM) platform to simulate the water budget storages and fluxes over the Canadian southern boreal forest (SBF). Evaluation of the CRHM-based model in a well-gauged SBF basin, White Gull Creek (WGC), Saskatchewan, Canada, indicated quite good performance in reproducing historical observations of streamflow, snow water equivalent (SWE), evapotranspiration (ET) and soil moisture without parameter calibration from streamflow. The entire SBF was then evenly divided into 2243 virtual basins, each of which was structured and parameterized with the same land cover, soil and hydrological parameters as the WGC basin, but with local latitude and topography in order to examine the sensitivity of governing hydrological processes to future climate variability and perturbation. Hydrological sensitivity in the virtual basins was assessed by examining the differences between hydrological simulations driven by 4-km gridded convection-permitting Weather Research and Forecasting (WRF) outputs in the current period (ctrl, 2001–2013) and a Pseudo Global Warming period (pgw, 2087–2099). The WRF simulation in the pgw period was forced by a perturbation of the same boundary conditions from ERA reanalysis data as for the ctrl period, and the perturbation was based on the ensemble-mean of projected changes from the CMIP5 RCP 8.5 emission scenario. Results showed that temperature would increase by 4.5℃ to 7℃ over the SBF but increases in annual precipitation of 15–24% would more than compensate for the effects of warming on runoff generation and result in greater streamflow volumes. Annual streamflow volumes would increase by 64 mm (35%) and 95 mm (16%) in the west and east, and by 48 mm (17%) in the central SBF. Annual snowfall and maximum SWE would decrease by 89–109 mm (∼29%) in the east, 3–8 mm (∼6%) in the west, and 31–50 mm (∼20%) in the central SBF. Annual mean soil moisture storage would decrease by 54–56 mm (27%) in the west and central, and by only 37 mm (14%) in the east SBF. Decreases in soil moisture would be caused by reduced soil freezing and enhanced thawing under future warming which would increase soil water loss from ET, subsurface runoff and percolation into groundwater storage. The larger sensitivity of streamflow and snow processes in the east SBF is partly due to the wetter climate and the larger increase in annual precipitation, the later also buffered the sensitivity of soil moisture to warming. These results show that the SBF would switch to a higher water yield region, dominated by rainfall-runoff fed streamflow over a longer snow-free season, and provide first-order guidance for sustainable water management of the SBF in the future.

Integration of a Groundwater Model to the Noah Land Surface Model for Aquifer‐Soil Interaction

AbstractSoil water‐groundwater interactions are important in determining the moisture profile in a soil column. However, the Noah Land Surface Model (Noah LSM), the land surface component of several general circulation models, does not consider groundwater effects. The present study investigates the impact of integrating a groundwater model into the Noah LSM. By performing experiments forced by observations at the surface, insights into the characteristics of soil moisture evolution with and without the aquifer are investigated. In the absence of surface precipitation, soil moisture is shown to exhibit an exponential decay rate without the aquifer (termed as Noah‐Cntl). This is because evapotranspiration dominates other processes governing the soil moisture evolution. Based on scale analysis, we derived an analytical equation that could well represent the aforementioned exponential variation in soil moisture. Presence of the aquifer (termed as Noah‐GW) makes the capillary processes important as well. The result is a slower decay of soil moisture as compared to Noah‐Cntl without surface precipitation. Soil moisture evolution in both the models is a function of vegetation and soil types. As a result, the aforementioned decay timescale displays significant heterogeneity over the Indian region, with Central India exhibiting relatively faster decay rates. We also performed 24‐year long experiments with observed interannually varying forcing that indicates an enhancement in the annual mean soil moisture at all levels due to groundwater effects. This enhancement is particularly prominent during the post‐monsoon season, reaffirming the results of the drain‐out experiments mentioned above.

The UKSCAPE-G2G river flow and soil moisture datasets: Grid-to-Grid model estimates for the UK for historical and potential future climates

Abstract. Appropriate adaptation planning is contingent upon information about the potential future impacts of climate change, and hydrological impact assessments are of particular importance. The UKSCAPE-G2G datasets were produced, as part of the Natural Environment Research Council (NERC) UK-SCAPE (UK Status, Change and Projections of the Environment) programme, to contribute to this information requirement. They use the Grid-to-Grid (G2G) national-scale hydrological model configured for both Great Britain and Northern Ireland (and the parts of the Republic of Ireland that drain to rivers in NI). Six separate datasets are provided, for two sets of driving data – one observation-based (1980–2011) and one climate-projection-based (1980–2080) – for both river flows and soil moisture on 1 km × 1 km grids across Great Britain and Northern Ireland. The river flow datasets include grids of monthly mean flow, annual maxima of daily mean flow, and annual minima of 7 d mean flow (m3 s−1). The soil moisture datasets are grids of monthly mean soil moisture content (m water / m soil), which should be interpreted as depth-integrated values for the whole soil column. The climate-projection-based datasets are produced using data from the 12-member 12 km regional climate model ensemble of the latest UK climate projections (UKCP18), which uses RCP8.5 emissions. The production of the datasets is described, along with details of the file format and how the data should be used. Example maps are provided, as well as simple UK-wide analyses of the various outputs. These suggest potential future decreases in summer flows, annual minimum 7 d flows, and summer/autumn soil moisture, along with possible future increases in winter flows and annual maximum flows. References are given for published papers providing more detailed spatial analyses, and some further potential uses of the data are suggested. The datasets are listed in Table 1.

Spatio-Temporal Heterogeneity of Soil Moisture on Shrub–Grass Hillslope in Karst Region

Influenced by the topography, the spatial variation of soil thickness on karst slopes is very large, and accordingly the spatial variation of soil moisture is also large. Therefore, analyzing the spatial heterogeneity of soil moisture on hillslopes is important for maintaining ecosystem stability. Combining geostatistical methods and GIS technology, the spatial variability and distribution pattern of soil moisture and the influencing factors of spatial variation and surface soil moisture (0–7 cm) on a typical karst shrub–grass hillslope were analyzed. The results showed that the mean soil moisture and coefficient of variation (CV) ranged between 25.7–42.6% and 10.3–20.9%, respectively, showing a moderate variation. The soil moisture presented a moderate or strong spatial autocorrelation in the sampling scale. The occurrence of rainfall events can exert a great influence on reducing the spatial heterogeneity of soil moisture. The spatial distribution pattern of soil moisture showed roughly plaque or stripe distribution. When soil moisture was much lower, the patch space fragmentation of soil moisture was higher. The soil moisture was higher in the low and middle parts of the plot. We can conclude that factors such as topography, vegetation, and weather conditions will exert a significant effect on soil moisture spatial variability. Areas with lower slope and higher vegetation coverage were more conducive to the retention of soil moisture.

Effect of Hydrogel and Mulching on Soil Moisture, Yield and Economics on Yellow Passion Fruit in Embu and Kiambu Counties, Kenya

Inadequate rainfall is a significant problem hindering the production of most crops in dryland regions. The current study was carried out to assess the effect of selected soil-water conserving interventions on soil moisture, growth, yield, quality, and profitability of yellow passion fruit. The experiment was laid in a randomized complete block design (RCBD) at Kenyatta University (Kiambu County) and Ugweri (Embu County). The treatments were: grass mulch, plastic mulch, hydrogel 10 g per plant (49 g m-2) + plastic mulch, hydrogel 20 g per plant (98 g m-2), hydrogel 10 g per plant + grass mulch, and a control (no hydrogel, no mulch). Results showed significant effects of treatments on soil moisture, growth, and yield of yellow passion fruit at both sites. Hydrogel 10 g per plant + plastic mulch treatment had significantly higher mean soil moisture than other treatments during all the sampling dates. The treatments: hydrogel 10 g per plant + plastic mulch, hydrogel 10 g per plant + grass mulch, and plastic mulch, recorded the highest average vine length. Hydrogel 10 g per plant + plastic mulch treatment had significantly higher yields than other treatments during 46, 48, 50, and 52 weeks after transplanting (WAT) at both sites. Treatments did not significantly affect the total soluble solids (TSS) of fruits. At Ugweri, hydrogel 10 g per plant + plastic mulch and hydrogel 10 g per plant + grass mulch had higher net benefits; 2599.00 USD ha-1 and 2455.10 USD ha-1, respectively. At Kenyatta University, hydrogel 10 g per plant + plastic mulch provided significantly higher net benefit (3390.40 USD ha-1) than other treatments. Based on the results, hydrogel 10 g per plant + plastic mulch and hydrogel 10 g per plant + grass mulch are recommended for yellow passion fruit growers in regions facing water scarcity.

Soil habitat condition shapes Tamarix chinensis community diversity in the coastal saline-alkali soils.

Unfavorable coastal saline-alkali soil habitats degrade plant community diversity and reduce terrestrial ecological functions. Previous studies have been conducted on the mechanisms by which certain saline-alkali soil properties determine plant community diversity, however, how those properties synergistically affect plant community diversity remains unclear. Here, 36 plots of typical Tamarix chinensis communities were investigated for a range of parameters at three different distances (10, 20, and 40 km) from the coastline in the Yellow River Delta between 2020 and 2022, and corresponding soil samples were taken and analyzed. Our results suggest that although T. chinensis density, ground diameter, and canopy coverage significantly increased (P<0.05) with increasing distance from the coast, the communities with the most plant species were found at 10 to 20 km distance from the coastline, indicating the effects of soil habitat on T. chinensis community diversity. Simpson dominance (species dominance), Margalef (species richness), and Pielou indices (species evenness) differed significantly among the three distances (P<0.05) and were significantly correlated with soil sand content, mean soil moisture, and electrical conductivity (P<0.05), indicating that soil texture, water, and salinity were the main factors governing T. chinensis community diversity. Principal component analysis (PCA) was performed to construct an integrated soil habitat index (SHI) representing the synthesis of the soil texture-water-salinity condition. The estimated SHI quantified a 64.2% variation in the synthetic soil texture-water-salinity condition and was significantly higher at the 10 km distance than at the 40 and 20 km distances. The SHI linearly predicted T. chinensis community diversity (R2 = 0.12-0.17, P<0.05), suggesting that greater SHI (coarser soil texture, wetter soil moisture regime, and higher soil salinity) was found closer to the coast and coincided with higher species dominance and evenness and lower species richness in the T. chinensis community. These findings on the relationship between T. chinensis communities and soil habitat conditions will be valuable in planning the restoration and protection of the ecological functions of T. chinensis shrubs in the Yellow River Delta.

Global variations and controlling factors of anammox rates.

Soil anammox is an environmentally-friendly way to eliminate reactive nitrogen (N) without generating nitrous oxide. Nevertheless, the current earth system models have not incorporated the anammox due to the lack of parameters in anammox rates on a global scale, limiting the accurate projection for N cycling. A global synthesis with 1212 observations from 89 peer-reviewed papers showed that the average anammox rate was 1.60 ± 0.17 nmol N g-1 h-1 in terrestrial ecosystems, with significant variations across different ecosystems. Wetlands exhibited the highest rate (2.17 ± 0.31 nmol N g-1 h-1 ), followed by croplands at 1.02 ± 0.09 nmol N g-1 h-1 . The lowest anammox rates were observed in forests and grasslands. The anammox rates were positively correlated with the mean annual temperature, mean annual precipitation, soil moisture, organic carbon (C), total N, as well as nitrite and ammonium concentrations, but negatively with the soil C:N ratio. Structural equation models revealed that the geographical variations in anammox rates were primarily influenced by the N contents (such as nitrite and ammonium) and abundance of anammox bacteria, which collectively accounted for 42% of the observed variance. Furthermore, the abundance of anammox bacteria was well simulated by the mean annual precipitation, soil moisture, and ammonium concentrations, and 51% variance of the anammox bacteria was accounted for. The key controlling factors for soil anammox rates differed from ecosystem type, e.g., organic C, total N, and ammonium contents in croplands, versus soil C:N ratio and nitrite concentrations in wetlands. The controlling factors in soil anammox rate identified by this study are useful to construct an accurate anammox module for N cycling in earth system models.

Multifrequency electromagnetic induction soil moisture characterization under different land uses in western Newfoundland

Identifying and characterizing the spatial patterns in soil moisture variability under different land use conditions is crucial for agriculture, forestry, and civil and environmental engineering. Yet employing multifrequency (MF) electromagnetic induction (EMI) techniques to carry out this task is under-represented in boreal podzolic soils. This study ( i) compared four frequencies (∼2.8–80 kHz) for shallow mapping of soil moisture measured with a time–domain reflectometry at 0–20 cm soil depth under three different land use conditions (agricultural land, field road, and a recently cleared natural forest), ( ii) developed a relationship between apparent electrical conductivity (ECa) measured using multifrequency EMI (GEM-2) and soil moisture, and ( iii) assessed the effectiveness of ECa as an auxiliary variable in predicting soil moisture variations under different land use conditions. The means of ECa measurements were calculated for the exact sampling location (ground truth data) in each land use condition at a research site, Pasadena, NL, Canada. Soil moisture–ECa linear regression models for the three land use conditions were only statistically significant for 38.3 kHz frequency and were further analyzed. Further statistical analysis revealed that ECa was primarily controlled by soil moisture for the three land use conditions, with the natural forest possessing the highest mean ECa and soil moisture. Geostatistical analysis revealed that cokriging ECa with less densely collected soil moisture improved the characterization accuracy of soil moisture variability across the different land use conditions. These results reveal the effectiveness of the georeferenced MF–EMI technique to rapidly assess intrafield soil moisture variability under different land uses.

Assessing and comparing the subseasonal variations of summer soil moisture of satellite products over eastern China

AbstractUsing the 43‐station soil moisture (SM) at a depth of 0–10 cm over eastern China, the near‐surface (about 0–5 cm) SM products from satellite missions including the level‐2 product of Chinese FengYun 3C (FY3C), level‐2 neural network product of the European Soil Moisture and Ocean Salinity (SMOS) and level‐4 product of the US Soil Moisture Active Passive (SMAP) were evaluated and compared during summers of 2015–2018. Thus, features of those products can be identified for their further application of climate study over eastern China. Large diversity is found among the satellite and field SM in terms of spatial distribution. This disagreement may cause the poor performance of the relationship between spatial coefficients of variation (CV) and mean SM. Compared with the field SM, the FY3C SM has smaller bias than the other satellite products. FY3C also performs better than SMOS in terms of the root‐mean‐square error (RMSE) and unbiased RMSE (ubRMSE) but has the smallest correlation coefficient (R). The SMAP product is generally the best among the three products in terms of RMSE, ubRMSE and R. However, a good performance in those metrics does not guarantee the same results on various time scales. On subseasonal time scales, the R in FY3C is the smallest among the three products, and the SMAP product has the largest R, but its amplitudes of the subseasonal variations are much smaller than the field observations. This indicates that when the SMAP products are applied for the analysis on subseasonal SM–atmosphere interactions, the effects of SM may be underestimated. On 10–30 days and above 60 days, dry period tends to have large spatial CV but this phenomenon is weak in all the satellite products. On the other hand, dry area tends to have large temporal CV on the four time scales, and this relationship is the strongest in SMAP but the weakest in FY3C. Therefore, there are large uncertainties of variability among different satellite SM products on subseasonal time scales over eastern China. Besides seasonal and overall performance, more attention is called to the variations on different time scales.

Diagnosing Product Variability in the Soil Moisture Response to Precipitation on the Tibetan Plateau

Abstract Previous studies show that some soil moisture products have a good agreement with in situ measurements on the Tibetan Plateau (TP). However, the soil moisture response to precipitation variability in different products is yet to be assessed. In this study, we focus on the soil moisture response to precipitation variability across weekly to decadal time scales in satellite observations and reanalyses. The response of soil moisture to precipitation variability differs between products, with large uncertainties observed for variations in weekly accumulated precipitation. Using June 2009 as an example, weekly mean anomalous soil moisture varies by up to 25% between products. Across decadal time scales, soil moisture trends vary spatially and across different products. In light of the soil moisture response to precipitation at different time scales, we conclude that remote sensing products developed as part of the European Space Agency’s (ESA) Water Cycle Multimission Observation Strategy and Soil Moisture Climate Change Initiative (CCI) projects are the most reliable, followed by the Global Land Evaporation Amsterdam Model (GLEAM) dataset. Even products that strongly agree with in situ observations on daily time scales, such as the Global Land Data Assimilation System (GLDAS), show inconsistent soil moisture responses to decadal precipitation trends. European Centre for Medium-Range Weather Forecasts (ECWMF) reanalysis products have a relatively poor agreement with in situ observations compared to satellite observations and land-only reanalysis datasets. Unsurprisingly, products which show a consistent soil moisture response to precipitation variability are those mostly aligned to observations or describe the physical relationship between soil moisture and precipitation well. Significance Statement We focus on soil moisture responses to precipitation across weekly to decadal time scales by using multiple satellite observations and reanalysis products. Several soil moisture products illustrate good consistency with in situ measurements in different biomes on the Tibetan Plateau, while the response to precipitation variability differs between products, with large uncertainties observed for variations in weekly accumulated precipitation. The response of soil moisture to decadal trends in boreal summer precipitation varies spatially and temporally across products. Based on the assessments of the soil moisture response to precipitation variability across different time scales, we conclude that remote sensing products developed as part of the European Space Agency’s Water Cycle Multimission Observation Strategy and Soil Moisture Climate Change Initiative (CCI) projects are the most reliable, followed by the Global Land Evaporation Amsterdam Model (GLEAM) dataset. Reanalysis products from ECWMF show inconsistent soil moisture responses to precipitation. The results highlight the importance of using multiple soil moisture products to understand the surface response to precipitation variability and to inform developments in soil moisture modeling and satellite retrievals.

Effect of moisture conservation techniques on almond (Prunus dulcis) productivity under rainfed conditions

A field experiment was conducted to study the effect of moisture conservation techniques on productivity enhancement in almond (Prunus dulcis Mill) during 2010-11 to 2012-13 at Srinagar, Jammu and Kashmir. Treatment sincluded five water harvesting techniques, viz. half-moon (W1), full-moon (W2), cup and plate (W3), trench (W4) and control (W5) and three mulch materials, viz. plastic mulch (M1), organic mulch (M2) and control (M3) were laid out in factorial randomized block design with three replication. The experimental results revealed that maximum mean trunk cross sectional area (266.82 cm2), nut yield (5.37 kg/tree), productivity efficiency (19.47 g/cm2 TCSA) and soil moisture content (13.48%) were recorded in full moon water harvesting technique. Among mulch material, plastic mulch found better in respect to TCSA (255.59 cm2), nut yield (4.99 kg/tree), productivity efficiency (18.88 g/cm2 TCSA) and soil moisture content (12.86%) compared to other treatments. The combined effect of water harvesting and mulching indicated that maximum TCSA (270.48 cm2), nut yield (5.82 kg/tree), productivity efficiency (20.92 g/cm2 TCSA) and soil moisture (13.98 %) were recorded in full moon water harvesting + plastic mulch in almond cultivar Non Pareil under rainfed conditions of north west Himalayan regions of India.

Latitudinal and Altitudinal Patterns and Influencing Factors of Soil Humus Carbon in the Low-Latitude Plateau Regions

The composition of forest soil organic matter is an important part of the global carbon cycle, which is effective by temperature and moisture. As we all know, the temperature and moisture in the low-latitude plateau regions are very sensitive to changes in latitude and altitude. However, the composition of soil organic matter response to changes in latitude and altitude in the low-latitude plateau regions is unknown. In this study, the effects of latitude (21–29° N) and altitude (500–4000 m) on soil organic carbon (SOC) and humic acid carbon (HAC), fulvic acid carbon (FAC), and humin carbon (HMC) in forest surface soil (0–10 cm) were investigated. The results showed that the contents of soil organic carbon and humus increased with the increase in altitude and latitude. The effect of altitude on the composition of organic matter was significant only at 23° N to 25° N. The composition of organic matter is not only regulated by mean annual temperature (MAT) and soil moisture content (SMC) but also affected by soil pH, carbon to nitrogen ratio (C/N), and powder. The soil surface layer (0–10 cm) carbon sequestration capacity in high-latitude and high-altitude areas is stronger than that in low-latitude and low-altitude areas. As a consequence, in today’s response to global climate change, the high carbon sequestration capacity of high latitude and high altitude areas should be given attention and protection.

Dynamics of global dryland vegetation were more sensitive to soil moisture: Evidence from multiple vegetation indices

The prominent role of drylands in the global ecosystem calls for a deeper understanding of the responses of dryland vegetation to ongoing environmental drivers in the context of global climate change. Here, we first investigated the spatial and temporal trends of global dryland vegetation based on multiple satellite- and model-based indices, including the normalized difference vegetation index (NDVI), leaf area index (LAI), vegetation optical depth (VOD), and gross primary productivity (GPP) during 1988–2018. Then, the impacts of a set of environmental drivers (i.e. mean annual precipitation (MAP), mean annual temperature (MAT), soil moisture (SM), and vapor pressure deficit (VPD)) on vegetation dynamics were quantified using partial correlation analysis and structural equation model. All four indices increased strongly before 2000 but slowed afterward. The variation in dryland vegetation was more related to SM anomaly in comparison with other environmental drivers. The variation induced by SM was amplified by high VOD in some continents. Furthermore, MAT contributed similarly as SM to vegetation dynamics in North America. The four vegetation indices exhibited divergent responses to environmental drivers due to their characteristics. At the continental scale, NDVI was only relevant to variation in VPD in North America. In contrast to NDVI, LAI, and VOD, GPP was more closely associated with the variation in SM and VPD. Roughly half of the GPP variation was attributable to the combination of SM and MAT in North America and Australia, whereas they have low predictive power (∼30%) in Eurasia, Africa, and South America. SM was closely linked to the vegetation changes in grasslands and shrublands; however, this impact varied among the continents. Our results advance the current understanding of dryland vegetation dynamics and shed new light on improving dryland carbon flux simulation by fully considering the role of soil moisture.

Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index

ABSTRACT This study presents a global, hourly surface soil moisture estimation procedure based on precipitation and temperature data. Information on soil composition further helps to define the local characteristics of soil moisture development. An advanced antecedent precipitation index (API) is utilized to generate a global soil moisture product of high temporal resolution with the Global Precipitation Measurement (GPM) Missions Integrated Multi-Satellite Retrievals for GPM (IMERG) as main driver. The resulting global GPM API data set is compared against in situ measurements from the International Soil Moisture Network (ISMN) and is also evaluated against the soil moisture data set from the European Space Agency’s Climate Change Initiative (ESA CCI SM). The study shows that with empirically derived dampening factors the GPM API achieves a mean ubRMSD across the utilized in situ stations in different climates and vegetation zones of 4.68 and a bias of 0.88 . The data set clearly represents the local soil moisture schemes with seasonal variations. When comparing with ESA CCI SM, the GPM API does perform better at the measurement sites concerning bias, correlation and error values. The data set is in most parts negatively biased compared to the ESA CCI SM, however better matches the mean soil moisture at ISMN stations. Overall, the GPM API delivers a very promising global, hourly surface soil moisture product at 0.1 0.1 spatial resolution.

Characterisation of the vertical temperature gradient in the canopy reveals increased trunk height to be a potential adaptation to climate change

Given the important role of temperature in vine development and grape composition, climate change has already impacted wine production. Adaptation strategies are needed in order to sustain the production of wines and maintain their typicity. Several levers of adaptation are possible, including the use of more heat and drought tolerant plant material, relocating the vineyard and adaptations in the cellar. The training system is also a potential lever for adaptation that is relatively easy to implement. Taking that avenue, a study of the vertical thermal gradient in the vine canopy was carried out in order to determine whether trunk height could be an adaptation strategy for manipulating micro-climate in the bunch zone. Temperature was measured at four different heights from the soil (30, 60, 90 and 120 cm) in two adjacent vineyard parcels. One parcel was managed with cover crop and the other by tilling the soil. The results of this study show that increased trunk height is not likely to significantly delay ripeness, but it could minimise the potential damages of both frost and heat wave events. Type of parcel management was found to have an effect: close to the ground, the cover crop parcel generally had lower minimum temperatures and higher maximum temperatures in comparison to the tilled parcel, exposing the vines to an increased risk of both frost and heat wave damage. When investigating the factors driving the vertical thermal gradient, soil moisture and weather type were found to have an impact. Some of these factors, like mean temperature and soil moisture, may exacerbate the vertical temperature gradient of maximum temperature in a climate change context and increase the risk of damages due to extreme temperatures.

Monthly variation of local land–atmosphere coupling over the Tibetan Plateau in the rainy season and its relationship with the South Asian summer monsoon

AbstractThe local land–atmosphere coupling (LoCo) over the Tibetan Plateau (TP) in the rainy season and the possible influence of the South Asian monsoon (SAM) on LoCo characteristics are investigated by applying two LoCo diagnostic metrics to 12‐year observational data at six stations over the TP and the ERA5 from June to September (JJAS) from 1980 to 2018. The validation of ERA5 in estimating monthly mean diurnal evolutions of T2m, q2m, and surface fluxes (Hsfc and LEsfc) at the six stations reveals that these variables in the ERA5 are relatively reliable for the LoCo analysis. The mixing diagram analysis based on the observational data and ERA5 suggests that the monthly variations in the planetary boundary layer (PBL) energy budget are strong and vary from station to station, revealing the complexity of the influence of the SAM on the PBL energy budget over TP. The relationship between Hsfc and PBL height (PBLH) and lifted condensation level (LCL) deficit at the six stations based on the ERA5 reveal a strong influence of soil moisture on PBL growth and convective cloud formation, although the topography may also exert its influence. Analyses of the correlations of the PBL energy budget, PBLH, and LCL deficit over the TP with the SAM in JJAS indicate that the LoCo characteristics over the TP show large spatial variability between the southeastern and central‐western parts of the TP. Such spatial variability can be attributed to the spatial differences in monthly mean soil moisture and rainfall closely related to the evolution of SAM's influence and the complex topography of the TP. The topography of the TP can also affect the LoCo characteristics in JJAS, but the mechanisms in the areas dominated by SAM and by the westerly winds are different.

Impact of land use types on soil moisture dynamics of loamy soils

In the last decades, drought has been a significant climate hazard in the Carpathian Basin. In this study, we investigated the soil moisture dynamics (SM) of three different land use types (pasture, ploughland, and orchard) in the Transdanubian Hills (SW Hungary). The soil moisture, matric potential and rainfall were measured between January 1, 2019 and February 28, 2023. Two monitoring stations were installed at each study site on the shoulder and at the toeslope positions. The study has revealed that the textural types of the study sites were silt loam, clay loam, and silt. The pasture had the most positive water balance, whereas the orchard had the most negative, especially in 2022 when trees were removed. The mean soil moisture values were 0.26, 0.21, and 0.21 for the pasture, ploughland and orchard for 10 cm, and 0.3, 0.22, and 0.22 for the pasture, ploughland and orchard for 30 cm, respectively. Moisture differences were relatively minor between the three sites, however, soil moisture dynamics were influenced by farming practices. Hence, site-specific mapping and analyses of factors responsible for efficient moisture retention are indispensable for the maximization of agricultural productivity and the optimization of the efficiency of ecosystem services. Our results could be used for the promotion of sustainable agricultural activities where loamy soils and subhumid continental climates prevail.

Hourly Water Level Forecasting in an Hydroelectric Basin Using Spatial Interpolation and Artificial Intelligence.

In this work, a new hydroelectric basin modelling approach is described and applied to the Pontecosi basin, Italy. Several types of data sources were used to learn the model: a number of weather stations, satellite observations, the reanalysis dataset, and basin data. With the goal of predicting the water level of the basin, the model was composed by three cascade modules. Firstly, different spatial interpolation methods, such as Kriging, Radial Basis Function, and Natural Neighbours, were compared and applied to interpolate the weather stations data nearby the basin area to infer the main environmental variables (air temperature, air humidity, precipitation, and wind speed) in the basin area. Then, using these variables as inputs, a neural network was trained to predict the mean soil moisture concentration over the area, also to improve the low availability due to satellite orbits. Finally, a non-linear auto regressive exogenous input (NARX) model was trained to simulate the basin level with different prediction horizons, using the data from the previous modules and past basin data (water level, discharge flow rate, and turbine flow rate). Accurate predictions of the basin water level were achieved within 1 to 6 h ahead, with mean absolute errors (MAE) between 2 cm and 10 cm, respectively.

A decadal record of soil moisture space–time variability over a south‐east Australian catchment

AbstractSemi‐arid to temperate south‐east Australian catchments with agricultural landscapes demonstrate unique hydro‐climatic characteristics. Understanding the behaviour of soil moisture over such catchments and the influence of driving factors are crucial for hydrologic, climatic and agricultural applications. However, this is challenging due the complex, non‐linear relationship between these factors and soil moisture, and the lack of long‐term catchment scale data records. To address this, spatial and temporal patterns of soil moisture over two south‐east Australian river catchments (i.e., Krui and Merriwa) and the influence of soil texture, topography, vegetation and rainfall on soil moisture variability were evaluated using a decadal in‐situ dataset. This unique in‐situ soil moisture monitoring network is established over a semi‐arid to temperate catchment representing typical south‐east Australian agricultural landscape and the data record has captured some major climatic events. Time stability of catchment‐scale soil moisture and the potential of predicting catchment mean soil moisture content using one representative station were also examined using a linear regression model. Soil texture was found as the dominating factor driving the spatial variability of soil moisture in the area. The temporal patterns of soil moisture showed a positive agreement with vegetation dynamics and rainfall at topsoil layers (0–5 cm and 0–30 cm). A higher spatial variability of soil moisture was observed during dry catchment conditions compared to wet catchment conditions. The deeper soil layers (30–60 cm and 60–90 cm) showed highly stable soil moisture values, which might be the driving force of the agriculture in the area. A linear regression based prediction model demonstrated a good potential in estimating spatial mean soil moisture content from one representative station. The results are useful in parameterization of soil moisture variability in land surface, climatic and hydrologic models, agricultural applications and in remote sensing of soil moisture.