Soil Moisture Deficit Research Articles

Dual controls of vapour pressure deficit and soil moisture on photosynthesis in a restored temperate bog.

Despite only covering ~3% of the land mass, peatlands store more carbon (C) per unit area than any other ecosystem. This is due to the discrepancy between C fixed by the plants (Gross primary productivity (GPP)) and decomposition. However, this C is vulnerable to frequent, severe droughts and changes in the peatland microclimate. Plants play a vital role in ecosystem C dynamics under drought by mediating water loss to the atmosphere (surface water vapour conductance) and GPP by the presence/absence of stomatal regulation. This is dependent on soil moisture, air temperature, and vapour pressure deficit (VPD). Although there is ample evidence of the role of VPD on stomatal regulation and GPP, the impact of soil moisture is still debated. We addressed this knowledge gap by investigating the role of bulk surface conductance of water vapour in shifts between climatic (Air temperature (Tair), incoming shortwave radiation (SWR) and VPD) and water limitation of GPP in a peat bog in Canada. A causal analysis process was used to investigate how environmental factors influenced GPP. The results suggested that stomatal regulation in response to increased VPD caused the reduction in GPP in 2016 (~2.5gCm-2day-1 as opposed to ~3gCm-2day-1 in 2018). In contrast, GPP was limited again in 2019 due to the dry surface. This was driven by the relaxed stomatal regulation adopted by the ecosystem following the initial drought to maximise C assimilation. We found the threshold at which surface water decline limited GPP was at about -8cm water table depth (82.5% soil moisture). The causal inference corroborated our findings. The temporal variations of water and energy limitation seen in this study could increasingly restrict GPP due to the projected climate warming.

Spatial and temporal distribution of infiltration, curve number and runoff coefficients using TOPMODEL and SCS-CN models

Infiltration, the process by which water enters the soil, is intricately intertwined with the attributes of the catchment, including soil composition and vegetation cover, both of which exhibit temporal and spatial variability. Accurate quantification of infiltration rates is imperative for enhancing the predictive capabilities of rainfall-runoff models, especially in regions with limited hydrological monitoring infrastructure, such as many developing countries where a significant portion of catchments remains ungauged. In this study, we integrate the Soil Conservation Service Curve Number (SCS-CN) model with the TOPography-based hydrological MODEL (TOPMODEL) to derive a comprehensive framework for estimating the spatial and temporal dynamics of infiltration and its associated parameters. By leveraging the complementary strengths of these two models, we aim to enhance our understanding of infiltration processes across diverse landscapes. The amalgamation of the SCS-CN model with TOPMODEL Dynamic, which incorporates topographic features, contributing areas, and soil moisture deficit (SMD) dynamics within the watershed, represents a novel approach for characterizing the spatiotemporal variability of infiltration, curve number (CN), and runoff coefficient (RC). This integrated model offers a refined mathematical representation, capable of capturing the intricate interactions between land surface characteristics and hydrological processes, thereby advancing our ability to simulate and predict runoff responses in complex environmental settings.

Impacts of Climate Change on Forest Resources in Mexico: Regional Projections and Vulnerabilities

Anthropogenic climate change can affect forest ecosystems and is one of the causes of deforestation, which leads to desertification. Deforestation is one of the main drivers of climate change due to the release of large amounts of CO2 stored in forests and jungles. Rising temperatures, decreased rainfall and drought have negative impacts on the diversity of forest species, as well as on forest ecosystem goods and services. Thus, this research focuses on climate change and its impact on Mexico's forests, evaluating deforestation and its effects on climate change and, subsequently, the effects of that climate change on forests; and the role of forests in carbon sequestration. Aims: The objective of this study is to present a set of regional temperature, precipitation, and drought projections for Mexico under the IPCC's AR6 climate change scenarios, and to estimate the potential impacts on Mexico's forest resources. Methodology: The methodology consists of obtaining a set of regional projections of temperature, precipitation, vegetation, drought and soil moisture under the conditions of the IPCC AR6 climate change scenarios for Mexico, with the changes of the aforementioned variables the vulnerability of the forestry sector in Mexico will be evaluated through models of the influence of these factors. Results: Regional models for Mexico show temperature increases ranging from 0.5 to 5 °C, while the percentage change in precipitation will range from -20.3% to 13.5% depending on the scenario and period of analysis. The low soil moisture (mm), the negative changes of the NDVI and the SPEI12 show that the North, West and Bajío zones will present reductions in precipitation and temperature increase that will cause a severe deficit of soil moisture and water stress in the plants, considering these areas with scarce vegetation and the presence of semi-permanent meteorological drought. Under these scenarios, it is expected that practically the entire country will be subject to moderate droughts (Central and South) to extremely severe droughts (North) that will last and worsen between now and the end of the century. These conditions have already been recorded in Mexico's main forest areas and if adaptation and mitigation measures are not adopted, the country's forest resources are at risk. Likewise, in addition to the decrease in rainfall, it is estimated that it will be more intense with the presence of extreme events, which will increase the vulnerability of some basins throughout the country, in the Central and Northern areas with extreme drought events, while in the Southeast with floods. Conclusion: Temperate and cloud forests, xerophilous scrub and grasslands will be the most affected forest ecosystems in Mexico, to reduce the effects of climate change on forest resources, it is necessary to design and prioritize adaptation actions and implement mitigation measures that prevent the effects of climate change from intensifying in the most vulnerable regions of the country.

Declining resilience of forest carbon sinks linked to increased atmospheric water deficit during droughts in the Northern Hemisphere

Abstract Droughts have posed intense threats to the forest carbon sink (i.e. net ecosystem productivity, NEP), potentially elevating the risk of forest degradation and challenging the achievement of climatic and ecological goals. While global forest NEP endured, the resilience of NEP served as the ability of ecosystems to withstand and recover from perturbations and the underlying maintenance mechanisms during droughts remain unclear. Here, we explored the responses of NEP resilience, quantified by the lag-1 temporal autocorrelation coefficient (TAC) of two consecutive time series, to droughts based on 87 drought events across 45 forest sites with flux and meteorological observations in the Northern Hemisphere. Furthermore, an interpretable machine learning algorithm was utilized to disentangle the synergistic effects of environmental and biotic factors on TAC, achieving a mean coefficient of determination of 0.87 for drought events with significantly increasing TAC and 0.91 for other drought events. Here, we found that the increase in NEP resilience could alleviate the negative effects of droughts, in contrast to a 2.5 times increase in the probability of NEP decline events associated with decreased NEP resilience. However, NEP resilience declined with the rise of drought intensification. The reduced reference canopy conductance (G cref) was the primary constraint on NEP resilience maintenance, contributing 48% to the total influence of biotic factors. In addition, high vapor pressure deficit (VPD) exacerbated the negative effects of soil moisture deficit, jointly leading to the decline in NEP resilience. Specifically, elevated VPD during droughts significantly reduced G cref, indicating the vulnerability of tree hydraulic systems to compound stress. Overall, our study emphasizes the potential risks of the compound soil and atmospheric water deficit on forest NEP resilience and carbon sink across the Northern Hemisphere in the future.

Frequent Irrigation Under Increasing Doses of Stabilized Sewage Sludge in The Soil Increases the Yield and Quality of Silage Maize

Achieving higher efficiency and better quality production with appropriate irrigation regimes in silage maize cultivation in soils mixed with urban sludge is a requirement of sustainable agriculture. Therefore, a two-year field study was carried out with three replicates with four different sewage sludge doses (D0: 0 t/ha, D1: 30 t/ha, D2: 60 t/ha, and D3: 90 t/ha), and three different irrigation regimes (S1, S2, and S3). In the S1, S2 and S3 regimes, when the sum of (Reference evapotranspiration - Effective rainfall) was 25 mm, 50 mm and 75 mm, respectively, irrigation was carried out and the soil moisture deficit was completed to the field capacity. Considering the two-year average, increasing sewage sludge dose and frequent irrigation significantly increased fresh and dry biomass yields and crude protein, while decreasing acid detergent fiber (ADF) and neutral detergent fiber (NDF). The fresh biomass yield in D3 treatment was 12.4%, 20.6%, and 42.1% higher than D2, D1, and D0, respectively. ADF in D3 was 5.6%, 2.1% and 1.7% lower than D0, D1 and D2, respectively, while NDF was 4.4%, 3.7% and 2.1% lower. D3 treatment increased the crude protein value by 27.3%, 15.5% and 7.7% compared to D0, D1 and D2 treatments, respectively. S1 provided 12.9% and 28.3% higher fresh biomass yield compared to S2 and S3. ADF value in S1 was 0.69% and 2.4% lower than S2 and S3, respectively, and NDF value was 0.86% lower compared to S3. There was a positive linear relationship with a high correlation between fresh and dry biomass yields. It could be concluded that D3-S1 treatment is the most effective practice for higher and quality yields, and followed by D3-S2 treatment.

Summer atmospheric drying could contribute more to soil moisture change than spring vegetation greening

Widespread spring vegetation greening (inferred by LAI) in the Northern Hemisphere leads to additional summer soil moisture (SM) deficits through increasing transpiration. Meanwhile, vapor pressure deficit (VPD) has also been rising, which can increase atmospheric evaporative demand. However, the extent and magnitude of influence of these two factors on SM changes have not been elucidated. Here, based on the state-of-the-art reanalysis and remote sensing data, we use three statistical methods to quantify the contributions of spring LAI and summer VPD to summer SM deficit. The results show that summer VPD contributes more to SM change than spring LAI in the southwestern and northern regions of North America, northeastern Europe, and central and southeastern Asia, covering 13.4% of the vegetated areas despite of the certain influence of spring vegetation greening on summer soil drying. The results are of great significance for climate change adaptation and the enhancement of surface water management.

Quantifying the Impacts of Precipitation, Vegetation, and Soil Properties on Soil Moisture Dynamics in Desert Steppe Herbaceous Communities Under Extreme Drought

The security of water resources in the desert steppe ecosystem faces threats due to large-scale vegetation restoration. Dynamic changes in soil moisture result from the interplay of precipitation replenishment and evapotranspiration depletion, both directly regulated by vegetation and soil. To achieve sustainable vegetation restoration, understanding the quantifiable impacts of precipitation, evapotranspiration, soil, and vegetation on spatiotemporal soil moisture dynamics is crucial. However, these effects remain insufficiently understood. In this study, against the background of an extreme drought from 2020 to 2022, four typical herbaceous plant communities—Agropyron mongolicum, Sophora alopecuroides, Stipa breviflora, and Achnatherum splendens—were selected for investigation in Yanchi County, Ningxia Province, Northwest China. We analyzed dynamic changes in soil moisture at 0–120 cm during depletion, recovery, and stability periods, quantifying the relative contributions of precipitation, evapotranspiration, soil clay/sand ratio (C/S), and biomass to soil moisture dynamics. The results showed that the 0–120 cm soil moisture of the four plant communities in the depletion, recovery, and stability periods decreased from 7.38% to 6.81%, 11.22% to 8.08%, and 11.70% to 5.84%, respectively. In terms of relative importance, precipitation and evapotranspiration accounted for 25% to 50% and 23.6% to 39.6% of the total explanation for the soil moisture in each plant community, respectively. C/S primarily influenced soil moisture in the S. alopecuroides community, demonstrating a significant positive correlation with soil moisture and accounting for 49.1% of the total explanation. Biomass mainly affected soil moisture in the A. mongolicum, S. breviflora, and A. splendens communities and had a significant negative correlation with soil moisture, accounting for 5.7%, 13.1%, and 9.8% of the total interpretation, respectively. The continuous extreme drought caused the soil moisture deficit to extend from the shallow to the deep layers. The effects of C/S and biomass on soil moisture occurred in leguminous and gramineous communities, respectively.

Climate Change and its Economic Impacts in Mexico

Introduction: Climate change is one of the great challenges of the 21st century, due to its global causes and consequences and its heterogeneous and asymmetric regional impacts, with those who contribute the least to the phenomenon receiving the greatest negative impacts. Mexico has a minor contribution to climate change in terms of greenhouse gas emissions; however, it is highly vulnerable to its negative effects. Climate change is a long-term global phenomenon, with a high level of uncertainty. The economic analysis of climate change has a high level of risk, which makes it a process where risk must be managed. Despite these characteristics and limitations, it is a fundamental instrument for public policy and society since it identifies options and alternatives to build sustainable development strategies and protect natural resources and ecosystems for future generations beyond their economic value. However, it must be understood that projections are not one-off forecasts but only prospective scenarios. Objective: The objective of this study is to evaluate the economic impacts of Climate Change for Mexico during this century, according to the different scenarios of the IPCC AR6. Methodology: The methodology consists of presenting a set of regional projections of climate variables under the IPCC's AR6 climate change scenarios, and estimating the possible impacts of climate change on Mexico's economic sector. Results: Regional models show temperature increases between 0.5 and 5 °C, while changes in precipitation will range between -20.3% and 13.5%. Low soil moisture, negative changes in vegetation (NDVI) and drought (SPEI12) will lead to soil moisture deficits, water stress, sparse vegetation and semi-permanent drought. The entire country is expected to be subjected to moderate (Central and Southern) to extremely severe (Northern) droughts that will worsen by the end of the century. Climate change will have effects on crop yields, livestock reproduction, production of meat and its derivatives throughout the country in a differentiated way, with increases in food prices. It will also cause a deficit of land suitable for agricultural activities; and water availability. Diseases associated with climate change will become more acute by the end of the century, with increases in health spending. It is estimated that the investment of the Gross Domestic Product (GDP) in the health sector would go from 6.25% to more than 18%, which could be unfeasible for the national economy. Based on the likely temperatures by the end of the century, it is estimated that the risk of extinction of species will range between 3 and 48% in terrestrial, oceanic and coastal ecosystems, the risk of biodiversity loss will go from moderate to very high. The total costs of climate change reach 2050, with a discount rate of 4%, values that range between 9 and 13% of GDP in the most likely scenarios for the end of the century and reach between 28 and 36% of GDP at a discount rate of 0.5%, while by 2100 with a discount rate of 4%. around 16.5% of GDP and reach maximums between 69 and 76% of GDP at a discount rate of 0.5%. Conclusion: The results allow us to conclude that climate change has significant impacts on the Mexican economy and that the costs of inaction are higher. Thus, it is more efficient to act now than to leave the problem to future generations, beyond the ethical considerations that it implies.

Stronger effects of accumulated soil moisture deficit on gross primary productivity and light use efficiency than lagged soil moisture deficit for cropland and forest

Many studies have underscored the impacts of drought on ecosystems, and some researchers reported the effects of accumulated soil moisture deficit (ASMD) on light use efficiency (LUE) in grassland. However, the potential effects of ASMD on gross primary productivity (GPP) and LUE for both cropland and forest ecosystems are still not understood. This study elucidated the effects of accumulated and lagged soil moisture deficit (ASMD and LSMD, respectively) on GPP and LUE in these two ecosystems by using observations from 10 cropland and 25 forest flux sites during drought years. The results showed that the effects of ASMD and LSMD on LUE/GPP for both cropland and forests obviously surpass the concurrent effects (CSMD). For cropland, the mean R2 between CSMD/LSMD/ASMD with LUE were 0.22, 0.47, 0.56, respectively, and were 0.29, 0.54, 0.74 with GPP, respectively. For forest, the mean R2 between CSMD/LSMD/ASMD with LUE were 0.21, 0.36, 0.46, respectively, and were 0.34, 0.63, and 0.65 with GPP, respectively. Additionally, the effects of ASMD and LSMD on LUE are more pronounced for cropland than for forests, and for both cropland and forest, the effect of ASMD is stronger than that of LSMD. This study underscores the crucial role of ASMD in influencing LUE and GPP for cropland and forests, thereby offering a theoretical foundation for incorporating ASMD into LUE models to enhance the accuracy of GPP simulations, especially during drought periods.

Modelling of the trade-off between the deep soil moisture and vegetation restoration in the hilly area of the Loess Plateau, China

Excessive depletion of soil moisture by artificial forests in the vegetation restoration areas of the Loess Plateau has attracted widespread attention. To assess potential risks of soil moisture deficit, we needed on-site vegetation and soil sampling data, as well as UAV images from the Chaigou Watershed for three-dimensional analysis, combining both sampling and raster data. Three-dimensional surfaces for assessment of trade-off were established innovatively by the local regression and interpolation methods. The results indicated that soil moisture benefits at 20–40 cm depth are lower than at 0–20 cm due to infiltration and surface disturbance. In some areas of the Chaigou Watershed, grass and shrub-grass vegetation are facing risks of soil moisture deficit based on trade-off values (RSMD) and multiparameter evaluations. Analysis of deep soil water content variability revealed the moisture decreases significantly with the deepening of the soil layer in some plots. R (Richness), H (Shannon’s Diversity), Margalef, COHESION, and CONTAG were applicable in interpolation and fitted with the local regression model (R2 > 0.6) corresponding to the trade-off, but SPLIT was proven to be inapplicable in this study area. The zero trade-off inflection points were 6 %–8 %, while the trend inflection points were 7.5 %–9 % for soil moisture and vegetation indices of vegetation and landscape in typical sampling sites of the Chaigou Watershed. Three-dimensional fitting model is more comprehensive and effective in assessing deep soil moisture conditions and grass plots on shady slopes generally had the best trade-off status in this region.

Convolutional Neural Networks accurately predict soil matric potential from soil, weather, and satellite-derived data

Being able to predict soil moisture dynamics offers water managers the possibility to better plan irrigation events and prevent soil moisture deficits from reaching levels that reduce crop production. Machine learning (ML) model predictions can potentially assist farmers in managing irrigation water more efficiently. In this study, we aimed to assess the accuracy of a set of ML models in predicting soil matric potential seven days ahead in gravity-surface irrigated cotton paddocks and evaluate the models’ performance for longer term predictions (14 days). The ML models used past soil moisture, weather, and satellite-derived crop-related data as features for the input parameters. Input data were structured in tuples that were organised following a 20-day ‘window’ approach that ‘slid’ one position forward after each training round. A convolutional neural network (CNN) model outperformed a Long Short-Term Memory, Dense Multilayer Perceptron, and Linear Regression model, the latter of which produced the least accurate predictions. The accuracy of the soil matric potential predictions with the CNN model was stable over time (R2 ≥ 0.92 and root mean square deviation ≤ 7.5 kPa). However, less accurate predictions were obtained for a short period after emergence and at crop senescence. This study demonstrates the feasibility of producing accurate predictions of soil matric potential in cotton fields at 0.20 m soil depth with a CNN model, which can be integrated into irrigation decision support systems.

Satellite-Observed Hydrothermal Conditions Control the Effects of Soil and Atmospheric Drought on Peak Vegetation Growth on the Tibetan Plateau

Recent research has demonstrated that global warming significantly enhances peak vegetation growth on the Tibetan Plateau (TP), underscoring the influence of climatic factors on vegetation dynamics. Nevertheless, the effects of different drought types on peak vegetation growth remain underexplored. This study utilized satellite-derived gross primary productivity (GPP) and the normalized difference vegetation index (NDVI) to assess the impacts of soil moisture (SM) and vapor pressure deficit (VPD) on peak vegetation growth (GPPmax and NDVImax) across the TP from 2001 to 2022. Our findings indicate that NDVImax and GPPmax exhibited increasing trends in most regions, displaying similar spatial patterns, with 65.28% of pixels showing an increase in NDVImax and 72.98% in GPPmax. In contrast, the trend for SM primarily showed a decrease (80.86%), while VPD showed an increasing trend (74.75%). Through partial correlation analysis and ridge regression, we found that peak vegetation growth was significantly affected by SM or VPD in nearly 20% of the study areas, although the magnitude of these effects varied considerably. Furthermore, we revealed that hydrothermal conditions modulated the responses of peak vegetation growth to SM and VPD. In regions with annual precipitation less than 650 mm and an annual mean temperature below 10 °C, decreased SM and increased VPD generally inhibited peak vegetation growth. Conversely, in warm and humid areas, lower SM and higher VPD promoted peak vegetation growth. These findings are crucial for deepening our understanding of vegetation phenology and its future responses to climate change.

Geospatial monitoring and analysis of agricultural drought to identify hotspots and risk assessment for Senegal

Agricultural drought, characterized by insufficient soil moisture crucial for crop growth, poses significant challenges to food security and economic sustainability, particularly in water-scarce regions like Senegal. This study addresses this issue by developing a comprehensive geospatial monitoring system for agricultural drought using the Regional Hydrologic Extremes Assessment System (RHEAS). This system, with a high-resolution of 0.05°, effectively simulates daily soil moisture and generates the Soil Moisture Deficit Index (SMDI)-based agricultural drought monitoring. The SMDI derived from the RHEAS has effectively captured historical droughts in Senegal over the recent 30 years period from 1993 to 2022. The SMDI, also provides a comprehensive understanding of regional variations in drought severity (S), duration (D), and frequency (F), through S-D-F analysis to identify key drought hotspots across Senegal. Findings reveal a distinct north-south gradient in drought conditions, with the northern and central Senegal experiencing more frequent and severe droughts. The study highlights that Senegal experiences frequent short-duration droughts with high severity, resulting in extensive spatial impact. Additionally, increasing trends in drought severity and duration suggest evolving climate change effects. These findings emphasize the urgent need for sustainable interventions to mitigate drought impacts on agricultural productivity. Specifically, the study identifies recurrent and intense drought hotspots affecting yields of staple crops like maize and rice, as well as cash crops like peanuts. The developed high-resolution drought monitoring system for Senegal not only identifies hotspots but can also enables prioritizing sustainable approaches and adaptive strategies, ultimately sustaining agricultural productivity and resilience in Senegal's drought-prone regions.

Agricultural drought monitoring and early warning at the regional scale using a remote sensing-based combined index

Early detection of agricultural drought can alert farmers and authorities, enhancing the resilience of the food sector. A framework is proposed for developing a novel regional agricultural drought index (RegCDI) by combining remotely sensed vegetation health, soil moisture and crop water stress via a transparent Shannon’s entropy weighting method. The framework consists of the selection of suitable datasets based on their regional performance, the aggregation of selected drought indicators, the validation of the combined index against crop yield, and the testing of predictive capabilities. The creation and performance of RegCDI are demonstrated for the drought prone Indian state of Odisha. MODIS surface reflectance is selected for crop water stress and GLDAS-2 for assessing soil moisture deficits and vegetation health. Three selected indicators (SMCI, TCI, and SIWSI-1) are combined into RegCDI for Odisha. The performance of RegCDI is evaluated (a) against other popular drought indices and (b) by comparing with seasonal crop yields. RegCDI is used to identify drought hotspots based on drought severity, duration, and propensity over the study area. A reforecast evaluation of RegCDI (up to three months ahead) showed that the indicators based on soil moisture deficit and crop water stress could predict drought conditions up to two months ahead with no less than 80% accuracy. This demonstrated the potential of the RegCDI framework and its component indicators for early warning of drought in Odisha.

Global influence of soil texture on ecosystem water limitation

Low soil moisture and high vapour pressure deficit (VPD) cause plant water stress and lead to a variety of drought responses, including a reduction in transpiration and photosynthesis1,2. When soils dry below critical soil moisture thresholds, ecosystems transition from energy to water limitation as stomata close to alleviate water stress3,4. However, the mechanisms behind these thresholds remain poorly defined at the ecosystem scale. Here, by analysing observations of critical soil moisture thresholds globally, we show the prominent role of soil texture in modulating the onset of ecosystem water limitation through the soil hydraulic conductivity curve, whose steepness increases with sand fraction. This clarifies how ecosystem sensitivity to VPD versus soil moisture is shaped by soil texture, with ecosystems in sandy soils being relatively more sensitive to soil drying, whereas ecosystems in clayey soils are relatively more sensitive to VPD. For the same reason, plants in sandy soils have limited potential to adjust to water limitations, which has an impact on how climate change affects terrestrial ecosystems. In summary, although vegetation–atmosphere exchanges are driven by atmospheric conditions and mediated by plant adjustments, their fate is ultimately dependent on the soil.

Direct and lagged climate change effects intensified the 2022 European drought

In 2022, Europe faced an extensive summer drought with severe socioeconomic consequences. Quantifying the influence of human-induced climate change on such an extreme event can help prepare for future droughts. Here, by combining observations and climate model outputs with hydrological and land-surface simulations, we show that Central and Southern Europe experienced the highest observed total water storage deficit since satellite observations began in 2002, probably representing the highest and most widespread soil moisture deficit in the past six decades. While precipitation deficits primarily drove the soil moisture drought, human-induced global warming contributed to over 30% of the drought intensity and its spatial extent via enhanced evaporation. We identify that 14–41% of the climate change contribution was mediated by the warming-driven drying of the soil that occurred before the hydrological year of 2022, indicating the importance of considering lagged climate change effects to avoid underestimating associated risks. Human-induced climate change had qualitatively similar effects on the extremely low observed river discharges. These results highlight that global warming effects on droughts are already underway, widespread and long lasting, and that drought risk may escalate with further human-induced warming in the future.

Desertification of Bengal Dryland Areas Possible under Projected Climate Conditions

Drylands are some of the most sensitive areas to climate change and human activities around the globe. Assessment of future climate trend scenarios provides valuable practical information for dryland management decision-making. According to Huang et al. (2017), more than 50% of global drylands will expand by this century, with a maximum (78%) of newly expanded dryland occurring in developing countries. To understand the potential for expansion of drylands and desertification, we examine critical predictor variables (temperature and precipitation) of Bengal dryland expansion to guide early actions to mitigate and prevent desertification. Using trend analysis of bias-corrected CMIP6 projected climate change data for temperature and precipitation (2022-2041), results indicate future dryland expansion is possible from increases in temperature and declines in monsoonal precipitation. Over the next two decades (2022-2041), Bengal dryland areas will be 0.1-0.5°C warmer, and rainfall will decrease by 2.57-13.43cm total during the monsoon period. Given these variables are critical predictors of dryland expansion because of their role in driving evapotranspiration and soil moisture deficits; we anticipate an increase in the population affected by water scarcity, land degradation, and desertification. Our work provides information critical for effective dryland management, biodiversity conservation, and land-use planning under future climate conditions.

Increasing Optimum Temperature of Vegetation Activity Over the Past Four Decades

AbstractOver the past four decades, global temperatures have increased more rapidly than before, potentially reducing vegetation activity if temperatures exceed the optimum temperature (Topt). However, plants have the capacity to acclimate to rising temperatures by adjusting Topt, thereby maintaining or even enhancing photosynthesis and carbon uptake. Despite this, it remains unclear how Topt of vegetation activity changes over time and to what extent global vegetation can acclimate to current temperature changes. In this study, we evaluated the temporal trends of Topt of vegetation activity and the thermal acclimation magnitudes globally using three remote‐sensed vegetation indices and eddy‐covariance observations of gross primary productivity from 1982 to 2020. We found that the global Topt of vegetation activity has increased at an average rate of 0.63°C per decade over the past four decades. The increase in Topt closely tracked the rise in annual maximum daily mean temperature (Tmax), indicating that thermal acclimation has occurred widely across the globe. Globally, we found an average thermal acclimation magnitude of 0.38°C per 1°C increase in Tmax. Notably, polar and continental regions exhibited the highest thermal acclimation magnitudes, while arid areas showed the lowest. Additionally, the thermal acclimation magnitude was positively affected by interannual temperature variability and negatively affected by soil moisture and vapor pressure deficits. Our findings indicate that terrestrial ecosystems have acclimated to current climate warming trends with varying degrees, suggesting a greater potential for land carbon uptake. Moreover, these results highlight the necessity for earth system models to integrate the thermal acclimation of Topt to better forecast the global carbon cycle.

Effect of potassium application on maize to sandy soil under deficit irrigation conditions

Maize is widely growth in arid and semi-arid region where, drought is common and a limiting factor for crop production. Potassium plays a key role in enhancing plant growth under drought condition. The objective of this study is to determine the effect of K fertilization with and without NP on maize growth grown in sandy loam soil under adequate and deficit irrigation conditions. The following treatments were investigated in pot experiment: (1) control with no fertilizer application (C); (2) 128 kg N + 328 kg P2O5 ha-1 (NPK0); (3) 128 kg N + 328 kg P2O5 ha-1 + 152.5 kg K2O ha1 (NPK1); (4) 128 kg N + 328 kg P2O5 ha-1 + 305 kg K2O ha-1 (NPK2); and 128 kg N + 328 kg P2O5 ha-1 + 457.5 kg K2O ha-1 (NPK3). Treatments were investigated under adequate and deficit soil moisture content. Each pot filled with 3.5 kg air-dry soil and seeded with maize and pots were watered according to the treatments. The results indicated that plant growth and nutrient uptake were significantly reduced under water stress condition. The application of NP increased plant growth and nutrient uptake and further were increased with K application. K application also enhanced plant tolerance to deficit soil moisture condition. In addition, K enhanced nutrient uptake and leaf chlorophyll content. Based on the results, it can be concluded that application of NP for maize was not adequate to achieve the highest plant growth, unless it is combined with K application. In addition, K application enhances plant tolerance to water stress.

Impacts of changing weather patterns on the dynamics of water pollutants in agricultural catchments: Insights from 11-year high temporal resolution data analysis

Widespread and long-term shifts in weather patterns are contributing to further degradation of surface water quality. This challenge caused by the increasing frequency of extreme weather events requires appropriate adaptation of current mitigation strategies. But to confirm the need to redesign such strategies, an understanding of the impacts of increasing weather extremes on pollutant losses in different catchment types is required. With this in view, this study investigated the impact of changing weather patterns on the inter-seasonal and inter-annual dynamics of nutrient losses in six agricultural catchments in Ireland over 11 years. The high temporal resolution data (10-min) from these intensively managed catchments represented different characteristics and management practices. Mann-Kendall Trend Analysis and Generalised Additive Models were used to study nutrient concentration trends, and to investigate the significance of water discharge, precipitation, potential evapotranspiration, soil moisture deficit, air temperature, and soil temperature on the losses of nutrients, respectively. The analysis of historical data revealed changes in the trends of daily average nitrate (NO3-N), phosphorus (P), and suspended sediment (SS) concentrations in association with significant increasing trends in air temperature, soil temperature, and precipitation across the same month over 11 years of monitoring. While discharge was significantly contributing to the concentrations of NO3-N, P, and SS across different catchments, air and soil temperature were significantly correlated to NO3-N losses, and precipitation was the major contributor to regulating P (total P and total reactive P) concentrations. In short, air temperature, soil temperature, soil moisture deficit, and precipitation were the main climatic drivers regulating the nutrient concentrations while the soil chemistry and drainage status were the non-climatically related drivers. The results revealed that the extent of the impact of climatic drivers depends on catchment characteristics. Therefore, expanding the application of this type of study would facilitate better understanding of current and future challenges to water management and provision of climate-resilient mitigation strategies for different catchment typologies.