Soil Moisture Potential Research Articles

Quantitation of Rainfall Retention Capacity for Small Reservoirs Considering Spatial Soil Moisture

To realize the estimation of rainfall retention capacity for small reservoirs considering spatial soil moisture, a rainfall retention capacity model and its parameter schemes have been developed in this study. An iterative trial solution method considering potential rainfall and soil moisture for the model constructed was proposed for efficient computation. The rainfall retention capacity of 32 pilot small reservoirs located in ungauged basins of Hunan province was calculated starting from 21 August 2023. In addition, a continuous calculation was carried out from 1 August to 30 September 2023 using the proposed method for Heping reservoir. The results show that the Pearson’s correlation coefficients between rainfall retention capacity and available reservoir capacity and soil moisture, are 0.36 and −0.64, respectively. Using Heping reservoir as an example, this study effectively characterized the dynamic change in its rainfall retention capacity, which ranged from 123.6 mm to 68 mm in August 2023. The analysis indicates the rainfall retention capacity of the pilot small reservoirs calculated is reasonably related to the soil moisture, supporting risk visualization for small reservoirs within rain-affected regions. Furthermore, the impact of the antecedent precipitation on rainfall retention capacity can also be dynamically quantified in real time, which provides reference for the continuous management of small reservoirs.

Species-Specific Root Distribution and Leaf Iso/Anisohydric Tendencies Shape Transpiration Patterns Across Heterogeneous Karst Habitats.

The driving forces of transpiration are not only atmospheric evaporation but also root zone water supply and stomatal regulation among species. However, the biophysiological drivers of transpiration remain incompletely understood in heterogeneous karst habitats. This study investigated the commonly coexisting tree species Mallotus philippensis and Celtis biondii in two typical karst habitats: rock-dominated (RD) habitat and control soil-dominated (SD) habitat. Over 2 years, soil moisture, transpiration, root distribution, and leaf water potential were measured. The results showed that soil moisture in the RD habitat was significantly lower than in the SD habitat. Transpiration patterns also differed between habitats, with species-specific distinctions driven by biophysiological traits. M. philippensis showed small hydroscape areas and its root system mainly distributed in the soil zone in both habitats. The isohydric behaviour and lower root density in the RD habitat drove M. philippensis to reduce transpiration in response to soil water deficiency. Conversely, C. biondii had large hydroscape areas and roots capable of penetrating bedrock. It transpired higher relying on ample accessible water through anisohydric behaviour and having a more robust root system both in soil and bedrock zones in the RD habitat. Our study highlights the critical role of root water accessibility and leaf iso/anisohydric tendencies in driving transpiration.

Revisiting evapotranspiration inputs in eco-hydrological modeling for climate change assessment

Evapotranspiration (ET) is an essential variable linking hydrological and ecological processes and is typically modeled as a function of potential evapotranspiration and soil moisture in traditional hydrological models. However, commonly used empirical ET models typically do not recognize the underlying vegetation dynamics. This can have implications when models are extrapolated under future climate change. In this study, the traditional HYMOD-BVM (THV) eco-hydrological model is adopted as a benchmark model. A modified HYMOD-BVM (MHV) is developed using an actual ET substitute for the traditional PET input to consider vegetation dynamics under climate change. The THV and MHV models are compared to evaluate how streamflow (Q) and leaf area index (LAI) vary under future climate in the Florentine River (FR, energy-limited) catchment and Murray River (MR, water-limited) catchment in Australia. Six global climate models (GCMs) from the latest Coupled Model Intercomparison Project (CMIP6) are bias-corrected and employed in the calibrated THV and MHV models, and ensemble mean results are analyzed. Results suggest that the streamflow projected by the MHV model tends to be higher than that from the THV model, while LAI presents an opposite trend. The energy-limited catchment appears more susceptible to climate change whereas larger vegetation effects are seen in the water-limited catchment. Overall, our research highlights the effects of evapotranspiration on future climate change scenario analysis and prompts the need for the development of spatial and temporal continuously accurate ET models for both current and future climates.

Seasonal Variations in Hydraulic Regulation of Whole-Tree Transpiration in Mongolian Pine Plantations: Insights from Semiarid Deserts in Northern China

Seasonal precipitation variance significantly alters soil water content, potentially inducing water stress and affecting plant transpiration in semiarid deserts. This study explored the effects of environmental variables and hydraulic conductance on whole-tree transpiration (ET) in Mongolian pines (Pinus sylvestris var. mongolica) across different forest stages in the semiarid deserts of Northern China. We measured ET using sap flow in mature (MMP), half-mature (HMP), and young (YMP) Mongolian pine plantations. Measurements included soil-leaf water potential difference (ΔΨ), atmospheric conditions, and soil moisture contents on sunny days, both in dry and wet periods. Seasonally variable rainfall distinctly affected soil moisture; during the dry periods, both stomatal and hydraulic conductance influenced ET, whereas stomatal conductance primarily regulated it during the wet periods. Discrepancies between predicted and measured ET were noticed: compared to the predicted ET, the measured ET was lower during dry periods while higher during wet periods. Hydraulic conductance (KT) increased with tree height (H) and ΔΨ. The KT values in the dry period were lower than those in the wet period, indicating that the hydraulic resistance in the dry period was higher. The hydraulic compensation occurred and was observed between 11:00 and 13:00, aligned with increased hydraulic resistance during dry periods. Decreasing hydraulic conductance intensified leaf water stress in dry periods, especially when photosynthetically active radiation (PAR) and vapor pressure deficit (VPD) were heightened, potentially increasing stomatal sensitivity to drought, promoting water conservation and plant survival. A linear relationship between predawn and midday leaf water potentials was noticed, indicating extreme anisohydric behavior across forest stages during dry and wet periods. Although stomatal and hydraulic conductance influenced ET during the dry period, MMP and YMP were more susceptible to drought conditions. Understanding these dynamics could help evaluate semiarid desert ecological functions for water conservation amidst uneven seasonal precipitation in Northern China.

The influence of plant water stress on vegetation–atmosphere exchanges: implications for ozone modelling

Abstract. Evapotranspiration is important for Earth's water and energy cycles as it strongly affects air temperature, cloud cover, and precipitation. Leaf stomata are the conduit of transpiration, and their opening is sensitive to weather and climate conditions. This feedback can exacerbate heat waves and can play a role in their spatiotemporal propagation. Sustained high temperatures strongly favour high ozone levels, with significant negative impacts on air quality and thus on human health. Our study evaluates the process representation of evapotranspiration in the atmospheric chemistry–climate European Centre for Medium-Range Weather Forecasts – Hamburg(ECHAM)/Modular Earth Submodel System (MESSy) Atmospheric Chemistry model. Different water stress parameterizations are implemented in a stomatal model based on CO2 assimilation. The stress factors depend on either soil moisture or leaf water potential, which act on photosynthetic activity, and mesophyll and stomatal conductance. The new functionalities reduce the initial overestimation of evapotranspiration in the model globally by more than an order of magnitude, which is most important in the Southern Hemisphere. The intensity of simulated warm spells over continents is significantly improved. For ozone, we find that a realistic model representation of plant water stress suppresses uptake by vegetation and enhances photochemical production in the troposphere. These effects lead to an overall increase in simulated ground-level ozone, which is most pronounced in the Southern Hemisphere over the continents. More sophisticated land surface models with multi-layer soil schemes could address the uncertainties in representing plant dynamics representation due to too-shallow roots. In regions with low evaporative loss, the representation of precipitation remains the largest uncertainty.

Anthropogenic shrub encroachment has accelerated the degradation of desert steppe soil over the past four decades

Anthropogenic and natural shrub encroachment have similar ecological consequences on native grassland ecosystems. In fact, there is an accelerating trend toward anthropogenic shrub encroachment, as opposed to the century-long process of natural shrub encroachment. However, the soil quality during the transition of anthropogenic shrub encroachment into grasslands remains insufficiently understood. Here, we used a soil quality assessment method that utilized three datasets and two scoring methods to evaluate changes in soil quality during the anthropogenic transition from temperate desert grassland to shrubland. Our findings demonstrated that the soil quality index decreased with increasing shrub cover, from 0.49 in the desert grassland to 0.31 in the shrubland. Our final results revealed a gradual and significant decline of 36.73 % in soil quality during the transition from desert grassland to shrubland. Reduced soil moisture levels, nutrient availability, and microbial activity characterized this decline. Nearly four decades of anthropogenic shrub encroachment have exacerbated soil drought conditions while leading to a decrease in perennial herbaceous plants and an increase in bare ground cover; these factors can explain the observed decline in soil quality. These findings emphasize the importance of considering soil moisture availability and potential thresholds when implementing revegetation strategies in arid and semiarid regions.

Trends, turning points, and driving forces of desertification in global arid land based on the segmental trend method and SHAP model

ABSTRACT Desertification is a form of land degradation observed in arid, semiarid, and dry subhumid ecosystems. Assessing the global trends and drivers of desertification in arid land is crucial for developing effective land restoration policies and mitigating desertification. We aimed to evaluate global segmental trends in desertification in arid land using Moderate Resolution Imaging Spectroradiometer images from 2000 to 2022. By constructing a robust MSAVI-Albedo Desertification Distance Index (DDI), we assessed the segmented development characteristics of desertification on Google Earth Engine. Additionally, we employed the SHapley Additive exPlanations (SHAP) model and machine learning methods to analyze the individual and interactive driving mechanisms of desertification. The results indicated an overall reduction in desertification, with approximately 23.21 million km2 (39% of the global arid region area) exhibiting negative trends in DDI. Approximately 31% of the land area showed a DDI of −0.002/y. Precipitation was consistently the primary factor influencing desertification, with an average SHAP value of 11.42. Secondary influencing factors included potential evapotranspiration, soil moisture, and vapor pressure differences. Notably, the coupling between precipitation and soil moisture exhibited the most significant impact on the desertification process, with SHAP coupling values of approximately 3.28 and 5.06 before and after the DDI turning point, respectively. These findings provide new insights into desertification on a global scale and offer valuable scientific support for promoting effective prevention and control measures.

Estimation of water inflows to reservoirs of irrigation dams in the Guinea Savannah ecological zone of Ghana

ABSTRACT The study was carried out in the northern Ghana to estimate water inflows to nine reservoirs of irrigation dams for a period of 20 years (1999–2018) using the USDA Natural Resources Conservation Service Curve Number model. The key input parameters of the model were hydrologic soil group, rainfall amount, landuse/landcover, weighted curve number, antecedent moisture condition, and potential maximum soil moisture retention. The annual rainfall within the reservoir catchments was found to vary between 617.20 and 1,382.30 mm. The estimated annual runoff depths ranged from 54.10 to 125.55 mm. A high degree of positive correlation was found between rainfall and runoff depths with a coefficient of determination (R2) ranging from 79.80 to 90.70%. Hydrologic soil groups accounted for about 67.2 and 62.5% of the variation in runoff depth and percentage runoff, respectively. The estimated annual water inflows to the reservoirs ranged from 145,206 ± 125,814 m3/y at Gambibgo to 47,074,634 ± 4,395,860 m3/y at Tono. The variation in the annual water inflows and annual storage capacity recharge among the various reservoirs was noted to be influenced by reservoir size, catchment size, rainfall, and soil characteristics in the reservoir catchments.

Evaluation of Retrospective National Water Model Soil Moisture and Streamflow for Drought‐Monitoring Applications

AbstractThe National Oceanic and Atmospheric Administration (NOAA)’s National Water Model (NWM) provides analyses and predictions of hydrologic variables relevant to drought monitoring and forecasts at fine time and space scales (hourly, 0.25–1 km). We present results exploring the potential for NWM soil moisture and streamflow analyses to inform operational drought monitoring. Both agricultural and hydrologic drought monitoring rely either explicitly or implicitly on an accurate representation of anomalous soil moisture values. Much of our analysis focuses on comparisons of soil moisture anomalies in the NWM to those from in‐situ observations. To establish benchmarks for NWM soil moisture skill, we also include other gridded data sets currently used to inform the US Drought Monitor, specifically those from the North American Land Data Assimilation System phase 2 (NLDAS‐2) land surface models. We then compare NWM streamflow low flows with ∼500 stream gauges from the United States Geological Survey (USGS) Hydro‐Climatic Data Network of undisturbed basins. The NWM soil moisture simulation’s skill parallels that from NLDAS‐2. The accuracy of drought condition identification from NWM streamflow exceeds that based on soil moisture as determined by Critical Success Index scores for extreme dry percentiles. Different meteorological forcings are used in the operational NWM cycles than those used in this retrospective analysis. This forcing disconnect, together with concerns about current‐generation land surface model soil moisture‐transport schemes, inhibit its current operational use for drought monitoring.

The Impact of Satellite Soil Moisture Data Assimilation on the Hydrological Modeling of SWAT in a Highly Disturbed Catchment

The potential of satellite soil moisture (SM) in improving hydrological modeling has been addressed in synthetic experiments, but it is less explored in real data cases. Here, we investigate the added value of Soil Moisture and Passive (SMAP) and Advanced Scatterometer (ASCAT) SM data to distributed hydrological modeling with the soil and water assessment tool (SWAT) in a highly human disturbed catchment (126, 486 km2) featuring a network of SM and streamflow observations. The investigation is based on the ensemble Kalman filter (EnKF) considering SM errors from satellite data using the triple collocation. The assimilation of SMAP and ASCAT SM improved the surface (0–10 cm) and rootzone (10–30 cm) SM at >70% and > 50% stations of the basin, respectively. However, the assimilation effects on distributed streamflow simulation of the basin are un-significant and not robust. SM assimilation improved the simulated streamflow at two upstream stations, while it deteriorated the streamflow at the remaining stations. This can be largely attributed to the poor vertical soil water coupling of SWAT, suboptimal model parameters, satellite SM data quality, humid climate, and human disturbance to rainfall-runoff processes. This study offers strong evidence of integrating satellite SM into hydrological modeling in improving SM estimation and provides implications for achieving the added value of remotely sensed SM in streamflow improvement.

Concept and importance of soil moisture potentials in agriculture

The root design of crops in soils has a direct impact on yield potentials, which are controlled by soil moisture potential and its balancing components, because crops only benefit from transpiration sharing. The soil moisture potential, which governs the movement of soil moisture based on differential energy states, is a significant factor. In general, the kinetic energy of a soil moisture flow rate is too tiny to be taken into account. Due to its location in a force field that may help to increase water use efficiency, the corresponding potential energy of soil moisture is what defines its energy state. Significant irrigation water loss happens during flood irrigation through evaporation, seepage or drainage. On the other hand, tensiometer-based soil moisture potential delineation contributes significantly to irrigation water savings. Therefore, having greater yield potentials as well as improved lives in water-stressed regions of the world requires a proper understanding of soil moisture potentials, its components, and their relationship with soil water flow for reducing water footprints particularly of water stressed regions.

Soil Moisture Content Sensor: Utilizing Bacterial Cells as Living Sensors

Soil moisture affects water and heat exchange between soil and air, weather and climate, and plant growth. Therefore, controlling and predicting soil moisture is vital for irrigation, nutrient supply, absorption, and crop productivity. Soil microbial fuel cell (SMFC) is a recent technology that generates electricity from soil microorganisms and chemicals. SMFC can be used to sense soil moisture and treat polluted soil. In this study, we designed a low-cost, portable, and easy-to-install SMFC for soil moisture sensing, and it was tested at four moisture levels: 40%, 60%, 80%, and 100% soil water holding capacity (SWHC). The SMFC worked best at 60 - 80% SWHC, which is suitable moisture for many plants. The results suggest that the proposed SMFC can be a potential soil moisture sensor.

Effect of Plastic Membrane and Geotextile Cloth Mulching on Soil Moisture and Spring Maize Growth in the Loess–Hilly Region of Yan’an, China

In order to study the effect of a plastic membrane and geotextile cloth mulching on soil moisture and crop growth in the loess–hilly region, a one-year continuous field monitoring experiment was carried out in Ansai District, City of Yan’an, Shaanxi Province, China. The experimentation included three treatments: plastic membrane and geotextile cloth mulching on the ridge (MB), geotextile cloth mulching on the ridge (DB), and bare soil ridge (CK). Soil moisture and water potential sensors were installed to monitor the changes in soil moisture content and water potential at 5, 15, and 30 cm below the furrow surface and meteorological data above the soil surface, and the growth traits, yield, and quality of maize were analyzed. The results showed the following: (1) The soil water-storage capacity of the three treatments dropped to a minimum in the filling stage and gradually recovered in the mature stage. The average water-storage capacity for the MB treatment was 35.5% higher than that for the DB treatment and 85.1% higher than that for the CK treatment, significant throughout the whole growth period. (2) For four types of rainfall events, namely, light, medium, heavy, and storm rainfall, significant responses were observed at 5 cm below the ground for three treatments, and the fastest response was in MB due to its best rain-collection effect. A significant response was also observed at 15 and 30 cm below the surface of the furrow during medium, heavy, and storm rainfall, while no significant difference in response time was found between the three treatments due to the restriction of the soil infiltration capacity. (3) The differences between the three treatments in the agronomic traits of maize, except for plant height and stem thickness, were insignificant (p < 0.05). The seed moisture content and yield for the MB treatment were the highest, with values of 40.33% and 8366 kg/hm2, respectively, followed closely by the DB treatment, with values of 38.61% and 7780 kg/hm2, respectively, and the smallest values were observed in the CK treatment, with values of 35.80% and 6897 kg/hm2, respectively. Compared with those for the CK treatment, the average starch content and the average lipid content for the mulching treatments (MB, DB) decreased by 13.40% and 17.11%, respectively, while the average protein content of maize increased by 7.86%. Overall, a plastic membrane and geotextile cloth mulching could significantly increase soil moisture and spring maize yield due to their better rain-collection effect.

Effects of Agricultural Cropping Systems on Soil Water Capacity: The Case in Cross-Border Altai

Temperate grasslands are called the breadbaskets of the world. Due to most continental climate conditions, humus-rich soils have been developed. These soils are very well suited for grain production. This is why extensive conversions from natural steppe to arable land have been implemented in this biome. The Kulunda Steppe, in Southwest Siberia and Central Asia, occupies large parts of the driest regions of the Eurasian Steppe Belt. It was one of the sites of the Virgin Land Campaign realized in the former Soviet Union in the 1950s and 1960s. Intensive agricultural practices have caused significant soil degradation, mainly through humus loss and soil erosion. This results in the degradation of organic carbon, altering the physical and chemical structure of the chestnut soils and impacting their water storage capacity. Against the background of climatic changes, a further intensification of these processes and conditions is to be expected. To stabilize soil carbon and optimize moisture utilization, it is necessary to extensively introduce worldwide experiences in conservation cropping technologies (such as no-till, min-till, and direct seeding) in the area. This study aimed to determine the effects of different cropping systems on soil water storage and water availability. The study’s initial hypothesis was that the soil conservational cropping system has advantages against the traditional deep tillage (24 cm). This hypothesis was based on extensive global experience studying the effects of different agricultural management systems on soil-water balance. In 2013–2016, an experiment was conducted for the first time in the Kulunda steppe to instrumentally measure soil moisture and matrix potential at 30–60–120 cm depth under traditional and conservation technology using innovative meteorological and soil hydrological stations. Statistically significant advantages of no-till over deep tillage (24 cm) in terms of moisture retention were found, confirming the hypothesis of this study. Besides, this groundbreaking study reveals new possibilities for soil monitoring in the region. The acquired data are applicable for predictive models using remote sensing. Moreover, the results on the management effects for the soil water balance provide basic approaches to soil water monitoring, offering important data for evaluating model results and remote sensing products for the region.

Data worth analysis within a model-free data assimilation framework for soil moisture flow

Abstract. Conventional data worth (DW) analysis for soil water problems depends on physical dynamic models. The widespread occurrence of model structural errors and the strong nonlinearity of soil water flow may lead to biased or wrong worth assessment. By introducing the nonparametric data worth analysis (NP-DWA) framework coupled with the ensemble Kalman filter (EnKF), this real-world case study attempts to assess the worth of potential soil moisture observations regarding the reconstruction of fully data-driven soil water flow models prior to data gathering. The DW of real-time soil moisture observations after Gaussian process training and Kalman update was quantified with three representative information metrics, including the trace, Shannon entropy difference and relative entropy. The sequential NP-DWA framework was examined by a number of cases in terms of the variable of interest, spatial location, observation error, and prior data content. Our results indicated that, similarly to the traditional DW analysis based on physical models, the overall increasing trend of the DW from the sequential augmentation of additional observations within the NP-DWA framework was also susceptible to interruptions by localized surges due to never-experienced atmospheric conditions (i.e., rainfall events). The difference is that this biased DW in the former is caused by model structural errors triggered by contrasting scenarios, which is difficult to be compensated for by assimilating more prior data, while this performance degradation in the NP-DWA can be effectively alleviated by enriching training scenarios or the appropriate amplification of observational noise under extreme meteorological conditions. Nevertheless, a substantial expansion of the prior data content may cause an unexpected increase in the DW of future potential observations due to the possible introduction of ensuing observation noises. Hence, high-quality and representative small data may be a better choice than unfiltered big data. Compared with the observations in the surface layer with the strongest time variability, the soil water content in the middle layer robustly exhibited remarkable superiority in the construction of model-free soil moisture models. We also demonstrated that the DW assessment performance was jointly determined by 3C, i.e., the capacity of potential observation realizations to capture actual observations, the correlation of potential observations with the variables of interest and the choice of DW indicators. Direct mapping from regular meteorological data to soil water content within the NP-DWA mitigated the adverse effects of nonlinearity-related interference, which thus facilitated the identification of the soil moisture covariance matrix, especially the cross-covariance.

Assessment of projected climate change impact on agro‐climatic indicators in Poland

AbstractThis study presents a long‐term assessment of the impact of projected climate change on selected agro‐climatic indicators in Poland. The analysis was based on an ensemble of six bias‐corrected EURO‐CORDEX projections covering the period from 2006 to 2100 under Representative Concentration Pathways: RCP4.5 and 8.5, and partly on simulations of an agro‐hydrological Soil and Water Assessment Tool model. The indicators included growing season (length, beginning, and ending date), growing degree days, frost days, accumulated frost, and planting and harvest timing. The study focused on four major crops cultivated in Poland, namely spring barley, rapeseed, maize silage, and potato. The analysis revealed a significant future increase in precipitation and mean temperature in Poland reaching +10.3% and 3.4°C by the end of century, respectively. Warmer conditions will significantly accelerate the beginning of the growing season (by up to 24 days) and delay its ending (by up to 18 days), consequently significantly extending its length throughout the country (by up to 42 days). A decrease is projected in potential soil moisture deficit by up to 26 mm by the end of century in the high‐end emission scenario. A significant advancement (by up to 30 days) in planting timing is projected for the future for all crops under consideration. A significant increase in growing degree days (by up to 760°C‐days) will also advance the harvest timing (by up to 54 days). The projected temperature increase over the winter season will result in a significant decrease in frost days (by 56 days) and accumulated frost (by 375°C‐days), which may lead to an increased presence of pests. This study can provide a scientific basis for assessing the future risks to the local agriculture from climate change and will be beneficial in planning adaption and mitigation actions for food security at the national and local scale.

Development of a Solar-Powered Integrated Wireless Soil Moisture Meter

In this study, we developed a solar-powered integrated wireless soil moisture meter that can easily measure in situ soil moisture, soil temperature, and hydrogen potential (pH) using nature's solar energy. Knowledge of soil moisture content and other relevant soil-specific parameters is essential for irrigation scheduling, fertilizer selection, and fertigation. Also, considering that the electricity supply in some developing countries is either erratic or unavailable, this research aims to bridge the gap in electricity availability and ease of measurement and integrate more soil-specific parameters. The sensor system was developed using the frequency domain (FD) technique for fast response. These parameters were measured sequentially at an interval of about 5 seconds, with the readings displayed simultaneously on a Bluetooth-connected device (e.g., an Android phone) located about 50 meters away from the developed system. The different sensors are classified and adequately labeled to identify the parameter to be measured. The performance evaluation carried out indicated a reasonably functioning device that is cost-effective. The results obtained showed that the system was resourceful as it not only measured the parameters of interest (soil moisture, temperature, and pH) but also gave a prompt response in measurement and transmission. Overall, the developed wireless soil moisture meter provides instantaneous data on pH, moisture, and temperature circulation across soil layers. The system is promising as it can be integrated into large-scale automated irrigation systems for agricultural lands.

Improving Field Moisture Monitoring of Recycled and Virgin Aggregates

The structural capacity of pavement foundation is altered by moisture conditions. Reliable moisture monitoring of geomaterials throughout pavement service life is critical for asset management by transportation agencies. Improved moisture sensing enables transportation officials and practitioners to better understand performance of complex recycled materials under frequent extreme rain events. Although moisture sensing for geomaterials has improved significantly, there are still challenges when using sensors in recycled materials that may contain unhydrated cement, aged asphalt, or both. Challenges include the development of calibration functions that account for the presence of recycled materials and robust installation procedures, as technology was developed mostly for agricultural practices. In this study, a series of experiments were conducted to suggest improvements for installation techniques and data interpretation of soil moisture and water potential sensors. Suggested installation guidelines minimize wash-out and erosion potential at the soil–sensor interface. Experimental results indicate a strong bilinear relation between dielectric of recycled materials and water content, with a region of relatively small change governed by dielectric permittivity of air and a region of rapid change controlled by dielectric permittivity of water. Moreover, it was found that dielectric permittivity is not significantly affected by aggregate internal structure as dielectric for a specific moisture content for different compaction degrees is relatively similar. Furthermore, soil water characteristic curves obtained using the water potential sensor and improved installation technique compare reasonably well with laboratory results obtained with traditional equipment. Reliability of both moisture and water potential sensors was improved with the suggested guidelines.

Multi-Objective Optimization of an Intelligent Soil-Moisture-Monitoring Satellite Constellation

In early satellite mission design, requirements are not yet fixed, cost is sometimes negotiable, and designs are relatively unconstrained. During this period of design freedom, multi-objective optimization can provide a useful lens into the design space by showing theoretical performance limits and illuminating design tradeoffs. This work optimizes a radar constellation for a potential soil moisture mission. Several different optimization cases with different variables are considered and contrasted. The optimization of the instrument and constellation parameters is considered jointly and separately to better understand the effect of coupling on the optimization performance. A science-driven optimization based on soil moisture retrieval error is compared with a performance-metric-driven optimization. Pareto analysis and association rule mining are performed on the generated designs to provide insight into driving features. Design recommendations are made for several cost caps. Results show that optimization that considers the instrument and constellation design together find superior revisit metrics than treating instrument and constellation separately. The use of the science value metric as an optimization objective shows that while cost may always be increased to improve instrument and constellation performance, the difference in science value may be negligible. These findings can inform tradespace exploration studies for similar problems.

Spatiotemporal trends of NDVI and its response to climate variability in the Abbay River Basin, Ethiopia

Woody vegetation plays a vital role in regulating the water budget and energy exchange in the Earth's system. This study aimed at analyzing the spatiotemporal variability of Normalized Difference Vegetation Index (NDVI) and its response to Potential Evapotranspiration (PET), rainfall (RF), soil moisture (SM), and temperature (TEM) in the study area. The trends, correlations, and relationships between NDVI and climate variables were executed using Mann-Kendall monotonic trend (MKMT), partial correlation coefficients (PCC), and multiple linear regression (MLR) methods, respectively. Over the last 26 years, the interannual NDVI increased by 0.0065 yr−1 (R2 = 0.159, p = 0.157). The spatiotemporal MKMT and Theil-Sen slope analysis showed that interannual NDVI increased significantly in 78% of the basin's total area. Of the 78% of the basin, 31%, and 47%, of the total area showed extremely significant increasing (Zmk = 4.706, p ≤ 0.01), and significant increasing trends (Zmk = 2.378, p ≤ 0.05) respectively. The interannual variation of NDVI was well explained (R2 = 0.88, Adjusted R2 = 0.84) by the climate variables in the eastern, southeastern, and central sub-basins where agriculture, grass, sparse vegetation and barelands are the predominant land use land cover (LULC) classes. The main climatic factors that control vegetation growth and greenness during the rainy season were found to be PET, SM, and RF with 0.91, 0.99, and 0.86 PCC with NDVI respectively. The current study broadens the scientific community's understanding of the relationship between climate variables and vegetation growth in highland ecosystems. Understanding the seasonal and long-term relationship between climate and NDVI contributes to the scientific knowledge of highland ecosystems, which are extremely vulnerable to climate change.