With an increased interest in precision agriculture, it is important to identify efficient ways to monitor soil moisture. Soil moisture can be monitored using handheld sensors, but this method is laborious and time consuming. Remote methods, such as radar systems can be used as well, but these methods require ground truth data to verify their accuracy. It becomes clear that to collect this data regularly and reliably, a mobile robotic device is necessary. This thesis proposes to implement mobile robot take soil moisture measurements with less human effort than existing methods while maintaining the same accuracy. This soil moisture data collection system uses an unmanned ground vehicle (UGV) to take measurements with position data. This system uses an actuator inserted soil moisture probe, and a radio frequency identification (RFID) sensing system that uses buried moisture sensing tags. Field testing of both measurement systems showed that the actuator-based system worked reliably.

Suboptimal soil moisture during the growing season often limits growth and yield potential of corn. This study aimed to assess the impact of varying soil moisture regimes on corn growth at different growth phases involving vegetative, flowering, and grain-filling stages. Exposure to moisture stress (80% of the control) during the vegetative stage resulted in a 65% reduction in stomatal conductance and increased the canopy temperature by 5 oC, which led to a substantial decrease in total dry matter (56%). Moisture stress-induced reductions in silk length (19%) and fresh weight (34%), negatively affected kernel number (53%), and weight (54%). Unlike the flowering stage, extreme stress during grain-filling had a greater impact on kernel weight (19%) than the number (7%). During flowering, stress reduced kernel starch content with an increase in protein content. Our findings infer that improving the resilience of the corn flowering stage to soil moisture stress may help reduce the yield gap between irrigated and rainfed.

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An intrinsic link exists between soil moisture and soil nitrogen. Factors that increase or decrease soil moisture can have a profound effect on soil nitrogen cycling, which may have later repercussions in the plant community. Post-fire soil water repellency is one factor that can limit soil moisture acquisition and may indirectly affect nitrogen cycling and weed invasion in woody islands of fertility. Plots centered on burned Juniperus osteosperma trees were either left untreated or treated with a surfactant to ameliorate water repellency. Two years later, soils were excavated from the untreated and treated field plots. In the greenhouse, half of each soil type received a surfactant treatment while the other half was left untreated. Pots were seeded with either Bromus tectorum or Pseudoroegneria spicata. Analysis of field soil prior to the greenhouse trial showed that untreated, repellent soils had inorganic nitrogen levels an order of magnitude higher than wettable, surfactant-treated soils. Greenhouse pots that had received a surfactant treatment in the field and/or greenhouse had similar soil water content, plant density, and above ground biomass, which were, respectively, 55-101%, 31 to 34 -fold, and 16 to 18 -fold greater than pots without a surfactant treatment. No species effects were found. This study indicates that water repellency can reduce wetting and retention of water in the soil while promoting the retention of high levels of inorganic nitrogen. However, the effects of soil water repellency on inorganic nitrogen appeared to have a minimal effect on plant growth compared to the effect of soil water repellency on water availability.

The expected increase in precipitation and temperature in Scandinavia, and especially short-time heavy precipitation, will increase the frequency of flooding. Urban areas are the most vulnerable, and specifically, the road infrastructure. The accumulation of large volumes of water and sediments on road-stream intersections gets severe consequences for the road drainage structures. The need for a tool to identify characteristics that impacts the occurrence of flooding, and to predict future event is thus essential. This study integrates the spatial and temporal soil moisture properties into the research about flood prediction methods. Soil moisture data is derived from remote sensing techniques, with focus on the soil moisture specific satellites ASCAT and SMOS. Furthermore, several physical catchments descriptors (PCDs) are used to identify catchment characteristics that are prone to flooding and an inventory of current road drainage facilities are conducted. Finally, the index of sediment connectivity (IC) by Cavalli, Trevisani, Comiti, and Marchi (2013) is implemented to assess the flow of water and sediment within the catchment. A case study of two areas in Sweden, Västra Götaland and Värmland, that was affected by severe flooding in August 2014 are included. The results show that the method with using soil moisture satellite data is promising for the inclusion of soil moisture data into estimations of flooding and the index of sediment connectivity. / De förväntade ökningarna i nederbörd och temperatur i Skandinavien, och speciellt extrem korttidsnederbörd, kommer att öka frekvensen av översvämningar. Urbana områden är de mest sårbara, och speciellt väginfrastrukturen. Ackumuleringen av stora volymer av vatten och sediment där väg och vattendrag möts leder till allvarliga konsekvenser för dräneringskonstruktionerna. Behovet av ett verktyg för att identifiera egenskaper som påverkar förekomsten av översvämningar, och för att förutsäga framtida händelser är väsentligt. Studien integrerar markfuktighet både rumsligt och tidsmässigt i forskningen om metoder för översvämningsrisker. Markfuktighetsdata är inkluderat från fjärranalysteknik, med fokus på de specifika satelliterna för markfuktighet, ASCAT och SMOS. Vidare är flertalet faktorer (PCDs) inkluderade för att identifiera egenskaper i avrinningsområden som är benägna till översvämning samt en inventering av nuvarande vägdräneringskonstruktioner. Slutligen är ett index med sediment connectivity (Cavalli et al., 2013) implementerat för att se flödet av vatten och sediment inom avrinningsområdet. En fallstudie med två områden i Sverige, Västra Götaland och Värmland, som drabbades av allvarliga översvämningar i augusti 2014 är inkluderat. Resultaten visar att metoden att använda markfuktighet från satellitdata är lovande för inkludering i uppskattningar av översvämningsrisk och i indexet med sediment connectivity.

Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing-based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction.

Montane meadows provide essential habitat for a variety of unique species and important ecosystem services in the western United States. Although important, meadows have experienced increased rates of conifer encroachment due to climate change, fire suppression and grazing. To combat meadow degradation from conifer encroachment, land managers have employed various restoration strategies one of which is conifer removal. Multiple studies have investigated the relationship between meadow hydrology and vegetation; however, few have assessed the effect of conifer removal on meadow groundwater. The goal of this study is to determine if the removal of conifers from an encroached meadow has an effect on depth to the groundwater table (WTD) and soil moisture content (SMC), and to investigate the accuracy and potential usefulness of evapotranspiration (ET) calculation methodologies for montane meadows. This goal will be accomplished by the subsequent objectives: 1) perform an analysis of WTD and SMC in an encroached meadow preceding and following conifer removal and upland thinning; 2) calculate and compare daily ET estimates in a previously restored meadow using diurnal groundwater table fluctuation, diurnal groundwater fluctuation modelling, and SMC. Miranda Cabin Meadow (MC) is located within the Upper American River Watershed, southeast of French Meadows Reservoir, at an elevation of 6,200 feet. MC received conifer removal, upland thinning and road decommissioning in the fall of 2018 as part of the American River Conservancy’s American River Headwaters Restoration Project. This study found the average WTD in MC during the growing season decreased from 4.91 feet prior to restoration, to 3.39 feet after restoration. In addition, the number of days the WTD was within 0.98 feet and 3 feet increased from 12 days and 34 days, to 31 and 49 days. Analysis of SMC in MC was limited due to gaps in data, however this study found that after restoration the average weekly SMC decreased at a slower rate than prior to restoration, possibly indicating decreased hydrologic output from ET. Based upon WTD during the growing season and the limited SMC data it appears that removal of conifers and upland thinning at MM promotes SMC and WTD conditions conducive to meadow vegetation communities. Marian Meadow (MM), located in Plumas County, CA at an elevation of 4,900 feet, received conifer removal as part of a timber harvest plan carried out by Collins Pine Company in July 2015. The soil moisture sensors used in this study were installed in MM in September 2013 for previous graduate thesis research. Groundwater table data was collected using 10-foot wells installed in July of 2018. Daily ET was calculated during August 2018 using three methodologies, and during September 2018 using two methodologies. Daily ET estimates calculated using diurnal groundwater table fluctuation and the White method averaged 11.8 mm per day in August and 9.1 mm in September. Using diurnal groundwater table fluctuation modelling this study calculated an average daily ET of 4.2 mm in August and 3 mm in September. Daily ET estimates based on SMC were calculated for August 2018 using two methods which produced estimates of 0.9 mm and 1.2 mm per day. All three methods for calculating ET produced some daily estimates that compare well to previous research of Et in Sierra Nevada meadows, however the White method generally overestimated daily Et while SMC methods underestimated ET. Groundwater table fluctuation modelling produced the best estimates of daily ET for both August and September. ET results in this study support previous research on the applicability of the White method; and they also suggest that the applicability of groundwater fluctuation modelling to estimate meadow daily ET in Sierra Nevada montane meadows be investigated further.

Water is a limited and highly valued resource in the semi-arid Great Basin. Surface water sources are often small and widely spaced apart, comprising only 1-3% of the surface area of the overall landscape. Despite their small size, these springs and surrounding wet meadows have a substantial effect on the surrounding environment. Springs provide drinking water, forage and cover for livestock and wildlife, habitat for diversity of plant species and a resource for human-related activities. In recent years, many of these springs have become dewatered due to diversions of groundwater for municipal water and agriculture, and climatic shifts in precipitation affecting recharge. These hydrologic changes can cause a drop in the local water table that promotes a shift in the plant community from wetland-obligates to species that have more drought-tolerance. The root masses of the new plant community are insufficient to secure soils resulting in the erosion of the thalweg. This leads to channelization through the wet meadow, which drives the water table further underground. As degradation progresses, springs and wet meadows lose their ability to store water. The purpose of this thesis is to examine the responses of both the hydrologic and biologic factors to different springbox restoration techniques. Twenty-four spring sites were chosen in the Sheldon National Wildlife Refuge in northwestern Nevada. Each site was randomly assigned one of six different treatment designs. Variables for these studies included: surface soil moisture, soil moisture at varying depths, flow rates, water chemistry, plant community cover and frequency, biomass, wildlife visits and wildlife species numbers. We observed soil moisture increase over the majority of our sites, while flow rates only increased at the control sites. This may indicate that more water is being held in the soils around the spring source instead of being allowed to flow downstream. Biomass increased in four of our six treatments. All treatment types exhibited a similar effect on springs with none having a clearly more restorative effective than any others. This research suggests that springs in the Great Basin have unique characteristics and responses to restoration, and may need individualized approaches. Additionally, studies have shown that it may take many years for plant communities to recover after hydrologic restoration. Yearly variation caused by increased precipitation may be partially responsible for changes in hydrologic and biologic aspects of springs and wet meadows. Further data collection is needed to determine the true extent of treatment and yearly effects on spring restoration. In spite of the need for individualized approaches, restoration is possible. Simple solutions may be sufficient to recover hydrologic processes that maintain ecologic resilience.

Ecological aspects of desert vegetation in relation to a moisture gradient of an ephemeral stream in central Arizona were investigated. The stream channel, flood plain and north, west, south, east facing slopes represent a moisture gradient going from most mesic to most xeric conditions. In parts of the system, vegetation from the stream channel intergraded into flood plain vegetation which in turn intergraded into slope vegetation types. In other areas there are sharp delineations between stream channel and flood plain, and between flood plain and slope. Trees and legumes preferred medium moisture habitats, while forbs, shrubs and succulents preferred the dryer moisture areas. Family groups like the Compositeae and the Gramineae were found to be distributed ubiquitously. Niche widths were broadest for flood plain species. Diversity was highest on the slopes. Negative correlations between diversity and the Synthetic Stand Moisture Index existed, i.e. as moisture increased diversity decreased. It was postulated that factors other than moisture, i.e. disturbance also strongly influenced diversity.

Soil moisture is an important variable in many environmental researches and application areas as it affects the interactions between atmosphere and land surface by controlling the energy and water exchange. The current measurement techniques are insufficient to acquire accurate large-scale root zone soil moisture (RZSM) data at the spatial resolution of interest. Though assorted models have been successfully applied in relatively small areas to estimate RZSM, the large-scale estimation is still facing challenges as it requires the flexibility and practicality of the models for the applications under various conditions. Though physically based soil moisture models are widely used, the errors in model physics affect the flexibility of these models meanwhile their large demand of data and computational resources reduces the practicality. On the contrary, the statistical and data-driven methods have high potential but their applications for large-scale RZSM estimation have not been fully explored. To develop feasible models for large-scale RZSM estimation using the surface observations, artificial neural networks, specifically multilayer perceptrons (MLPs), were applied in this study to estimate RZSM at the depths of 20cm and 50cm, using the data of 557 stations in the United States. Two experiments including four models were developed and the input variables of the models were carefully selected. The sensitivity analysis found that surface soil moisture and the cumulative rainfall, snowfall, air temperature and surface soil temperature were important inputs. If given soil texture data as inputs, the models achieved better performance and were extremely sensitive to them. The results showed that the MLPs were effective and flexible for the estimation of soil moisture at 20cm under various climate types and were insensitive to the potential errors in soil moisture datasets. However, the results of the estimation at 50cm are not as good as that of the 20cm. / Thesis / Master of Science (MSc)