Associate Professor In-Young Yeo

It has been over ten years since the successful launch of the first-ever dedicated satellite for global soil moisture monitoring; Soil Moisture and Ocean Salinity (SMOS). Looking towards the future, P-band (0.3¿1 GHz) is a promising technique to replace or enhance the L-band (1.4 GHz) SMOS and SMAP (Soil Moisture Active Passive) missions because of an expected reduction in roughness and vegetation impact, leading to an improved soil moisture accuracy over rougher soil surfaces and more densely vegetated areas. Accordingly, this investigation evaluated the tau-omega model at P-band (0.75 GHz) using a tower-based experiment in Victoria, Australia, where brightness temperature observations were collected concurrently at P- and L-band over bare and wheat-covered flat and periodic soil surfaces. The potential to retrieve soil moisture without discriminating periodic and flat surfaces was investigated by applying the roughness and vegetation parameters calibrated for flat soil to retrieve the moisture of periodic soil. Results showed that P-band had a comparable RMSE across different roughness configurations (variations less than 0.016 m3/m3) for both bare and wheat-covered soil, while the L-band RMSE was only comparable for wheat-covered soil, indicating that periodic surfaces did not need to be discriminated in such scenarios. Conversely, a difference of 0.022 m3/m3 was observed for L-band with bare soil. A reduced vegetation impact was also demonstrated at P-band, with an RMSE of 0.029 m3/m3 achieved when completely ignoring the wheat existence with under 4-kg/m2 vegetation water content, whereas at L-band the RMSE increased to 0.063 m3/m3. This study therefore paves the way for a successful P-band radiometer mission for obtaining more accurate global soil moisture information.

L-band passive microwave remote sensing is currently considered a robust technique for global monitoring of soil moisture. However, soil roughness complicates the relationship between brightness temperature and soil moisture, with current soil moisture retrieval algorithms typically assuming a constant roughness parameter globally, leading to a potential degradation in retrieval accuracy. This current investigation established a tower-based experiment site in Victoria, Australia. P-band (~40-cm wavelength/0.75 GHz) was compared with L-band (~21-cm wavelength/1.41 GHz) over random and periodic soil surfaces to determine if there is an improvement in brightness temperature simulation and soil moisture retrieval accuracy for bare soil conditions, due to reduced roughness impact when using a longer wavelength. The results showed that P-band was less impacted by random and periodic roughness than L-band, evidenced by more comparable statistics across different roughness conditions. The roughness effect from smooth surfaces (e.g., 0.8-cm root-mean-square height and 11.1-cm correlation length) could be potentially ignored at both P- and L-band with satisfactory simulation and retrieval performance. However, for rougher soil (e.g., 1.6-cm root-mean-square height and 6.8-cm correlation length), the roughness impact needed to be accounted for at both P- and L-band, with P-band observations showing less impact than L-band. Moreover, a sinusoidal soil surface with 10-cm amplitude and 80-cm period substantially impacted the brightness temperature simulation and soil moisture retrieval at both P- and L-band, which could not be fully accounted for using the SMOS and SMAP default roughness parameters. However, when retrieving roughness parameters along with soil moisture, the ubRMSE at P-band over periodic soil was improved to a similar level (0.01-0.02 m3/m3) as that of smooth flat soil (0.01 m3/m3), while L-band showed higher ubRMSE over the periodic soil (0.03-0.04 m3/m3) than over smooth flat soil (0.01 m3/m3). Accordingly, periodic roughness effects were reduced by using observations at P-band.

In this study, we assessed the impacts of uncertainties arising from hydrologic model parameters and climate change data on streamflow and catchment-level non-floodplain wetland (NFW) water storage predictions for the Coastal Plain of the Chesapeake Bay watershed. The hydrologic model used in this study was the Soil and Water Assessment Tool (SWAT) coupled with improved wetland modules. Model uncertainty was represented using 12 parameter sets (PARs) with acceptable model performance. Different projections of future climate conditions (eight global circulation models [GCMs] under three representative concentration pathways [RCPs]) were used to represent climate change uncertainty. The ensemble method and analysis of variance were adopted for uncertainty assessment. The results showed that monthly streamflow projections did not substantially differ with respect to individual PARs, GCMs, and RCPs; by contrast, the projected NFW water storage varied significantly. However, the changes in projected hydrological values relative to historic conditions greatly differed with regard to the PARs, GCMs, and RCPs, leading to a high uncertainty in assessing climate change impacts. The variability of GCM projections was the most significant single contributor, accounting for 46% and 49% of the total streamflow and NFW water storage projection uncertainties, respectively, followed by PARs and RCPs. Our work assessed the impacts of different uncertainty sources on NFW hydrology under climate change, suggesting careful consideration of model and climate change uncertainties for the reliable projections of NFW hydrologic behaviors.

Cyclone global navigation satellite system (CYGNSS) has provided a valuable opportunity for high spatiotemporal monitoring of land surface reflectivity over the past few years. CYGNSS with a constellation of eight microsatellites is able to constantly observe the 'scattered' global positioning system signals from the land. In this study, we validate the CYGNSS land reflectivity data in Australia for mapping the spatial extent of the inundated area and for determining temporal changes in surface soil moisture. CYGNSS level 1 data acquired for the period of 2017-2020 is assessed against various measurements, including satellite and ground-based measurements. Empirical mode decomposition is used to better analyze the CYGNSS time series and their relationship with the independent measurements. Furthermore, the mission's ability to capture surface reflectivity changes in response to extreme climatic events is analyzed. The results show that high spatial and temporal resolution CYGNSS data can largely represent the top layer ($\sim$5 cm) soil moisture spatial and temporal variations close to soil moisture active passive. CYGNSS surface reflectivity results are also found to be sensitive to surface water changes and able to depict inundated land surface.

Following extreme drought during the 2019¿2020 bushfire summer, the eastern part of Australia suffered from a week-long intense rainfall and extensive flooding in March 2021. Understanding how much water storage changes in response to these climate extremes is critical for developing timely water management strategies. To quantify prompt water storage changes associated with the 2021 March flooding, we processed the low-latency (1¿3¿days), high-precision intersatellite laser ranging measurements from GRACE Follow-On spacecraft and determined instantaneous gravity changes along spacecraft orbital passes. Such new data processing detected an abrupt surge of water storage approaching 60¿70 trillion liters (km3 of water) over a week in the region, which concurrently caused land subsidence of ~5¿mm measured by a network of ground GPS stations. This was the highest speed of ground water recharge ever recorded in the region over the last two decades. Compared to the condition in February 2020, the amount of recharged water was similar but the recharge speed was much faster in March 2021. While these two events together replenished the region up to ~80% of the maximum storage over the last two decades, the wet antecedent condition of soils in 2021 was distinctly different from the dry conditions in 2020 and led to generating extensive runoff and flooding in 2021.

The overall size and timing of monsoon floods in Bangladesh are challenging to measure. The inundated area is extensive in low-lying Bangladesh, and observations of water storage are key to understanding floods. Laser-ranging instruments on Gravity Recovery and Climate Experiment (GRACE) Follow-On spacecraft detected the peak water storage anomaly of 75 gigatons across Bangladesh in late July 2020. This is in addition to, and three times larger than, the maximum storage anomaly in soil layers during the same period. A flood propagation model suggested that the water mass, as shown in satellite observations, is largely influenced by slow floodplain and groundwater flow processes. Independent global positioning system measurements confirmed the timing and total volume of the flood water estimates. According to land surface models, the soils were saturated a month earlier than the timing of the peak floodplain storage observed by GRACE Follow-On. The cyclone Amphan replenished soils with rainfall just before the monsoon rains started, and consequently, excessive runoff was produced and led to the early onset of the 2020 flooding. This study demonstrated how antecedent soil moisture conditions can influence the magnitude and duration of flooding. Continuous monitoring of storage change from GRACE Follow-On gravity measurements provides important information complementary to river gauges and well levels for enhancing hydrologic flood forecasting models and assisting surface water management.

Growing agricultural water demand is dramatically affecting the implementation of, and compliance with, water sharing plans in regions such as the Murray-Darling Basin (MDB). Problems can arise from water theft, poor resourcing or questionable actions from stakeholders. Recent actions from MDB governments have resulted in improved regulation, although more is required in a technical, governance and cultural space to create a comprehensive and transparent management framework. This is pivotal in improving overall trust in water regulators. We discuss an integrated water resource management approach for improved water regulation, involving the implementation of remote sensing technologies to complement metering, coupled with a focus on a stronger compliance culture in a range of stakeholder groups and regulatory changes that allow quicker adoption of unbiased best practice science and technology.

The moisture retrieval depth is commonly held to be the approximately top 5 cm at L-band (21-cm wavelength/1.41 GHz), which is seen as a limitation for hydrological applications. A widely held view is that this moisture retrieval depth increases with wavelength, ranging approximately from one-tenth to one-fourth of the wavelength. Accordingly, P-band (40-cm wavelength/0.75 GHz) is under investigation for soil moisture observation over a deeper layer of soil. However, there is no accepted method for predicting the moisture retrieval depth, and there has been no study to confirm that the actual retrieval depth at P-band is indeed deeper than that achieved at L-band. Consequently, this research has estimated the moisture retrieval depth from theory and compared with empirical evidence from tower-based observations. Model predictions and experimental observations agreed that P-band has the potential to retrieve soil moisture over a deeper layer (7 cm) than L-band (5 cm) while maintaining the same correlation. However, an alternate interpretation of experimental results is that P-band has a larger correlation with soil moisture (accuracy of retrieval) than L-band but for the same 5-cm moisture retrieval depth. The results also demonstrated the increasing trend of the moisture retrieval depth for increasing wavelength, with the potential to achieving a moisture retrieval depth greater than 10 cm for P-band below 0.5 GHz. Importantly, model predictions showed that moisture retrieval depth was not only dependent on soil moisture content and observation frequency, but also the moisture gradient of the profile.

Process based, distributed watershed models possess a large number of parameters that are not directly measured in field and need to be calibrated, in most cases through matching modeled in-stream fluxes with monitored data. Recently, concern has been raised regarding the reliability of this common calibration practice, because models that are deemed to be adequately calibrated based on commonly used metrics (e.g., Nash Sutcliffe efficiency) may not realistically represent intra-watershed responses or fluxes. Such shortcomings stem from the use of an evaluation criteria that only concerns the global in-stream responses of the model without investigating intra-watershed responses. In this study, we introduce a modification to the Soil and Water Assessment Tool (SWAT) model, and a new calibration technique that collectively reduce the chance of misrepresenting intra-watershed responses. The SWAT model was modified to better represent NO3 cycling in soils with various degrees of water holding capacity. The new calibration tool has the capacity to calibrate paired watersheds simultaneously within a single framework. It was found that when both proposed methodologies were applied jointly to two paired watersheds on the Delmarva Peninsula, the performance of the models as judged based on conventional metrics suffered, however, the intra-watershed responses (e.g., mass of NO3 lost to denitrification) in the two models automatically converged to realistic sums. This approach also demonstrates the capacity to spatially distinguish areas of high denitrification potential, an ability that has implications for improved management of prior converted wetlands under crop production and for identifying prominent areas for wetland restoration.

This study examines forest change processes, within the framework of forest transition theory (FTT), using Mississippi (USA) as a case study. The aim is to evaluate the assumption and theoretical basis of FTT with quantitative data, and to propose changes in forest management policy as a potential driver for reforestation. We compiled a number of historical records, geospatial data, and time series forest mapping products to reconstruct the last 100 years of forest trajectory. Forest changes are studied in relation to changes in society, over a range of temporal and spatial scales. Details of forest dynamics (e.g., the rate and extent of forest gain and loss) were quantified, while considering the ecological properties of the secondary forests. Mississippi forests are intensively managed and fragmented secondary forests, while regenerated entirely through plantation. The quantified forest transition (FT) curve indicated that forest dynamics have been nonlinear and have involved multiple reversals, resulting in multiple periods of forest expansion. The spatial and temporal analysis with time series remote sensing data over the last 30 years reveals that Mississippi forests have experienced very frequent changes and disturbance, even during the period of forest expansion. These change patterns are not apparent when considering total forest area estimates. The result illustrates that the " forest scarcity pathway" of FTT (Rudel et al., Global Environmental Change Part A 15(1) (2005) 23-31) worked to reverse the deforestation trend for the initial FT period. However, regenerated forests have experienced another episode of FT and expansion, and this cannot be explained by the forest scarcity pathway. Rather, the case of Mississippi suggests an alternative pathway (Mather, International Forestry Review 9(1) (2007) 491-502; Lambin and Meyfroidt, Land Use Policy 27 (2010) 108-118), distinct from the previous work, and highlights the importance of changes in policy incentives to account for forest recovery. The conceptual basis of FTT proposed by Mather (Area 24(4) (1992) 367-379) and Grainger (Area 27(3) (1995) 242-251) is revisited, showing how two alternative views are complementary, providing explanation for the repeated patterns of FT. This study presents empirical evidence to understand the theoretical basis and assumptions of FTT and suggests a new path for FT, " forest management policy pathway" © 2012 Elsevier Ltd.

We investigated the predictive strength of forested wetland maps produced using digital elevation models (DEMs) derived from Light Detection and Ranging (LiDAR) data and multiple topographic metrics, including multiple topographic wetness indices (TWIs), a TWI enhanced to incorporate information on water outlets, normalized relief, and hybrid TWI/relief in the Coastal Plain of Maryland. LiDAR DEM based wetland maps were compared to maps of inundation and existing wetland maps. TWIs based on the most distributed FD8 (8 cells) and somewhat distributed D8 (1-2 cells) flow routing algorithms were better correlated with inundation than a TWI based on a non-distributed D8 (1 cell) flow routing algorithm, but D8 TWI class boundaries appeared artificial. The enhanced FD8 TWI provided good prediction of wetland location but could not predict periodicity of inundation. Normalized relief provided good prediction of inundation periodicity but was less able to map wetland boundaries. A hybrid of these metrics provided good measurement of wetland location and inundation periodicity. Wetland maps based on topographic metrics included areas of flooded forest that were similar to an aerial photography based wetland map. These results indicate that LiDAR based topographic metrics have potential to improve accuracy and automation of wetland mapping. © 2012 US Government.

Understanding forest changes and its trajectory is important to develop policy options and future scenarios for climate analysis. This research is conducted to gain insights on secondary forests change using Mississippi, USA, as a case study. We investigate the spatial patterns and temporal dynamics of secondary forests at high resolution and examine the forces driving their changes. An extensive literature review is conducted to refine the conceptual framework of forest changes and identify the underlying key factors. Forest changes are quantified at high spatial (30-m) and temporal (biennial) resolutions, using time series remotely sensed data between 1984 and 2007. A number of geospatial and socioeconomic data were compiled to analyze the spatial variations of forest disturbances and their linkages to various socioeconomic, political, and biogeophysical factors. The results show that the secondary forests are highly dynamic and variable. Disturbances and regeneration occur continuously everywhere in a systematic and coordinated fashion. This pattern prevents an extensive disturbance and increases total forest cover. Market conditions (i.e., timber price) are the key predictor of the level and overall trend of forest disturbances. However, spatial patterns of forest dynamics cannot be explained by location-specific biophysical, socioeconomic, and policy factors identified in the literature. They can best be described by the ecological characteristics of the forests (i.e., the forest type and age distribution), which have a clear economic linkage. The research shows that regenerated forests frequently experience loss and gain of their extent, and their ecological characteristics change drastically on a short-term basis. These results point out challenges and opportunities in forest management and policy with regard to reforestation. © 2012 Springer-Verlag.

To effectively and systematically analyze this complex spatial watershed system, a hierarchical optimization approach is proposed. A top-down land allocation scheme is applied to determine an optimal land allocation, and then to identify spatial locations of protected lands at a high level of spatial resolution (30 m cell). At the top level, the stormwater runoff simulation model is used to generate peak discharge pseudo data, that are input to a regression analysis, where the functional relationship between peak discharge and land-use variables is approximated as a quadratic function (R 2=0.98%). This function is then used in an optimization model to allocate future land uses (urban, conservation, and agriculture at the subwatershed/catchement level), in a way that minimizes the resulting peak discharge at the watershed outlet. Then, an integrated hydrological-land use optimization (IHLUO) model scheme is developed to (1) investigate the NFS pollution generation/transport mechanisms and spatial variability/interdependencies of land use change impacts on watershed hydrology, (2) systematically evaluate different land-use patterns and their response to rainfall, and (3) search for the optimal land-use pattern at the cell level. The results are very promising, with a 44% reduction of the peak discharge rate as compared to the rate corresponding to the current land-use pattern. The most downstream and upstream areas are to be protected with more conservation, and most urban activities are allocated to the minimal impact subwatershed. Sensitivity analyses are performed and suggest maintaining at least 30% of the land in a conservation state, developing no more than 12% for urban purposes, and no more than 70% for agricultural purposes. The proposed methodology is applied to the Old Woman Creek watershed, located in the southwestern basin of Lake Erie (Ohio).