SOIL MOISTURE PREDICTION USING SUPPORT VECTOR MACHINES1

ABSTRACT: Herein, a recently developed methodology, Support Vector Machines (SVMs), is presented and applied to the challenge of soil moisture prediction. Support Vector Machines are derived from statistical learning theory and can be used to predict a quantity forward in time based on training that uses past data, hence providing a statistically sound approach to solving inverse problems. The principal strength of SVMs lies in the fact that they employ Structural Risk Minimization (SRM) instead of Empirical Risk Minimization (ERM). The SVMs formulate a quadratic optimization problem that ensures a global optimum, which makes them superior to traditional learning algorithms such as Artificial Neural Networks (ANNs). The resulting model is sparse and not characterized by the “curse of dimensionality.” Soil moisture distribution and variation is helpful in predicting and understanding various hydrologic processes, including weather changes, energy and moisture fluxes, drought, irrigation scheduling, and rainfall/runoff generation. Soil moisture and meteorological data are used to generate SVM predictions for four and seven days ahead. Predictions show good agreement with actual soil moisture measurements. Results from the SVM modeling are compared with predictions obtained from ANN models and show that SVM models performed better for soil moisture forecasting than ANN models.     

Soil moisture‐based drought monitoring at different time scales: a case study for the U.S. Great Plains

Short‐term agricultural drought and longer term hydrological drought have important ecological and socioeconomic impacts. Soil moisture monitoring networks have potential to assist in the quantification of drought conditions because soil moisture changes are mostly due to precipitation and evapotranspiration, the two dominant water balance components in most areas. In this study, the Palmer approach to calculating a drought index was combined with a soil water content‐based moisture anomaly calculation. A drought lag time parameter was introduced to quantify the time between the start of a moisture anomaly and the onset of drought. The methodology was applied to four shortgrass prairie sites along a North‐South transect in the U.S. Great Plains with an 18‐year soil moisture record. Short time lags led to high periodicity of the resulting drought index, appropriate for assessing short‐term drought conditions at the field scale (agricultural drought). Conversely, long time lags led to low periodicity of the drought index, being more indicative of long‐term drought conditions at the watershed or basin scale (hydrological drought). The influence of daily, weekly, and monthly time steps on the drought index was examined and found to be marginal. The drought index calculated with a short drought lag time showed evidence of being normally distributed. A longer data record is needed to assess the statistical distribution of the drought index for longer drought lag times.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow‐Dominated Watersheds?

Changing climate and growing water demand are increasing the need for robust streamflow forecasts. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service have relied on precipitation and snow water equivalent observations from Snow Telemetry (SNOTEL) sites. We investigate whether also including SNOTEL soil moisture observations improve April‐July streamflow volume forecast accuracy at 0, 1, 2, and 3‐month lead times at 12 watersheds in Utah and California. We found statistically significant improvement in 0 and 3‐month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2‐month lead times. Surprisingly, these improvements were insensitive to soil moisture metrics derived from soil physical properties. Forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. By including VWC at the 0‐month lead time the forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% on average compared to standard forecasts that already explained an average 77% of the variability. At 1 to 3‐month lead times, the inclusion of soil moisture explained 12.3‐26.3% more variability than the standard forecast on average. Our findings indicate including soil moisture observations increased statistical streamflow forecast accuracy and thus, could potentially improve water supply reliability in regions affected by changing snowpacks.     

IMPROVED RAINFALL/RUNOFF ESTIMATES USING REMOTELY SENSED SOIL MOISTURE1

ABSTRACT: Remotely sensed soil moisture data measured during the Southern Great Plains 1997 (SGP97) experiment in Oklahoma were used to characterize antecedent soil moisture conditions for the Soil Conservation Service (SCS) curve number method. The precipitation‐adjusted curve number and the soil moisture were strongly related (r²= 0.70). Remotely sensed soil moisture fields were used to adjust the curve numbers and the runoff estimates for five watersheds, in the Little Washita watershed; the results ranged from 2.8 km² to 601.6 km². The soil moisture data were applied at two spatial scales, a finer one (800 m) measuring spatial resolution and a coarser one (28 km). The root mean square error (RMSE) and the mean absolute error (MAE) of the runoff estimated by the standard SCS method was reduced by nearly 50 percent when the 800 m soil moisture data were used to adjust the curve number. The coarser scale soil moisture data also significantly reduced the error in the runoff predictions with 41 percent and 28 percent reductions in MAE and RMSE, respectively. The results suggest that remote sensing of soil moisture, when combined with the SCS method, can improve rainfall runoff predictions at a range of spatial scales.     

ASSESSMENT OF SAMPLING ERROR ASSOCIATED WITH SOIL MOISTURE ESTIMATION DESIGNS1

A spectral formalism was developed and applied to quantify the sampling errors due to spatial and/or temporal gaps in soil moisture measurements. A design filter was developed to compute the sampling errors for discrete measurements in space and time. This filter has as its advantage a general form applicable to various types of sampling design. The lack of temporal measurements of the two‐dimensional soil moisture field made it difficult to compute the spectra directly from observed records. Therefore, the wave number frequency spectra of soil moisture data derived from stochastic models of rainfall and soil moisture were used. Parameters for both models were estimated using data from the Southern Great Plains Hydrology Experiment (SGP97) and the Oklahoma Mesonet. The estimated sampling error of the spatial average soil moisture measurement by airborne L‐band microwave remote sensing during the SGP97 hydrology experiment is estimated to be 2.4 percent. Under the same climate conditions and soil properties as the SGP97 experiment, equally spaced ground probe networks at intervals of 25 and 50 km are expected to have about 16 percent and 27 percent sampling error, respectively. Satellite designs with temporal gaps of two and three days are expected to have about 6 percent and 9 percent sampling errors, respectively.     

A Multi‐Scale Soil Moisture Monitoring Strategy for California: Design and Validation

A multi‐scale soil moisture monitoring strategy for California was designed to inform water resource management. The proposed workflow classifies soil moisture response units (SMRUs) using publicly available datasets that represent soil, vegetation, climate, and hydrology variables, which control soil water storage. The SMRUs were classified, using principal component analysis and unsupervised K‐means clustering within a geographic information system, and validated, using summary statistics derived from measured soil moisture time series. Validation stations, located in the Sierra Nevada, include transect of sites that cross the rain‐to‐snow transition and a cluster of sites located at similar elevations in a snow‐dominated watershed. The SMRUs capture unique responses to varying climate conditions characterized by statistical measures of central tendency, dispersion, and extremes. A topographic position index and landform classification is the final step in the workflow to guide the optimal placement of soil moisture sensors at the local‐scale. The proposed workflow is highly flexible and can be implemented over a range of spatial scales and input datasets can be customized. Our approach captures a range of soil moisture responses to climate across California and can be used to design and optimize soil moisture monitoring strategies to support runoff forecasts for water supply management or to assess landscape conditions for forest and rangeland management.     

SOIL MOISTURE VARIATION PATTERNS OBSERVED IN HAND COUNTY,SOUTH DAKOTA1

ABSTRACT: Sail moisture data were taken during nine sampling events (1976-1978) at a test site in South Dakota as part of the ground truth used in NASA's aircraft experiments studying the microwave sensing of soil moisture. This portion of the study dealt only with the spatial variability observed with regard to the ground data. Samples were taken over three surface depths at each point, and the data reported as the mean field moisture content within each of three surface horizons. The results shed additional light on the relationship between ground sampling and remote sensing of soil moisture. First, it was found that it is best to partition data of well drained sites from poorly drained areas when attempting to characterize the surface moisture content throughout an area of varying soil and cover conditions. It was also found that the moisture coefficient of variation within a field decreased as the mean field soil moisture increased, and that the standard deviation was at a maximum in the mid-range of observed moisture conditions (15-25 percent). Within field sample variation also decreases as the sample is integrated over a greater surface depth. It was determined that a sampling intensity of 10 samples per kilometer was adequate to characterize the mean field soil moisture at all three depths along a transect in the areas of moderate to good drainage-.     

Modeling the Spatially Varying Water Balance Processes in a Semiarid Mountainous Watershed of Idaho1

Stratton, Benjamin T., Venakataramana Sridhar, Molly M. Gribb, James P. McNamara, and Balaji Narasimhan, 2009. Modeling the Spatially Varying Water Balance Processes in a Semiarid Mountainous Watershed of Idaho. Journal of the American Water Resources Association (JAWRA) 45(6):1390‐1408. Abstract: The distributed Soil Water Assessment Tool (SWAT) hydrologic model was applied to a research watershed, the Dry Creek Experimental Watershed, near Boise Idaho to investigate its water balance components both temporally and spatially. Calibrating and validating SWAT is necessary to enable our understanding of the water balance components in this semiarid watershed. Daily streamflow data from four streamflow gages were used for calibration and validation of the model. Monthly estimates of streamflow during the calibration phase by SWAT produced satisfactory results with a Nash Sutcliffe coefficient of model efficiency 0.79. Since it is a continuous simulation model, as opposed to an event‐based model, it demonstrated the limited ability in capturing both streamflow and soil moisture for selected rain‐on‐snow (ROS) events during the validation period between 2005 and 2007. Especially, soil moisture was generally underestimated compared with observations from two monitoring pits. However, our implementation of SWAT showed that seasonal and annual water balance partitioning of precipitation into evapotranspiration, streamflow, soil moisture, and drainage was not only possible but closely followed the trends of a typical semiarid watershed in the intermountain west. This study highlights the necessity for better techniques to precisely identify and drive the model with commonly observed climatic inversion‐related snowmelt or ROS weather events. Estimation of key parameters pertaining to soil (e.g., available water content and saturated hydraulic conductivity), snow (e.g., lapse rates, melting), and vegetation (e.g., leaf area index and maximum canopy index) using additional field observations in the watershed is critical for better prediction.     

FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES1

ABSTRACT: Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one‐lead day to seven‐lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four‐lead day to seven‐lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool.     

ESTIMATION OF SOIL MOISTURE WITH API ALGORITHMS AND MICROWAVE EMISSION1

Large area soil moisture estimations are required to describe input to cloud prediction models, rainfall distribution models, and global crop yield models. Satellite mounted microwave sensor systems that as yet can only detect moisture at the surface have been suggested as a means of acquiring large area estimates. Relations previously discovered between microwave emission at the 1.55 cm wavelength and surface moisture as represented by an antecedent precipitation index were used to provide a pseudo infiltration estimation. Infiltration estimates based on surface wetness on a daily basis were then used to calculate the soil moisture in the surface 0–23 cm of the soil by use of a modified antecedent precipitation index. Reasonably good results were obtained (R²= 0.7162) when predicted soil moisture for the surface 23 cm was compared to measured moisture. Where the technique was modified to use only an estimate of surface moisture each three days an R² value of 0.7116 resulted for the same data set. Correlations between predicted and actual soil moisture fall off rapidly for repeat observations more than three days apart. The algorithms developed in this study may be used over relatively flat agricultural lands to provide improved estimates of soil moisture to a depth greater than the depth of penetration for the sensor.     

GROUND WATER/SURFACE WATER RESPONSES TO GLOBAL CLIMATE SIMULATIONS,SANTA CLARA‐CALLEGUAS BASIN,VENTURA, CALIFORNIA1

ABSTRACT: Climate variations can play an important, if not always crucial, role in successful conjunctive management of ground water and surface water resources. This will require accurate accounting of the links between variations in climate, recharge, and withdrawal from the resource systems, accurate projection or predictions of the climate variations, and accurate simulation of the responses of the resource systems. To assess linkages and predictability of climate influences on conjunctive management, global climate model (GCM) simulated precipitation rates were used to estimate inflows and outflows from a regional ground water model (RGWM) of the coastal aquifers of the Santa Clara‐Calleguas Basin at Ventura, California, for 1950 to 1993. Interannual to interdecadal time scales of the El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) climate variations are imparted to simulated precipitation variations in the Southern California area and are realistically imparted to the simulated ground water level variations through the climate‐driven recharge (and discharge) variations. For example, the simulated average ground water level response at a key observation well in the basin to ENSO variations of tropical Pacific sea surface temperatures is 1.2 m/°C, compared to 0.9 m/°C in observations. This close agreement shows that the GCM‐RGWM combination can translate global scale climate variations into realistic local ground water responses. Probability distributions of simulated ground water level excursions above a local water level threshold for potential seawater intrusion compare well to the corresponding distributions from observations and historical RGWM simulations, demonstrating the combination's potential usefulness for water management and planning. Thus the GCM‐RGWM combination could be used for planning purposes and — when the GCM forecast skills are adequate — for near term predictions.     

Spatial Dynamics of Soil Moisture in a Complex Terrain in the Semi‐Arid Loess Plateau Region,China1

Abstract: Soil moisture is an important hydrological variable in reforestation practices in a water‐limited region of the Loess Plateau of northwestern China. The objective of this study was to quantify the spatial dynamics of soil moisture on a complex terrain. During 2004‐2006, a total of 313 sample points in two kinds of grid (2 × 2 m and 20 × 20 m) were arranged for soil moisture measurements (two soil layers: 0‐30 and 30‐60 cm) with Time Domain Reflectometry. The geostatistical properties of soil moisture patterns, the variance and correlation structure of the soil moisture, and the effects of terrain factors on soil moisture were analyzed. The results suggested that our sampling grid captured the spatial variability of soil moisture distributions for this complex terrain. Principal Component Analysis and Cluster Analysis statistics showed that soil moisture decreased as slope gradient increased; that sunny aspects (112.5°‐292.5°) had relatively lower soil moisture than did shady aspects (292.5°‐112.5°); that soil moisture was lowest in the SWW direction and highest in the NWN direction; and that hillslope aspect was the main factor affecting soil moisture in the 0‐ to 30‐cm soil layer, whereas the main factor for the 30‐ to 60‐cm layer was slope gradient. It was found that the relative values of soil moisture for steep slopes (>36%) with shady aspect (292.5°‐112.5°), gentle slopes (<36%) with sunny aspect (112.5°‐292.5°), and steep slopes with sunny aspect were 99, 82, and 80, respectively – assuming a soil moisture value of 100 for gentle slopes with shady aspect. The results of this study are expected to be relevant to and useful for reforestation planning and design, parameterization of distributed hydrology models, and land productivity assessment in the study region.     

APPLICATION OF THE RHESSys MODEL TO A CALIFORNIA SEMIARID SHRUBLAND WATERSHED1

ABSTRACT: Distributed hydrologic models which link seasonal streamflow and soil moisture patterns with spatial patterns of vegetation are important tools for understanding the sensitivity of Mediterranean type ecosystems to future climate and land use change. RHESSys (Regional Hydro‐Ecologic Simulation System) is a coupled spatially distributed hydroecological model that is designed to be able to represent these feedbacks between hydrologic and vegetation carbon and nutrient cycling processes. However, RHESSys has not previously been applied to semiarid shrubland watersheds. In this study, the hydrologic submodel of RHESSys is evaluated by comparing model predictions of monthly and annual streamflow to stream gage data and by comparing RHESSys behavior to that of another hydrologic model of similar complexity, MIKESHE, for a 34 km² watershed near Santa Barbara, California. In model intercomparison, the differences in predictions of temporal patterns in streamflow, sensitivity of model predictions to calibration parameters and landscape representation, and differences in model estimates of soil moisture patterns are explored. Results from this study show that both models adequately predict seasonal patterns of streamflow response relative to observed data, but differ significantly in terms of estimates of soil moisture patterns and sensitivity of those patterns to the scale of landscape tessellation used to derive spatially distributed elements. This sensitivity has implications for implementing RHESSys as a tool to investigate interactions between hydrology and ecosystem processes.     

Comparison of Field Performance of Multiple Soil Moisture Sensors in a Semi‐Arid Rangeland1

Abstract:  Automated electronic soil moisture sensors, such as time domain reflectometry (TDR) and capacitance probes are being used extensively to monitor and measure soil moisture in a variety of scientific and land management applications. These sensors are often used for a wide range of soil moisture applications such as drought forage prediction or validation of large‐scale remote sensing instruments. The convergence of three different research projects facilitated the evaluation and comparison of three commercially available electronic soil moisture probes under field application conditions. The sensors are all installed in shallow soil profiles in a well instrumented small semi‐arid shrub covered subwatershed in Southeastern Arizona. The sensors use either a TDR or a capacitance technique; both of which indirectly measure the soil dielectric constant to determine the soil moisture content. Sensors are evaluated over a range of conditions during three seasons comparing responses to natural wetting and drying sequences and using water balance and infiltration simulation models. Each of the sensors responded to the majority of precipitation events; however, they varied greatly in response time and magnitude from each other. Measured profile soil moisture storage compared better to water balance estimates when soil moisture in deeper layers was accounted for in the calculations. No distinct or consistent trend was detected when comparing the responses from the sensors or the infiltration model to individual precipitation events. The results underscore the need to understand how the sensors respond under field application and recognize the limitations of soil moisture sensors and the factors that can affect their accuracy in predicting soil moisture in situ.     

EVAPOTRANSPIRATION CONCEPTUALIZATION IN THE HSPF‐MODFLOW INTEGRATED MODELS1

In 1988, the Florida Institute of Phosphate Research (FIPR) funded project to develop an advanced hydrologic model for shallow water table systems. The FIPR hydrologic model (FHM) was developed to provide an improved predictive capability of the interactions of surface water and ground water using its component models, HSPF and MODFLOW. The Integrated Surface and Ground Water (ISGW) model was developed from an early version of FHM and the two models were developed relatively independently in the late 1990s. Hydrologic processes including precipitation, interception, evapotranspiration, runoff, recharge, streamflow, and base flow are explicitly accounted for in both models. Considerable review of FHM and ISGW and their applications occurred through a series of projects. One model evolved, known as the Integrated Hydrological Model IHM. This model more appropriately describes hydrologic processes, including evapotranspiration fluxes within small distributed land‐based discretization. There is a significant departure of many IHM algorithms from FHM and ISGW, especially for soil water and evapotranspiration (ET). In this paper, the ET concepts in FHM, ISGW, and IHM will be presented. The paper also identifies the advantages and data costs of the improved methods. In FHM and IHM, ground water ET algorithms of the MODFLOW ET package replace those of HSPF (ISGW used a different model for ground water ET). However, IHM builds on an improved understanding and characterization of ET partitioning between surface storages, vadose zone storage, and saturated ground water storage. The IHM considers evaporative flux from surface sources, proximity of the water table to land surface, relative moisture condition of the unsaturated zone, thickness of the capillary zone, thickness of the root zone, and relative plant cover density. The improvements provide a smooth transition to satisfy ET demand between the vadose zone and deeper saturated ground water. While the IHM approach provides a more sound representation of the actual soil profile than FHM, and has shown promise at reproducing soil moisture and water table fluctuations as well as field measured ET rates, more rigorous testing is necessary to understand the robustness and/or limitations of this methodology.     

Calibration and Verification of SWMM for Low Impact Development

The Storm Water Management Model was used to simulate runoff and nutrient export from a low impact development (LID) watershed and a watershed using traditional runoff controls. Predictions were compared to observed values. Uncalibrated simulations underpredicted weekly runoff volume and average peak flow rates from the multiple subcatchment LID watershed by over 80%; the single subcatchment traditional watershed had better predictions. Saturated hydraulic conductivity, Manning's n for swales, and initial soil moisture deficit were sensitive parameters. After calibration, prediction of total weekly runoff volume for the LID and traditional watersheds improved to within 12 and 5% of observed values, respectively. For the validation period, predicted total weekly runoff volumes for the LID and traditional watersheds were within 6 and 2% of observed values, respectively. Water quality simulation was less successful, Nash–Sutcliffe coefficients >0.5 for both calibration and validation periods were only achieved for prediction of total nitrogen export from the LID watershed. Simulation of a 100‐year, 24‐h storm resulted in a runoff coefficient of 0.46 for the LID watershed and 0.59 for the traditional watershed. Results suggest either calibration is needed to improve predictions for LID watersheds or expanded look‐up tables for Green–Ampt infiltration parameter values that account for compaction of urban soil and antecedent conditions are needed.     

A COMPARISON OF MODELS PREDICTING GROUND WATER LEVELS ON HILLSIDE SLOPES1

ABSTRACT: A comparative study of ground water level predictions on hillside slopes using two models is presented. The models are a simplified mass balance model that has components for evapotran-spiration, recharge, and drainage; and a two-dimensional finite difference model that employs kriging to estimate soil parameters and accounts for non-uniform thickness of the soil layer. These models are representative of a wide range of modeling capabilities and are used to illustrate the sensitivity of ground water level predictions to the sophistication of the modeling techniques. The drainage and recharge components of the two models are evaluated and the importance of unsaturated flow in recharge computations is underscored. Piezometric observations in a small drainage depression on the slope of Kennel Creek Valley in Tongass National Forest, Alaska, were used to evaluate the two models. The results show that, although the predictions differ from the field observations, the simple physically-based mass balance model predicts the ground water levels as well as the two-dimensional model. It is suggested that caution should be exercised in using complex models to validate simpler models.     

Reconstructions of Soil Moisture for the Upper Colorado River Basin Using Tree‐Ring Chronologies1

Anderson, SallyRose, Glenn Tootle, and Henri Grissino‐Mayer, 2012. Reconstructions of Soil Moisture for the Upper Colorado River Basin Using Tree‐Ring Chronologies. Journal of the American Water Resources Association (JAWRA) 48(4): 849‐858. DOI: 10.1111/j.1752‐1688.2012.00651.x Abstract: Soil moisture is an important factor in the global hydrologic cycle, but existing reconstructions of historic soil moisture are limited. We used tree‐ring chronologies to reconstruct annual soil moisture in the Upper Colorado River Basin (UCRB). Gridded soil moisture data were spatially regionalized using principal components analysis and k‐nearest neighbor techniques. We correlated moisture sensitive tree‐ring chronologies in and adjacent to the UCRB with regional soil moisture and tested the relationships for temporal stability. Chronologies that were positively correlated and stable for the calibration period were retained. We used stepwise linear regression to identify the best predictor combinations for each soil moisture region. The regressions explained 42‐78% of the variability in soil moisture data. We performed reconstructions for individual soil moisture grid cells to enhance understanding of the disparity in reconstructive skill across the regions. Reconstructions that used chronologies based on ponderosa pines (Pinus ponderosa) and pinyon pines (Pinus edulis) explained more variance in the datasets. Reconstructed soil moisture data was standardized and compared with standardized reconstructed streamflow and snow water equivalent data from the same region. Soil moisture and other hydrologic variables were highly correlated, indicating reconstructions of soil moisture in the UCRB using tree‐ring chronologies successfully represent hydrologic trends.     

PREPLANTING SOIL MOISTURE USING PASSIVE MICROWAVE SENSORS1

ABSTRACT: Accurate assessment of preplanting soil moisture conditions is necessary for good agricultural management, and can have a significant influence on crop yield in the Texas Panhandle region. The Texas High Plains Underground Water Conservation District invests considerable time and money in developing a soil moisture deficit map each year in the hopes of achieving optimal use of irrigation water. Microwave sensors are responsive to surface soil moisture and, if used in this application, can provide timely and detailed information on root zone soil moisture. For this reason, an experiment was conducted in 1984 to evaluate the potential of aircraft-mounted passive microwave sensors. Microwave radiometer data were collected over a 2700 km² area near Lubbock, Texas, with a processed resolution of 0.32 km². These data were ground registered and converted to estimates of soil moisture using an appropriate model and land cover and soil texture information. Analyses indicate that the system provides an efficient means for mapping variations in soil moisture over large areas.