Scaling characteristics of spatial patterns of soil moisture from distributed modelling

Characterizing the spatial dynamics of soil moisture fields is a key issue in hydrology, offering an avenue to improve our understanding of complex land surface–atmosphere interactions. In this paper, the statistical structure of soil moisture patterns is examined using modelled soil moisture obtained from the North American Land Data Assimilation System (NLDAS) at 0.125° resolution. The study focuses on the vertically averaged soil moisture in the top 10 cm and 100 cm layers. The two variables display a weak dependence for lower values of surface soil moisture, with the strength of the relationship increasing with the water content of the top layer. In both cases, the variance of the soil moisture follows a power law decay as a function of the averaging area. The superficial layer shows a lower degree of spatial organization and higher temporal variability, which is reflected in rapid changes in time of the slope of the scaling functions of the soil moisture variance. Conversely, the soil moisture in the top 100 cm has lower variability in time and larger spatial correlation. The scaling of these patterns was found to be controlled by the changes in the soil water content. Results have implications for the downscaling of soil moisture to prevent model bias.     

Utilization of satellite data in land surface hydrology: sensitivity and assimilation

This paper investigates the sensitivity of potential evapotranspiration to input meteorological variables, i.e. surface air temperature and surface vapor pressure. The sensitivity studies have been carried out for a wide range of land surface variables such as wind speed, leaf area index and surface temperatures. Errors in the surface air temperature and surface vapor pressure result in errors of different signs in the computed potential evapotranspiration. This result has implications for use of estimated values from satellite data or analysis of surface air temperature and surface vapor pressure in large‐scale hydrological modeling. The comparison of cumulative potential evapotranspiration estimates using ground observations and satellite observations over Manhattan, Kansas for a period of several months shows a variable difference between the two estimates. The use of satellite estimates of surface skin temperature in hydrological modeling to update the soil moisture using a physical adjustment concept is studied in detail, including the extent of changes in soil moisture resulting from the assimilation of surface skin temperature. The soil moisture of the 1 cm surface layer was adjusted by 0·9 mm over a 10‐day period as a result of a 3 K difference between the predicted and the observed surface temperature. This is a considerable amount given the fact that the top layer can hold only 5 mm of moisture. Copyright © 2001 John Wiley & Sons, Ltd.     

Beyond the semivariogram: Patterns,scale, and hydrology in a semi-arid landscape

As an alternative to geostatistical modeling, we characterized the hydrology of a semi-arid landscape in southeastern Washington state, USA, by coupling spatial patterns identified in the distributions of relative relief and vegetation with the influence each has on soil moisture storage and evapotranspiration at the appropriate scale. Gauging precipitation, soil moisture, and evapotranspiration over a two-year period while concurrently mapping relative relief and vegetation distributions at three scales ranging from centimeters to 90 m, we determined that soil moisture and soil moisture storage are significantly greater in topographic concavities than in convexities at the microrelief (20–50 cm) scale but are not significantly different in relief features at larger scales. A generalized microrelief surface produced using a two-dimensional Fourier transformation provided a good representation of the distribution of soil moisture within microrelief when scaled to soil moisture values. Applying a spatial point process analysis we determined that big sage are randomly distributed across the landscape at all scales, suggesting that lysimeter-derived sage evapotranspiration rates also be distributed randomly across the landscape. Where sage were not present, we applied an autoregressive moving-average model conditioned on grass lysimeter measurements to derive evapotranspiration rates. Combining these hydrologic spatial patterns derived from distributions in relief and vegetation with measured precipitation inputs and evapotranspiration outputs, we created a spatially distributed model of soil moisture which we tested against measured values over an eight-week period. The model provides accurate characterization of soil moisture, allows estimates of soil moisture between measurement points, permits extrapolation of soil moisture distributions outside the gauged area, and maintains small-scale variability when aggregating soil moisture to successively larger scales.     

A Raman lidar to measure water vapor in the atmospheric boundary layer

A new multi-telescope scanning Raman lidar designed to measure the water vapor mixing ratio in the atmospheric boundary layer for a complete diurnal cycle with high resolution spatial (1.25 m) and temporal (1 s) resolutions is presented. The high resolution allows detailed measurements of the lower atmosphere and offers new opportunities for evaporation and boundary layer research, atmospheric profiling and visualization. This lidar utilizes a multi-telescope design that provides for an operational range with a nearly constant signal-to-noise ratio, which allows for statistical investigations of atmospheric turbulence. This new generation ground-based water vapor Raman lidar is described, and first observations from the Turbulent Atmospheric Boundary Layer Experiment (TABLE) are presented. Direct comparison with in-situ point measurements obtained during the field campaign demonstrate the ability of the lidar to reliably measure the water vapor mixing ratio. Horizontal measurements taken with time are used to determine the geometric characteristics of coherent structures. Vertical scans are used to visualize nocturnal jet features, layered structures within a stably stratified atmosphere and the internal boundary layer structure over a lake.     

The Representation of Tropospheric Water Vapor Over Low-Latitude Oceans in (Re-)analysis: Errors,Impacts, and the Ability to Exploit Current and Prospective Observations

This paper addresses the representation of lower tropospheric water vapor in the meteorological analyses—fully detailed estimates of atmospheric state—providing the wide temporal and spatial coverage used in many process studies. Analyses are produced in a cycle combining short forecasts from initial conditions with data assimilation that optimally estimates the state of the atmosphere from the previous forecasts and new observations, providing initial conditions for the next set of forecasts. Estimates of water vapor are among the less certain aspects of the state because the quantity poses special challenges for data assimilation while being particularly sensitive to the details of model parameterizations. Over remote tropical oceans observations of water vapor come from two sources: passive observations at microwave or infrared wavelengths that provide relatively strong constraints over large areas on column-integrated moisture but relatively coarse vertical resolution, and occultations of Global Positioning System provide much higher accuracy and vertical resolution but are relatively spatially coarse. Over low-latitude oceans, experiences with two systems suggest that current analyses reproduce much of the large-scale variability in integrated water vapor but have systematic errors in the representation of the boundary layer with compensating errors in the free troposphere; these errors introduce errors of order 10% in radiative heating rates through the free troposphere. New observations, such as might be obtained by future observing systems, improve the estimates of water vapor but this improvement is lost relatively quickly, suggesting that exploiting better observations will require targeted improvements to global forecast models.     

Seasonal ground ice impacts on spring ecohydrological conditions in a western boreal plains peatland

Peatlands in the Western Boreal Plains act as important water sources in the landscape. Their persistence, despite potential evapotranspiration (PET) often exceeding annual precipitation, is attributed to various water storage mechanisms. One storage element that has been understudied is seasonal ground ice (SGI). This study characterized spring SGI conditions and explored its impacts on available energy, actual evapotranspiration, water table, and near surface soil moisture in a western boreal plains peatland. The majority of SGI melt took place over May 2017. Microtopography had limited impact on melt rates due to wet conditions. SGI melt released 139mm in ice water equivalent (IWE) within the top 30cm of the peat, and weak significant relationships with water table and surface moisture suggest that SGI could be important for maintaining vegetation transpiration during dry springs. Melting SGI decreased available energy causing small reductions in PET (<10mm over the melt period) and appeared to reduce actual evapotranspiration variability but not mean rates, likely due to slow melt rates. This suggests that melting SGI supplies water, allowing evapotranspiration to occur at near potential rates, but reduces the overall rate at which evapotranspiration could occur (PET). The role of SGI may help peatlands in headwater catchments act as a conveyor of water to downstream landscapes during the spring while acting as a supply of water for the peatland. Future work should investigate SGI influences on evapotranspiration under differing peatland types, wet and dry spring conditions, and if the spatial variability of SGI melt leads to spatial variability in evapotranspiration.     

Modelling the spatial variation in soil moisture at the landscape scale: an application to five areas of ecological interest in the UK

A simple conceptual hydrological model that explicitly includes the lateral movement of soil water and operates efficiently at the landscape scale is outlined. It is applied to five areas of ecological interest in the UK to provide distributed mean monthly soil moisture on a 50 m grid. As the model's driving variables—daily rainfall and potential evapotranspiration—are assumed constant over each of the tracts of land, the variability in soil moisture is due to different soil types and to topographic effects. Box plots of the mean monthly simulated soil moisture clearly show the spread of values occasioned by modelling the lateral water movement down the hillslope. The general magnitude of the results are compared with published data wherever possible and there is some discussion of the form of the curve used in the model to describe the attenuation of evapotranspiration with decreasing soil moisture. Copyright © 2000 John Wiley & Sons, Ltd.     

Remote sensing in hydrology

Remote sensing provides a means of observing hydrological state variables over large areas. The ones which we will consider in this paper are land surface temperature from thermal infrared data, surface soil moisture from passive microwave data, snow cover using both visible and microwave data, water quality using visible and near-infrared data and estimating landscape surface roughness using lidar. Methods for estimating the hydrometeorlogical fluxes, evapotranspiration and snowmelt runoff, using these state variables are also described.     

Vegetation structure controls on snow and soil moisture in restored ponderosa pine forests

Thinning of semi-arid forests to reduce wildfire risk is believed to improve forest health by increasing soil moisture. Increased snowpack, reduced transpiration and reduced rainfall interception are frequently cited mechanisms by which reduced canopy density may increase soil moisture. However, the relative importance of these factors has not been rigorously evaluated in field studies. We measured snow depth, snow water equivalent (SWE) and the spatial and temporal variation in soil moisture at four experimental paired treatment-control thinning sites in high elevation ponderosa pine forest northern Arizona, USA. We compared snow and soil moisture measurements with forest structure metrics derived from aerial imagery and 3-dimensional lidar data to determine the relationship between vegetation structure, snow and soil moisture throughout the annual hydrologic cycle. Soil moisture was consistently and significantly higher in thinned forest plots, even though the treatments were performed 8–11 years before this study. However, we did not find evidence that SWE was higher in thinned forests across a range of snow conditions. Regression tree analysis of soil moisture and vegetation structure data provided some evidence that localized differences in transpiration and interception of precipitation influence the spatial pattern of soil moisture at points in the annual hydrologic cycle when the system is becoming increasingly water limited. However, vegetation structure explained a relatively low amount of the spatial variance (R² < 0.23) in soil moisture. Continuous measurements of soil moisture in depth profiles showed stronger attenuation of soil moisture peaks in thinned sites, suggesting differences in infiltration dynamics may explain the difference in soil moisture between treatments as opposed to overlying vegetation alone. Our results show limited support for commonly cited relationships between vegetation structure, snow and soil moisture and indicate that future research is needed to understand how reduction in tree density alters soil hydraulic properties.     

The NAFE’06 data set: Towards soil moisture retrieval at intermediate resolution

The National Airborne Field Experiment 2006 (NAFE’06) was conducted during a three week period of November 2006 in the Murrumbidgee River catchment, located in southeastern Australia. One objective of NAFE’06 was to explore the suitability of the area for SMOS (Soil Moisture and Ocean Salinity) calibration/validation and develop downscaling and assimilation techniques for when SMOS does come on line. Airborne L-band brightness temperature was mapped at 1 km resolution 11 times (every 1–3 days) over a 40 by 55 km area in the Yanco region and 3 times over a 40 by 50 km area that includes Kyeamba Creek catchment. Moreover, multi-resolution, multi-angle and multi-spectral airborne data including surface temperature, surface reflectance (green, read and near infrared), lidar data and aerial photos were acquired over selected areas to develop downscaling algorithms and test multi-angle and multi-spectral retrieval approaches. The near-surface soil moisture was measured extensively on the ground in eight sampling areas concurrently with aircraft flights, and the soil moisture profile was continuously monitored at 41 sites. Preliminary analyses indicate that (i) the uncertainty of a single ground measurement was typically less than 5% vol. (ii) the spatial variability of ground measurements at 1 km resolution was up to 10% vol. and (iii) the validation of 1 km resolution L-band data is facilitated by selecting pixels with a spatial soil moisture variability lower than the point-scale uncertainty. The sensitivity of passive microwave and thermal data is also compared at 1 km resolution to illustrate the multi-spectral synergy for soil moisture monitoring at improved accuracy and resolution. The data described in this paper are available at www.nafe.unimelb.edu.au.     

Evaluating the spatial distribution of water balance in a small watershed,Pennsylvania

A conceptual water‐balance model was modified from a point application to be distributed for evaluating the spatial distribution of watershed water balance based on daily precipitation, temperature and other hydrological parameters. The model was calibrated by comparing simulated daily variation in soil moisture with field observed data and results of another model that simulates the vertical soil moisture flow by numerically solving Richards' equation. The impacts of soil and land use on the hydrological components of the water balance, such as evapotranspiration, soil moisture deficit, runoff and subsurface drainage, were evaluated with the calibrated model in this study. Given the same meteorological conditions and land use, the soil moisture deficit, evapotranspiration and surface runoff increase, and subsurface drainage decreases, as the available water capacity of soil increases. Among various land uses, alfalfa produced high soil moisture deficit and evapotranspiration and lower surface runoff and subsurface drainage, whereas soybeans produced an opposite trend. The simulated distribution of various hydrological components shows the combined effect of soil and land use. Simulated hydrological components compare well with observed data. The study demonstrated that the distributed water balance approach is efficient and has advantages over the use of single average value of hydrological variables and the application at a single point in the traditional practice. Copyright © 2000 John Wiley & Sons, Ltd.     

Hillslope‐scale probabilistic characterization of soil moisture dynamics and average water balance

Probabilistic water balance modelling provides a useful framework for investigating the interactions between soil, vegetation, and the atmosphere. It has been used to estimate temporal soil moisture dynamics and ecohydrological responses at a point. This study combines a nonlinear rainfall–runoff theory with probabilistic water balance model to represent varied source area runoff as a function of rainfall depth and a runoff coefficient at hillslope scale. Analytical solutions of the soil‐moisture probability density function and average water balance model are then developed. Based on a sensitivity analysis of soil moisture dynamics, we show that when varied source area runoff is incorporated, mean soil moisture is always lower and total runoff higher, compared with the original probabilistic water balance model. The increased runoff from the inclusion of varied source area runoff is mainly because of a reduction in leakage when the index of dryness is less than one and evapotranspiration when the index of dryness is greater than one. Inclusion of varied source area runoff in the model means that the actual evapotranspiration is limited by less available water (i.e. water limit), which is stricter than Budyko’s and Milly’s water limit. Application of the model to a catchment located in Western Australia showed that the method can predict annual value of actual evapotranspiration and streamflow accurately. Copyright © 2012 John Wiley & Sons, Ltd.     

Spatial soil moisture scaling structure during Soil Moisture Experiment 2005

Soil moisture state and variability control many hydrological and ecological processes as well as exchanges of energy and water between the land surface and the atmosphere. However, its state and variability are poorly understood at spatial scales larger than the fields (i.e. 1 km²) as well as the ability to extrapolate field scale to larger spatial scales. This study investigates soil moisture profiles, their spatial organization, and physical drivers of variability within the Walnut Creek watershed, Iowa, during Soil Moisture Experiment 2005 and relates the watershed scale findings to previous field‐scale results. For all depths, the watershed soil moisture variability was negatively correlated with the watershed mean soil moisture and followed an exponential relationship that was nearly identical to that for field scales. This relationship differed during drying and wetting. While the overall time stability characteristics were improved with observation depth, the relatively wet and dry locations were consistent for all depths. The most time stable locations, capturing the mean soil moisture of the watershed within ± 0·9% volumetric soil moisture, were typically found on hill slopes regardless of vegetation type. These mild slope locations consistently preserve the time stability patterns from field to watershed scales. Soil properties also appear to impact stability but the findings are sensitive to local variations that may not be well defined by existing soil maps. Copyright © 2010 John Wiley & Sons, Ltd.     

Role of time variant and invariant terms on soil moisture spatio-temporal variability in a subhumid coastal wetland

The knowledge of soil moisture spatio-temporal variability is highly relevant for water resources management. This paper reports an analysis of the spatial–temporal variability of soil moisture data for a small to medium-scale soil-sensors network in a coastal wetland of southwestern Spain. Measurements were taken from five sites located in the Doñana National Park over the time-period of one hydrological year from September 2017 to September 2018. The total area of the soil-sensors network shows an extension about 25 × 3 km. Soil moisture data was separated into time invariant (the temporal mean of the whole period at each site) and time-variant terms (the deviations of soil moisture from the mean, or anomalies). The time-invariant component was generally the main contributor to the total spatial variance of soil moisture and it was mostly controlled by the groundwater levels in the area. Nevertheless, the time variant terms have a huge effect on soil moisture variability in very dry states. Characteristic convex time-dependent patterns for this field site were found between spatially averaged soil moisture and its variability. This information could be used for the up and downscaling of soil moisture from satellite data. Those patterns of relation between spatial mean and variability of soil moisture were only affected by heavy rainfalls giving rise to hysteretic behaviour. This study shows that even though groundwater level is a time-variant variable, it significantly affects soil moisture's time-variant but also time-invariant terms due to the different average groundwater level depths at the different sites.     

Soil moisture variability and scale-dependency of nonlinear parameterizations in coupled land–atmosphere models

Many of the relationships used in coupled land–atmosphere models to describe interactions between the land surface and the atmosphere have been empirically parameterized and thus are inherently dependent on the observational scale for which they were derived and tested. However, they are often applied at scales quite different than the ones they were intended for due to practical necessity. In this paper, a study is presented on the scale-dependency of parameterizations which are nonlinear functions of variables exhibiting considerable spatial variability across a wide range of scales. For illustration purposes, we focus on parameterizations which are explicit nonlinear functions of soil moisture. We use data from the 1997 Southern Great Plains Hydrology Experiment (SGP97) to quantify the spatial variability of soil moisture as a function of scale. By assuming that a parameterization keeps its general form the same over a range of scales, we quantify how the values of its parameters should change with scale in order to preserve the spatially averaged predicted fluxes at any scale of interest. The findings of this study illustrate that if modifications are not made to nonlinear parameterizations to account for the mismatch of scales between optimization and application, then significant systematic biases may result in model-predicted water and energy fluxes.     

Estimating water balance components in irrigated agriculture using a combined approach of soil moisture and energy balance monitoring,and numerical modelling

Information on water balance components such as evapotranspiration and groundwater recharge are crucial for water management. Due to differences in physical conditions, but also due to limited budgets, there is not one universal best practice, but a wide range of different methods with specific advantages and disadvantages. In this study, we propose an approach to quantify actual evapotranspiration, groundwater recharge and water inflow, i.e. precipitation and irrigation, that considers the specific conditions of irrigated agriculture in warm, arid environments. This approach does not require direct measurements of precipitation or irrigation quantities and is therefore suitable for sites with an uncertain data basis. For this purpose, we combine soil moisture and energy balance monitoring, remote sensing data analysis and numerical modelling using Hydrus. Energy balance data and routine weather data serve to estimate ET₀. Surface reflectance data from satellite images (Sentinel-2) are used to derive leaf area indices, which help to partition ET₀ into energy limited evaporation and transpiration. Subsequently, first approximations of water inflow are derived based on observed soil moisture changes. These inflow estimates are used in a series of forward simulations that produce initial estimates of drainage and ET_act, which in turn help improve the estimate of water inflow. Finally, the improved inflow estimates are incorporated into the model and then a parameter optimization is performed using the observed soil moisture as the reference figure. Forward simulations with calibrated soil parameters result in final estimates for ET_act and groundwater recharge. The presented method is applied to an agricultural test site with a crop rotation of cotton and wheat in Punjab, Pakistan. The final model results, with an RMSE of 2.2% in volumetric water content, suggest a cumulative ET_act and groundwater recharge of 769 and 297 mm over a period of 281 days, respectively. The total estimated water inflow accounts for 946 mm, of which 77% originates from irrigation.     

Spatially distributed hydrotope‐based modelling of evapotranspiration and runoff in mountainous basins

River basins in mountainous regions are characterized by strong variations in topography, vegetation, soils, climatic conditions and snow cover conditions, and all are strongly related to altitude. The high spatial variation needs to be considered when modelling hydrological processes in such catchments. A complex hydrological model, with a great potential to account for spatial variability, was developed and applied for the hourly simulation of evapotranspiration, soil moisture, water balance and the runoff components for the period 1993 and 1994 in 12 subcatchments of the alpine/pre‐alpine basin of the River Thur (area 1703 km²). The basin is located in the north‐east of the Swiss part of the Rhine Basin and has an elevation range from 350 to 2500 m a.s.l. A considerable part of the Thur Basin is high mountain area, some of it above the tree‐line and a great part of the basin is snow covered during the winter season. In the distributed hydrological model, the 12 sub‐basins of the Thur catchment were spatially subdivided into sub‐areas (hydrologically similar response units—HRUs or hydrotopes) using a GIS. Within the HRUs a hydrologically similar behaviour was assumed. Spatial interpolations of the meteorological input variables wereemployed for each altitudinal zone. The structure of the model components for snow accumulation and melt, interception, soil water storage and uptake by evapotranspiration, runoff generation and flow routing are briefly outlined. The results of the simulated potential evapotranspiration reflect the dominant role of altitudinal change in radiation and albedo of exposure, followed by the influence of slope. The actual evapotranspiration shows, in comparison with the potential evapotranspiration, a greater variability in the lower and medium altitudinal zones and a smaller variability in the upper elevation zones, which was associated with limitations of available moisture in soil and surface depression storages as well as with the evaporative demand of the local vegetation. The higher altitudinal dependency and variability of runoff results from the strong increase in precipitation and the decrease in evaporation with increased altitude. An increasing influence of snow cover on runoff as well as evapotranspiration with altitude is obvious. The computed actual evapotranspiration and runoff were evaluated against the observed values of a weighting lysimeter and against runoff hydrographs. Copyright © 1999 John Wiley & Sons, Ltd.     

Macroscale hydrological modelling approach for study of large scale hydrologic impacts under climate change in Indian river basins

In climate‐change studies, a macroscale hydrologic model (MHM) operating over large scales can be an important tool in developing consistent hydrological variability estimates over large basins. MHMs, which can operate at coarse grid resolutions of about 1° latitude by longitude, have been used previously to study climate change impacts on the hydrology of continental scale or global river basins. They can provide a connection between global atmospheric models and water resource systems on large spatial scales and long timescales. In this study, the variable infiltration capacity (VIC) MHM is used to study large scale hydrologic impacts of climate change for Indian river basins. Large‐scale changes in runoff, evapotranspiration and soil moisture for India, as well as station‐scale changes in discharges for three major river basins with distinct climatic and geographic characteristics are examined in this study. Climate model projections for meteorological variables (precipitation, temperature and wind speed) from three general circulation models (GCMs) and three emissions scenarios are used to drive the VIC MHM. GCM projections are first interpolated to a 1° by 1° hydrologic model grid and then bias‐corrected using a quantile–quantile mapping. The VIC model is able to reproduce observed statistics for discharges in the Ganga, Narmada and Krishna basins reasonably well, even at the coarse grid resolution employed using a calibration period for years 1965–1970 and testing period from 1971–1973/1974. An increasing trend is projected for summer monsoon surface runoff, evapotranspiration and soil moisture in most central Indian river basins, whereas a decrease in runoff and soil moisture is projected for some regions in southern India, with important differences arising from GCM and scenario variability. Discharge statistics show increases in mid‐flow and low flow at Farakka station on Ganga River, increased high flows at Jamtara station upstream of Narmada, and increased high, mid‐flow and low flow for Vijayawada station on Krishna River in the future. Copyright © 2013 John Wiley & Sons, Ltd.