Venkataramana Sridhar

Development of the Soil Moisture Index to Quantify Agricultural Drought and Its “User Friendliness” in Severity-Area-Duration Assessment

Abstract This paper examines the role of soil moisture in quantifying drought through the development of a drought index using observed and modeled soil moisture. In Nebraska, rainfall is received primarily during the crop-growing season and the supply of moisture from the Gulf of Mexico determines if the impending crop year is either normal or anomalous and any deficit of rain leads to a lack of soil moisture storage. Using observed soil moisture from the Automated Weather Data Network (AWDN), the actual available water content for plants is calculated as the difference between observed or modeled soil moisture and wilting point, which is subsequently normalized with the site-specific, soil property–based, idealistic available water for plants that is calculated as the difference between field capacity and wilting point to derive the soil moisture index (SMI). This index is categorized into five classes from no drought to extreme drought to quantitatively assess drought in both space and time. Additional...

Evaluation of the Complementary Relationship Using Noah Land Surface Model and North American Regional Reanalysis (NARR) Data to Estimate Evapotranspiration in Semiarid Ecosystems

Estimating evapotranspiration using the complementary relationship can serve as a proxy to more sophisticated physically based approaches and can be used to better understand water and energy budget feedbacks. The authors investigated the existence of complementarity between actual evapotranspiration (ET) and potential ET (ETp) over natural vegetation in semiarid desert ecosystems of southern Idaho using only the forcing data and simulated fluxes obtained from Noah land surface model (LSM) and North American Regional Reanalysis (NARR) data. To mitigate the paucity of long-term meteorological data, the Noah LSM-simulated fluxes and the NARR forcing data were used in the advection‐aridity (AA) model to derive the complementary relationship (CR) for the sagebrush and cheatgrass ecosystems. When soil moisture was a limiting factor for ET, the CR was stable and asymmetric, with b values of 2.43 and 1.43 for sagebrush and cheatgrass, respectively. Higher b values contributed to decreased ET and increased ETp, and as a result ET from the sagebrush community was less compared to that of cheatgrass. Validation of the derived CR showed that correlations between daily ET from the Noah LSM and CR-based ET were 0.76 and 0.80 for sagebrush and cheatgrass, respectively, while the root-mean-square errors were 0.53 and 0.61 mm day 21 .

Assessing snowmelt dynamics with NASA scatterometer (NSCAT) data and a hydrologic process model

Abstract The presence of snow strongly impacts the exchange of moisture and energy between the land surface and atmosphere. In the interior of the northern hemisphere continents, snowmelt on frozen soils can cause or exacerbate major floods. Microwave remote sensing from satellite platforms has the potential to monitor the freeze–thaw status of soil and overlying snow packs over large areas. We evaluate the backscatter response of the NSCAT scatterometer to changing snow surface conditions, especially freeze and thaw status, using a macroscale hydrology model and the NSCAT backscatter data for the upper Mississippi River basin of the north central U.S. and the Boreal Ecosystem Atmosphere Study (BOREAS) region in central Canada. We compared the snowmelt conditions simulated by the Variable Infiltration Capacity (VIC) macroscale hydrology model driven with surface meteorological observations with NSCAT measurements for 1996–1997 snow season. A mid-winter thaw event (in February) and late season melt (April–May) are evaluated for both regions. Comparison of backscatter images with daily and hourly-modeled snow surface wetness and temperature showed that the model agreed with the backscatter for snow surface wetness on some days but not on others. Factors such as NSCAT overpass times, vegetation on the ground and their freeze–thaw state, and liquid moisture content appear to contribute to these discrepancies.

Quality Control of Soil Water Data in Applied Climate Information System—Case Study in Nebraska

Soil moisture is a key state variable from both climate and hydrologic cycle assessment perspectives. Recently, automated measurements of soil moisture with sensors deployed at sites in a real-time monitoring network have provided valuable new data to monitor the soil water resource. However, to assure the quality of the data, quality control QC tools are needed. Earlier studies left little literature on the QC of soil water data as measurements were generally not part of a network that routinely collected measurements. This paper presents a systematic QC analysis and methodology to evaluate the performance of candidate QC techniques using a spatially- extensive soil water data set. The six tests included are based on the general behavior of soil moisture, the statistical characteristics of the measurements, the soil properties, and the precipitation measurements. The threshold, step change, and spatial regression test proved most effective in identifying data problems. The results demonstrate that these methods will lead to early identification of potential instrument failures and other disturbances to the soil water measurements.

Estimation of the Water Balance Using Observed Soil Water in the Nebraska Sandhills

Analyzing the dynamic hydrologic conditions of the Sandhills is critical for water and range management and sustainability of the sandhills ecosystem as well as for dune stability. There are complex models available to quantify both surface and subsurface hydrological processes. However, we present in this study an application of a relatively simple model to arrive at best estimates of the water balance components. Using the Thornthwaite-Mather model, water balance components were estimated for four automated weather data network weather monitoring stations. Estimated averages of the water balance components suggested that mean annual precipitation of these four sites was only about 420 mm but water loss through plant evapotranspiration (ET) was 861 mm, with potential ET of about 1,214 mm. Our investigation shows that there was surplus of water between December and March and a deficit occurs at the start of the growing season in May and extends through senescence in September–October. This study also sugg...

A non-linear and non-stationary perspective for downscaling mean monthly temperature: a wavelet coupled second order Volterra model

This study presents a multiscale framework for downscaling of the General Circulation Model (GCM) outputs to the mean monthly temperature at regional scale using a wavelet based Second order Voltera (SoV) model. The models are developed using the reanalysis climatic data from the National Centers for Environmental Prediction (NCEP) and are validated using the simulated climatic dataset from the Can CM4 GCM for five locations in the Krishna river basin, India. K-means clustering, based on the multiscale wavelet entropy of the predictors, is used for obtaining the clusters of the input climatic variables. Principal component analysis (PCA) is used to obtain the representative variables from each cluster. These input variables are then used to develop a wavelet based multiscale model using Second order Volterra approach to simulate observed mean monthly temperature for the selected locations in the basin. These models are called W-P-SoV models in this paper. For the purpose of comparison, linear multi-resolution models are developed using Multiple Linear regression (MLR) and are called W-P MLR models. The performance of the models is further compared with other Wavelet-PCA based models coupled with Multiple linear regression models (P-MLR) and Artificial Neural Networks (P-ANN), and, stand-alone MLR and ANN to establish the superiority of the proposed approach. The results indicate that the performance of the wavelet based models is superior in terms of downscaling accuracy when compared with the other models used.

Coupled Surface and Groundwater Hydrological Modeling in a Changing Climate

Many current watershed modeling efforts now incorporate surface water and groundwater for managing water resources since the exchanges between groundwater and surface water need a special focus considering the changing climate. The influence of groundwater dynamics on water and energy balance components is investigated in the Snake River Basin (SRB) by coupling the Variable Infiltration Capacity (VIC) and MODFLOW models (VIC-MF) for the period of 1986 through 2042. A 4.4% increase in base flows and a 10.3% decrease in peak flows are estimated by VIC-MF compared to the VIC model in SRB. The VIC-MF model shows significant improvement in the streamflow simulation (Nash-Sutcliffe efficiency [NSE] of 0.84) at King Hill, where the VIC model could not capture the effect of spring discharge in the streamflow simulation (NSE of -0.30); however, the streamflow estimates show an overall decreasing trend. Two climate scenarios representing median and high radiative-forcings such as representative concentration pathways 4.5 and 8.5 show an average increase in the water table elevations between 2.1 and 2.6 m (6.9 and 8.5 feet) through the year 2042. The spatial patterns of these exchanges show a higher groundwater elevation of 15 m (50 feet) in the downstream area and a lower elevation of up to 3 m (10 feet) in the upstream area. Broadly, this study supports results of previous work demonstrating that integrated assessment of groundwater-surface water enables stakeholders to balance pumping, recharge and base flow needs and to manage the watersheds that are subjected to human pressures more sustainably.

A comprehensive analysis of interseasonal and interannual energy and water balance dynamics in semiarid shrubland and forest ecosystems

Accurate estimation of ecosystem-scale land surface energy and water balance has great importance in weather and climate studies. This paper summarizes seasonal and interannual fluctuations of energy and water balance components in two distinctive semiarid ecosystems, sagebrush (SB) and lodgepole pine (LP) in the Snake River basin of Idaho. This study includes 6 years (2011-2016) of eddy covariance (EC) along with modeled estimates. An analysis of the energy balance indicated a higher energy balance ratio (0.88) for SB than for LP (0.86). The inclusion of canopy storage (CS) increased the association between turbulent fluxes and available energy in LP. Green vegetation fraction (GVF) significantly controlled evapotranspiration (ET) and surface energy partitioning when available energy and soil moisture were not limited. Seasonal water balance in the Budyko framework showed severe water-limited conditions in SB (6-9 months) compared to LP (6-7 months). Based on the validated Noah land surface model estimates, direct soil evaporation (ESoil) is the main component of ET (62 to 79%) in SB due to a large proportion of bare soil (60%), whereas at the lodgepole pine site, it was transpiration (ETran, 42-52%). A complementary ratio (CR) analysis on ET and potential ET (PET) showed a strong asymmetric CR in SB, indicating significant advection. Both SG and LP showed strong coupling between soil moisture (SM) and air temperature (Ta). However, a weak coupling was observed in SB when the soil was dry and Ta was high. This weak coupling was due to the presence of net advection. The results presented here have a wider application: to help us understand and predict the survival, productivity, and hydroclimatology of water-limited ecosystems.

Comparative Analysis of the Performance of Wavelet-Based and Stand-alone Models in Capturing Non-stationarity in Climate Downscaling

Non-stationarity is an intrinsic property of all natural processes, and addressing the same is crucial for climatic downscaling models. Wavelet-based models have been used to address the non-stationarity in the individual predictor (explanatory) time series where each predictor is “decomposed” into its discrete wavelet components at multiple time–frequency resolutions. However, in the warming climate, the predictor–predictand relationships (PPRs) are getting unpredictable. Hence, it is important to understand if the wavelet-based approach can capture the non-stationary PPR better than the stand-alone models. This paper provides an experimental approach to compare the strength of wavelet-based and stand-alone regression models when applied to downscale mean monthly temperature, from general circulation models (GCMs). For this study, we use Can CM4 GCM model to downscale temperature at multiple locations in the Krishna River Basin. Regression coefficients of the recursively updated models are compared for the wavelet-based and the stand-alone models for the length of the validation period. The comparison shows that the regression coefficients from the wavelet-based models capture higher variance compared to the stand-alone models and hence were able to capture the changing PPRs in the downscaling models with greater accuracy. The statistical performance indices reinforce the finding that wavelet-based models consistently outperformed the stand-alone models.

Land–Atmosphere Interactions in South Asia: A Regional Earth Systems Perspective

Exchanges between the land and the atmosphere are complex and dynamic. Capturing these interactions provides a better understanding of the earth systems across multiple scales. Studies highlighting the energy budget partitioning and water cycle assessment have immensely contributed to defining better monsoon, weather and climate predictions as well as regional water vapor transport impacted by anthropogenic influences in South Asia. In this chapter, we provide examples of how the land–atmosphere interactions are influenced by changing soil moisture, evapotranspiration, water vapor transport, and planetary boundary layer depth over this region. Climate change adaptation requires the knowledge of how large-scale atmospheric and climate-induced forcings impact at a local scale. Therefore, proper physical understanding of the processes that influence the feedbacks between the vegetation, water, and atmosphere becomes essential for future management of natural resources and water management.