Venkat Lakshmi

IAHS decade on predictions in ungauged basins (PUB), 2003-2012: Shaping an exciting future for the hydrological sciences

Abstract Drainage basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The problem is compounded by the impacts of human-induced changes to the land surface and climate, occurring at the local, regional and global scales. Predictions of ungauged or poorly gauged basins under these conditions are highly uncertain. The IAHS Decade on Predictions in Ungauged Basins, or PUB, is a new initiative launched by the International Association of Hydrological Sciences (IAHS), aimed at formulating and implementing appropriate science programmes to engage and energize the scientific community, in a coordinated manner, towards achieving major advances in the capacity to make predictions in ungauged basins. The PUB scientific programme focuses on the estimation of predictive uncertainty, and its subsequent reduction, as its central theme. A general hydrological prediction system contains three components: (a) a model that describes the key processes of interest, (b) a set of parameters that represent those landscape properties that govern critical processes, and (c) appropriate meteorological inputs (where needed) that drive the basin response. Each of these three components of the prediction system, is either not known at all, or at best known imperfectly, due to the inherent multi-scale space—time heterogeneity of the hydrological system, especially in ungauged basins. PUB will therefore include a set of targeted scientific programmes that attempt to make inferences about climatic inputs, parameters and model structures from available but inadequate data and process knowledge, at the basin of interest and/or from other similar basins, with robust measures of the uncertainties involved, and their impacts on predictive uncertainty. Through generation of improved understanding, and methods for the efficient quantification of the underlying multi-scale heterogeneity of the basin and its response, PUB will inexorably lead to new, innovative methods for hydrological predictions in ungauged basins in different parts of the world, combined with significant reductions of predictive uncertainty. In this way, PUB will demonstrate the value of data, as well as provide the information needed to make predictions in ungauged basins, and assist in capacity building in the use of new technologies. This paper presents a summary of the science and implementation plan of PUB, with a call to the hydrological community to participate actively in the realization of these goals.

Soil moisture estimates from TRMM Microwave Imager observations over the Southern United States

Abstract The lack of continuous soil moisture fields at large spatial scales, based on observations, has hampered hydrologists from understanding its role in weather and climate. The most readily available observations from which a surface wetness state could be derived is the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations at 10.65 GHz. This paper describes the first attempt to map daily soil moisture from space over an extended period of time. Methods to adjust for diurnal changes associated with this temporal variability and how to mosaic these orbits are presented. The algorithm for deriving soil moisture and temperature from TMI observations is based on a physical model of microwave emission from a layered soil–vegetation–atmosphere medium. An iterative, least-squares minimization method, which uses dual polarization observations at 10.65 GHz, is employed in the retrieval algorithm. Soil moisture estimates were compared with ground measurements over the U.S. Southern Great Plains (SGP) in Oklahoma and the Little River Watershed, Georgia. The soil moisture experiment in Oklahoma was conducted in July 1999 and Little River in June 2000. During both the experiments, the region was dry at the onset of the experiment, and experienced moderate rainfall during the course of the experiment. The regions experienced a quick dry-down before the end of the experiment. The estimated soil moisture compared well with the ground observations for these experiments (standard error of 2.5%). The TMI-estimated soil moisture during 6–22 July over Southern U.S. was analyzed and found to be consistent with the observed meteorological conditions.

Effects of vegetation and soil moisture on the simulated land surface processes from the coupled WRF/Noah model

[1] The coupled Weather Research and Forecasting (WRF) model with the Noah land surface model (Noah LSM) is an attempt of the modeling community to embody the complex interrelationship between land surface and atmosphere into numerical weather or climate prediction. This study describes coupled WRF/Noah model tests to evaluate the model sensitivity and improvement through vegetation fraction (Fg) parameterizations and soil moisture initialization. We utilized the 500 m 8-day Moderate Resolution Imaging Spectroradiometer reflectance data to derive the model Fg parameter using two different methods: the linear and quadric methods. In addition, combining the Fg quadric method, we initialized soil moisture simulated by High-Resolution Land Data Assimilation System, which has been developed for providing better soil moisture data in high spatial resolution by National Center for Atmospheric Research. We performed temporal comparisons of the simulated land surface variables: surface temperature (TS), sensible heat flux (SH), ground heat flux (GH), and latent heat flux (LH) to observed data during 2002 International H2O Project. Then these results were statistically validated with correlation coefficients and root mean square errors. The results indicate high sensitivity of the coupled model to vegetation fluctuations, showing overestimation of vegetation transpiration and very low variability of GH in highly vegetated area.

Land surface air temperature mapping using TOVS and AVHRR

Surface air temperature is an important variable in land surface hydrological studies. This paper evaluates the ability of satellites to map air temperature across large land surface areas. Algorithms recently have been developed that derive surface air temperature using observations from the TOVS (TIROS Operational Vertical Sounder) suite of instruments and also from the AVHRR (Advanced Very High Resolution Radiometer), which have flown on the NOAA operational sun synchronous satellites TIROS-N NOAA-14. In this study we evaluate TOVS soundings from NOAA-10 (nominal local time of overpass 7:30 a.m./p.m.) and data from AVHRR aboard NOAA-9 (nominal local time 2:30 a.m./p.m.). Instantaneous estimates from the AVHRR and TOVS were compared with the hourly ground observations collected from 26 meteorological stations in the Red River-Arkansas River basin for a 3-month period from May to July 1987. Detailed comparisons between the satellite and ground estimates of surface air temperatures are reported and the feasibility of estimating the diurnal variation is explored. The comparisons are interpreted in the geographical context, i.e. land cover and topography, and in the seasonal context, i.e. early and midsummer. The results show that the average bias over the 3-month period compared with ground-based observations is approximately 2°C or less for the three times of day with TOVS having lower biases than AVHRR. Knowledge of these error estimates will greatly benefit use of satellite data in hydrological modelling.

Predictions in ungauged basins as a catalyst for multidisciplinary hydrology

The face of hydrologic science is changing rapidly, on national as well as on international scales.The increasing complexity of the problems hydrology is asked to investigate in research and practice today often requires solutions that can no longer be obtained by a single hydrologist, but require a multidisciplinary team. One consequence of this trend is the establishment of initiatives that help formulate and implement science programs to engage and energize the scientific community toward achieving major advances. The IAHS Decade on Predictions in Ungauged Basins (PUB) is an initiative of the International Association of Hydrological Sciences (IAHS) [Sivapalan et al., 2003] to advance our ability to make reliable predictions in ungauged basins. Within PUB, the drainage basin (at various scales) is seen as the element that integrates all aspects of the hydrological cycle within a defined area that can be studied, quantified, and acted upon.

Remote Sensing of Soil Moisture

Soil moisture is an important variable in land surface hydrology as it controls the amount of water that infiltrates into the soil and replenishes the water table versus the amount that contributes to surface runoff and to channel flow. However observations of soil moisture at a point scale are very sparse and observing networks are expensive to maintain. Satellite sensors can observe large areas but the spatial resolution of these is dependent on microwave frequency, antenna dimensions, and height above the earth’s surface. The higher the sensor, the lower the spatial resolution and at low elevations the spacecraft would use more fuel. Higher spatial resolution requires larger diameter antennas that in turn require more fuel to maintain in space. Given these competing issues most passive radiometers have spatial resolutions in 10s of kilometers that are too coarse for catchment hydrology applications. Most local applications require higher-spatial-resolution soil moisture data. Downscaling of the data requires ancillary data and model products, all of which are used here to develop high-spatial-resolution soil moisture for catchment applications in hydrology. In this paper the author will outline and explain the methodology for downscaling passive microwave estimation of soil moisture.

Validation of the ASAR Global Monitoring Mode Soil Moisture Product Using the NAFE'05 Data Set

The Advanced Synthetic Aperture Radar (ASAR) Global Monitoring (GM) mode offers an opportunity for global soil moisture (SM) monitoring at much finer spatial resolution than that provided by the currently operational Advanced Microwave Scanning Radiometer for the Earth Observing System and future planned missions such as Soil Moisture and Ocean Salinity and Soil Moisture Active Passive. Considering the difficulties in modeling the complex soil-vegetation scattering mechanisms and the great need of ancillary data for microwave backscatter SM inversion, algorithms based on temporal change are currently the best method to examine SM variability. This paper evaluates the spatial sensitivity of the ASAR GM surface SM product derived using the temporal change detection methodology developed by the Vienna University of Technology. This evaluation is made for an area in southeastern Australia using data from the National Airborne Field Experiment 2005. The spatial evaluation is made using three different types of SM data (station, field, and airborne) across several different scales (1-25 km). Results confirmed the expected better agreement when using point (R station = 0.75) data as compared to spatial (R PLMR, 1 km = 0.4) data. While the aircraft-ASAR GM correlation values at 1-km resolution were low, they significantly improved when averaged to 5 km (R PLMR, 5 km = 0.67) or coarser. Consequently, this assessment shows the ASAR GM potential for monitoring SM when averaged to a spatial resolution of at least 5 km.

An Assessment of QuikSCAT Ku-Band Scatterometer Data for Soil Moisture Sensitivity

The QuikSCAT enhanced (2.225-km) backscattering product is investigated for sensitivity to changes in soil moisture and its potential for spatial disaggregation of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture. Specifically, an active-passive methodology based on temporal change detection is tested using data from the 2006 National Airborne Field Experiment data set. This campaign was carried out from October 29 to November 20, 2006 in a 60 km times 40 km area of the Murrumbidgee catchment, southeast Australia. Temporal change detection analysis and accuracy in terms of spatial pattern distribution throughout the domain were assessed using a passive microwave airborne product derived from the Polarimetric L-band Multibeam Radiometer at 1-km spatial resolution. QuikSCAT-AMSR-E intercomparisons indicated higher correlations when using C-band observations. The greatest sensitivity to soil moisture was observed when using V-polarized backscatter measurement. While backscattering data showed adequate temporal sensitivity to changes in soil moisture due to precipitation events, the spatial agreement was complicated by the presence of irrigation and standing water (rice fields). This resulted in low Cramers Phi values (less than 0.06), which were used as a measure of spatial correspondence in terms of change in soil moisture and backscatter. In addition, the high QuikSCAT sensor frequency and existence of noise in the observed data contributed to the observed discrepancies.

Web service based hydrologic data distribution system

Hydrological modeling has been undergoing an exciting phase of transformation driven by rapid advances in remote sensing technology and computing power. In particular, NASAs Earth Observation System (EOS) suite of satellite platforms and sensors has made available a variety of biophysical measurements that have the potential to immensely advance the science and application of hydrology. These sensors use a variety of remote sensing technologies to make measurements of hydrological variables at several scales of spatial and temporal resolution. These rich datasets are available in a variety of data formats with different data access techniques, data quality issues and temporal and spatial extents that may be intimidating to the end user. As more and more watersheds become gauged and satellite instruments get deployed, it becomes important to make data availability and usage as streamlined as possible for potential users. We propose a Web Services based data distribution system to deliver remote sensing data to the hydrologic community. The use of Web Services for data delivery will permit hydrologists to directly integrate data sources into their hydrological models with minimal effort.

Using a data grid to automate data preparation pipelines required for regional-scale hydrologic modeling

Modeling a regional-scale hydrologic system introduces major data challenges related to the access and transformation of heterogeneous datasets into the information needed to execute a hydrologic model. These data preparation activities are difficult to automate, making the reproducibility and extensibility of model simulations conducted by others difficult or even impossible. This study addresses this challenge by demonstrating how the integrated Rule Oriented Data Management System (iRODS) can be used to support data processing pipelines needed when using data-intensive models to simulate regional-scale hydrologic systems. Focusing on the Variable Infiltration Capacity (VIC) model as a case study, data preparation steps are sequenced using rules within iRODS. VIC and iRODS are applied to study hydrologic conditions in the Carolinas, USA during the period 1998-2007 to better understand impacts of drought within the region. The application demonstrates how iRODS can support hydrologic modelers to create more reproducible and extensible model-based analyses. An approach for data processing to support hydrologic modeling is presented.The approach uses federated data grids and server-side data processing.The approach is demonstrated using the Variable Infiltration Capacity (VIC) model.The demonstration focuses on simulating drought in the Carolinas, USA.