Xianwu Xue

Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia

This paper examines the spatial error structures of eight precipitation estimates derived from four different satellite retrieval algorithms including TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). All the original satellite and bias-corrected products of each algorithm (3B42RTV7 and 3B42V7, CMORPH_RAW and CMORPH_CRT, GSMaP_MVK and GSMaP_Gauge, PERSIANN_RAW and PERSIANN_CDR) are evaluated against ground-based Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) over Central Asia for the period of 2004 to 2006. The analyses show that all products except PERSIANN exhibit overestimation over Aral Sea and its surrounding areas. The bias-correction improves the quality of the original satellite TMPA products and GSMaP significantly but slightly in CMORPH and PERSIANN over Central Asia. 3B42RTV7 overestimates precipitation significantly with large Relative Bias (RB) (128.17%) while GSMaP_Gauge shows consistent high correlation coefficient (CC) (>0.8) but RB fluctuates between −57.95% and 112.63%. The PERSIANN_CDR outperforms other products in winter with the highest CC (0.67). Both the satellite-only and gauge adjusted products have particularly poor performance in detecting rainfall events in terms of lower POD (less than 65%), CSI (less than 45%) and relatively high FAR (more than 35%).

Evaluation of Spatial Errors of Precipitation Rates and Types from TRMM Spaceborne Radar over the Southern CONUS

AbstractIn this paper, the authors estimate the uncertainty of the rainfall products from NASA and Japan Aerospace Exploration Agencys (JAXA) Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) so that they may be used in a quantitative manner for applications like hydrologic modeling or merging with other rainfall products. The spatial error structure of TRMM PR surface rain rates and types was systematically studied by comparing them with NOAA/National Severe Storms Laboratorys (NSSL) next generation, high-resolution (1 km/5 min) National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (QPE; NMQ/Q2) over the TRMM-covered continental United States (CONUS). Data pairs are first matched at the PR footprint scale (5 km/instantaneous) and then grouped into 0.25° grid cells to yield spatially distributed error maps and statistics using data from December 2009 through November 2010. Careful quality control steps (including bias correction with rain gauges and quality filtering...

Water balance‐based actual evapotranspiration reconstruction from ground and satellite observations over the conterminous United States

The objective of this study is to produce an observationally based monthly evapotranspiration (ET) product using the simple water balance equation across the conterminous United States (CONUS). We adopted the best quality ground and satellite-based observations of the water budget components, i.e., precipitation, runoff, and water storage change, while ET is computed as the residual. Precipitation data are provided by the bias-corrected PRISM observation-based precipitation data set, while runoff comes from observed monthly streamflow values at 592 USGS stream gauging stations that have been screened by strict quality controls. We developed a land surface model-based downscaling approach to disaggregate the monthly GRACE equivalent water thickness data to daily, 0.125° values. The derived ET computed as the residual from the water balance equation is evaluated against three sets of existing ET products. The similar spatial patterns and small differences between the reconstructed ET in this study and the other three products show the reliability of the observationally based approach. The new ET product and the disaggregated GRACE data provide a unique, important hydro-meteorological data set that can be used to evaluate the other ET products as a benchmark data set, assess recent hydrological and climatological changes, and terrestrial water and energy cycle dynamics across the CONUS. These products will also be valuable for studies and applications in drought assessment, water resources management, and climate change evaluation.

Hydrometeorological Analysis and Remote Sensing of Extremes: Was the July 2012 Beijing Flood Event Detectable and Predictable by Global Satellite Observing and Global Weather Modeling Systems?

AbstractPrediction, and thus preparedness, in advance of flood events is crucial for proactively reducing their impacts. In the summer of 2012, Beijing, China, experienced extreme rainfall and flooding that caused 79 fatalities and economic losses of

Evaluation of a Method to Enhance Real-Time, Ground Radar–Based Rainfall Estimates Using Climatological Profiles of Reflectivity from Space

1.6 billion. Using rain gauge networks as a benchmark, this study investigated the detectability and predictability of the 2012 Beijing event via the Global Hydrological Prediction System (GHPS), forced by the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis at near–real time and by the deterministic and ensemble precipitation forecast products from the NOAA Global Forecast System (GFS) at several lead times. The results indicate that the disastrous flooding event was detectable by the satellite-based global precipitation observing system and predictable by the GHPS forced by the GFS 4 days in advance. However, the GFS demonstrated inconsistencies from run to run, limiting the confidence in predicting the extreme event. T...

New Multisite Cascading Calibration Approach for Hydrological Models: Case Study in the Red River Basin Using the VIC Model

AbstractOver mountainous terrain, ground weather radars face limitations in monitoring surface precipitation as they are affected by radar beam blockages along with the range-dependent biases due to beam broadening and increase in altitude with range. These issues are compounded by precipitation structures that are relatively shallow and experience growth at low levels due to orographic enhancement. To improve surface precipitation estimation, researchers at the University of Oklahoma have demonstrated the benefits of integrating the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) products into the ground-based NEXRAD rainfall estimation system using a vertical profile of reflectivity (VPR) identification and enhancement (VPR-IE) approach. However, the temporal resolution of TRMM limits the application of VPR-IE method operationally. To implement the VPR-IE concept into the National Mosaic and Multi-Sensor QPE (NMQ) system in real time, climatological VPRs from 11 years of TRMM PR obse...

A threshold artificial neural network model for improving runoff prediction in a karst watershed

AbstractA novel multisite cascading calibration (MSCC) approach using the shuffled complex evolution–University of Arizona (SCE-UA) optimization method, developed at the University of Arizona, was employed to calibrate the variable infiltration capacity (VIC) model in the Red River Basin. Model simulations were conducted at 35 nested gauging stations. Compared with simulated results using a priori parameters, single-site calibration can improve VIC model performance at specific calibration sites; however, improvement is still limited in upstream locations. The newly developed MSCC approach overcomes this limitation. Simulations using MSCC not only utilize all of the available streamflow observations but also better represent spatial heterogeneities in the model parameters. Results indicate that MSCC largely improves model performance by decreasing the number of stations with negative Nash-Sutcliffe coefficient of efficiency (NSCE) values from 69% (66%) for a priori parameters to 37% (34%) for single-site ...

Systematical evaluation of VPR- identification and enhancement (VPR-IE) approach for different precipitation types

Abstract Artificial neural network model (ANN) has been extensively used in hydrological prediction. Generally, most existing rainfall-runoff models including artificial neural network model are not very successful at simulating streamflow in karst watersheds. Due to the complex physical structure of karst aquifer systems, runoff generation processes are quite different during flood and non-flood periods. In this paper, an ANN model based on back-propagation algorithm was developed to simulate and predict daily streamflow in karst watersheds. The idea of threshold was introduced into artificial neural network model [hereafter called Threshold-ANN model (T-ANN)] to represent the nonlinear characteristics of the runoff generation processes in the flood and non-flood periods. The T-ANN model is applied to the Hamajing watershed, which is a small karst watershed in Hubei Province, China. The network input, the previous discharge, is determined by the correlative analysis, and the network structure is optimized with the maximum Nash coefficient as the objective function. And the precipitation and previous discharge are chosen as the threshold factors to reflect the effect of specificity of karst aquifer systems, respectively. By using the T-ANN, the simulation errors of streamflow have been reduced, and the simulation becomes more successful, which would be helpful for runoff prediction in karst watersheds.

Advancement in Research of Hydrological Processes Simulation for Karst Region and Future Challenges

Over complex terrains, ground radars usually rely on scans at higher elevation angles to observe precipitating systems. The surface quantitative precipitation estimation (QPE) might have considerable errors if veridical structure of precipitation is not considered because radar reflectivity varies with height due to evaporation at low levels as well as processes of melting, aggregation, and drop break-up. The vertical profile of reflectivity (VPR) links the surface precipitation to the radar observation at higher levels, which is very useful for accurately estimating the surface rainfall. Researchers at the University of Oklahoma have demonstrated the integration of the Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar products (4-km precipitation quantity, types, and 250-meter vertical profile of reflectivity (VPR)) into the NEXRAD ground-based radar rainfall estimation system. In the latest progress in the VPRIdentification and Enhancement (VPR-IE) approach, we have optimally combined the climatological VPR information to the National Mosaic QPE (NMQ) system from 1 January 2011 to 31 December 2011 over the Mountainous West Region of the U.S. Performance of latest VPR-IE is systematically evaluated by rain gauges measurements for different precipitation types. The results indicate improvements in precipitation detection and estimation following the incorporation of space-based radar information into ground radar networks.

Statistical and hydrological evaluation of TRMM-based Multi-satellite Precipitation Analysis over the Wangchu Basin of Bhutan: Are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins?

Hydrological processes simulation is an effective way for water resources evaluation and can provide scientific basis for sustainable utilization of water resources and ecological environment restoration. Compared with traditional watershed hydrological processes, hydrological processes in karst region have their unique in runoff generation and concentration stage because of the complexity and multiplicity of karst aquifer system. This paper reviews the two stages of hydrological processes simulation method in karst region: 1. systematic simulation model stage; 2. process based mechanism model stage. By analyzing the characteristics and limitation of two kinds of models, the tendency of future karst hydrological processes simulation method in two aspects are discussed: 1. quasi physically based model balancing physical senses and data richness; 2. scale adaptable model based on macro-scale applicable equations.