Faisal Hossain

Investigating Error Metrics for Satellite Rainfall Data at Hydrologically Relevant Scales

Abstract This paper addresses the following open question: What set of error metrics for satellite rainfall data can advance the hydrologic application of new-generation, high-resolution rainfall products over land? The authors’ primary aim is to initiate a framework for building metrics that are mutually interpretable by hydrologists (users) and algorithm developers (data producers) and to provide more insightful information on the quality of the satellite estimates. In addition, hydrologists can use the framework to develop a space–time error model for simulating stochastic realizations of satellite estimates for quantification of the implication on hydrologic simulation uncertainty. First, the authors conceptualize the error metrics in three general dimensions: 1) spatial (how does the error vary in space?); 2) retrieval (how “off” is each rainfall estimate from the true value over rainy areas?); and 3) temporal (how does the error vary in time?). They suggest formulations for error metrics specific to...

The influence of large dams on surrounding climate and precipitation patterns

Understanding the forcings exerted by large dams on local climate is key to establishing if artificial reservoirs inadvertently modify precipitation patterns in impounded river basins. Using a 30 year record of reanalysis data, the spatial gradients of atmospheric variables related to precipitation formation are identified around the reservoir shoreline for 92 large dams of North America. Our study reports that large dams influence local climate most in Mediterranean, arid and semi-arid climates, while for humid climates the influence is least. During the growing season, large dams in Mediterranean climates increase CAPE 2-3 times near the reservoir compared to the non-growing season. Clear spatial gradients of CAPE, specific humidity and surface evaporation are also observed around the fringes between the reservoir shoreline and further from these dams. Because of the increasing correlation observed between higher percentile of rain and CAPE, our findings point to the possibility of storm intensification in impounded basins of the Mediterranean and arid climates of the United States.

Benchmarking High-Resolution Global Satellite Rainfall Products to Radar and Rain-Gauge Rainfall Estimates

This paper presents an in-depth investigation of the error properties of two high-resolution global-scale satellite rain retrievals verified against rainfall fields derived from a moderate-resolution rain-gauge network (25-30-km intergage distances) covering a region in the midwestern U.S. (Oklahoma Mesonet). Evaluated satellite retrievals include the NASA Tropical Rainfall Measuring Mission multisatellite precipitation analysis and the National Oceanic and Atmospheric Administration Climate Prediction Center morphing technique. The two satellite products are contrasted against a rain-gauge-adjusted radar rainfall product from the WSR-88D network in continental U.S. This paper presents an error characterization of the Mesonet rainfall fields based on an independent small-scale, but very dense (100-m intergage distances), rain-gauge network (named Micronet). The Mesonet error analysis, although significantly lower than the corresponding error statistics derived for the satellite and radar products, demonstrates the need to benchmark reference data sources prior to their quantitative use in validating remote sensing retrievals. In terms of the remote sensing rainfall products, this paper provides quantitative comparisons between the two satellite estimates and the most definitive rain-gauge-adjusted radar rainfall estimates at corresponding spatial and temporal resolutions (25 km and 3 hourly). Error quantification presented herein includes zero- (rain detection probability and false alarm), first- (bias ratio), and second-order (root mean square error and correlation) statistics as well as an evaluation of the spatial structure of error at warm and cold seasons of 2004 and 2006.

Dealing With Complexity and Extreme Events Using a Bottom-Up, Resource-Based Vulnerability Perspective

We discuss the adoption of a bottom-up, resource-based vulnerability approach in evaluating the effect of climate and other environmental and societal threats to societally critical resources. This vulnerability concept requires the determination of the major threats to local and regional water, food, energy, human health, and ecosystem function resources from extreme events including those from climate but also from other social and environmental issues. After these threats are identified for each resource, then the relative risks can be compared with other risks in order to adopt optimal preferred mitigation/adaptation strategies. This is a more inclusive way of assessing risks, including from climate variability and climate change, than using the outcome vulnerability approach adopted by the Intergovernmental Panel on Climate Change (IPCC). A contextual vulnerability assessment using the bottom-up, resource-based framework is a more inclusive approach for policy makers to adopt effective mitigation and adaptation methodologies to deal with the complexity of the spectrum of social and environmental extreme events that will occurinthecomingdecadesastherangeofthreatsareassessed,beyondjustthefocus on CO2 and a few other greenhouse gases as emphasized in the IPCC assessments.

Understanding the Scale Relationships of Uncertainty Propagation of Satellite Rainfall through a Distributed Hydrologic Model

The study presents a data-based numerical experiment performed to understand the scale relationships of the error propagation of satellite rainfall for flood evaluation applications in complex terrain basins. A satellite rainfall error model is devised to generate rainfall ensembles based on two satellite products with different retrieval accuracies and space‐time resolutions. The generated ensembles are propagated through a distributed physics-based hydrologic model to simulate the rainfall‐runoff processes at different basin scales. The resulted hydrographs are compared against the hydrograph obtained by using high-resolution radar rainfall as the ‘‘reference’’ rainfall input. The error propagation of rainfall to stream runoff is evaluated for a number of basin scales ranging between 100 and 1200 km 2 . The results from this study show that (i) use of satellite rainfall for flood simulation depends strongly on the scale of application (catchment area) and the satellite product resolution, (ii) different satellite products perform differently in terms of hydrologic error propagation, and (iii) the propagation of error depends on the basin size; for example, this study shows that small watersheds (,400 km 2 ) exhibit a higher ability in dampening the error from rainfall to runoff than larger-sized watersheds, although the actual error increases as drainage area decreases.

Understanding the Dependence of Satellite Rainfall Uncertainty on Topography and Climate for Hydrologic Model Simulation

A quantitative and physical understanding of satellite rainfall uncertainties provides meaningful guidance on improving algorithms to advance hydrologic prediction. The aim of this study is to characterize satellite rainfall errors and their impact on hydrologic fluxes based on fundamental governing factors that dictate the accuracy of passive remote sensing of precipitation. These governing factors are land features-comprising topography (elevation)-and climate type, representing the average ambient atmospheric conditions. First, the study examines satellite rainfall errors of three major products, 3B42RT, Climate prediction center MORHing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), by breaking the errors down into independent components (hit, miss-rain, and false-rain biases) and investigating their contribution to runoff and soil moisture errors. The uncertainties of three satellite rainfall products are explored for five regions of the Mississippi River basin that are categorized grid cell by grid cell (at the native spatial resolution of satellite products) based on topography and climate. It is found that total and hit biases dictate the temporal trend of soil moisture and runoff errors, respectively. Miss-rain and hit biases are the leading errors in the 3B42RT and CMORPH products, respectively, whereas false-rain bias is a pervasive problem of the PERSIANN product. For 3B42RT and CMORPH, about 50%-60% of grid cells are influenced by the total bias during winter and 60%-70% of grid cells during summer. For PERSIANN, about 70%-80% of the grid cells are marked by total bias during the summer and winter seasons. False-rain bias gradually increases from lowland to highland regions universally for all three satellite rainfall products. Overall, the study reveals that satellite rainfall uncertainty is dependent more on topography than the climate of the region. This studys results indicate that it is now worthwhile to assimilate the static knowledge of topography in the satellite estimation of precipitation to minimize the uncertainty in anticipation of the Global Precipitation Measurement mission.

Satellite-based Flood Modeling Using TRMM-based Rainfall Products

Increasingly available and a virtually uninterrupted supply of satellite-estimated rainfall data is gradually becoming a cost-effective source of input for flood prediction under a variety of circumstances. However, most real-time and quasi-global satellite rainfall products are currently available at spatial scales ranging from 0.25° to 0.50° and hence, are considered somewhat coarse for dynamic hydrologic modeling of basin-scale flood events. This study assesses the question: what are the hydrologic implications of uncertainty of satellite rainfall data at the coarse scale? We investigated this question on the 970 km2 Upper Cumberland river basin of Kentucky. The satellite rainfall product assessed was NASAs Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product called 3B41RT that is available in pseudo real time with a latency of 6-10 hours. We observed that bias adjustment of satellite rainfall data can improve application in flood prediction to some extent with the trade-off of more false alarms in peak flow. However, a more rational and regime-based adjustment procedure needs to be identified before the use of satellite data can be institutionalized among flood modelers.

Sensitivity analyses of satellite rainfall retrieval and sampling error on flood prediction uncertainty

The Global Precipitation Measurement mission planned jointly by the United States, Japanese, and European space agencies envisions providing global rainfall products from a constellation of passive microwave (PM) satellite sensors at time scales ranging from 3-6 h. In this paper, a sensitivity analysis was carried out to understand the implication of satellite PM rainfall retrieval and sampling errors on flood prediction uncertainty for medium-sized (/spl sim/100 km/sup 2/) watersheds. The 3-h rainfall sampling gave comparable flood prediction uncertainties with respect to the hourly sampling, typically used in runoff modeling, for a major flood event in Northern Italy. The runoff prediction error, though, was magnified up to a factor of 3 when rainfall estimates were derived from 6-h PM sampling intervals. The systematic and random error components in PM retrieval are shown to interact with PM sampling introducing added uncertainty in runoff simulation. The temporal correlation in the PM retrieval error was found to have a negligible effect in runoff prediction. It is shown that merging rain retrievals from hourly infrared (IR) and PM observations generally reduces flood prediction uncertainty. The error reduction varied between 50% (0%) and 80% (50%) for the 6-h (3-h) PM sampling scenarios, depending on the relative magnitudes of PM and IR retrieval errors. Findings from this paper are potentially useful for the design, planning, and application assessment of satellite remote sensing in flood and flash flood forecasting.

Improving flood forecasting in international river basins

In flood-prone international river basins (IRBs), many riparian nations that are located close to a basins outlet face a major problem in effectively forecasting flooding because they are unable to assimilate in situ rainfall data in real time across geopolitical boundaries. NASAs propose Global Precipitation Measurement (GPM) mission, which is expected to begin in 2010, will comprise high-resolution passive microwave (PM) sensors (at resolution ∼3-6 hours, 10 x 10 square kilometers) that may provide new opportunities to improve flood forecasting in these river basins. Research is now needed to realize the potential of GPM. With adequate research in the coming years, it may be possible to identify the specific IRBs that would benefit cost-effectively from a preprogrammed satellite-based forecasting system in anticipation of GPM. Acceleration of such a research initiative is worthwhile because it could reduce the risk of the cancellation of GPM [see Zielinski, 2005].

Proof of Concept of an Altimeter-Based River Forecasting System for Transboundary Flow Inside Bangladesh

Recent work by Biancamaria (Geophysical Research Letters, 2011) has demonstrated the potential of satellite altimetry to forecast incoming transboundary flow for downstream nations by detecting river levels at locations in upstream nations. Using the Ganges-Brahmaputra (GB) basin as an example, we assessed the operational feasibility of using JASON-2 satellite altimetry for forecasting such transboundary flow at locations further inside the downstream nation of Bangladesh by propagating forecasts derived from upstream (Indian) locations through a hydrodynamic river model. The 5-day forecast of river levels at upstream boundary points inside Bangladesh were used to initialize daily simulation of the hydrodynamic river model and yield the 5-day forecast river level further downstream inside Bangladesh. The forecast river levels were then compared with the 5-day-later “nowcast” simulation by the river model based on in-situ river level at the upstream boundary points in Bangladesh. Results show that JASON-2 retains good fidelity at 5-day lead forecast with an average RMSE (relative to nowcast) ranging from 0.5 m to 1.5 m and a mean bias (underestimation) of 0.25 m to 1.25 m in river water level estimation. Based on the proof-of-concept feasibility, a 4 month-long capacity building of the Bangladesh flood forecasting agency was undertaken. This facilitated a 20-day JASON-2 based forecasting of flooding during Aug 1, 2012 to Aug 20, 2012 up to a 5 day lead time in a real-time operational environment. Comparison against observed water levels at select river stations revealed an average error of forecast ranging from -0.4 m to 0.4 m and an RMSE ranging from 0.2 m to 0.7 m. In general, this study shows that satellite altimeter such as JASON-2 can indeed be an efficient and practical tool for building a robust forecasting system for transboundary flow.