Seasonal effect on spatial and temporal consistency of the new GPM-based IMERG-v5 and GSMaP-v7 satellite precipitation estimates in Brazil’s Central Plateau Region

This study assesses the performance of the new Global Precipitation Measurement (GPM)-based satellite precipitation estimates (SPEs) datasets in the Brazilian Central Plateau and compares it with the previous Tropical Rainfall Measurement Mission (TRMM)-era datasets. To do so, the Integrated Multi-satellitE Retrievals for GPM (IMERG)-v5 and the Global Satellite Mapping of Precipitation (GSMaP)-v7 were evaluated at their original 0.1 spatial resolution and for a 0.25 grid for comparison with TRMM Multi-satellite Precipitation Analysis (TMPA). The assessment was made on an annual, monthly, and daily basis for both wet and dry seasons. Overall, IMERG presents the best annual and monthly results. In both time steps, IMERG’s precipitation estimations present bias with lower magnitudes and smaller root-mean-square error. However, GSMaP performs slightly better for the daily time step based on categorical and quantitative statistical analysis. Both IMERG and GSMaP estimates are seasonally influenced, with the highest difficulty in estimating precipitation occurring during the dry season. Additionally, the study indicates that GPM-based SPEs products are capable of continuing TRMM-based precipitation monitoring with similar or even better accuracy than obtained previously with the widely used TMPA product

Remote Sensing of Precipitation: Part II

Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products

Decadal development of CREST hydrological model family: review, improvements, applications, and outlook

A hydrological model is an indispensable tool in Earth system science and engineering operations to understand, predict, and manage water resources on Earth. The Coupled Routing and Excess Storage (CREST) model, released in 2011, is one such to simulate distributed hydrologic states and fluxes at variable scales. Over the last decade, CREST model has been actively under development and applied by different sectors to tackle water-related problems worldwide. This dissertation is dedicated to expanding the capacity of CREST model from three main fronts: (1) hydrologic data, (2) model development, and (3) applications. To start, the decadal development and applications of CREST model family were reviewed to lay the foundation for my contribution (Chapter 1). First, uncertainties in hydrologic input data were evaluated comprehensively for three state-of-the-science precipitation datasets derived from in-situ instruments, ground weather radar, and satellites during extreme events (Chapter 2); then a 120-year CONUS-wide flood database was compiled into a unified format as a validation source for models and hydroclimatic research (Chapter 3). From the model development front, a Hydrologic&Hydraulic (H&H) framework was developed to empower flood inundation mapping capacity for CREST (Chapter 4); furthermore, the re-infiltration, an important yet often ignored hydrologic process during the flooding period, was incorporated to improve the more realistic rainfall-runoff modeling representation (Chapter 5). To further improve the model efficiency, a vector-based CREST model was developed that can achieve 10x speedup for a continental-scale simulation, as well as improved model accuracy (Chapter 6). Finally on the model application, the high-resolution CREST model was applied in quantifying future US floods in a warmer climate: flood flashiness is becoming 7.9% higher for the continent (Chapter 7); and extreme rainfall and floods are becoming more frequent, widespread, and less seasonal (Chapter 8). The final Chapter 9 summarizes the contributions to the CREST model family development, outlooks, and general remarks for advancing our understanding of hydrologic science and engineering

Statistical and Hydrological Evaluations of Multi-Satellite Precipitation Products over Fujiang River Basin in Humid Southeast China

The purpose of the paper is to evaluate the quality and hydrological utility of four popular satellite precipitation products, including the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product (3B42V7), near real-time product (3B42RT), and the Climate Prediction Center (CPC) MORPHing technique (CMORPH) satellite⁻gauge merged product (CMORPH BLD) and bias-corrected product (CMORPH CRT) over Fujiang River basin, China. First, we provided a statistical assessment of the four precipitation products at multiple spatiotemporal scales. The results show that: (1) all the products except 3B42RT capture the spatial pattern of annual precipitation fairly well; (2) in general, CMORPH BLD benefits from the application of the probability density function-optimal interpolation (PDF-OI) gauge adjustment algorithm and performs best among all the products with Pearson correlation coefficients (CC) of 0.84 and 0.94, equitable threat score (ETS) of 0.56 and 0.63 in grid and basin scales, respectively, followed by 3B42V7 and CMORPH CRT; whereas 3B42RT performs worst across all the metrics; (3) according to the occurrence frequencies of rainfall, satellite estimates mainly fall into the bin of 0⁻1 mm/day and tend to underestimate light precipitation. In addition, the performance of all the products in warm season is much better than in cold season in both grid and basin scales. Subsequently, a physically based distributed model is established to further evaluate the hydrological utility of different precipitation products. The results reveal that: (1) the errors in precipitation products mainly propagate into hydrological simulations, resulting in the best hydrological performance in CMORPH BLD in both daily and monthly scales after recalibrating the model, while 3B42RT shows limited skills in reproducing the daily observed hydrograph; (2) after recalibrating the model with the respective satellite data, significant improvements are observed for all the products; (3) CMORPH BLD no longer shows its superiority during near-real-time monitoring of floods. There is still a great challenge for the application of current satellite-based estimates into local flood monitoring. This study could be used as guidance for choosing alternative satellite precipitation products for hydrological applications in a local community, particularly in basins in which rainfall gauges are scarce