Advanced Remote Sensing Precipitation Input for Improved Runoff Simulation : Local to regional scale modelling

Accurate precipitation data are crucial for hydrological modelling and rainwater runoff management. Precipitation variability exists through a wide range of spatial and temporal scales and cannot be captured well using sparse rain gauge networks. This limitation is further emphasised for urban and mountainous catchments, especially under global warming, causing an increased frequency of extreme events. Recent advances in remote sensing (RS) techniques make monitoring precipitation possible over larger areas at more regular resolutions than conventional rain gauge networks. The RS data can be biased mainly due to the indirect estimations prone to multiple error sources and temporally discrete observations. The wealth of spatiotemporal precipitation data by RS, however, calls for developing data-driven solutions for both the bias correction and hydrological modelling that, in turn, requires new procedures to assure generalization of the existing methods. The present dissertation comprises a comprehensive summary followed by five appended papers, attempting to evaluate quantitative precipitation estimations (QPE) by state-of-the-art instruments/products for local and regional hydrological applications. Accordingly, two recently installed dual polarimetric doppler X-band weather radars (X-WRs) in southern Sweden and multiple Global Precipitation Mission (GPM) products in Iran were studied at the relevant scales for urban hydrology (1–5-min and sub-km) and large water supply river–reservoir system operation (daily-monthly and 0.1°), respectively. The validation against rain gauge observations (Paper I and II) showed a significant dependency of the X-WR and GPM precipitation errors on the radial distance and regional precipitation pattern, respectively. Taking observations from local tipping bucket rain gauges at the 1–30-km ranges as a reference, the apparent problems with a single X-WR is related to the attenuation during heavy rains and overshooting (at higher elevation angle scans). An internationally bias-corrected GPM product called GPM-IMERG-Final shows a generally good correlation to synoptic observations of over 300 rain gauges in Iran except for extreme observations that are much better predicted by the GPM-IMERG Late product during spring, summer, and autumn seasons. To leverage the wealth of spatiotemporally complete and validated precipitation data for hydrological modelling, two novel data-driven procedures using artificial neural networks (ANNs) were developed. As in Paper III, the formulation of the new ANN input variables, namely, ECOVs and CCOVs, representing the event- and catchment-specific areal precipitation coverage ratios, improve monthly runoff estimations in all the studied sub-catchments of the Karkheh River basin (KRB) in the mountainous semi-arid climate of western Iran. Merging the doppler and dual-polarization data in the overlapping coverage of the two XWRs (Paper IV) via an ANN-based QPE improves rainfall detection and accuracy. ANN-assisted estimation of rainfall quantiles, compared to the merging with an empirically based regression model, also shows better results especially related to the extreme 5-min data. Finally, Paper V describes the impact of human activities such as agricultural developments that can equally affect the runoff variation. This fact is considered in Paper III by including MODIS Terra products as additional inputs

Benefits of the successive GPM based satellite precipitation estimates IMERG–V03, –V04, –V05 and GSMaP–V06, –V07 over diverse geomorphic and meteorological regions of Pakistan

Launched in 2014, the Global Precipitation Measurement (GPM) mission aimed at ensuring the continuity with the Tropical Rainfall Measuring Mission (TRMM) launched in 1997 that has provided unprecedented accuracy in Satellite Precipitation Estimates (SPEs) on the near-global scale. Since then, various SPE versions have been successively made available from the GPM mission. The present study assesses the potential benefits of the successive GPM based SPEs product versions that include the Integrated Multi–Satellite Retrievals for GPM (IMERG) version 3 to 5 (–v03, –v04, –v05) and the Global Satellite Mapping of Precipitation (GSMaP) version 6 to 7 (–v06, –v07). Additionally, the most effective TRMM based SPEs products are also considered to provide a first insight into the GPM effectiveness in ensuring TRMM continuity. The analysis is conducted over different geomorphic and meteorological regions of Pakistan while using 88 precipitations gauges as the reference. Results show a clear enhancement in precipitation estimates that were derived from the very last IMERG–v05 in comparison to its two previous versions IMERG–v03 and –v04. Interestingly, based on the considered statistical metrics, IMERG–v03 provides more consistent precipitation estimate than IMERG–v04, which should be considered as a transition IMERG version. As expected, GSMaP–v07 precipitation estimates are more accurate than the previous GSMaP–v06. However, the enhancement from the old to the new version is very low. More generally, the transition from TRMM to GPM is successful with an overall better performance of GPM based SPEs than TRMM ones. Finally, all of the considered SPEs have presented a strong spatial variability in terms of accuracy with none of them outperforming the others, for all of the gauges locations over the considered regions

How accurate are satellite estimates of precipitation over the north Indian Ocean?

Following the launch of the Global Precipitation Measurement (GPM) Core Observatory in early 2014, motivated from the successful Tropical Rainfall Measurement Mission (TRMM) satellite, an advanced and sophisticated global multi-satellite precipitation product – Integrated Multi- satellitE Retrievals for GPM (IMERG) was released at finer spatio-temporal scales. This precipitation product has been upgraded recently after several refinements and supposed to be superior to other existing global or quasi-global multi-satellite precipitation estimates. In the present study, IMERG precipitation is comprehensively evaluated for the first time against moored buoy observations over the north Indian Ocean at hourly scale for the study period of March 2014 to December 2015. IMERG precipitation performs considerably better over the Bay of Bengal than the Arabian Sea in both detection and estimation. The systematic error in IMERG is appreciably lower by about 14%, however, it generally overestimates in-situ precipitation and also exhibits noticeable false alarms. Furthermore, IMERG essentially shows an improvement over the TRMM Multi-satellite Precipitation Analysis (TMPA) at a daily scale over the north Indian Ocean. IMERG precipitation estimates show overall promising error characteristics, but there is still a need of substantial efforts for improvement in the satellite-based precipitation estimation algorithms especially over data sparse regions such as north Indian Ocean

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of 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 a 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

Extension of the TAMSAT satellite-based rainfall monitoring over Africa and from 1983 to present

Tropical Applications of Meteorology Using Satellite Data and Ground-Based Observations (TAMSAT) rainfall monitoring products have been extended to provide spatially contiguous rainfall estimates across Africa. This has been achieved through a new, climatology-based calibration, which varies in both space and time. As a result, cumulative estimates of rainfall are now issued at the end of each 10-day period (dekad) at 4-km spatial resolution with pan-African coverage. The utility of the products for decision making is improved by the routine provision of validation reports, for which the 10-day (dekadal) TAMSAT rainfall estimates are compared with independent gauge observations. This paper describes the methodology by which the TAMSAT method has been applied to generate the pan-African rainfall monitoring products. It is demonstrated through comparison with gauge measurements that the method provides skillful estimates, although with a systematic dry bias. This study illustrates TAMSAT’s value as a complementary method of estimating rainfall through examples of successful operational application

Geoinformatic and Hydrologic Analysis using Open Source Data for Floods Management in Pakistan

There is being observed high variability in the spatial and temporal rainfall patterns under changing climate, enhancing both the intensity and frequency of the natural disasters like floods. Pakistan, a country which is highly prone to climate change, is recently facing the challenges of both flooding and severe water shortage as the surface water storage capacity is too limited to cope with heavy flows during rainy months. Thus, an effective and timely predication and management of high flows is a dire need to address both flooding and long term water shortage issues. The work of this thesis was aimed at developing and evaluating different open source data based methodologies for floods detection and analysis in Pakistan. Specifically, the research work was conducted for developing and evaluating a hydrologic model being able to run in real time based on satellite rainfall data, as well as to perform flood hazard mapping by analyzing seasonality of flooded areas using MODIS classification approach. In the first phase, TRMM monthly rainfall data (TMPA 3B43) was evaluated for Pakistan by comparison with rain gauge data, as well as by further focusing on its analysis and evaluation for different time periods and climatic zones of Pakistan. In the next phase, TRMM rainfall data and other open source datasets like digital soil map and global land cover map were utilized to develop and evaluate an event-based hydrologic model using HEC-HMS, which may be able to be run in real time for predicting peak flows due to any extreme rainfall event. Finally, to broaden the study canvas from a river catchment to the whole country scale, MODIS automated water bodies classification approach with MODIS daily surface reflectance products was utilized to develop a historical archive of reference water bodies and perform seasonal analysis of flooded areas for Pakistan. The approach was found well capable for its application for floods detection in plain areas of Pakistan. The open source data based hydrologic modeling approach devised in this study can be helpful for conducting similar rainfall-runoff modeling studies for the other river catchments and predicting peak flows at a river catchment scale, particularly in mountainous topography. Similarly, the outcomes of MODIS classification analysis regarding reference and seasonal water and flood hazard maps may be helpful for planning any management interventions in the flood prone areas of Pakistan

High-resolution precipitation datasets in South America and West Africa based on satellite-derived rainfall, Enhanced Vegetation Index and Digital Elevation Model

Mean Annual Precipitation is one of the most important variables used in water resource management. However, quantifying Mean Annual Precipitation at high spatial resolution, needed for advanced hydrological analysis, is challenging in developing countries which often present a sparse gauge network and a highly variable climate. In this work, we present a methodology to quantify Mean Annual Precipitation at 1 km spatial resolution using different precipitation products from satellite estimates and gauge observations at coarse spatial resolution (i.e., ranging from 4 km to 25 km). Examples of this methodology are given for South America and West Africa. We develop a downscaling method that exploits the relationship among satellite-derived rainfall, Digital Elevation Model and Enhanced Vegetation Index. At last, we validate its performance using rain gauge measurements: comparable annual precipitation estimates for both South America and West Africa are retrieved. Validation indicates that high resolution Mean Annual Precipitation downscaled from CHIRP (Climate Hazards Group Infrared Precipitation) and GPCC (Global Precipitation Climatology Centre) datasets present the best ensemble of performance statistics for both South America and West Africa. Results also highlight the potential of the presented technique to downscale satellite-derived rainfall worldwide.JRC.H.1-Water Resource

Estimating Precipitation from WSR-88D Observations and Rain Gauge Data: Potential for Drought Monitoring

Since its deployment, the precipitation estimates from the network of National Weather Service (NWS) Weather Surveillance Radars-1988 Doppler (WSR-88D) have become widely used. These precipitation estimates are used for the flash flood warning program at NWS Weather Forecast Offices (WFOs) and the hydrologic program at NWS River Forecast Centers (RFCs), and they also show potential as an input data set for drought monitoring. However, radar-based precipitation estimates can contain considerable error because of radar limitations such as range degradation and radar beam blockage or false precipitation estimates from anomalous propagation (AP) of the radar beam itself. Because of these errors, for operational applications, the RFCs adjust the WSR-88D precipitation estimates using a multisensor approach. The primary goal of this approach is to reduce both areal-mean and local bias errors in radar-derived precipitation by using rain gauge data so that the final estimate of rainfall is better than an estimate from a single sensor. This chapter briefly discusses the past efforts for estimating mean areal precipitation (MAP). Although there are currently several radar and rain gauge estimation techniques, such as Process 3, Mountain Mapper, and Daily Quality Control (QC), this chapter will emphasize the Multisensor Precipitation Estimator (MPE) Precipitation Processing System (PPS). The challenges faced by the Hydrometeorological Analysis and Support (HAS) forecasters at RFCs to quality control all sources of precipitation data in the MPE program, including the WSR-88D estimates, will be discussed. The HAS forecaster must determine in real time if a particular radar is correctly estimating, overestimating, or underestimating precipitation and make adjustments within the MPE program so the proper amount of precipitation is determined. In this chapter, we discuss procedures used by the HAS forecasters to improve initial best estimates of precipitation using 24 h rain gauge data, achieving correlation coefficients greater than 0.85. Finally, since several organizations are now using the output of MPE for deriving short- and long-term Standardized Precipitation Indices (SPIs), this chapter will discuss how spatially distributed estimates of precipitation can be used for drought monitoring