Assessment of satellite rainfall products for streamflow simulation in medium watersheds of the Ethiopian highlands

The objective is to assess the suitability of commonly used high-resolution satellite rainfall products (CMORPH, TMPA 3B42RT, TMPA 3B42 and PERSIANN) as input to the semi-distributed hydrological model SWAT for daily streamflow simulation in two watersheds (Koga at 299 km<sup>2</sup> and Gilgel Abay at 1656 km<sup>2</sup>) of the Ethiopian highlands. First, the model is calibrated for each watershed with respect to each rainfall product input for the period 2003–2004. Then daily streamflow simulations for the validation period 2006–2007 are made from SWAT using rainfall input from each source and corresponding model parameters; comparison of the simulations to the observed streamflow at the outlet of each watershed forms the basis for the conclusions of this study. Results reveal that the utility of satellite rainfall products as input to SWAT for daily streamflow simulation strongly depends on the product type. The 3B42RT and CMORPH simulations show consistent and modest skills in their simulations but underestimate the large flood peaks, while the 3B42 and PERSIANN simulations have inconsistent performance with poor or no skills. Not only are the microwave-based algorithms (3B42RT, CMORPH) better than the infrared-based algorithm (PERSIANN), but the infrared-based algorithm PERSIANN also has poor or no skills for streamflow simulations. The satellite-only product (3B42RT) performs much better than the satellite-gauge product (3B42), indicating that the algorithm used to incorporate rain gauge information with the goal of improving the accuracy of the satellite rainfall products is actually making the products worse, pointing to problems in the algorithm. The effect of watershed area on the suitability of satellite rainfall products for streamflow simulation also depends on the rainfall product. Increasing the watershed area from 299 km<sup>2</sup> to 1656 km<sup>2</sup> improves the simulations obtained from the 3B42RT and CMORPH (i.e. products that are more reliable and consistent) rainfall inputs while it deteriorates the simulations obtained from the 3B42 and PERSIANN (i.e. products that are unstable and inconsistent) rainfall inputs

Deep Neural Network Cloud-Type Classification (DeepCTC) model and its application in evaluating PERSIANN-CCS

Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from multispectral data from the Advanced Baseline Imager (ABI) onboard the GOES-16 platform. The DeepCTC model is trained and tested using coincident data from both CloudSat and ABI over the CONUS region. Evaluations of DeepCTC indicate that the model performs well for a variety of cloud types including Altostratus, Altocumulus, Cumulus, Nimbostratus, Deep Convective and High clouds. However, capturing low-level clouds remains a challenge for the model. Results from simulated GOES-16 ABI imageries of the Hurricane Harvey event show a large-scale perspective of the rapid and consistent cloud-type monitoring is possible using the DeepCTC model. Additionally, assessments using half-hourly Multi-Radar/Multi-Sensor (MRMS) precipitation rate data (for Hurricane Harvey as a case study) show the ability of DeepCTC in identifying rainy clouds, including Deep Convective and Nimbostratus and their precipitation potential. We also use DeepCTC to evaluate the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) product over different cloud types with respect to MRMS referenced at a half-hourly time scale for July 2018. Our analysis suggests that DeepCTC provides supplementary insights into the variability of cloud types to diagnose the weakness and strength of near real-time GEO-based precipitation retrievals. With additional training and testing, we believe DeepCTC has the potential to augment the widely used PERSIANN-CCS algorithm for estimating precipitation

Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau

On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellite-based rainfall products, which allow for a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitable alternatives for studies on investigating the hydrological processes and climate change. In this study, a newly developed daily satellite-based precipitation product, termed Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR), is used as input for a hydrologic model to simulate streamflow in the upper Yellow and Yangtze River basins on the Tibetan Plateau. The results show that the simulated streamflows using PERSIANN-CDR precipitation and the Global Land Data Assimilation System (GLDAS) precipitation are closer to observation than that using limited gauge-based precipitation interpolation in the upper Yangtze River basin. The simulated streamflow using gauge-based precipitation are higher than the streamflow observation during the wet season. In the upper Yellow River basin, gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation have similar good performance in simulating streamflow. The evaluation of streamflow simulation capability in this study partly indicates that the PERSIANN-CDR rainfall product has good potential to be a reliable dataset and an alternative information source of a limited gauge network for conducting long-term hydrological and climate studies on the Tibetan Plateau

Cloud Impact Parameters Derived from A-Train Satellite, ERA-Interim, MERRA-2 and Their Relationship to the Environment

Cloud feedback remains one of the largest sources of uncertainty in model climate sensitivity estimates, partly because of the complicated interactions between convective processes, radiative effects, and the large-scale circulation. Cloud radiative effects and precipitation processes have been linked in both deep convective clouds (DC) and low cloud regimes, which points to the importance of understanding the connections between the latent heating from precipitation and surface and atmospheric cloud radiative effects. In this paper, cloud impact parameters (CIPs), including Gvc, Avc and Nvc and energy and water coupling parameters (EWCPs) are examined. The two EWCPs, the surface radiative cooling efficiency, Rvc and the atmospheric heating efficiency, Rvh are used to characterize how efficiently a cloud can heat the atmosphere or cool the surface per unit rain. EWCPs link both cloud radiative properties and precipitation properties together to demonstrate the synergistic effects of the cloud-precipitation-radiation interaction (CPRI). Global distributions of CIPs and EWCPs are highly dependent on cloud regimes and reanalyses fail to simulate strong Rvc and Rvh over deep convection regions in the Indo-Pacific warm pool region, but produce stronger Rvc and Rvh over marine stratocumulus regions. Together, these indicate the possibility that the variability of the Walker circulation simulated by reanalysis is underestimated. To understand how the environment modulates the EWCPs, the EWCPs from A-Train observations, ERA-Interim and MERRA-2 datasets are conditionally sampled by dynamic and thermodynamic variables including vertical pressure velocity (w), sea surface temperature (SST), and column water vapor (CWV). The dynamic regime controls the sign of Rvh, while the CWV appears to be the larger control on the magnitude. The magnitude of Rvc is highly coupled to the dynamic regime. Observations also show two thermodynamic regions of strong Rvc, at low SST and CWV and at high SST and CWV, only the former of which is captured by the reanalyses. The results in this paper can be a reference for improving parameterizations important for coupling the energy and water cycles in global climate models

Cloud Impact Parameters Derived from A-Train Satellite, ERA-Interim, MERRA-2 and Their Relationship to the Environment

Cloud feedback remains one of the largest sources of uncertainty in model climate sensitivity estimates, partly because of the complicated interactions between convective processes, radiative effects, and the large-scale circulation. Cloud radiative effects and precipitation processes have been linked in both deep convective clouds (DC) and low cloud regimes, which points to the importance of understanding the connections between the latent heating from precipitation and surface and atmospheric cloud radiative effects. In this paper, cloud impact parameters (CIPs), including Gvc, Avc and Nvc and energy and water coupling parameters (EWCPs) are examined. The two EWCPs, the surface radiative cooling efficiency, Rvc and the atmospheric heating efficiency, Rvh are used to characterize how efficiently a cloud can heat the atmosphere or cool the surface per unit rain. EWCPs link both cloud radiative properties and precipitation properties together to demonstrate the synergistic effects of the cloud-precipitation-radiation interaction (CPRI). Global distributions of CIPs and EWCPs are highly dependent on cloud regimes and reanalyses fail to simulate strong Rvc and Rvh over deep convection regions in the Indo-Pacific warm pool region, but produce stronger Rvc and Rvh over marine stratocumulus regions. Together, these indicate the possibility that the variability of the Walker circulation simulated by reanalysis is underestimated. To understand how the environment modulates the EWCPs, the EWCPs from A-Train observations, ERA-Interim and MERRA-2 datasets are conditionally sampled by dynamic and thermodynamic variables including vertical pressure velocity (w), sea surface temperature (SST), and column water vapor (CWV). The dynamic regime controls the sign of Rvh, while the CWV appears to be the larger control on the magnitude. The magnitude of Rvc is highly coupled to the dynamic regime. Observations also show two thermodynamic regions of strong Rvc, at low SST and CWV and at high SST and CWV, only the former of which is captured by the reanalyses. The results in this paper can be a reference for improving parameterizations important for coupling the energy and water cycles in global climate models

DEVELOPMENT AND EVALUATION OF AN ADVANCED REGIONAL AND GLOBAL HYDROLOGICAL PREDICTION SYSTEM ENABLED BY SATELLITE REMOTE SENSING, NUMERICAL WEATHER FORECASTING, AND ENSEMBLE DATA ASSIMILATION

This dissertation advanced the traditional hydrological prediction via multi-sensor satellite remote sensing products, numerical weather forecasts and advanced data assimilation approach in sparsely gauged or even ungauged regions and then extend this approach to global scale with enhanced efficiency for prototyping a flood early warning system on a global basis. This dissertation consists of six chapters: the first chapter is the introductive chapter which describes the problem and raises the hypotheses, Chapters 2 to 5 are the four main Chapters followed by Chapter 6 which is an overall summary of this dissertation. For regional hydrological prediction in Chapter 2 and 3, two rainfall – runoff hydrological models: the HyMOD (Hydrological MODel) and the simplified version of CREST (Coupled Routing and Excess Storage) Model were set up and tested in Cubango River basin, Africa. In Chapter 2, first, the AMSR-E (Advanced Microwave Scanning Radiometer for Earth observing system) signal/TMI (TRMM Microwave Imager) passive microwave streamflow signals are converted into actual streamflow domain with the unit of m3/s by adopting the algorithm from Brakenridge et al. (2007); then the HyMOD was coupled with Ensemble Square Root Filter (EnSRF) to account for uncertainty in both forcing data and model initial conditions and thus improve the flood prediction accuracy by assimilating the signal converted streamflow, in comparison to the benchmark assimilation of in-situ streamflow observations in actual streamflow domain with the unit of m3/s. In Chapter 3, the remote-sensing streamflow signals, without conventional in-situ hydrological measurements, was applied to force, calibrate and update the hydrologic model coupled with EnSRF data assimilation approach in the same research region, but resulting in exceedance probability-based flood prediction. For global hydrological predictions in Chapter 4 and 5, a physical based distributed hydrological model CREST is set up at 1/8 degree from 50°N to 50°S and forms the Real Time Hydrological Prediction System (http://eos.ou.edu) which was co-developed by HyDROS (Hydrometeorology and Remote Sensing Laboratory) lab at the University of Oklahoma and NASA Goddard center. In Chapter 4, the CREST model is described with details and then the Real Time Global Hydrological Monitoring System will be comprehensively evaluated on basis of gauge based streamflow observation and gridded global runoff data from GRDC (Global Runoff Data Center, http://www.bafg.de/GRDC/EN/Home/homepage_node.html). In order to extend the hydrological forecast horizon for the Real Time Global Hydrological Prediction System, the deterministic precipitation forecast fields from a numerical meteorological model GFS (Global Forecasting System) as well as the ensemble precipitation forecast fields are introduced as the forcing data to be coupled into the global CREST model in order to generate the global hydrological forecasting up to around 7 days lead time in Chapter 5. The July 21, 2012 Beijing extreme flooding event is selected to evaluate the hydrological prediction skills for extremes of both the deterministic and the ensemble GFS products

Spatio-temporal appraisal of water-borne erosion using optical remote sensing and GIS in the Umzintlava catchement (T32E), Eastern Cape, South Africa.

Globally, soil erosion by water is often reported as the worst form of land degradation owing to its adverse effects, cutting across the ecological and socio-economic spectrum. In general, soil erosion negatively affects the soil fertility, effectively rendering the soil unproductive. This poses a serious threat to food security especially in the developing world including South Africa where about 6 million households derive their income from agriculture, and yet more than 70% of the country’s land is subject to erosion of varying intensities. The Eastern Cape in particular is often considered the most hard-hit province in South Africa due to meteorological and geomorphological factors. It is on this premise the present study is aimed at assessing the spatial and temporal patterns of water-borne erosion in the Umzintlava Catchment, Eastern Cape, using the Revised Universal Soil Loss Equation (RUSLE) model together with geospatial technologies, namely Geographic Information System (GIS) and remote sensing. Specific objectives were to: (1) review recent developments on the use of GIS and remote sensing technologies in assessing and deriving soil erosion factors as represented by RUSLE parameters, (2) assess soil erosion vulnerability of the Umzintlava Catchment using geospatial driven RUSLE model, and (3) assess the impact of landuse/landcover (LULC) change dynamics on soil erosion in the study area during the period 1989-2017. To gain an understanding of recent developments including related successes and challenges on the use of geospatial technologies in deriving individual RUSLE parameters, extensive literature survey was conducted. An integrative methodology, spatially combining the RUSLE model with Systeme Pour l’Obsevation de la Terre (SPOT7) imagery within a digital GIS environment was used to generate relevant information on erosion vulnerability of the Umzintlava Catchment. The results indicated that the catchment suffered from unprecedented rates of soil loss during the study period recording the mean annual soil loss as high as 11 752 t ha−1yr−1. Topography as represented by the LS-factor was the most sensitive parameter to soil loss occurring in hillslopes, whereas in gully-dominated areas, soil type (K-factor) was the overriding factor. In an attempt to understand the impact of LULC change dynamics on soil erosion in the Umzintlava Catchment from the period 1989-2017 (28 years), multi-temporal Landsat data together with RUSLE was used. A post-classification change detection comparison showed that water bodies, agriculture, and grassland decreased by 0.038%, 1.796%, and 13.417%, respectively, whereas areas covered by forest, badlands, and bare soil and built-up area increased by 3.733%, 1.778%, and 9.741% respectively, during the study period. The mean annual soil loss declined from 1027.36 t ha−1yr−1 in 1989 to 138.71 t ha−1yr−1 in 2017. Though soil loss decreased during the observed period, there were however apparent indications of consistent increase in soil loss intensity (risk), most notably, in the elevated parts of the catchment. The proportion of the catchment area with high (25 – 60 t ha−1yr−1) to extremely high (>150 t ha−1yr−1) soil loss risk increased from 0.006% in 1989 to 0.362% in 2017. Further analysis of soil loss results by different LULC classes revealed that some LULC classes, i.e. bare soil and built-up area, agriculture, grassland, and forest, experienced increased soil loss rates during the 28 years study period. Overall, the study concluded that the methodology integrating the RUSLE model with GIS and remote sensing is not only accurate and time-efficient in identifying erosion prone areas in both spatial and temporal terms, but is also a cost-effective alternative to traditional field-based methods. Although successful, few issues were encountered in this study. The estimated soil loss rates in Chapter 3 are above tolerable limits, whereas in Chapter 4, soil loss rates are within tolerable limits. The discrepancy in these results could be explained by the differences in the spatial resolution of SPOT (5m * 5m) and Landsat (30m * 30m) images used in chapters 3 and 4, respectively. Further research should therefore investigate the impact of spatial resolution on RUSLE-estimated soil loss in which case optical sensors including Landsat, Sentinel, and SPOT images may be compared