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Study region: The Source Region of the Yellow River Basin (SRYRB), China Study focus: To improve daily runoff prediction accuracy in data-scarce areas, this study focuses on incorporating multiple grid-based data (precipitation, EVI, soil moisture (SM)) to drive the CNN-LSTM hybrid model. The spatial features of precipitation and underlying surface of the basin can be extracted by CNN, while the temporal features of the input data series can be captured by the LSTM. The hybrid model is compared with the single models (CNN, LSTM), and hybrid model performances under different driven data are also investigated. New hydrological insights for the region: Driven by the in-situ precipitation, grid-based precipitation (GPM) and SM data, the CNN-LSTM hybrid model achieved the best prediction result with NSE of 0.834, outperforming the single LSTM model (NSE=0.510) and the CNN model (NSE=0.612). It indicates that the hybrid model captures the spatiotemporal change features of precipitation and underlying surface of the basin. When using only GPM and SM data as input, the hybrid model achieved comparable result with NSE of 0.827. It implies that GPM could serve as a good alternative of in-situ precipitation and SM could provide additional value to improve prediction. This study highlights the value of using multiple grid-based data to drive the hybrid model, which provides new insights into runoff prediction in data-scarce regions.

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Predicting discharge in contiguously data‐scarce or ungauged regions is needed for quantifying the global hydrologic cycle. We show that prediction in ungauged regions (PUR) has major, underrecognized uncertainty and is drastically more difficult than previous problems where basins can be represented by neighboring or similar basins (known as prediction in ungauged basins). While deep neural networks demonstrated stellar performance for streamflow predictions, performance nonetheless declined for PUR, benchmarked here with a new stringent region‐based holdout test on a US data set with 671 basins. We tested approaches to reduce such errors, leveraging deep network's flexibility to integrate "soft" data, such as satellite‐based soil moisture product, or daily flow distributions which improved low flow simulations. A novel input‐selection ensemble improved average performance and greatly reduced catastrophic failures. Despite challenges, deep networks showed stronger performance metrics for PUR than traditional hydrologic models. They appear competitive for geoscientific modeling even in data‐scarce settings. Plain Language Summary: Large areas of land exist on all continents where we do not have access to daily streamflow rate measurements, but predictions are still needed to plan for a changing climate. In such a scenario, if we apply hydrologic models calibrated elsewhere, how reliable are they? Previously, hydrologists studied the problem of prediction in ungauged basins (PUB), where a basin needing predictions can be represented by neighboring or similar basins. There has not been ample recognition that prediction in large, contiguously data‐sparse regions, which we call prediction in ungauged regions (PUR), is a substantially more difficult problem than PUB. Using a new benchmark problem with 671 US basins, we highlight this danger using machine learning models: performance dropped significantly from PUB to PUR scenarios. We proposed multiple approaches to improve model robustness, including a novel input‐selection ensemble method which combines models with different input options, as well as assimilating more widely available data. Despite the challenges with PUR, the machine learning model appears to be the best available tool for PUB and PUR applications. Perhaps counterintuitively, deep networks seem to be highly competitive even in data‐scarce settings, and geoscientific domains should carefully consider the extrapolation performance of their models. Key Points: We highlight increased model error with predictions in contiguously data‐scarce (or ungauged) regions (PUR) and propose a streamflow benchmarkWe introduce methods to mitigate error, including an input‐selection ensemble and integrating satellite data or flow distributionsWith these methods, our machine learning PUR model presents metrics competitive with basin‐by‐basin calibrated hydrologic models in the conterminous United States [ABSTRACT FROM AUTHOR]

Flood risk mapping forms the basis for disaster risk management and the associated decision-making systems. The effectiveness of this process is highly dependent on the quality of the input data of both hazard and vulnerability maps and the method utilized. On the one hand, for higher-quality hazard maps, the use of 2D models is generally suggested. However, in ungauged regions, such usage becomes a difficult task, especially at the microscale. On the other hand, vulnerability mapping at the microscale suffers limitations as a result of the failure to consider vulnerability components, the low spatial resolution of the input data, and the omission of urban planning aspects that have crucial impacts on the resulting quality. This paper aims to enhance the quality of both hazard and vulnerability maps at the urban microscale in ungauged regions. The proposed methodology integrates remote sensing data and high-quality city strategic plans (CSPs) using geographic information systems (GISs), a 2D rainfall-runoff-inundation (RRI) simulation model, and multicriteria decision-making analysis (MCDA, i.e., the analytic hierarchy process (AHP)). This method was implemented in Hurghada, Egypt, which from 1996 to 2019 was prone to several urban flood events. Current and future physical, social, and economic vulnerability maps were produced based on seven indicators (land use, building height, building conditions, building materials, total population, population density, and land value). The total vulnerability maps were combined with the hazard maps based on the Kron equation for three different return periods (REPs) 50, 10, and 5 years to create the corresponding flood risk maps. In general, this integrated methodology proved to be an economical tool to overcome the scarcity of data, to fill the gap between urban planning and flood risk management (FRM), and to produce comprehensive and high-quality flood risk maps that aid decision-making systems.

Precipitation is the main component of global water cycle. At present, satellite quantitative precipitation estimates (QPEs) are widely applied in the scientific community. However, the evaluations of satellite QPEs have some limitations in terms of the deficiency in observation, evaluation methodology, the selection of time windows for evaluation and short periods for evaluation. The objective of this work is to make some improvements by evaluating the spatiotemporal pattern of the long-terms Climate Hazard Group InfraRed Precipitation Satellite';s (CHIRPS';s) QPEs over mainland China. In this study, we compared the daily precipitation estimates from CHIRPS with 2480 rain gauges across China and gridded observation using several statistical metrics in the long-term period of 1981-2014. The results show that there is significant difference between point evaluation and grid evaluation for CHIRPS. CHIRPS has better performance for a large amount of precipitation than it does for arid and semi-arid land. The change in good performance zones has strong relationship with monsoon';s movement. Therefore, CHIRPS performs better in river basins of southern China and exhibits poor performance in river basins in northwestern and northern China. Moreover, CHIRPS exhibits better in warm season than in Winter, owing to its limited ability to detect snowfall. Nevertheless, CHIRPS is moderately sensitive to the precipitation from typhoon weather systems. The limitations for CHIRPS result from the Tropical Rainfall Measuring Mission (TRMM) 3B42 estimates'; accuracy and valid spatial coverage. [ABSTRACT FROM AUTHOR]

Precipitation is the main component of global water cycle. At present, satellite quantitative precipitation estimates (QPEs) are widely applied in the scientific community. However, the evaluations of satellite QPEs have some limitations in terms of the deficiency in observation, evaluation methodology, the selection of time windows for evaluation and short periods for evaluation. The objective of this work is to make some improvements by evaluating the spatio-temporal pattern of the long-terms Climate Hazard Group InfraRed Precipitation Satellite’s (CHIRPS’s) QPEs over mainland China. In this study, we compared the daily precipitation estimates from CHIRPS with 2480 rain gauges across China and gridded observation using several statistical metrics in the long-term period of 1981–2014. The results show that there is significant difference between point evaluation and grid evaluation for CHIRPS. CHIRPS has better performance for a large amount of precipitation than it does for arid and semi-arid land. The change in good performance zones has strong relationship with monsoon’s movement. Therefore, CHIRPS performs better in river basins of southern China and exhibits poor performance in river basins in northwestern and northern China. Moreover, CHIRPS exhibits better in warm season than in Winter, owing to its limited ability to detect snowfall. Nevertheless, CHIRPS is moderately sensitive to the precipitation from typhoon weather systems. The limitations for CHIRPS result from the Tropical Rainfall Measuring Mission (TRMM) 3B42 estimates’ accuracy and valid spatial coverage.

Providing error information associated with existing satellite precipitation estimates is crucial to advancing applications in hydrologic modeling. In this study, we present a method of estimating the square difference prediction of satellite precipitation (hereafter used synonymously with “error variance”) using regression model for three satellite precipitation products (3B42RT, CMORPH, and PERSIANN-CCS) using easily available geophysical features and satellite precipitation rate. Building on a suite of recent studies that have developed the error variance models, the goal of this work is to explore how well the method works around the world in diverse geophysical settings. Topography, climate, and seasons are considered as the governing factors to segregate the satellite precipitation uncertainty and fit a nonlinear regression equation as a function of satellite precipitation rate. The error variance models were tested on USA, Asia, Middle East, and Mediterranean region. Rain-gauge based precipitation product was used to validate the error variance of satellite precipitation products. The regression approach yielded good performance skill with high correlation between simulated and observed error variances. The correlation ranged from 0.46 to 0.98 during the independent validation period. In most cases (~ 85% of the scenarios), the correlation was higher than 0.72. The error variance models also captured the spatial distribution of observed error variance adequately for all study regions while producing unbiased residual error. The approach is promising for regions where missed precipitation is not a common occurrence in satellite precipitation estimation. Our study attests that transferability of model estimators (which help to estimate the error variance) from one region to another is practically possible by leveraging the similarity in geophysical features. Therefore, the quantitative picture of satellite precipitation error over ungauged regions can be discerned even in the absence of ground truth data. [ABSTRACT FROM AUTHOR]

Flood risk mapping forms the basis for disaster risk management and the associated decision-making systems. The effectiveness of this process is highly dependent on the quality of the input data of both hazard and vulnerability maps and the method utilized. On the one hand, for higher-quality hazard maps, the use of 2D models is generally suggested. However, in ungauged regions, such usage becomes a difficult task, especially at the microscale. On the other hand, vulnerability mapping at the microscale suffers limitations as a result of the failure to consider vulnerability components, the low spatial resolution of the input data, and the omission of urban planning aspects that have crucial impacts on the resulting quality. This paper aims to enhance the quality of both hazard and vulnerability maps at the urban microscale in ungauged regions. The proposed methodology integrates remote sensing data and high-quality city strategic plans (CSPs) using geographic information systems (GISs), a 2D rainfall-runoff-inundation (RRI) simulation model, and multicriteria decision-making analysis (MCDA, i.e., the analytic hierarchy process (AHP)). This method was implemented in Hurghada, Egypt, which from 1996 to 2019 was prone to several urban flood events. Current and future physical, social, and economic vulnerability maps were produced based on seven indicators (land use, building height, building conditions, building materials, total population, population density, and land value). The total vulnerability maps were combined with the hazard maps based on the Kron equation for three different return periods (REPs) 50, 10, and 5 years to create the corresponding flood risk maps. In general, this integrated methodology proved to be an economical tool to overcome the scarcity of data, to fill the gap between urban planning and flood risk management (FRM), and to produce comprehensive and high-quality flood risk maps that aid decision-making systems. [ABSTRACT FROM AUTHOR]