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Water resources reanalysis (WRR) can be used as a numerical tool to advance our understanding of hydrological processes where in situ observations are limited. However, WRR products are associated with uncertainty that needs to be quantified to improve usability of such products in water resources applications. In this study, we evaluate estimates of water cycle components from 18 state-of-the-art WRR datasets derived from different land surface/hydrological models, meteorological forcing, and precipitation datasets. The evaluation was conducted at three spatial scales in the upper Blue Nile basin in Ethiopia. Precipitation, streamflow, evapotranspiration (ET), and terrestrial water storage (TWS) were evaluated against in situ daily precipitation and streamflow measurements, remote sensing–derived ET, and the NASA Gravity Recovery and Climate Experiment (GRACE) product, respectively. Our results highlight the current strengths and limitations of the available WRR datasets in analyzing the hydrological cycle and dynamics of the study basins, showing an overall underestimation of ET and TWS and overestimation of streamflow. While calibration improves streamflow simulation, it results in a relatively poorer performance in terms of ET. In addition, we show that the differences in the schemes used in the various land surface models resulted in significant differences in the estimation of the water cycle components from the respective WRR products, while we noted small differences among the products related to precipitation forcing. We did not identify a single product that consistently outperformed others; however, we found that there are specific WRR products that provided accurate representation of a single component of the water cycle (e.g., only runoff) in the area. [ABSTRACT FROM AUTHOR]

This study evaluates a machine learning-based precipitation ensemble technique (MLPET) over three mountainous tropical regions. The technique, based on quantile regression forests, integrates global satellite precipitation datasets from CMORPH, PERSIANN, GSMaP (V6), and 3B42 (V7) and an atmospheric reanalysis precipitation product (EI_GPCC) with daily soil moisture, specific humidity, and terrain elevation datasets. The complex terrain study areas include the Peruvian and Colombian Andes in South America and the Blue Nile in East Africa. Evaluation is performed at a daily time scale and 0.258 spatial resolution based on 13 years (2000-12) of reference rainfall data derived from dense in situ rain gauge networks. The technique is evaluated using K-fold, separately in each region, and leave-one-region-out validation experiments. Comparison of MLPET with the individual satellite and reanalysis precipitation datasets used for the blending and the recent Multi-Source Weighted-Ensemble Precipitation (MSWEP) global precipitation product exhibited improved systematic and random error statistics for all regions. In addition, it is shown that observations are encapsulated well within the ensemble envelope generated by the blending technique. [ABSTRACT FROM AUTHOR]

• State of the art on precipitation frequency analysis based on remotely sensed datasets is depicted. • Investigation of climatic gradients and explicit inclusion of the areal scale is highly promising. • Current main issues: estimation accuracy, statistical methods, reference/validation methods. • Recommended research: uncertainty partition, dedicated statistical and validation methods. Information on extreme precipitation is essential to managing weather-related risks and designing hydraulic structures. Research attention to frequency analyses based on remotely sensed precipitation datasets, such as those obtained from weather radars and satellites, has been rapidly increasing owing to their potential to provide information for ungauged regions worldwide. Together with the ability to measure the areal scale directly, these analyses promise to overcome the sampling limitations of traditional methods based on rain gauges. This focused review of the literature depicts the state of the art after a decade of efforts, and identifies the crucial gaps in knowledge and methodology that currently hinder the quantitative use of remotely sensed datasets in water resources system design and operation. It concludes by highlighting a set of research directions promising immediate impact with regard to the separation of the sources of uncertainty currently affecting applications based on remotely sensed datasets, the development of statistical methods that can cope with the peculiar characteristics of these datasets, and the improvement of validation methods. Important gains in knowledge are expected from the explicit inclusion of the areal dimension in the analyses and from the fine-scale investigation of extreme precipitation climatology. [ABSTRACT FROM AUTHOR]

• Precipitation annual maxima estimated from NEXRAD are evaluated over the CONUS. • Accuracy of radar-based estimates varies with magnitude and duration. • Uncertainty in radar-based IDF curves exhibits climate dependence. The lack of knowledge on precipitation frequency over ungauged areas introduces a significant source of uncertainty in relevant engineering designs and risk estimation procedures. Radar-based observations offer precipitation information over ungauged areas and thus have gained increasing attention as a potential solution to this problem. However, due to their relative short data records and inherent uncertainty sources, their ability to provide accurate estimates on the frequency of precipitation extremes requires evaluation. This study involves the evaluation of at-site precipitation frequency estimates from NEXRAD Stage IV radar precipitation dataset. We derive precipitation annual maxima series from the 16yrs record (2002–2017) of NEXRAD and we compare against 539 long-term (50yrs) hourly gauge records. In addition, Intensity-Duration-Frequency (IDF) curves are estimated from both radar and gauge dataset and compared. IDF estimation is based on fitting the Generalize Extreme Value distribution to annual precipitation maxima. Evaluation is carried out over the contiguous United States and results are grouped and presented for five dominant climate classes and for a range of return period and precipitation durations. NEXRAD was shown to overestimate intensities at shorter durations (1- and 3-h) and low quantiles, while it tends to underestimate higher quantiles at longer durations (24 h). In addition, evaluation of the IDF curves estimated from NEXRAD revealed a distinct geographic dependence with certain regions exhibiting a tendency to overestimation (e.g. east of the Rocky Mountains) or underestimation (Midwest). Overall, this analysis suggests that, while significant discrepancies may exist, there are several cases where NEXRAD provide estimates within the uncertainty bounds of the reference rain gauge dataset. The climate/geographic region and the temporal duration are important aspects to consider. Findings provided in this work on these aspects will hopefully serve as a general guideline for those interested in using NEXRAD estimates for further research or applications on precipitation extremes. [ABSTRACT FROM AUTHOR]

This study focuses on the Iberian Peninsula and investigates the propagation of precipitation uncertainty, and its interaction with hydrologic modeling, in global water resource reanalysis. Analysis is based on ensemble hydrologic simulations for a period spanning 11 years (2000–2010). To simulate the hydrological variables of surface runoff, subsurface runoff, and evapotranspiration, we used four land surface models (LSMs) – JULES (Joint UK Land Environment Simulator), ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems), SURFEX (Surface Externalisée), and HTESSEL (Hydrology – Tiled European Centre for Medium-Range Weather Forecasts – ECMWF – Scheme for Surface Exchanges over Land) – and one global hydrological model, WaterGAP3 (Water – a Global Assessment and Prognosis). Simulations were carried out for five precipitation products – CMORPH (the Climate Prediction Center Morphing technique of the National Oceanic and Atmospheric Administration, or NOAA), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), 3B42V(7), ECMWF reanalysis, and a machine-learning-based blended product. As a reference, we used a ground-based observation-driven precipitation dataset, named SAFRAN, available at 5 km, 1 h resolution. We present relative performances of hydrologic variables for the different multi-model and multi-forcing scenarios. Overall, results reveal the complexity of the interaction between precipitation characteristics and different modeling schemes and show that uncertainties in the model simulations are attributed to both uncertainty in precipitation forcing and the model structure. Surface runoff is strongly sensitive to precipitation uncertainty, and the degree of sensitivity depends significantly on the runoff generation scheme of each model examined. Evapotranspiration fluxes are comparatively less sensitive for this study region. Finally, our results suggest that there is no single model–forcing combination that can outperform all others consistently for all variables examined and thus reinforce the fact that there are significant benefits to exploring different model structures as part of the overall modeling approaches used for water resource applications. [ABSTRACT FROM AUTHOR]

Of the boundary conditions that affect the simulation of convective precipitation, soil moisture is one of the most important. In this study, we explore the impact of the soil moisture on convective precipitation, and factors affecting it, through an extensive numerical experiment based on four convective precipitation events that caused moderate to severe flooding in the Gard region of southern France. High-spatial-resolution (1 km) weather simulations were performed using the integrated atmospheric model Regional Atmospheric Modeling System/Integrated Community Limited Area Modeling System (RAMS/ICLAMS). The experimental framework included comparative analysis of five simulation scenarios for each event, in which we varied the magnitude and spatial distribution of the initial volumetric water content using realistic soil moisture fields with different spatial resolution. We used precipitation and surface soil moisture from radar and satellite sensors as references for the comparison of the sensitivity tests. Our results elucidate the complexity of the relationship between soil moisture and convective precipitation, showing that the control of soil water content on partitioning land surface heat fluxes has significant impacts on convective precipitation. Additionally, it is shown how different soil moisture conditions affect the modeled microphysical structure of the clouds, which translates into further changes in the magnitude and distribution of precipitation. [ABSTRACT FROM AUTHOR]

This study investigates the use of a nonparametric, tree-based model, quantile regression forests (QRF), for combining multiple global precipitation datasets and characterizing the uncertainty of the combined product. We used the Iberian Peninsula as the study area, with a study period spanning 11 years (2000-2010). Inputs to the QRF model included three satellite precipitation products, CMORPH, PERSIANN, and 3B42 (V7); an atmospheric reanalysis precipitation and air temperature dataset; satellite-derived nearsurface daily soil moisture data; and a terrain elevation dataset. We calibrated the QRF model for two seasons and two terrain elevation categories and used it to generate ensemble for these conditions. Evaluation of the combined product was based on a high-resolution, ground-reference precipitation dataset (SAFRAN) available at 5 km1h-1 resolution. Furthermore, to evaluate relative improvements and the overall impact of the combined product in hydrological response, we used the generated ensemble to force a distributed hydrological model (the SURFEX land surface model and the RAPID river routing scheme) and compared its streamflow simulation results with the corresponding simulations from the individual global precipitation and reference datasets. We concluded that the proposed technique could generate realizations that successfully encapsulate the reference precipitation and provide significant improvement in streamflow simulations, with reduction in systematic and random error on the order of 20-99 and 44-88 %, respectively, when considering the ensemble mean. [ABSTRACT FROM AUTHOR]

The performance of six satellite-based and three newly released reanalysis rainfall estimates are evaluated at daily time scale and spatial grid size of 0.25 degrees during the period of 2000 to 2013 over the Upper Blue Nile Basin, Ethiopia, with the view of improving the reliability of precipitation estimates of the wet (June to September) and secondary rainy (March to May) seasons. The study evaluated both adjusted and unadjusted satellite-based products of TMPA, CMORPH, PERSIANN, and ECMWF ERA-Interim reanalysis as well as Multi-Source Weighted-Ensemble Precipitation (MSWEP) estimates. Among the six satellite-based rainfall products, adjusted CMORPH exhibits the best accuracy of the wet season rainfall estimate. In the secondary rainy season, unadjusted CMORPH and 3B42V7 are nearly equivalent in terms of bias, POD, and CSI error metrics. All error metric statistics show that MSWEP outperform both unadjusted and gauge adjusted ERA-Interim estimates. The magnitude of error metrics is linearly increasing with increasing percentile threshold values of gauge rainfall categories. Overall, all precipitation datasets need further improvement in terms of detection during the occurrence of high rainfall intensity. MSWEP detects higher percentiles values better than satellite estimate in the wet and poor in the secondary rainy seasons. [ABSTRACT FROM AUTHOR]

This paper contributes to the predictive understanding of satellite precipitation estimation errors over complex terrain, which is fundamental to the development of error models for improving hydrological applications. This paper focuses on the Trentino-Alto Adige region of the eastern Italian Alps. Rainfall observations over a 10-year period (2000–2009) from a dense rain gauge network in the region are used as reference precipitation. A number of satellite precipitation error properties (probability of detection, false alarm rates, missed events, spatial correlation of the error, and hit biases) are investigated in terms of seasonality, satellite algorithm, rainfall intensity, gauge density, and temporal resolution dependencies. These error parameters are typically used in error models (e.g., SREM2D) and provide the basis for enhancing error scheme development. Three widely used satellite-based precipitation products are employed: 1) the Climate Prediction Center morphing product; 2) the precipitation estimation from remotely sensed imagery using artificial neural networks; and 3) the Tropical Rainfall Measuring Mission multisatellite precipitation analysis 3B42 near-real-time product. The three products show similar performances, with larger errors during the warm season, characterized by convective storms, and less variability in the cold season, characterized by more organized stratiform systems. Lower biases are depicted at the daily scale with respect to the 3-hourly resolution. The SREM2D error model has the ability to correct the satellite precipitation products, even though attention is needed for potential systematic errors when applying the calibrated model to independent periods or regions. [ABSTRACT FROM PUBLISHER]

Flash floods develop over small spatiotemporal scales, an attribute that makes their predictability a particularly challenging task. The serious threat they pose for human lives, along with damage estimates that can exceed one billion U.S. dollars in some cases, urge toward more accurate forecasting. Recent advances in computational science combined with state-of-the-art atmospheric models allow atmospheric simulations at very fine (i.e., subkilometer) grid scales, an element that is deemed important for capturing the initiation and evolution of flash flood-triggering storms. This work provides some evidence on the relative gain that can be expected from the adoption of such subkilomeler model grids. A necessary insight into the complex processes of these severe incidents is provided through the simulation of three flood-inducing heavy precipitation events in the Alps for a range of model grid scales (0.25, 1, and 4 km) with the Regional Atmospheric Modeling System-Integrated Community Limited Area Modeling System (RAMS-ICLAMS) atmospheric model. A distributed hydrologic model [Kinematic Local Excess Model (KLEM)] is forced with the various atmospheric simulation outputs to further evaluate the relative impact of atmospheric model resolution on the hydrologic prediction. The use of a finer grid is beneficial in most cases, yet there are events where the improvement is marginal. This underlines why the use of finer scales is a step in the right direction but not a solitary component of a successful flash flood-forecasting recipe. [ABSTRACT FROM AUTHOR]

This work presents a first evaluation of the performance of the Integrated Multisatellite Retrievals for GPM (IMERG) precipitation product over the upper Blue Nile basin of Ethiopia. One of the unique features of this study is the availability of hourly rainfall measurements from an experimental rain gauge network in the area. Both the uncalibrated and calibrated versions of IMERG are evaluated, and their performance is contrasted against another high-resolution satellite product, which is the Kalman filter (KF)-based Climate Prediction Center (CPC) morphing technique (CMORPH). The analysis is performed for hourly and daily time scales and at spatial scales that correspond to the nominal resolution of satellite products, which is 0.1° spatial resolution. The period analyzed is focused on a single wet season (May-October 2014). Evaluation is performed using several statistical and categorical error metrics, as well as spatial correlation analysis to assess the ability of satellite products to represent spatial variability of precipitation in the area. Results show that both IMERG products have a better bias ratio and correlation coefficient on both time scales as compared to CMORPH. Comparison statistics show a slight improvement in the skill of detecting rainfall events in IMERG products compared to CMORPH. Results also show a decreasing trend in the detection ability of satellite products for increasing threshold values, highlighting the need to further improve detection during heavy precipitation. [ABSTRACT FROM AUTHOR]

This study investigates the error characteristics of six quasi-global satellite precipitation products and their error propagation in flow simulations for a range of mountainous basin scales (255-6967 km2) and two different periods (May-August and September-November) in northeast Italy. Statistics describing the systematic and random error, the temporal similarity, and error ratios between precipitation and runoff are presented. Overall, strong over-/underestimation associated with the near-real-time 3B42/Climate Prediction Center morphing technique (CMORPH) products is shown. Results suggest positive correlation between the systematic error and basin elevation. Performance evaluation of flow simulations yields a higher degree of consistency for the moderate to large basin scales and the May-August period. Gauge adjustment for the different satellite products is shown to moderate their error magnitude and increase their correlation with reference precipitation and streamflow simulations. Moreover, ratios of precipitation to streamflow simulation error metrics show dependencies in terms of magnitude and variability. Random error and temporal dissimilarity are shown to reduce from basin-average rainfall to the streamflow simulations, while the systematic error exhibits no clear pattern in the rainfall-runoff transformation. [ABSTRACT FROM AUTHOR]

Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events (HPEs). In situ observations over mountainous areas are limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for hydrological applications. In this study, four widely used satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, version 7 (3B42-V7), and in near-real time (3B42-RT); Climate Prediction Center (CPC) morphing technique (CMORPH); and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)] are evaluated with respect to their performance in capturing the properties of HPEs over different basin scales. Evaluation is carried out over the upper Adige River basin (eastern Italian Alps) for an 8-yr period (2003-10). Basin-averaged rainfall derived from a dense rain gauge network in the region is used as a reference. Satellite precipitation error analysis is performed for warm (May-August) and cold (September-December) season months as well as for different quantile ranges of basin-averaged precipitation accumulations. Three error metrics and a score system are introduced to quantify the performances of the various satellite products. Overall, no single precipitation product can be considered ideal for detecting and quantifying HPE. Results show better consistency between gauges and the two 3B42 products, particularly during warm season months that are associated with high-intensity convective events. All satellite products are shown to have a magnitude-dependent error ranging from overestimation at low precipitation regimes to underestimation at high precipitation accumulations; this effect is more pronounced in the CMORPH and PERSIANN products. [ABSTRACT FROM AUTHOR]

In mountain basins, the use of long-range operational weather radars is often associated with poor quantitative precipitation estimation due to a number of challenges posed by the complexity of terrain. As a result, the applicability of radar-based precipitation estimates for hydrological studies is often limited over areas that are in close proximity to the radar. This study evaluates the advantages of using X-band polarimetric (XPOL) radar as a means to fill the coverage gaps and improve complex terrain precipitation estimation and associated hydrological applications based on a field experiment conducted in an area of Northeast Italian Alps characterized by large elevation differences. The corresponding rainfall estimates from two operational C-band weather radar observations are compared to the XPOL rainfall estimates for a near-range (10–35 km) mountainous basin (64 km2). In situ rainfall observations from a dense rain gauge network and two disdrometers (a 2D-video and a Parsivel) are used for ground validation of the radar-rainfall estimates. Ten storm events over a period of two years are used to explore the differences between the locally deployed XPOL vs. longer-range operational radar-rainfall error statistics. Hourly aggregate rainfall estimates by XPOL, corrected for rain-path attenuation and vertical reflectivity profile, exhibited correlations between 0.70 and 0.99 against reference rainfall data and 21% mean relative error for rainfall rates above 0.2 mm h−1. The corresponding metrics from the operational radar-network rainfall products gave a strong underestimation (50–70%) and lower correlations (0.48–0.81). For the two highest flow-peak events, a hydrological model (Kinematic Local Excess Model) was forced with the different radar-rainfall estimations and in situ rain gauge precipitation data at hourly resolution, exhibiting close agreement between the XPOL and gauge-based driven runoff simulations, while the simulations obtained by the operational radar rainfall products resulted in a greatly underestimated runoff response. [ABSTRACT FROM AUTHOR]