Rainfall Error Research Articles

Direct and Indirect Effects of Mountain Heights on Heavy Rainfall in the Hokitika Region of New Zealand

In the Hokitika region, on the west coast of the South Island of New Zealand on 18 June 2015, very heavy stratiform precipitation (>200 mm/per day) occurred under north-westerlies with small CAPE (<25 J/kg). Analyses of model simulations and observations showed that this heavy rainfall was due to cold-front lifting enhanced by orographic lifting over the Southern Alps. At 1.5 km grid-length, the model terrain underestimated the average height of the 103 tallest mountains over the South Island by ~800 m. This led to weaker orographic lifting and mountain blocking, and a faster-moving and stronger cold front in the Hokitika region. As a result, large errors in the heavy rainfall prediction occurred. By increasing either the resolved or the sub-grid mountain heights, the simulated rainfall errors were largely reduced through stronger orographic lifting and mountain blocking, and simulation of the cold front movement and strength was improved. All the experiments have the same “flow-over” regime with mountain waves and/or wave breaking (Fm ranges 0.61–1.21). However, the rainfall amount and distribution on the windward side of the mountains varied significantly. Our new findings were that the Southern Alps can have significant indirect effects on heavy rainfall by altering the speed and strength of the cold front, in addition to the well-known direct dynamical effects (i.e., orographic lifting and mountain blocking). A combination of these direct and indirect effects makes the heavy rainfall simulation sensitive to mountain heights even under the same “flow-over” regime.

Precipitation uncertainty estimation and rainfall-runoff model calibration using iterative ensemble smoothers

The introduction of iterative ensemble smoothers (IES) for parameter calibration opens avenues for expanding parameter space in surface water hydrologic modeling. Here, we have introduced independent parameters into a model calibration experiment to estimate errors in rainfall forcing data. This approach has the potential to estimate rainfall errors using other hydrological observations and to improve model calibration. Using high-resolution rain gauge data, we estimated “real” rainfall errors across the Turkey River watershed at storm and daily scales. Tests on synthetic and real-world scenarios successfully estimated errors correlated with observed values – even at daily scales. However, a bias remained from model parameter compensation, and identifying errors was challenging for low precipitation and snowfall. Despite synthetic results showing good error correlation, the biases in parameter identification masked potential improvements in hydrological calibration. This study highlights the potential of IES to provide additional information on rainfall errors, even only using streamflow observations.

Tropical Atlantic rainfall drives bias in extratropical seasonal forecasts

AbstractWe investigate the impact of seasonal forecast biases in the Tropical Atlantic on the North Atlantic. The analysis uses a novel ensemble‐based method to estimate the impact of tropical rainfall bias on forecasts of the Extratropical North Atlantic. The inter‐ensemble spread of the forecast model is used to estimate the impact of the bias in Tropical Atlantic rainfall on the North Atlantic by selecting model members that happen to produce forecast anomalies that most closely resemble the tropical rainfall bias and using these as a proxy for the model error. The Tropical Atlantic rainfall bias impacts Rossby wave sources over the Subtropical Atlantic and there is a clear Rossby wave pattern originating from this area which is comparable to the mean bias in hindcasts. We argue that Tropical Atlantic rainfall errors explain a significant amount of the bias in seasonal forecasts over the Extratropical North Atlantic.

Error modeling and hierarchical Bayesian fusion for spaceborne and ground radar rainfall data

The optimal fusion of multisource rainfall data can improve the accuracy and resolution of a posteriori estimates, which is beneficial for hydrological and meteorological applications. In this study, the errors of ground-based dual-polarization radar (GR) and dual-frequency precipitation radar (DPR) on global precipitation measurement (GPM) satellite rainfall estimation were modeled, and a hierarchical Bayesian (HB) framework was developed for GR and DPR rainfall data fusion. According to the matched optimal estimated GR rainfall and rain gauge data, the GR rainfall error was analyzed, and the error variance varying with rain type and intensity was described and modeled. The rain types were classified based on disdrometer and dual-polarization radar data. By comparing the DPR rainfall product with GR rainfall, the DPR rainfall error was divided into systematic and random errors. The systematic error was linearly fitted, and the probability distributions for the random error and the variable error parameters were analyzed and modeled. Because the parameters for GR and DPR rainfall errors varied with rain type and intensity, we developed a HB fusion approach considering the varying parameters in rainfall error modeling. The performance of the fusion algorithm was evaluated using matched GR and DPR cases, which showed that the HB fusion can effectively improve the consistency between the original DPR rainfall product and rain gauge data, reducing the relative bias by an average of 30% and improving the correlation by an average of 20%. Compared with the conventional Bayesian (CB) fusion algorithm, which is based on overall error modeling, the HB fusion results can improve the root mean square error by an average of 24% and relative bias by 14%.

Assimilation of Sentinel-1 Backscatter into a Land Surface Model with River Routing and Its Impact on Streamflow Simulations in Two Belgian Catchments

Abstract Accurate streamflow simulations rely on good estimates of the catchment-scale soil moisture distribution. Here, we evaluated the potential of Sentinel-1 backscatter data assimilation (DA) to improve soil moisture and streamflow estimates. Our DA system consisted of the Noah-MP land surface model coupled to the HyMAP river routing model and the water cloud model as a backscatter observation operator. The DA system was set up at 0.01° resolution for two contrasting catchments in Belgium: (i) the Demer catchment dominated by agriculture and (ii) the Ourthe catchment dominated by mixed forests. We present the results of two experiments with an ensemble Kalman filter updating either soil moisture only or soil moisture and leaf area index (LAI). The DA experiments covered the period from January 2015 through August 2021 and were evaluated with independent rainfall error estimates based on station data, LAI from optical remote sensing, soil moisture retrievals from passive microwave observations, and streamflow measurements. Our results indicate that the assimilation of Sentinel-1 backscatter observations can partly correct errors in surface soil moisture due to rainfall errors and overall improve surface soil moisture estimates. However, updating soil moisture and LAI simultaneously did not bring any benefit over updating soil moisture only. Our results further indicate that streamflow estimates can be improved through Sentinel-1 DA in a catchment with strong soil moisture–runoff coupling, as observed for the Ourthe catchment, suggesting that there is potential for Sentinel-1 DA even for forested catchments. Significance Statement The purpose of this study is to improve streamflow estimation by integrating soil moisture information from satellite observations into a hydrological modeling framework. This is important preparatory work for operational centers that are responsible for producing the most accurate flood forecasts for the society. Our results provide new insights into how and where streamflow forecasting could benefit from high-spatial-resolution Sentinel-1 radar backscatter observations.

Comparison between statistical and dynamical downscaling of rainfall over the Gwadar‐Ormara basin, Pakistan

AbstractThis paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study.

A calibration method for SWMM to mitigate the impact of the structure defect without considering runoff on building walls

In this study, we propose a hypothesis that an automatic calibration framework can address modeling uncertainties in the Storm Water Management Model (SWMM) due to structural defects that result in the inability of the model to account for runoff generated on building walls from wind-driven rain. To test this hypothesis, we introduce a rainfall error model into the calibration framework to indirectly consider the effects of inclined wind-driven rain on building walls. We couple the optimization algorithm Differential Evolution Adaptive Metropolis (DREAM) with SWMM using newly developed API functions. To demonstrate the effectiveness of the framework, we conduct a case study in Guangzhou, China and assess the impacts of rainfall uncertainty on model parameter estimations and simulated runoff boundaries. The results show that the framework can improve the average Nash–Sutcliffe index of selected events by more than 5%. It also captures peak flow more accurately. This framework contributes to the theory of SWMM calibration by accounting for structural defects and considering rainfall uncertainty.

Improving Rainfall Fields in Data-Scarce Basins: Influence of the Kernel Bandwidth Value of Merging on Hydrometeorological Modeling

Accurate rainfall fields are important for several hydrological and meteorological applications. In poorly instrumented basins, the lack of rain gauges heavily affects the spatial rainfall estimation, yet neither remote sensed nor climate model estimates are good enough for management applications. To tackle this problem, we investigate the impacts of combining both sources of information by varying the kernel bandwidth value of the double smoothing merging algorithm and analyzing the error of the rainfall fields. We explored the correlation between rain gauge density and bandwidth and compared the results against classical geostatistical interpolation methods based solely on in-situ measurements. Propagation of rainfall error into hydrological modelling is usual, and therefore we evaluated the influence of the bandwidth in streamflow simulations implementing two hydrological models. The hydrological evaluation considered the analysis of hydrological signatures rather than just performance metrics. We found that there is a clear correlation between kernel bandwidth and monitoring network density and that the bandwidth also affects hydrological performance. Simple bilinear downscaling did not produce a significant difference in meteorological or hydrological errors, and rain gauge network configuration also impacts the error of the field. We conclude that merging outperforms the results of classical interpolation methods, in some cases by 20% or 50%, suggesting the suitability of the method for being applied in data-scarce domains.

Estimation of extreme precipitation events in Estonia and Italy using dual-polarization weather radar quantitative precipitation estimations

Abstract. Evaluating extreme rainfall for a certain location is commonly considered when designing stormwater management systems. Rain gauge data are widely used to estimate rainfall intensities for a given return period. However, the poor spatial and temporal resolution of operational gauges is the main limiting factor. Several studies have used rainfall estimates based on weather radar horizontal reflectivity (Zh), but they come with a great caveat: while proven reliable for low or moderate rainfall rates, they are subject to major errors in extreme rainfall and convective cases. It is widely known that C-band weather radar can underestimate precipitation intensity due to signal attenuation or overestimate it due to hail and clutter contamination. From the late 1990s, dual-polarization weather radar started to become operational in the national surveillance radar network in Europe, providing innovative quantitative precipitation estimation (QPE) based on polarimetric variables. This study circumvents Zh shortcomings by using specific differential-phase (Kdp) data from operational dual-polarization C-band weather radars. The rain intensity estimates based on a specific differential-phase data are immune to attenuation and less affected by hail contamination. In this study, for the first time, QPEs based on polarimetric observations by operational C-band weather radars and without any rain gauge adjustments are analyzed. The purpose is to estimate return periods for 1 h rainfall total computed from polarimetric weather radar data using non-adjusted QPEs based on R(Zh,Kdp) data and to compare the results with those derived using R(Zh) and rain gauge data. Only the warm period during the year is considered here, as most of the extreme precipitation events for such a duration occur for both places studied (Italy and Estonia) at this time. Limiting the dataset to warm periods also allows us to use the radar-based rainfall quantitative precipitation estimations, which are more reliable than the snowfall ones. Data from operational dual polarimetric C-band weather radar sites are used from both Italy and Estonia. Given climatologically homogeneous regions, this study demonstrates that polarimetric weather radar observations can provide reliable QPEs compared to single-polarization estimates with respect to rain gauges and that they can provide a reliable estimation of return periods of 1 h rainfall total, even for relatively short time series.

On constructing limits-of-acceptability in watershed hydrology using decision trees

A hydrological model incurs three types of uncertainties: measurement, structural and parametric uncertainty. For instance, in rainfall-runoff models, measurement uncertainty exists due to errors in measurements of rainfall and streamflow data. Structural uncertainty exists due to errors in mathematical representation of hydrological processes. Parametric uncertainty is a consequence of our inability to measure effective model parameters, limited data available to calibrate model parameters, and measurement and structural uncertainties. The existence of these predominantly epistemic uncertainties makes the model inference difficult. Limits-of-acceptability (LOA) framework has been proposed in the literature for model inference under a rejectionist framework. LOAs can be useful in model inference if they reflect the effect of errors in rainfall and streamflow measurements. In this study, the usefulness of quantile random forest (QRF) algorithm has been explored for constructing LOAs. LOAs obtained by QRF were compared to the uncertainty bounds obtained by rating-curve analysis and the LOAs obtained by runoff ratio method. Rating curve analysis yields uncertainty in streamflow measurements only and the runoff ratio method is expected to reflect uncertainty in rainfall and streamflow volume measurements. LOAs obtained by using QRF were found to envelop the uncertainty bounds due to streamflow measurement errors. LOAs obtained by QRF and runoff ratio methods were similar. Further, QRF LOAs were scrutinized in terms of their ability to reflect the effect of rainfall uncertainty, both qualitatively and quantitatively. Results indicate that QRF LOAs reflect the effect of rainfall uncertainty: increase in standard deviation with increase in mean streamflow values and decrease in coefficient of variation with increase in mean streamflow values. A mathematical analysis of the LOAs obtained by the QRF method is presented to provide a theoretical foundation.

The Support Vector Regression with L1 Norm: Application to Weather Radar Data in Adjusting Rainfall Errors

In hydrological research, accurate rainfall data is the primary subject for the minimization of potential loss of life and property that is mainly caused by floods. However, there is a difficulty in getting precise rainfall data for poorly gauged locations, especially in mountainous areas. Weather radar instruments can be the remedy accompanied by some errors. And, these errors should be removed before the implementation of this product. This paper presents the results of the research on radar rainfall estimate errors with support vector regression (SVR) method using the observed rain gauge data. The paper depicts the methodological base of the algorithm that covers additive and multiplicative corrections and the results of practical implementations considering the locations of gauge measurements. The preliminary results show that the SVR has a location-oriented performance. The multiplicative and additive correction factors show decreasing and polynomial trends respectively, as the distance from the radar location increase. Another particular outcome is that the SVR shows better results for the stations located in the mid-range (mainly for 40-60 km) contrary to the nearest ones. Since the systematic error in the radar data is nonlinear, the SVR method would show a promising result with a combination of other optimization techniques.

Incorporating IMERG satellite precipitation uncertainty into seasonal and peak streamflow predictions using the Hillslope Link hydrological model

In global applications and data sparse regions, which comprise most of the earth, hydrologic model-based flood monitoring relies on precipitation data from satellite multisensor precipitation products or numerical weather forecasts. However, these products often exhibit substantial errors during the meteorological conditions that lead to flooding, including extreme rainfall. The propagation of precipitation forcing errors to predicted runoff and streamflow is scale-dependent and requires an understanding of the autocorrelation structure of precipitation errors, since error autocorrelation impacts the accumulation of precipitation errors over space and time in hydrologic models. Previous efforts to account for satellite precipitation uncertainty in hydrologic models have demonstrated the potential for improving streamflow estimates; however, these efforts use satellite precipitation error models that rely heavily on ground reference data such as rain gages or weather radar and do not characterize the nonstationarity of precipitation error autocorrelation structures. This work evaluates a new method, the Space-Time Rainfall Error and Autocorrelation Model (STREAM), which stochastically generates possible true precipitation fields, as input to the Hillslope Link Model to generate ensemble streamflow estimates. Unlike previous error models, STREAM represents the nonstationary and anisotropic autocorrelation structure of satellite precipitation error and does not use any ground reference to do so. Ensemble streamflow predictions are compared with streamflow generated using satellite precipitation fields as well as a radar-gage precipitation dataset during peak flow events. Results demonstrate that this approach to accounting for precipitation uncertainty effectively characterizes the uncertainty in streamflow estimates and reduces the error of predicted streamflow. Streamflow ensembles forced by STREAM improve streamflow prediction nearly to the level obtained using ground-reference forcing data across basin sizes.

WITHDRAWN: Incorporating IMERG satellite precipitation uncertainty into seasonal and peak streamflow predictions using the Hillslope Link hydrological model

In global applications and data sparse regions, which comprise most of the earth, hydrologic model-based flood monitoring relies on precipitation data from satellite multisensor precipitation products or numerical weather forecasts. However, these products often exhibit substantial errors during the meteorological conditions that lead to flooding, including extreme rainfall. The propagation of precipitation forcing errors to predicted runoff and streamflow is scale-dependent and requires an understanding of the autocorrelation structure of precipitation errors, since error autocorrelation impacts the accumulation of precipitation errors over space and time in hydrologic models. Previous efforts to account for satellite precipitation uncertainty in hydrologic models have demonstrated the potential for improving streamflow estimates; however, these efforts use satellite precipitation error models that rely heavily on ground reference data such as rain gages or weather radar and do not characterize the nonstationarity of precipitation error autocorrelation structures. This work evaluates a new method, the Space-Time Rainfall Error and Autocorrelation Model (STREAM), which stochastically generates possible true precipitation fields, as input to the Hillslope Link Model to generate ensemble streamflow estimates. Unlike previous error models, STREAM represents the nonstationary and anisotropic autocorrelation structure of satellite precipitation error and does not use any ground reference to do so. Ensemble streamflow predictions are compared with streamflow generated using satellite precipitation fields as well as a radar-gage precipitation dataset during peak flow events. Results demonstrate that this approach to accounting for precipitation uncertainty effectively characterizes the uncertainty in streamflow estimates and reduces the error of predicted streamflow. Streamflow ensembles forced by STREAM improve streamflow prediction nearly to the level obtained using ground-reference forcing data across basin sizes.

Assessment of the performance of satellite rainfall products over Makkah watershed using a physically based hydrologic model

Makkah region is one of the most flash flood-prone areas of Saudi Arabia due to terrain characteristics and the synoptic-scale weather conditions that intensify through interaction with the local topography causing high convective short-lived rainfall events, although these conditions are quite infrequent. Most of these events last for less than two hours. This study aims to assess the performance of five satellite precipitation products over a 1725 km2 sparsely gauged, arid basin. A fully distributed, physically based hydrologic model was forced by the five satellite precipitation products, and the evaluation included the hydrographs and runoff maps predicted by the model. Moreover, the propagation of the satellite rainfall errors into runoff predictions was quantified. Large variations and significant biases were found in satellites precipitation estimates compared to the available ground rainfall measurements. The Early IMERG product showed the best agreement with the reported total rainfall accumulations followed by Late IMERG while the other products significantly underestimated precipitation accumulations. Comparison with estimated runoff peaks showed that the Early IMERG product has the lowest errors in runoff peaks. Therefore, the hydrographs produced by the Early IMERG product were used as a reference to quantify the propagation of satellite precipitation errors into runoff predictions over the Makkah watershed. The results clearly indicated that both systematic and random rainfall errors were significantly amplified in runoff predictions.

On (in)validating environmental models. 2. Implementation of a Turing‐like test to modelling hydrological processes

AbstractPart 1 of this study discussed the concept of using a form of Turing‐like test for model evaluation, together with eight principles for implementing such an approach. In this part, the framing of fitness‐for‐purpose as a Turing‐like test is discussed, together with an example application of trying to assess whether a rainfall‐runoff model might be an adequate representation of the discharge response in a catchment for predicting future natural flood management scenarios. It is shown that the variation between event runoff coefficients in the record can be used to create some limits of acceptability that implicitly take some account of the epistemic uncertainties arising from lack of knowledge about errors in rainfall and discharge observations. In the case study it is demonstrated that the model used cannot be validated in this way across all the range of observed discharges, but that behavioural models can be found for the peak flows that are the subject of interest in the application. Thinking in terms of the Turing‐like test focusses attention on the critical observations needed to test whether streamflow is being produced in the right way so that a model is considered as fit‐for‐purpose in predicting the impacts of future change scenarios. As is the case for uncertainty estimation in general, it is argued that the assumptions made in setting behavioural limits of acceptability should be stated explicitly to leave an audit trail in any application that can be reviewed by users of the model outputs.

Improvement of displacement error of rainfall and wind field forecast associated with landfalling tropical cyclone AMPHAN

Spatial distribution of rainfall and wind speed forecast errors associated with landfalling tropical cyclones (TC) occur significantly due to incorrect location forecast by numerical models. Two major areas of errors are: (i) over-estimation over the model forecast locations and (ii) underestimation over the observed locations of the TCs. A modification method is proposed for real-time improvement of rainfall and wind field forecasts and demonstrated for the typical TC AMPHAN over the Bay of Bengal in 2020. The proposed method to improve the model forecasts is a relocation method through shifting of model forecast locations of TC to the real-time official forecast locations of India Meteorological Department (IMD). The modification is applied to the forecasts obtained from the operational numerical model, the Global Forecast System (GFS) of IMD. Application of the proposed method shows considerable improvement of both the parameters over both the locations. The rainfall forecast errors due to displacement are found to have improved by 44.1%–69.8% and 72.1%–85.2% over the GFS forecast locations and over the observed locations respectively for the respective forecast lead times 48 h, 72 h, and 96 h. Similarly, the wind speed forecasts have improved by 27.6%–56.0% and 63.7%–84.6% over the GFS forecast locations and over the observed locations respectively for the respective forecast lead times 60 h, 72 h, and 84 h. The results show that the proposed technique has capacity to provide improved spatial distributions of rainfall and wind speed forecasts associated with landfalling TCs and useful guidance to operational forecasters.

Ensemble Representation of Satellite Precipitation Uncertainty Using a Nonstationary, Anisotropic Autocorrelation Model

AbstractThe usefulness of satellite multi‐sensor precipitation (SMP) and other satellite‐informed precipitation products in water resources modeling can be hindered by substantial errors which vary considerably with spatiotemporal scale. One approach to cope with these errors is to combine SMPs with ensemble generation methods, such that each ensemble member reflects one plausible realization of the true—but unknown—precipitation. This requires replicating the spatiotemporal autocorrelation structure of SMP errors. The climatology of this structure is unknown for most locations due to a lack of ground‐reference observations, while the unique anisotropy and nonstationarity within any particular precipitation system limit the relevance of this climatology to the depiction of individual storm systems. Characterizing and simulating autocorrelation across spatiotemporal scales has thus been called a grand challenge within the precipitation community. We introduce the Space‐Time Rainfall Error and Autocorrelation Model (STREAM), which combines uncalibrated, anisotropic and nonstationary SMP spatiotemporal correlation modeling with a pixel‐scale precipitation error model to stochastically generate ensemble precipitation fields that resemble “ground truth” precipitation. We generate STREAM precipitation ensembles at high resolution (1‐hr, 0.1°) and evaluate these ensembles at multiple scales. STREAM ensembles consistently bracket ground‐truth observations and replicate the autocorrelation structure of ground‐truth precipitation fields. STREAM is compatible with pixel‐scale error/uncertainty formulations beyond those presented here, and could be applied to other precipitation sources such as numerical weather predictions or blended products. Although ground truth is used here to parameterize pixel‐scale uncertainty, if combined with other recent work in SMP uncertainty characterization, STREAM could be used without any ground data.

Which Rainfall Errors Can Hydrologic Models Handle? Implications for Using Satellite‐Derived Products in Sparsely Gauged Catchments

AbstractThere is interest in applying satellite‐derived rainfall products for water management in data‐sparse areas. However, questions remain around how uncertainties in different products interact with hydrologic models to determine simulation skill. Most related work uses performance statistics that inherently combine rainfall magnitude, timing and persistence, making it unclear which product improvements should be prioritized. We applied six satellite‐derived rainfall products in a conceptual hydrologic model (GR4J) across four Australian catchments with dense gauge data for comparison. We found that GR4J's inherent flexibility allowed it to filter errors in rainfall magnitude and variance through parameterization. Therefore, when rainfall observations for bias correction are unavailable, calibration of a flexible model could prove a useful alternative. However, the model was less able to compensate for errors in rainfall occurrence. In fact, the Probability of Detection score explained 59% of the variance in calibration performance (26% for validation), while overall bias explained just 14% (8% for validation). All products underestimated rainfall state persistence, but this had less influence on model skill. We then removed gauges from the observed data set to mimic data sparsity, finding that even a few gauges could reproduce rainfall occurrence and outperform satellite‐derived products. Two data‐sparse catchments in Vietnam were modeled to check whether the same learnings applied. The gauge data also performed best in Vietnam, and performance of most satellite‐derived products was comparable to the Australian case. Efforts to increase the spatial and temporal resolution of satellite observations, which could improve rainfall detection, will enhance satellite‐derived precipitation for hydrologic modeling.

An assessment study on the impact of land use land cover on monsoon depressions over India

This study investigates the influence of LULC representation on surface meteorological conditions associated with monsoon depressions (MDs) in the Advanced Research Weather Research and Forecasting (WRF) model. A total of 18 MDs were considered during 2007–2018, with a life period of at least two days. Two simulations are performed at a 5-km grid-spacing, ingesting the LULC from the United States Geological Survey (USGS) and National Remote Sensing Centre (NRSC) for each MD case. The urban area has been increased from 0.13% in USGS to 1.21% in NRSC over the north-central and east coastal states, reducing the soil moisture (SM) errors by 0.015 m3 m−3 (25%) in the NRSC run. The SM in the NRSC run has a correlation of 0.53, which is 15% higher than that in the USGS run. The NRSC has a larger forest area (22%) than the USGS (7%) over the north-western parts and some parts of Maharashtra, which helps increase latent heat flux (LHF) by 30 Wm−2. Verifying with ERA analysis, the USGS and the NRSC simulations underestimate the LHF in most parts of India and overestimate in orographic areas. The NRSC-simulation shows fewer improvements (<5%) in LHF across the central and southern areas, unlike in the East and West parts (>13%). The surface temperature, moisture, and rainfall have been noticeably improved in the NRSC run for day-2 and day-3 simulation, unlike in USGS, while they are comparable in day-1. The spatial error of rainfall increases with forecast length, with NRSC having 10–15% less error than the USGS run. Further, the NRSC run could identify the rainfall peaks of 3 mm h−1 with a mean error of 1.5 mm h−1 as compared to 2.2 mm h−1 error in the USGS. This study demonstrates the positive impact of NRSC-developed LULC in the MD simulations over India.

Error propagation of climate model rainfall to streamflow simulation in the Gidabo sub-basin, Ethiopian Rift Valley Lakes Basin

ABSTRACT This study assesses bias error of rainfall from climate models and related error propagation effects to simulated streamflow in the Gidabo sub-basin, Ethiopia. Rainfall is obtained from a combination of four global and regional climate models (GCM-RCMs), and streamflow is simulated by means of the Hydrologiska Byråns Vattenbalansavdelning (HBV-96) rainfall-runoff model. Five bias correction methods were tested to reduce the rainfall bias. To assess the effects of rainfall bias error propagation, percent bias (PBIAS), difference in coefficient of variation (CV), and 10th and 90th percentile indicators were applied. Findings indicate that the bias of the uncorrected rainfall caused large errors in simulated streamflow. All five bias correction methods improved the HBV-96 model performance in terms of capturing the observed streamflow. Overall, the findings of this study indicate that the magnitude of the error propagation varies subject to the selected performance indicator, bias correction method and climate model.