Multiple Precipitation Research Articles

Subsurface Irrigation with Ceramic Emitters: Evaluating Soil Water Effects Under Multiple Precipitation Scenarios

Subsurface Irrigation with Ceramic Emitters: Evaluating Soil Water Effects Under Multiple Precipitation Scenarios

A multi-model data fusion methodology for seasonal drought forecasting under uncertainty: Application of Bayesian maximum entropy

In this paper, we present a new methodology for improving the results of seasonal drought forecasting by developing a Bayesian Maximum Entropy-based fusion (BMEF) model. The BMEF model combines the forecasts done by four individual (single-source) data-driven models to achieve better outcomes. Regional drought indices of Effective Drought Index (EDI) and Multiple Standard Precipitation Index (MSPI) are forecasted using the individual forecasting models of Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), and M5tree. The outputs of the individual models with the best performances are selected to be fused using the BMEF model and the results are analyzed and compared. The effect of different large-scale climate signals on rainfall and drought forecasting is analyzed and the most effective climate variables are selected as predictors in the forecasting models. Next, the uncertainty analysis on the results of the individual models as well as those of the BMEF model is carried out by deriving the probability mass functions of the drought indices using a resampling technique and Monte Carlo analysis. Finally, the results of the uncertainty analysis are evaluated to compare the performance of individual models and the BME-based fusion model in decreasing the uncertainty of seasonal drought forecasting. The performance of the proposed methodology is evaluated by using it to forecast seasonal drought conditions in the southwest of Iran. Based on the results of the uncertainty analysis, the BMEF model provides more reliable forecasts particularly for severe drought events than the individual models. It is also inferred that adding the SST to the predictors, decreases the uncertainty of drought forecasts.

Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.

Improved Flood Forecasting in Basins With No Precipitation Stations: Constrained Runoff Correction Using Multiple Satellite Precipitation Products

AbstractSatellite‐based precipitation products (SPPs) with short latencies provide a new opportunity for flood forecasting in basins with no precipitation stations. However, the larger uncertainties associated with these near‐real‐time SPPs can influence the accuracy of flood forecasts. Here, we propose a real‐time updating method, referred to as “Constrained Runoff Correction using Multiple SPPs” (CRC‐M). The method is based on the hypothesis that the range of runoff volumes computed using different near‐real‐time SPPs provides an indication of the approximate range in which the true runoff volume lies. Accordingly, a constrained runoff correction is performed using the discharge observed at the basin outlet within this range. Evaluation using real data indicates that the new method performs well with Nash–Sutcliffe (NS) values of 0.85 and 0.87 during calibration and validation, respectively. The necessity of imposing constraints using multiple SPPs is demonstrated by comparing CRC‐M with 2 controls, referred to as “Unconstrained Runoff Correction using Single SPP” (URC‐S) and “Constrained Runoff Correction using Single SPP with perturbations” (CRC‐S). Experiments indicate that the key factors that result in good performance are (a) relatively reliable SPPs and (b) wider constraint ranges. Overall, the CRC‐M method can result in accurate and stable flood forecasts in basins with no precipitation stations, without the shortening of leading time and the need for increased model complexity (i.e., the numbers of model parameters).

Spatiotemporal variation of precipitation on a global scale from 1960 to 2016 in a new normalized daily precipitation dataset

AbstractIncreases in total and extreme precipitation have been investigated worldwide using limited spatiotemporal data based on surface observations and satellite remote sensing during recent decades. However, it remains unclear whether rain gauge daily precipitation records also show a significant increase in global precipitation. In this study, we first apply the transformed version of the penalized maximum F test (“transPMFred”) to homogenize daily precipitation (P) records from 10,629 stations around the world during 1960–2016 from a dataset (GHCN‐CMA) that combines the daily precipitation data from the Global Historical Climatology Network (GHCN‐Daily) dataset and the China Meteorological Administration (CMA), and then utilize the homogenized data to study the frequency of daily precipitation on a global scale. The results indicate that precipitation frequency exhibited significant increasing trends from 1960 to 2016 across most of the globe, except for East Asia. Moreover, the variability of global precipitation frequency was more pronounced in wet regions than dry regions, especially in North America, Europe and East Asia based multiple daily precipitation datasets. The regional average frequency for moderate (30th < P ≤ 60th percentile), heavy (60th < P ≤ 90th percentile) and very heavy (P > 90th percentile) precipitation increased in North America and Europe. Changes in precipitation frequency decreased for light (P ≤ 30th percentile) and moderate precipitation but increased for heavy and very heavy precipitation, which mainly occurs in dry regions of Australia. Moreover, precipitation frequency in wet and dry regions of East Asia showed opposite trends, with a decrease in wet regions and an increase in dry regions.

A streamflow-oriented ranking-based methodological framework to combine multiple precipitation datasets across large river basins

Hydrologic-Land Surface Models (H-LSMs) are subject to input uncertainties arising from climate forcing data, especially precipitation. For better streamflow simulations and predictions, the generation of a hybrid dataset by combining existing precipitation products has attracted considerable interest in recent years. To assess the accuracy of the hybrid dataset, in-situ precipitation-gauge stations are used as a reference point. However, the robustness of the hybrid dataset in representing spatial details can be problematic when the evaluation uses only a sparse network of in-situ observations at regional or basin scales. This study aims to develop a methodological framework to generate hybrid precipitation datasets based on the model performance of streamflow simulations that are spatially representative across large river basins. The framework is illustrated using a Canadian H-LSM known as MESH (Modélisation Environmentale communautaire – Surface Hydrology) in the Saskatchewan River basin, Canada, for the period 2002–2010. Five regional and global precipitation products (Global Meteorological Forcing Dataset at Princeton University (Princeton); the WATCH Forcing Data methodology applied to the ERA-Interim (WFDEI) augmented by Climatic Research Unit (WFDEI [CRU]) and Global Precipitation Climatology Centre (WFDEI [GPCC]); North American Regional Reanalysis (NARR); and Canadian Precipitation Analysis (CaPA)) were included as candidates in this study. Results indicate that the generation of a hybrid dataset based on hydrological evaluation was useful for improving H-LSM modelling skills. Hybrid datasets showed a similar or better model performance compared to that of the best basin-wide precipitation product in the headwaters and gradually performed better downstream and at the basin outlet. When multiple products are combined model performance can be further enhanced by considering seasonality with respect to the hydrological regime of the river basin. This study demonstrates the usefulness of hybrid datasets in a large-scale river basin with low climate station network density.

Improving Hourly Precipitation Estimates for Flash Flood Modeling in Data-Scarce Andean-Amazon Basins: An Integrative Framework Based on Machine Learning and Multiple Remotely Sensed Data

Accurate estimation of spatiotemporal precipitation dynamics is crucial for flash flood forecasting; however, it is still a challenge in Andean-Amazon sub-basins due to the lack of suitable rain gauge networks. This study proposes a framework to improve hourly precipitation estimates by integrating multiple satellite-based precipitation and soil-moisture products using random forest modeling and bias correction techniques. The proposed framework is also used to force the GR4H model in three Andean-Amazon sub-basins that suffer frequent flash flood events: upper Napo River Basin (NRB), Jatunyacu River Basin (JRB), and Tena River Basin (TRB). Overall, precipitation estimates derived from the framework (BC-RFP) showed a high ability to reproduce the intensity, distribution, and occurrence of hourly events. In fact, the BC-RFP model improved the detection ability between 43% and 88%, reducing the estimation error between 72% and 93%, compared to the original satellite-based precipitation products (i.e., IMERG-E/L, GSMAP, and PERSIANN). Likewise, simulations of flash flood events by coupling the GR4H model with BC-RFP presented satisfactory performances (KGE* between 0.56 and 0.94). The BC-RFP model not only contributes to the implementation of future flood forecast systems but also provides relevant insights to several water-related research fields and hence to integrated water resources management of the Andean-Amazon region.

Application of Random Forest Algorithm for Merging Multiple Satellite Precipitation Products across South Korea

Precipitation is a crucial component of the water cycle and plays a key role in hydrological processes. Recently, satellite-based precipitation products (SPPs) have provided grid-based precipitation with spatiotemporal variability. However, SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution of these products is still relatively coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation based on a combination of rainfall observation data with multiple SPPs for the period of 2003–2017 across South Korea. A Random Forest (RF) machine-learning algorithm model was applied for producing a new merged precipitation product. In addition, several statistical linear merging methods have been adopted to compare with the results achieved from the RF model. To investigate the efficiency of RF, rainfall data from 64 observed Automated Synoptic Observation System (ASOS) installations were collected to analyze the accuracy of products through several continuous as well as categorical indicators. The new precipitation values produced by the merging procedure generally not only report higher accuracy than a single satellite rainfall product but also indicate that RF is more effective than the statistical merging method. Thus, the achievements from this study point out that the RF model might be applied for merging multiple satellite precipitation products, especially in sparse region areas.

A Comparison of the Performance of Different Interpolation Methods in Replicating Rainfall Magnitudes under Different Climatic Conditions in Chongqing Province (China)

Precipitation is considered a crucial component in the hydrological cycle and changes in its spatial pattern directly influence the water resources. We compare different interpolation techniques in predicting the spatial distribution pattern of precipitation in Chongqing. Six interpolation methods, i.e., Inverse Distance Weighting (IDW), Radial Basis Function (RBF), Diffusion Interpolation with Barrier (DIB), Kernel Interpolation with Barrier (KIB), Ordinary Kriging (OK) and Empirical Bayesian Kriging (EBK), were applied to estimate different rainfall patterns. Annual mean, rainy season and dry-season precipitation was calculated from the daily precipitation time series of 34 meteorological stations with a time span of 1991 to 2019, based on Leave-One-Out Cross-Validation (LOOCV), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE) and Nash–Sutcliffe Efficiency coefficient (NSE) as validation indexes of the applied models for calculating the error degree and accuracy. Correlation test and Spearman coefficient was performed on the estimated and observed values. A method combining Entropy Weight and Technique for Order Preference by Similarity to Ideal Solution (Entropy-Weighted TOPSIS) was introduced to rank the performance of six interpolation methods. The results indicate that interpolation technique performs better in estimating during periods of low precipitation (i.e., dry season, relative to rainy season and mean annual). The performance priorities of the six methods under the combined multiple precipitation distribution patterns are KIB > EBK > OK > RBF > DIB > IDW. Among them, KIB method has the highest accuracy which maps more accurate precipitation surfaces, with the disadvantage that estimation error is prone to outliers. EBK method is the second highest, and IDW method has the lowest accuracy with a high degree of error. This paper provides information for the application of interpolation methods in estimating rainfall spatial pattern and for water resource management of concerned regions.

Comparison of traditional method and triple collocation analysis for evaluation of multiple gridded precipitation products across Germany

AbstractEvaluating the accuracy of precipitation products is essential for many applications. The traditional method for evaluation is to calculate error metrics of products with gauge measurements that are considered as ground-truth. The multiplicative triple collocation (MTC) method has been demonstrated powerful in error quantification of precipitation products when ground-truth is not known. This study applied MTC to evaluate five precipitation products in Germany: two raw satellite-based (CMORPH and PERSIANN), one reanalysis (ERA-Interim), one soil moisture-based (SM2RAIN-ASCAT), and one gauge-based (REGNIE) products. Evaluation was performed at the 0.5° -daily spatial-temporal scales. MTC involves a log transformation of data, necessitating dealing with zero values in daily precipitation. Effects of 12 different strategies for dealing with zero value on MTC results were investigated. Seven different triplet combinations were tested to evaluate the stability of MTC. Results showed that different strategies for replacing zero values had considerable effects on MTC-derived error metrics particularly for root mean squared error (RMSE). MTC with different triplet combinations generated different error metrics for individual products. MTC-derived correlation coefficient (CC) was more reliable than RMSE. It is more appropriate to use MTC to compare the relative accuracy of different precipitation products. Based on CC with unknown truth, MTC with different triplet combinations produced the same ranking of products as the traditional method. A comparison of results from MTC and the classic TC with additive error model showed the potential limitation of MTC in arid area or dry time periods with large ratio of zero daily precipitation.

Inferring precipitation thresholds of landslide activity from long-term dendrochronological and precipitation data: Case study on the unstable slope at Karpenciny, Poland

An extended statistical comparison of numerous precipitation parameters (absolute, minimum, and maximum totals; number of days with precipitation; and duration of precipitation in particular months, seasons, and longer periods) and the 48-y long dendrochronological reconstruction of landslide activity was conducted for a generally unmonitored landslide slope at Karpenciny, Western Carpathians, Poland. The Karpenciny landslide is a deep (approximately 40 m) translational landslide consisting of large, poorly fragmented, and almost intact landslide blocks. It is located only 2.4 km from the meteorological gauging station. We aimed to explore the long-term dependence of this landslide on precipitation conditions and reveal the precipitation parameters crucial for its activity, that is, direct triggers and preparatory antecedent precipitation. Therefore, dendrochronological records of tree-ring eccentricity and compression wood in 35 Norway spruce trees were correlated with 520 precipitation parameters (including seasonality, duration, and total), which might influence the activity of the studied landslide. Ten best-correlated precipitation parameters and seasonal sums of summer half-years and preceding winter half-years (separately and in total, i.e. as 12-month sums), were then tested for their potential in establishing precipitation thresholds for landslide activity. The analysis of multiple precipitation parameters enabled us to develop precipitation thresholds based on both direct causes and long-term preparatory precipitation necessary to accelerate the studied landslide. From the tree-ring data, we established landslide-safe conditions and 0.5-probability thresholds for a particular landslide slope. This study also generated comprehensive data on the seasonality of antecedent and triggering precipitation, long-term periods of antecedent precipitation, critical minimum levels of precipitation that sustain slope imbalance, and the importance of generally wet conditions (demonstrated as a high total number of days with precipitation regardless of its totals) as a factor in landslide activity. These patterns of landslide-precipitation dependence would not have been revealed with standard methods and standard precipitation parameters applied in threshold analyses. Thus, long-term reconstructions can be a valuable source of data for precipitation thresholds of landslide activity.

Reconstructing Terrestrial Water Storage Anomalies Using Satellite Data to Evaluate Water Resource Shortages from 1980 to 2016 in the Inland Yongding River Basin, China

Water resources in the Yongding River basin (YRB) are one of the important fundamental conditions in supporting regional water conservation and ecological development. However, the historical changes in water resources under recent human activities remain unknown due to very limited observation data. In this study, terrestrial water storage anomalies (TWSA) as well as multiple precipitation and actual evapotranspiration products from satellites were collected, and the accuracy of the data was verified by observed data or pairwise comparisons. The TWSA during 1980-2016 was reconstructed by using the water balance method, and the reconstructed TWSA was verified using GRACE-observed TWSA, the average depth to groundwater in the Beijing Plain from historical document records and the observed runoff from Guanting Reservoir. The reconstructed TWSA data indicated that the significant decrease occurred during 2000–2016 and the average rate of decreasing trend was -11 mm/year, which may have been caused by a decrease in groundwater storage due to agricultural development. However, the reconstructed TWSA decreased slightly during 1980-1999. The establishment of the water storage deficit index (WSDI) showed that there was no drought or mild drought during 1980-1999; however, the water resource shortage during 2000-2016 was more serious due to groundwater storage decreases caused by agricultural development. The WSDI was verified by using the commonly used self-calibrated Palmer drought severity index. The findings are valuable for sustainable water resource management in the YRB.

Using the Global Hydrodynamic Model and GRACE Follow-On Data to Access the 2020 Catastrophic Flood in Yangtze River Basin

Flooding is one of the most widespread and frequent weather-related hazards that has devastating impacts on the society and ecosystem. Monitoring flooding is a vital issue for water resources management, socioeconomic sustainable development, and maintaining life safety. By integrating multiple precipitation, evapotranspiration, and GRACE-Follow On (GRAFO) terrestrial water storage anomaly (TWSA) datasets, this study uses the water balance principle coupled with the CaMa-Flood hydrodynamic model to access the spatiotemporal discharge variations in the Yangtze River basin during the 2020 catastrophic flood. The results show that: (1) TWSA bias dominates the overall uncertainty in runoff at the basin scale, which is spatially governed by uncertainty in TWSA and precipitation; (2) spatially, a field significance at the 5% level is discovered for the correlations between GRAFO-based runoff and GLDAS results. The GRAFO-derived discharge series has a high correlation coefficient with either in situ observations and hydrological simulations for the Yangtze River basin, at the 0.01 significance level; (3) the GRAFO-derived discharge observes the flood peaks in July and August and the recession process in October 2020. Our developed approach provides an alternative way of monitoring large-scale extreme hydrological events with the latest GRAFO release and CaMa-Flood model.

Forests buffer against variations in precipitation.

Atmospheric moisture recycling effectively increases the amount of usable water over land as the water can undergo multiple precipitation–evapotranspiration cycles. Differences in land cover and climate regulate the evapotranspiration flux. Forests can have deep roots that access groundwater facilitating transpiration throughout the dry season independent of precipitation. This stable transpiration buffers the forest against precipitation variability. However, it is not known whether the buffering effect, already modeled for tropical forests, is common to all forests globally. Here we apply a state‐of‐the‐art Lagrangian moisture tracking model (UTrack) to study whether forest land cover in the upwind precipitationshed can lead to a reduction in monthly precipitation variability downwind. We found a significant buffering effect of forests in the precipitation variability of 10 out of 14 biomes globally. On average, if 50% of precipitation originates from forest, then we find a reduction in the coefficient of variation of monthly precipitation of 60%. We also observed that a high fraction of precipitation from non‐forest land sources tends to have the opposite effect, that is, no buffering effect. The average variation of monthly precipitation was 69% higher in areas where 50% of precipitation originates from non‐forest land sources in the precipitationshed. Our results emphasize the importance of land cover composition in the precipitationshed to buffer precipitation variability downwind, in particular forest cover. Understanding the influence of land cover in a precipitationshed on atmospheric moisture transport is key for evaluating an area's water‐climate regulatory ecosystem services and may become increasingly important due to continued changes in land cover and climate change.

Evaluation and Hydrological Application of a Data Fusing Method of Multi-Source Precipitation Products-A Case Study over Tuojiang River Basin

Precipitation is an essential driving factor of hydrological models. Its temporal and spatial resolution and reliability directly affect the accuracy of hydrological modeling. Acquiring accurate areal precipitation needs substantial ground rainfall stations in space. In many basins, ground rainfall stations are sparse and uneven, so real-time satellite precipitation products (SPPs) have become an important supplement to ground-gauged precipitation (GGP). A multi-source precipitation fusion method suitable for the Soil and Water Assessment Tool (SWAT) model has been proposed in this paper. First, the multivariate inverse distance similarity method (MIDSM) was proposed to search for the optimal representative precipitation points of GGP and SPPs in sub-basins. Subsequently, the correlation-coefficient-based weighted average method (CCBWA) was presented and applied to calculate the fused multi-source precipitation product (FMSPP), which combined GGP and multiple satellite precipitation products. The effectiveness of the FMSPP was proven over the Tuojiang River Basin. In the case study, three SPPs were chosen as the satellite precipitation sources, namely the Climate Forecast System Reanalysis (CFSR), Tropical Rainfall Measuring Mission Project (TRMM), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network Climate Data Record (PERSIANN-CDR). The evaluation indicators illustrated that FMSPP could capture the occurrence of rainfall events very well, with a maximum Probability of Detection (POD) and Critical Success Index (CSI) of 0.92 and 0.83, respectively. Furthermore, its correlation with GGP, changing in the range of 0.84–0.96, was higher in most sub-basins on the monthly scale than the other three SPPs. These results demonstrated that the performance of FMSPP was the best compared with the original SPPs. Finally, FMSPP was applied in the SWAT model and was found to effectively drive the SWAT model in contrast with a single precipitation source. The FMSPP manifested the highest accuracy in hydrological modeling, with the Coefficient of Determination (R2) of 0.84, Nash Sutcliff (NS) of 0.83, and Percent Bias (PBIAS) of only −1.9%.

Changes to the 1979–2013 summer Convective Available Potential Energy (CAPE) and extreme precipitation over North America

Trends of monthly Convective Available Potential Energy (CAPE) and of single or multiple event extreme precipitation indices, namely RX1day, RX5day and R20mm, were analyzed for the summer of 1979–2013 over the United States (US) and southern Canada. A homogeneity test in CAPE data using the Wilcoxon-rank-sum test showed that statistically significant change points mainly occurred between 2000 and 2003 probably due to changes in humidity sensors during this period. Therefore, the trend analysis was split into two categories: 1979–2013 for regions where no statistically significant change points in CAPE were detected and 1979–2001 and 2002–2013 for regions where statistically significant change points in CAPE were detected. The results show that increasing trends in CAPE and extreme precipitation indices dominate the eastern Atlantic coast of the US and some parts of the Great Lakes. Mixed increasing and decreasing trends were detected in the US mid-west while the western US and the Canadian Pacific coast had little trend in CAPE and extreme precipitation indices. Although both increasing and decreasing trends were detected, the average trends in RX1day and RX5day were about 0.2 mm/decade and 0.5 mm/decade over 1979–2013. However, the average trend for the 1979–2001 period was 0.2 mm/decade (RX1day) and 1.2 mm/decade (RX5day) as compared 1.2 mm/decade (RX1day) and 1.7 mm/decade (RX5day) for the 2002–2013 period. Temperatures also increased more during the 2002–2013 period (0.3 °C/decade) as compared to the 1979–2001 period (0.1 °C/decade). The higher increasing trend of the recent decades suggests that increasing temperatures could potentially lead to an overall increase in CAPE and extreme precipitation over the US and southern Canada.

A multi-sensor evaluation of precipitation uncertainty for landslide-triggering storm events.

Extreme precipitation can have profound consequences for communities, resulting in natural hazards such as rainfall‐triggered landslides that cause casualties and extensive property damage. A key challenge to understanding and predicting rainfall‐triggered landslides comes from observational uncertainties in the depth and intensity of precipitation preceding the event. Practitioners and researchers must select from a wide range of precipitation products, often with little guidance. Here we evaluate the degree of precipitation uncertainty across multiple precipitation products for a large set of landslide‐triggering storm events and investigate the impact of these uncertainties on predicted landslide probability using published intensity–duration thresholds. The average intensity, peak intensity, duration, and NOAA‐Atlas return periods are compared ahead of 177 reported landslides across the continental United States and Canada. Precipitation data are taken from four products that cover disparate measurement methods: near real‐time and post‐processed satellite (IMERG), radar (MRMS), and gauge‐based (NLDAS‐2). Landslide‐triggering precipitation was found to vary widely across precipitation products with the depth of individual storm events diverging by as much as 296 mm with an average range of 51 mm. Peak intensity measurements, which are typically influential in triggering landslides, were also highly variable with an average range of 7.8 mm/h and as much as 57 mm/h. The two products more reliant upon ground‐based observations (MRMS and NLDAS‐2) performed better at identifying landslides according to published intensity–duration storm thresholds, but all products exhibited hit ratios of greater than 0.56. A greater proportion of landslides were predicted when including only manually verified landslide locations. We recommend practitioners consider low‐latency products like MRMS for investigating landslides, given their near‐real time data availability and good performance in detecting landslides. Practitioners would be well‐served considering more than one product as a way to confirm intense storm signals and minimize the influence of noise and false alarms.

Finding the Optimal Multimodel Averaging Method for Global Hydrological Simulations

Global gridded precipitations have been extensively considered as the input of hydrological models for runoff simulations around the world. However, the limitations of hydrologic models and the inaccuracies of the precipitation datasets could result in large uncertainty in hydrological forecasts and water resource estimations. Therefore, it is of great importance to investigate the hydrological value of a weighted combination of hydrological models driven by different precipitation datasets. In addition, due to the diversities of combination members and climate conditions, hydrological simulation for watersheds under different climate conditions may show various sensitivities to the weighted combinations. This study undertakes a comprehensive analysis of various multimodel averaging methods and schemes (i.e., the combination of the members in averaging) to identify the most skillful and reliable multimodel averaging application. To achieve this, four hydrological models driven by six precipitation datasets were used as averaging members. The behaviors of 9 averaging methods and 11 averaging schemes in hydrological simulations were tested over 2277 watersheds distributed in different climate regions in the world. The results show the following: (1) The multi-input averaging schemes (i.e., members consist of one model driven by multiple precipitation datasets) generally perform better than the multimodel averaging schemes (i.e., members consist of multiple models driven by the same precipitation dataset) for each averaging method; (2) The use of multiple members can improve the averaging performances. Six averaging members are found to be necessary and advisable, since using more than six members only imrpoves the estimation results slightly, as compared with using all 24 members; (3) The advantage of using averaging methods for hydrological modeling is region dependent. The averaging methods, in general, produced the best results in the warm temperate region, followed by the snow and equatorial regions, while a large difference among various averaging methods is found in arid and arctic regions. This is mainly due to the different averaging methods being affected to a different extent by the poorly performed members in the arid and arctic regions; (4) the multimodel superensemble method (MMSE) is recommended for its robust and outstanding performance among various climatic regions.

Precipitation influence on and response to early and late Arctic sea ice melt onset during melt season

AbstractThe region containing portions of the East Siberian Sea and Laptev Sea (73°–84°N, 90°–155°E) is the area of focus (AOF) for this study. The impacts of precipitation, latent heat (LH) and sensible heat (SH) fluxes on sea ice melt onset in the AOF are investigated. Four early melting years (1990, 2012, 2003, and 1991) and four late melting years (1982, 1983, 1984, and 1996) are compared to better identify the different responses to melt onset timing. A consistency check is performed between multiple Arctic precipitation products (including NASA MERRA‐2, ECMWF ERA‐Interim [ERA‐I], and ECMWF ERA5 reanalyses as well as GPCP V2.3 observations) since there is not yet a high‐quality ground‐truth Arctic precipitation data product. MERRA‐2 has the greatest monthly average precipitation, snowfall, evaporation, and net LH flux. ERA‐I suggests that liquid precipitation starts earlier in the year than MERRA‐2 and ERA5, while GPCP shows different seasonal precipitation variations from the reanalyses. MERRA‐2 has the clearest and most amplified seasonal trends for the parameters used in this study, so the daily time series and anomalies of MERRA‐2 variables before and after the first major melt event are investigated. ERA5 is used to check these results because ERA‐I and ERA5 display similar seasonal trends. According to MERRA‐2, during early melt years, surface SH flux loss and precipitation are above average in the days before and after the first major melt event. During late melt years, surface SH flux loss and precipitation are below average in the month leading up to the first major melt event.

Assessment of Precipitation Error Propagation in Discharge Simulations over the Contiguous United States

AbstractThis study characterizes precipitation error propagation through a distributed hydrological model based on the river basins across the Contiguous United States (CONUS), to better understand the relationship between errors in precipitation inputs and simulated discharge (i.e., P-Q error relationship). The NLDAS-2 precipitation and its simulated discharge are used as the reference to compare with TMPA-3B42 V7, TMPA-3B42RT V7, StageIV, CPC-U, MERRA-2, and MSWEP-2.2 for 1,548 well gauged river basins. The relative errors in multiple conventional precipitation products and their corresponding discharges are analysed for the period of 2002-2013. The results reveal positive linear P-Q error relationships at annual and monthly timescales, and the stronger linearity for larger temporal accumulations. Precipitation errors can be doubled in simulated annual accumulated discharge. Moreover, precipitation errors are strongly dampened in basins characterized by temperate and continental climate regimes, particularly for peak discharges, showing highly nonlinear relationships. Radar-based precipitation product consistently shows dampening effects on error propagation through discharge simulations at different accumulation timescales compared to the other precipitation products. Although basin size and topography also influence the P-Q error relationship and propagation of precipitation errors, their roles depend largely on precipitation products, seasons and climate regimes.