Journal of Hydrometeorology最新文献

{"title":"Performance evaluation of GPM IMERG precipitation products over the tropical oceans using Buoys","authors":"R. Pradhan, Y. Markonis","doi":"10.1175/jhm-d-22-0216.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0216.1","url":null,"abstract":"\u0000The major fraction of the global precipitation falls in tropical oceans. Nonetheless, due to the lack of in-situ precipitation measurements, the number of studies over the tropical oceans remains limited. Similarly, the performance of IMERG products over the tropical oceans, is yet to be known. In this context, this study quantitatively evaluates the 20 years (2001 – 2020) of IMERG V06 Early, Late, and Final products against the in-situ buoys estimates using the pixel-point approach at a daily scale across the tropical oceans. Results show that IMERG represents well the mean spatial pattern and spatial variation of precipitation, though significant differences exist in the magnitude of precipitation amount. Overall, IMERG notably overestimates precipitation across the tropical ocean, with maxima over the West Pacific and Indian oceans, while it performs better over the East Pacific and Atlantic oceans. Moreover, irrespective of the region, IMERG sufficiently detects precipitation events (i.e., > 0.1 mm/day) for high-precipitation regions, though it significantly overestimates the magnitude. Despite IMERG’s detection issues of precipitation events over the regions with lower precipitation, it depicts good agreement with the buoys in total precipitation estimation. The positive hit bias and false alarm bias are the major contributions to the overall total positive bias. Furthermore, the detection capability of IMERG tends to decline with increasing precipitation rates. In terms of IMERG runs, IMERG-F shows slightly better performance than the −E, −L runs. More detailed studies over the tropical oceans are required to better characterize the biases and their sources.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"394 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77348806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the CFSv2 Seasonal Precipitation Forecasts across the U.S. by Combining Weather Regimes and Gaussian Mixture Models","authors":"Cody L. Ratterman, Wei Zhang, Grace Affram, Bradley Vernon","doi":"10.1175/jhm-d-22-0188.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0188.1","url":null,"abstract":"\u0000While seasonal climate forecasts have major socio-economic impacts, current forecast products, especially those for precipitation, are not yet reliable for forecasters and decision makers. Here we developed a novel statistical-dynamical hybrid model for precipitation by applying Weather Regimes (WRs) and Gaussian Mixture Models (WR-GMM) to the National Oceanic and Atmospheric Administration’s Climate Forecast System Version 2 (CFSv2) precipitation forecasts across the continental United States. Instead of directly forecasting precipitation, WR-GMM uses observed precipitation from synoptic patterns similar to the future CFSv2 forecast. Traditionally K-means has been used to classify daily synoptic patterns into individual WRs, but the new GMM approach allows multiple WRs to be represented for the same day. The novel WR-GMM forecast model is trained on daily Climate Forecast System Reanalysis (CFSR) geopotential height and observed precipitation data during a 1981-2010 period, and verified for years 2011-2022. Overall, the WR-GMM method outperforms the CFSv2 ensemble forecast precipitation in terms of root mean square error, and Pearson correlation coefficient for lead months 1 through 4. Previous studies have used global climate models to forecast WRs in the Pacific and Mediterranean regions, usually with an emphasis on winter months, but the WR-GMM model is the first of its kind that promises great untapped potential to improve precipitation forecasts produced by CFSv2 across the continental United States.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"74 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86299314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A neural network classification framework for monthly and high spatial resolution surface water mapping in the Qinghai-Tibet plateau from Landsat observations","authors":"Qinwei Ran, F. Aires, P. Ciais, Chunjing Qiu, Yanfen Wang","doi":"10.1175/jhm-d-22-0211.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0211.1","url":null,"abstract":"\u0000The Qinghai-Tibet plateau, known as the Asian Water Tower, has a significant area of water bodies that provide a wide range of valuable ecosystem services. In the context of climate change, the formation condition of surface water and water extent is changing fast. Thus, there is a critical need for monthly detection algorithms at high spatial resolution (~30 m) with good accuracy. Multiple sensors observations are available but producing reliable long time series surface water mapping at a sub-annual temporal frequency still remains a challenge, mainly due to data limitations. In this study, we proposed a neural network-based monthly surface water classification framework relying on Landsat 5/7/8 images in 2000-2020 and topographic indices, and retrieved monthly water mask for the year 2020. The surface water was mainly distributed in the central and western parts of the plateau and the maximum area of permanent surface water (water frequency > 60%) was 26.66*103 km2 in 2020. The overall, producer and user accuracies of our surface water map were 0.96, 0.94 and 0.98, respectively; and the kappa coefficient reached 0.90, demonstrating a better performance than existing products (i.e. JRC Monthly Water History with overall accuracy 0.94, producer accuracy 0.89, user accuracy 0.99, and kappa coefficient 0.89). Our framework efficiently solved the problem of missing data in Landsat images referring to the JRC and priori information and performed well in dealing with ice/snow cover issues. We showed that higher uncertainties exist on wetlands and recommended exploring relationships between water and wetlands in the future.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"102 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86022392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seasonal variations of recharge-storage-runoff process over the Tibetan Plateau","authors":"Yonghui Lei, Rui Li, H. Letu, Jiancheng Shi","doi":"10.1175/jhm-d-23-0045.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0045.1","url":null,"abstract":"\u0000The Tibetan Plateau (TP) is a vital and vulnerable water tower that supports the livelihoods of billions of people. The use of a data-driven recharge-storage-runoff perspective enables a more comprehensive estimation of multiple aspects of the water cycle. Through an analysis of the diagnostic net water flux from ERA5, water storage changes (dS/dt) from GRACE, runoff estimations (R) from the land-atmosphere water balance, and river discharge measurements (Rd), the annual cycle of recharge-storage-runoff has been studied over the TP and its basins. The net water flux determines a recharge of 326 mm/yr over the TP. Recharge in coupled storages, leading to an increase in water mass (dS/dt >0) and runoff (R >0) during the wet season, is considered the fast response and measured using the ratio of runoff to net water flux (r1). Conversely, the slow response determined by the water storage release (dS/dt <0) during the dry season, is quantified by the ratio of storage release to runoff (r2). The ratios of r1 and r2 are influenced by climatic and terrain drivers, indicating specific characteristics of recharge-storage-runoff at the basin scale. Small r1 values and large r2 values suggest high buffer capacity, while the basin of Amu Darya (Salween) is characterized by the highest (lowest) buffer capacity over the TP. However, measurements of river discharge at Amu Darya suggest an uncoupled recharge-storage-runoff. The imbalance between river discharge and runoff estimation was most severe in the first decade of the 21st century but has been mitigated since 2012. River discharge at Amu Darya is likely constrained by energy during summer.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"8 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90455951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IMERG Precipitation Improves the SMAP Level-4 Soil Moisture Product","authors":"R. Reichle, Qing Liu, J. Ardizzone, W. Crow, Gabrielle J. M. De Lannoy, J. Kimball, R. Koster","doi":"10.1175/jhm-d-23-0063.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0063.1","url":null,"abstract":"\u0000The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"1 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83834790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization-based prediction uncertainty qualification of climatic parameters","authors":"V. Nourani, Mina Sayyah-Fard, S. Kantoush, K. P. Bharambe, T. Sumi, M. Saber","doi":"10.1175/jhm-d-23-0043.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0043.1","url":null,"abstract":"\u0000Point predictions of hydroclimatic processes through nonlinear modeling tools are associated with uncertainty. The main goal of this research was to construct Prediction Intervals (PIs) for nonlinear Artificial Neural Network (ANN)-based models of evaporation and the Standardized Precipitation Index (SPI). These are two critical indicators for climate for four stations in Iran (i.e., Tabriz, Urmia, Ardabil and Ahvaz) to qualify their predicted Uncertainty Values (UVs). We used classical techniques of Bootstrap (BS), Mean-Variance Estimation (MVE), and Delta, as well as an optimization-based method of Lower-Upper Bound Estimation (LUBE), to construct and compare the PIs. The wavelet-based denoising method was also adopted to denoise input data, enhancing the modeling performance. The obtained results indicate the ability of the BS and LUBE methods to estimate the uncertainty bound. The Delta method mostly failed to find the desired coverage due to its narrow PIs. On the other hand, the MVE method, due to its wide bound, did not convey valuable information about uncertainty. According to the obtained results, denoising the input vector could enhance the PI quality in the modeling of the SPI by up to 76%. It was more prominent than reducing the UV for evaporation models, which was observed the most at the Ardabil station, up to 30%. The inherently more random nature of drought than the evaporation process was interpreted as the cause of this reaction. From the results, Urmia station seems the riskiest regarding drought ventures.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"125 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77194507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-Temporal Evaluation of Satellite-based Precipitation Products in the Colorado River Basin","authors":"Heechan Han, T. Abitew, Seonggyu Park, C. Green, Jaehak Jeong","doi":"10.1175/jhm-d-23-0003.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0003.1","url":null,"abstract":"\u0000Gridded precipitation products from satellite-based systems provide continuous and seamless data that can overcome the limitations of ground-based precipitation data. Remote sensing (RS) products can provide efficient precipitation data in the desert rangelands and the Rocky Mountains of the western United States, where ground-based rain gauges are sparse. In this study, we evaluated the quality of precipitation estimates from Tropical Rainfall Measuring Mission (TRMM), Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN) in the Upper Colorado River Basin (UCRB) for the period 2000-2020. The reliability of daily precipitation data from these products was tested against ground-based observations from the National Oceanic and Atmospheric Administration (NOAA) using two continuous and four categorical statistical evaluation metrics. We investigated the effects of topographical conditions on the quality of precipitation estimates. Results show that all three products have 3 - 4 mm/day differences in daily precipitation rates compared to ground observations. In addition, the difference in monthly precipitation rates was more prominent in the wet season (November to April) than in the dry season (May to October). The margin of errors varied with the type of RS system and by location. A categorical evaluation suggests a moderate ability to detect precipitation occurrence with 50% - 60% detection ability. The reliability of precipitation estimates is mainly limited by elevation and different ecoregions and climate features.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"12 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88622863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Seasonal Differences Among Three NOAA Climate Data Records of Precipitation","authors":"O. Prat, B. Nelson","doi":"10.1175/jhm-d-22-0108.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0108.1","url":null,"abstract":"\u0000Three satellite precipitation datasets – CMORPH, PERSIANN-CDR, and GPCP – from the NOAA/Climate Data Record program were evaluated in their ability to capture seasonal differences in precipitation for the period 2007-2018 over the conterminous United States. Data from the in-situ US Climate Reference Network (USCRN) provided reference precipitation measurements and collocated atmospheric conditions (temperature) at the daily scale. Satellite precipitation products’ (SPP) performance with respect to cold season precipitation was compared to warm season and full-year analysis for benchmarking purposes. Considering an ensemble of typical performance metrics including accuracy, false alarm ratio, probability of detection, probability of false detection, and the King-Gupta efficiency (KGE) that combines correlation, bias, and variability, we found that the three SPPs displayed better performances during the warm season than during the cold season. Among the three datasets, CMORPH displayed better performance – smaller bias, higher correlation, and a better KGE score – than the two other SPPs on an annual basis and during the warm season. During the cold season, CMORPH showed the worst performance at higher latitudes over areas experiencing recurring snow, or frozen and mixed precipitation. CMORPH’s performances were further degraded compared to PERSIANN-CDR and GPCP when considering freezing temperatures (T<0°C) due to the inability to microwave sensors to retrieve precipitation over snow-covered surface. However, for cold rainfall events detected simultaneously by the satellite and the USCRN stations (i.e., conditional case), CMORPH performance noticeably improved but remained inferior to the two other datasets. The quantification of seasonal precipitation errors and biases for three satellite precipitation datasets presented in this work provides an objective basis for the improvement of rainfall retrieval algorithms of the next generation of satellite precipitation products.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"1 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84028510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic generation of plausible hydroclimate futures using climate teleconnections for South-Eastern Australia","authors":"N. Potter, F. Chiew, D. Robertson","doi":"10.1175/jhm-d-22-0206.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0206.1","url":null,"abstract":"\u0000Generating plausible future climate timeseries is needed for bottom-up climate impact modelling, as well as downscaling climate model output for hydrological applications. A novel method for generating multisite daily stochastic climate series is developed based on: 1) linear regression between climate teleconnection timeseries (e.g. IPO/SOI) and annual rainfall, 2) clustered method of fragments for subannual disaggregation, and 3) a regression-based approach to daily potential evapotranspiration (PET) for hydrological modelling. We demonstrate that bias (i.e. oversampling) occurs with the standard method of fragments disaggregation in the multisite context; and show that selection of an analogue year from clustered rainfall amounts provides better sampling properties than the standard method of fragments. Using hydrological data for south-eastern Australia, we model runoff from observed and simulated rainfall and PET using the GR4J model. Simulated annual and daily rainfall and runoff characteristics from the new method are similar to existing methods, with improvements demonstrated in wet-wet transition probabilities and spatial (between-site) correlations.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"100 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79286924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of Underlying Surface Datasets on Simulated Hydrological Variables in the Xijiang River Basin","authors":"Songnan Liu, Jun Wang, Huijun Wang, Shilong Ge","doi":"10.1175/jhm-d-22-0095.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0095.1","url":null,"abstract":"\u0000Hydrological models play an important role in water resources management and extreme events forecasting, and they are sensitive to the underlying conditions. This study aims to evaluate the impact of different soil-type maps and land-use maps on hydrological simulations and watershed responses by applying the WRF-Hydro (Weather Research and Forecasting Model Hydrological modeling system) distributed hydrological model to the Xijiang River basin. WRF-Hydro runs for four different scenarios for the period 1992–2013. FAO (Food and Agriculture Organization) and GSDE (Global Soil Dataset for Earth System Science) soil-type maps, and MODIS (Moderate-Resolution Imaging Spectroradiometer) and CNLUCC (China Land Use Land Cover Remote Sensing Monitoring Dataset) land-use maps are used in this study. These soil-type maps and land-use maps are freely combined to form four scenarios. It is found that soil moisture and surface runoff are sensitive to soil-type maps, and absorbed shortwave radiation is found to be the least sensitive to soil-type maps. Absorbed shortwave radiation and heat flux are sensitive to land-use maps. The model performance of simulating soil moisture has increased when the soil-type map changes from FAO to GSDE and the land-use map changes from MODIS to CNLUCC for most stations. When the soil-type map changes from FAO to GSDE and the land-use map changes from MODIS to CNLUCC, the biases of simulating streamflow decrease. This study shows that the performance of the offline WRF-Hydro is significantly influenced by soil-type and land-use maps, and better simulation results can be obtained with more realistic underlying surface maps.\u0000\u0000\u0000The purpose of this study is to evaluate the impacts of land-use and soil-type maps on hydrological processes at the watershed scale by applying a distributed hydrological model WRF-Hydro model for the Xijiang River basin and reveal the importance of choosing land-use and soil-type maps. In this study, two soil-type maps and two land-use maps are used. It is found that soil moisture and surface runoff are sensitive to soil-type maps, and absorbed shortwave radiation and heat flux are sensitive to land-use maps. When using GSDE soil-type and CNLUCC land-use maps, the performance of the model is improved. The underlying conditions should be considered when applying the models in practice.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"25 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81034519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}