The 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.
{"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}
Cody L. Ratterman, Wei Zhang, Grace Affram, Bradley Vernon
While 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.
{"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}
Qinwei Ran, F. Aires, P. Ciais, Chunjing Qiu, Yanfen Wang
The 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.
被称为“亚洲水塔”的青藏高原拥有大面积的水体，提供了广泛的生态系统服务。在气候变化的背景下，地表水的形成条件和水的范围正在发生快速变化。因此，迫切需要高精度的高空间分辨率(~30 m)月检测算法。虽然可以使用多个传感器观测，但由于数据的限制，以次年的时间频率绘制可靠的长时间序列地表水地图仍然是一个挑战。基于2000-2020年Landsat 5/7/8影像和地形指数，提出了基于神经网络的月度地表水分类框架，并检索了2020年的月度水掩膜。地表水主要分布在高原中部和西部，2020年永久地表水面积最大(水频率> 60%)为26.66*103 km2。地表水图的总体精度、生产者精度和使用者精度分别为0.96、0.94和0.98;kappa系数达到0.90，优于现有产品(即JRC Monthly Water History，总体精度0.94，生产者精度0.89，用户精度0.99,kappa系数0.89)。我们的框架有效地解决了参考JRC和先验信息的Landsat图像数据缺失问题，并在处理冰雪覆盖问题方面表现良好。我们发现湿地存在较高的不确定性，并建议在未来探索水与湿地之间的关系。
{"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}
The 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.
{"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}
R. Reichle, Qing Liu, J. Ardizzone, W. Crow, Gabrielle J. M. De Lannoy, J. Kimball, R. Koster
The 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.
NASA主动被动土壤湿度(SMAP)任务4级土壤湿度(L4_SM)产品提供2015年4月至今的全球9公里分辨率、每3小时的地表和根区土壤湿度，平均延迟时间为2.5天。L4_SM算法将SMAP l波段(1.4 GHz)亮度温度(Tb)观测数据同化到NASA集水区地表模型中，因为该模型是由观测降水驱动的。本文描述并评估了从L4_SM版本6开始的L4_SM算法中，来自NASA综合多卫星检索全球降水测量(IMERG)产品的基于卫星和基于仪表的降水的使用。具体来说，IMERG有两种使用方式:(1) L4_SM降水参考气气学主要基于IMERG- final (Version 06B)数据，取代了以前L4_SM版本中使用的全球降水气气学项目2.2版本数据;(2)对北美和高纬度地区以外的降水强迫进行了校正，以匹配IMERG的日总量，取代了早期L4_SM版本中使用的仅仪表、每日产品或未经校正的天气分析降水。IMERG降水的使用使L4_SM表层土壤湿度的异常时间序列相关系数(相对于独立卫星估计)在全球平均水平上提高了0.03，在南美洲、非洲、澳大利亚和东亚部分地区提高了~ 0.3，这些地区早期L4_SM版本中使用的仅仪表降水产品的质量较差。这些改进还将Tb观测减去预测残差的时间序列标准差从L4_SM版本5的5.5 K降低到版本6的5.1 K。
{"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}
V. Nourani, Mina Sayyah-Fard, S. Kantoush, K. P. Bharambe, T. Sumi, M. Saber
Point 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.
{"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}
Heechan Han, T. Abitew, Seonggyu Park, C. Green, Jaehak Jeong
Gridded 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.
{"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}
Three 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.
{"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}
Generating 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.
{"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}
Hydrological 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. The 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.
水文模型在水资源管理和极端事件预报中发挥着重要作用，对潜在条件非常敏感。以西江流域为研究对象，应用WRF-Hydro (Weather Research and Forecasting Model水文模拟系统)分布式水文模型，评价不同土壤类型图和土地利用图对水文模拟和流域响应的影响。WRF-Hydro在1992年至2013年期间运行了四种不同的情景。利用FAO (Food and Agriculture Organization)和GSDE (Global Soil Dataset for Earth System Science)土壤类型图，以及MODIS (Moderate-Resolution Imaging Spectroradiometer)和CNLUCC (China Land Use - Land Cover遥感监测数据集)土地利用图进行研究。这些土壤类型图和土地利用图可以自由组合，形成四个场景。研究发现，土壤湿度和地表径流对土壤类型图最敏感，而吸收短波辐射对土壤类型图最不敏感。吸收的短波辐射和热通量对土地利用图很敏感。大多数台站土壤类型图由FAO改为GSDE，土地利用图由MODIS改为CNLUCC时，模型模拟土壤湿度的性能有所提高。当土壤类型图由FAO转换为GSDE，土地利用图由MODIS转换为CNLUCC时，模拟径流的偏差减小。研究表明，WRF-Hydro的离线模拟性能受土壤类型和土地利用图的显著影响，下垫面图越逼真，模拟效果越好。本研究旨在应用分布式水文模型WRF-Hydro模型，评价西江流域土地利用和土壤类型图对流域水文过程的影响，揭示土地利用和土壤类型图选择的重要性。本研究使用了2张土壤类型图和2张土地利用图。土壤湿度和地表径流对土壤类型图敏感，吸收短波辐射和热通量对土地利用图敏感。当使用GSDE土壤类型和CNLUCC土地利用图时，模型的性能得到了提高。在实际应用模型时，应考虑潜在的条件。
{"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}
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