Mijael Rodrigo Vargas Godoy, Y. Markonis, O. Rakovec, Michal Jeníček, Riya Dutta, R. Pradhan, Zuzana Bešťáková, Jan Kyselý, Roman Juras, S. Papalexiou, M. Hanel
Abstract. The water cycle in Czechia has been observed to be changing in recent years, with precipitation and evapotranspiration rates exhibiting a trend of acceleration. However, the spatial patterns of such changes remain poorly understood due to the heterogeneous network of ground observations. This study relied on multiple state-of-the-art reanalyses and hydrological modeling. Herein, we propose a novel method for benchmarking hydroclimatic data fusion based on water cycle budget closure. We ranked water cycle budget closure of 96 different combinations for precipitation, evapotranspiration, and runoff using CRU TS v4.06, E-OBS, ERA5-Land, mHM, NCEP/NCAR R1, PREC/L, and TerraClimate. Then, we used the best-ranked data to describe changes in the water cycle in Czechia over the last 60 years. We determined that Czechia is undergoing water cycle acceleration, evinced by increased atmospheric water fluxes. However, the increase in annual total precipitation is not as pronounced nor as consistent as evapotranspiration, resulting in an overall decrease in the runoff. Furthermore, non-parametric bootstrapping revealed that only evapotranspiration changes are statistically significant at the annual scale. At higher frequencies, we identified significant spatial heterogeneity when assessing the water cycle budget at a seasonal scale. Interestingly, the most significant temporal changes in Czechia occur during spring, while the spatial pattern of the change in median values stems from summer changes in the water cycle, which are the seasons within the months with statistically significant changes.
{"title":"Water cycle changes in Czechia: a multi-source water budget perspective","authors":"Mijael Rodrigo Vargas Godoy, Y. Markonis, O. Rakovec, Michal Jeníček, Riya Dutta, R. Pradhan, Zuzana Bešťáková, Jan Kyselý, Roman Juras, S. Papalexiou, M. Hanel","doi":"10.5194/hess-28-1-2024","DOIUrl":"https://doi.org/10.5194/hess-28-1-2024","url":null,"abstract":"Abstract. The water cycle in Czechia has been observed to be changing in recent years, with precipitation and evapotranspiration rates exhibiting a trend of acceleration. However, the spatial patterns of such changes remain poorly understood due to the heterogeneous network of ground observations. This study relied on multiple state-of-the-art reanalyses and hydrological modeling. Herein, we propose a novel method for benchmarking hydroclimatic data fusion based on water cycle budget closure. We ranked water cycle budget closure of 96 different combinations for precipitation, evapotranspiration, and runoff using CRU TS v4.06, E-OBS, ERA5-Land, mHM, NCEP/NCAR R1, PREC/L, and TerraClimate. Then, we used the best-ranked data to describe changes in the water cycle in Czechia over the last 60 years. We determined that Czechia is undergoing water cycle acceleration, evinced by increased atmospheric water fluxes. However, the increase in annual total precipitation is not as pronounced nor as consistent as evapotranspiration, resulting in an overall decrease in the runoff. Furthermore, non-parametric bootstrapping revealed that only evapotranspiration changes are statistically significant at the annual scale. At higher frequencies, we identified significant spatial heterogeneity when assessing the water cycle budget at a seasonal scale. Interestingly, the most significant temporal changes in Czechia occur during spring, while the spatial pattern of the change in median values stems from summer changes in the water cycle, which are the seasons within the months with statistically significant changes.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"82 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139452112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Evapotranspiration (ET) from tropical forests serves as a critical moisture source for regional and global climate cycles. However, the magnitude, seasonality, and interannual variability of ET in the Congo Basin remain poorly constrained due to a scarcity of direct observations, despite the Congo being the second-largest river basin in the world and containing a vast region of tropical forest. In this study, we applied a water balance model to an array of remotely sensed and in situ datasets to produce monthly, basin-wide ET estimates spanning April 2002 to November 2016. Data sources include water storage changes estimated from the Gravity Recovery and Climate Experiment (GRACE) satellites, in situ measurements of river discharge, and precipitation from several remotely sensed and gauge-based sources. An optimal precipitation dataset was determined as a weighted average of interpolated data by Nicholson et al. (2018), Climate Hazards InfraRed Precipitation with Station data version 2 (CHIRPS2) , and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record product (PERSIANN-CDR), with the relative weights based on the error magnitudes of each dataset as determined by triple collocation. The resulting water-balance-derived ET (ETwb) features a long-term average that is consistent with previous studies (117.2±3.5 cm yr−1) but displays greater seasonal and interannual variability than seven global ET products. The seasonal cycle of ETwb generally tracks that of precipitation over the basin, with the exception that ETwb is greater in March–April–May (MAM) than in the relatively wetter September–October–November (SON) periods. This pattern appears to be driven by seasonal variations in the diffuse photosynthetically active radiation (PAR) fraction, net radiation (Rn), and soil water availability. From 2002 to 2016, Rn, PAR, and vapor-pressure deficit (VPD) all increased significantly within the Congo Basin; however, no corresponding trend occurred in ETwb. We hypothesize that the stability of ETwb over the study period despite sunnier and less humid conditions may be due to increasing atmospheric CO2 concentrations that offset the impacts of rising VPD and irradiance on stomatal water use efficiency (WUE).
摘要热带森林的蒸散发（ET）是区域和全球气候循环的重要湿度来源。然而，尽管刚果河流域是世界第二大河流流域，并包含大量热带雨林，但由于缺乏直接观测数据，对刚果河流域蒸散发的大小、季节性和年际变化的限制仍然很差。在这项研究中，我们将水量平衡模型应用于一系列遥感和原位数据集，得出了从 2002 年 4 月到 2016 年 11 月的全流域月度蒸散发估算值。数据源包括重力恢复与气候实验（GRACE）卫星估算的蓄水量变化、河流排水量的原位测量数据，以及来自多个遥感和测量数据源的降水量。最佳降水量数据集被确定为 Nicholson 等人（2018 年）的插值数据、Climate HazardsInfraRed Precipitation with Station data version 2（CHIRPS2）和利用人工神经网络从遥感信息中估计降水量-气候数据记录产品（PERSIANN-CDR）的加权平均值，相对权重基于三重定位确定的每个数据集的误差大小。由此得出的水平衡 ET（ETwb）的长期平均值（117.2±3.5 厘米/年-1）与之前的研究结果一致，但与七个全球 ET 产品相比，其季节和年际变化更大。ETwb 的季节周期与盆地降水的季节周期基本一致，但 3 月-4 月-5 月（MAM）的 ETwb 大于相对较湿的 9 月-10 月-11 月（SON）的 ETwb。这种模式似乎是由漫射光合有效辐射（PAR）部分、净辐射（Rn）和土壤水分可用性的季节性变化驱动的。从 2002 年到 2016 年，刚果盆地的光合有效辐射（Rn）、净辐射（PAR）和水汽压力亏缺（VPD）都显著增加，但 ETwb 没有出现相应的趋势。我们假设，在研究期间，尽管阳光更充足、湿度更低，但ETwb却保持稳定，这可能是由于大气中二氧化碳浓度的增加抵消了VPD和辐照度上升对气孔水分利用效率（WUE）的影响。
{"title":"Data-driven estimates of evapotranspiration and its controls in the Congo Basin","authors":"M. W. Burnett, G. Quetin, A. Konings","doi":"10.5194/hess-2020-186","DOIUrl":"https://doi.org/10.5194/hess-2020-186","url":null,"abstract":"Abstract. Evapotranspiration (ET) from tropical forests serves as a critical moisture\u0000source for regional and global climate cycles. However, the magnitude,\u0000seasonality, and interannual variability of ET in the Congo Basin remain\u0000poorly constrained due to a scarcity of direct observations, despite the\u0000Congo being the second-largest river basin in the world and containing a\u0000vast region of tropical forest. In this study, we applied a water balance\u0000model to an array of remotely sensed and in situ datasets to produce\u0000monthly, basin-wide ET estimates spanning April 2002 to November 2016. Data\u0000sources include water storage changes estimated from the Gravity Recovery\u0000and Climate Experiment (GRACE) satellites, in situ measurements of river\u0000discharge, and precipitation from several remotely sensed and gauge-based\u0000sources. An optimal precipitation dataset was determined as a weighted\u0000average of interpolated data by Nicholson et al. (2018), Climate Hazards\u0000InfraRed Precipitation with Station data version 2 (CHIRPS2) , and the\u0000Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record product (PERSIANN-CDR), with the relative weights based on the error magnitudes of each dataset as determined by triple collocation. The resulting water-balance-derived ET (ETwb) features a long-term average that is consistent with previous studies (117.2±3.5 cm yr−1) but displays greater seasonal and interannual variability than seven global ET products. The seasonal cycle of ETwb generally tracks that of precipitation over the basin, with the exception that ETwb is greater in March–April–May (MAM) than in the relatively wetter September–October–November (SON) periods. This pattern appears to be\u0000driven by seasonal variations in the diffuse photosynthetically active radiation (PAR) fraction, net radiation (Rn), and soil water availability. From 2002 to 2016, Rn, PAR, and vapor-pressure deficit (VPD) all increased significantly within the Congo Basin; however, no corresponding trend occurred in ETwb. We hypothesize that the stability of ETwb over the study period despite sunnier and less humid conditions may be due to increasing atmospheric CO2 concentrations that offset the impacts of rising VPD and irradiance on stomatal water use efficiency (WUE).\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"43 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141206939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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