Pub Date : 2024-10-24DOI: 10.1016/j.jhydrol.2024.132180
Charlotte Hacker, Jürgen Kusche
Reconstructions allow us to extend the Gravity Recovery And Climate Experiment (GRACE) data record into the past and bridge the one-year gap between GRACE and its successor, GRACE-FO (Follow on). Reconstructed total water storage anomalies (TWSA) are obtained by identifying relations between GRACE-derived TWSA and climate variables via statistical and machine learning techniques. However, a comparative analysis of the characteristics and realism of reconstructions is missing.
In this contribution, we close this gap by comparing three global reconstructions by Humphrey and Gudmundsson (2019), Li et al. (2021) and Chandanpurkar et al. (2022) mutually and against output from the Water Global Analysis and Prognosis (WaterGAP) hydrological model from 1979 onwards, against large-scale mass-change derived from geodetic satellite laser ranging (SLR) from 1992 onwards, and finally against differing GRACE and GRACE-FO solutions from 2002 onwards. The reconstructions vary regarding design and trained GRACE solution.
Reconstructions recover the TWSA signal for humid climate regions but disagree for arid climate regions, which is evident on the inter-annual timescales. At seasonal and sub-seasonal timescales, the reconstructions agree surprisingly well in many regions. Our comparison against independent SLR data reveals that reconstructions (only) partially succeed in representing anomalous TWSA for areas that are influenced by significant climate modes such as El Niño-Southern Oscillation (ENSO).
{"title":"How realistic are multi-decadal reconstructions of GRACE-like total water storage anomalies?","authors":"Charlotte Hacker, Jürgen Kusche","doi":"10.1016/j.jhydrol.2024.132180","DOIUrl":"10.1016/j.jhydrol.2024.132180","url":null,"abstract":"<div><div>Reconstructions allow us to extend the Gravity Recovery And Climate Experiment (GRACE) data record into the past and bridge the one-year gap between GRACE and its successor, GRACE-FO (Follow on). Reconstructed total water storage anomalies (TWSA) are obtained by identifying relations between GRACE-derived TWSA and climate variables via statistical and machine learning techniques. However, a comparative analysis of the characteristics and realism of reconstructions is missing.</div><div>In this contribution, we close this gap by comparing three global reconstructions by Humphrey and Gudmundsson (2019), Li et al. (2021) and Chandanpurkar et al. (2022) mutually and against output from the Water Global Analysis and Prognosis (WaterGAP) hydrological model from 1979 onwards, against large-scale mass-change derived from geodetic satellite laser ranging (SLR) from 1992 onwards, and finally against differing GRACE and GRACE-FO solutions from 2002 onwards. The reconstructions vary regarding design and trained GRACE solution.</div><div>Reconstructions recover the TWSA signal for humid climate regions but disagree for arid climate regions, which is evident on the inter-annual timescales. At seasonal and sub-seasonal timescales, the reconstructions agree surprisingly well in many regions. Our comparison against independent SLR data reveals that reconstructions (only) partially succeed in representing anomalous TWSA for areas that are influenced by significant climate modes such as El Niño-Southern Oscillation (ENSO).</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132180"},"PeriodicalIF":5.9,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1016/j.jhydrol.2024.132194
Metehan Uz , Orhan Akyilmaz , C.K. Shum
The Gravity Recovery And Climate Experiment (GRACE) and GRACE-FollowOn (GRACE(−FO)) satellites have been monitoring Earth’s changes in terrestrial water storage (TWS) or surficial mass changes at monthly sampling and a spatial scale longer than ∼330 km (half wavelength) over the past two decades. At monthly sampling or revisit time, the use of satellite gravimetry is difficult to effectively monitor abrupt extreme weather events which are high-frequency, including the climate-induced hurricanes/cyclones, flash floods and droughts. The majority of the contemporary studies have focused on satellite gravimetry spatial downscaling, and not on reducing the temporal resolution of Earth’s mass change. Here, we developed a Deep Learning (DL) algorithm to downscale monthly GRACE/GRACE(−FO) Mass Concentration (Mascon) TWS anomaly (TWSA) solutions to daily sampling over the Contiguous United States (CONUS), with the aim of monitoring rapidly evolving natural hazard episodes. The simulative performance of the DL algorithm is validated by comparing the modeling to an independent observation and the land hydrology model (LHM) predicted TWSA. To confirm that our daily and monthly simulations captured the climatic variations, we first compared our simulations with El Niño/La Niña Southern Oscillation (ENSO) circulation system index, which has a dominant climatological and socioeconomic impact across CONUS, and results reveal high correlations which are statistically significant. Next, we assessed the feasibilities to detect long- and short-term variations in the TWSA signals triggered by hydrological extremes, including the 2011 and 2019 Missouri River Floods, the August 2017 Atlantic Hurricane Harvey landfalls in Texas, the 2012–2017 drought in California, and the flash drought in the Northern Great Plains in 2017. Additional validation results using independent in situ observations reveal that our DL-aided gravimetry downscaled daily simulations are capable of elucidating hazards and water cycle evolutions at high temporal resolution.
{"title":"Deep learning-aided temporal downscaling of GRACE-derived terrestrial water storage anomalies across the Contiguous United States","authors":"Metehan Uz , Orhan Akyilmaz , C.K. Shum","doi":"10.1016/j.jhydrol.2024.132194","DOIUrl":"10.1016/j.jhydrol.2024.132194","url":null,"abstract":"<div><div>The Gravity Recovery And Climate Experiment (GRACE) and GRACE-FollowOn (GRACE(−FO)) satellites have been monitoring Earth’s changes in terrestrial water storage (TWS) or surficial mass changes at monthly sampling and a spatial scale longer than ∼330 km (half wavelength) over the past two decades. At monthly sampling or revisit time, the use of satellite gravimetry is difficult to effectively monitor abrupt extreme weather events which are high-frequency, including the climate-induced hurricanes/cyclones, flash floods and droughts. The majority of the contemporary studies have focused on satellite gravimetry spatial downscaling, and not on reducing the temporal resolution of Earth’s mass change. Here, we developed a Deep Learning (DL) algorithm to downscale monthly GRACE/GRACE(−FO) Mass Concentration (Mascon) TWS anomaly (TWSA) solutions to daily sampling over the Contiguous United States (CONUS), with the aim of monitoring rapidly evolving natural hazard episodes. The simulative performance of the DL algorithm is validated by comparing the modeling to an independent observation and the land hydrology model (LHM) predicted TWSA. To confirm that our daily and monthly simulations captured the climatic variations, we first compared our simulations with El Niño/La Niña Southern Oscillation (ENSO) circulation system index, which has a dominant climatological and socioeconomic impact across CONUS, and results reveal high correlations which are statistically significant. Next, we assessed the feasibilities to detect long- and short-term variations in the TWSA signals triggered by hydrological extremes, including the 2011 and 2019 Missouri River Floods, the August 2017 Atlantic Hurricane Harvey landfalls in Texas, the 2012–2017 drought in California, and the flash drought in the Northern Great Plains in 2017. Additional validation results using independent in situ observations reveal that our DL-aided gravimetry downscaled daily simulations are capable of elucidating hazards and water cycle evolutions at high temporal resolution.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132194"},"PeriodicalIF":5.9,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1016/j.jhydrol.2024.132200
Tingting Zhang , Yue Dai , Anwar Abdureyim , Jiabing Kang
Tamarix ramosissima is a dominant species in desert ecosystems and an ecological barrier species in arid areas, playing a crucial role in stabilizing dunes and preventing desertification. In this study, river water, groundwater, soil water, and T. ramosissima individual samples were collected from three sites in July and October 2023 at the Daliyaboyi Oasis located at the tail of the Kriya River in the hinterland of the Taklamakan Desert. The three sites are referred to as the center (SE), west (SW), and north (SN) sites within the Daliyaboyi Oasis, and each experienced different flood frequencies. The SE site experienced flooding in July and October, the SW site experienced flooding only in July, and the SN site experienced no flooding in July or October. The spatial and temporal variation in hydrogen and oxygen stable isotopes and line-conditional excess (lc-excess) in water and plant samples were analyzed, and the potential changes in water use of T. ramosissima were analyzed by the hydrogen and oxygen stable isotope and MixSIAR model. The findings indicated that the slope of SWL at the SE, SW, and SN sites was higher in July (6.77, 6.42, and 3.05, respectively) than in October (7.37, 3.30, and 2.14, respectively). The lc-excess value of the SE site did not exhibit seasonal changes; only the lc-excess values of soil water in SW and SN sites showed seasonal changes. MixSIAR results indicated frequent flood events at the SE site, with relatively constant proportions of water source utilization by T. ramosissima in July and October. In addition, shallow soil water (0–60 cm) and deeper soil water (60–80 cm) were the main water sources of T. ramosissima at SE. The SN site was slightly influenced by surface water, resulting in statistically non-significant changes in the water source utilization by T. ramosissima. Indeed, deep soil water (60–200 cm) and groundwater were the sources of water for T. ramosissima at this site. In contrast with October, the SW site experienced flood events in July, resulting in the utilization of water by T. ramosissima from the shallow soil (0–60 cm) and deep soil (60–280 cm) in July and October, respectively. Different surface water flow patterns led to different water use characteristics of T. ramosissima, which further demonstrated that T. ramosissima has high resilience and ecological plasticity. This work provides a useful reference for the implementation of effective ecological water transport measures in the Daliyaboyi Oasis and similar arid habitats.
{"title":"Effects of different surface water flow frequencies on water use characteristics of Tamarix ramosissima in the hinterland of the Taklamakan Desert","authors":"Tingting Zhang , Yue Dai , Anwar Abdureyim , Jiabing Kang","doi":"10.1016/j.jhydrol.2024.132200","DOIUrl":"10.1016/j.jhydrol.2024.132200","url":null,"abstract":"<div><div><em>Tamarix ramosissima</em> is a dominant species in desert ecosystems and an ecological barrier species in arid areas, playing a crucial role in stabilizing dunes and preventing desertification. In this study, river water, groundwater, soil water, and <em>T. ramosissima</em> individual samples were collected from three sites in July and October 2023 at the Daliyaboyi Oasis located at the tail of the Kriya River in the hinterland of the Taklamakan Desert. The three sites are referred to as the center (SE), west (SW), and north (SN) sites within the Daliyaboyi Oasis, and each experienced different flood frequencies. The SE site experienced flooding in July and October, the SW site experienced flooding only in July, and the SN site experienced no flooding in July or October. The spatial and temporal variation in hydrogen and oxygen stable isotopes and <em>line-conditional excess</em> (<em>lc-excess</em>) in water and plant samples were analyzed, and the potential changes in water use of <em>T. ramosissima</em> were analyzed by the hydrogen and oxygen stable isotope and MixSIAR model. The findings indicated that the slope of SWL at the SE, SW, and SN sites was higher in July (6.77, 6.42, and 3.05, respectively) than in October (7.37, 3.30, and 2.14, respectively). The <em>lc-excess</em> value of the SE site did not exhibit seasonal changes; only the <em>lc-excess</em> values of soil water in SW and SN sites showed seasonal changes. MixSIAR results indicated frequent flood events at the SE site, with relatively constant proportions of water source utilization by <em>T. ramosissima</em> in July and October. In addition, shallow soil water (0–60 cm) and deeper soil water (60–80 cm) were the main water sources of <em>T. ramosissima</em> at SE. The SN site was slightly influenced by surface water, resulting in statistically non-significant changes in the water source utilization by <em>T. ramosissima.</em> Indeed, deep soil water (60–200 cm) and groundwater were the sources of water for <em>T. ramosissima</em> at this site. In contrast with October, the SW site experienced flood events in July, resulting in the utilization of water by <em>T. ramosissima</em> from the shallow soil (0–60 cm) and deep soil (60–280 cm) in July and October, respectively. Different surface water flow patterns led to different water use characteristics of <em>T. ramosissima</em>, which further demonstrated that <em>T. ramosissima</em> has high resilience and ecological plasticity. This work provides a useful reference for the implementation of effective ecological water transport measures in the Daliyaboyi Oasis and similar arid habitats.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132200"},"PeriodicalIF":5.9,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.jhydrol.2024.132223
Menghang Li , Qingyun Zhou , Xin Han , Pingan Lv
The accurate prediction of reference crop evapotranspiration (ET0) is essential to better manage crop irrigation water consumption and improve crop water use efficiency. To effectively improve the accuracy of ET0 simulated by machine learning models, five meteorological stations in Hailaer, Harbin, Hohhot, Changchun, and Dalian were taken as representative stations, daily and monthly ET0 data from 1952 to 2020 were used, and empirical mode decomposition (EMD) and wavelet threshold denoising (WD) were considered. The convolutional neural network (CNN) and long short-term memory network (LSTM) models were improved, and two new hybrid neural network models (EMD–WD–CNN and EMD–WD–LSTM) were constructed. Using the ET0 calculated using the FAO-56 Penman–Monteith (P–M) formula as the standard value, the applicability of the improved machine learning model was evaluated. Results showed the following: i) the daily ET0-PM minimum values of five stations were close to 0, the average values were not significantly increased, and the maximum values significantly fluctuated (the fluctuations in Hailaer and Hohhot showed an upward trend, and the fluctuations in Harbin, Changchun, and Dalian showed a downward trend). The annual average monthly ET0-PM varied seasonally, with the peak in June in the Hailaer station and May in all other stations (the peak in Hohhot was the largest, and the peak in Dalian was the smallest). ii) The daily and monthly ET0 values predicted by the proposed EMD–WD–CNN and EMD–WD–LSTM models were highly consistent with the calculated results of the P–M model, showing high accuracy on the daily and monthly ET0 of the simulated five stations (daily: mean absolute error (MAE), 0.30–0.41 mm/day; root mean square error (RMSE), 0.46–0.60 mm/day; R2, 0.86–0.95; monthly: MAE, 5.66–13.71 mm/month; RMSE, 8.97–18.04 mm/month; R2, 0.91–0.95). iii) The EMD–WD–CNN model was suitable for daily scale ET0 simulation and prediction in Northeast China and monthly scale in Harbin, Changchun, and Hohhot. The EMD–WD–LSTM model was suitable for monthly ET0 simulation and prediction in Hailaer and Dalian in Northeast China. The mixed models of EMD–WD–CNN and EMD–WD–CNN can effectively improve the prediction accuracy of ET0 and can provide a new method for agricultural development and irrigation regulation in Northeast China.
{"title":"Prediction of reference crop evapotranspiration based on improved convolutional neural network (CNN) and long short-term memory network (LSTM) models in Northeast China","authors":"Menghang Li , Qingyun Zhou , Xin Han , Pingan Lv","doi":"10.1016/j.jhydrol.2024.132223","DOIUrl":"10.1016/j.jhydrol.2024.132223","url":null,"abstract":"<div><div>The accurate prediction of reference crop evapotranspiration (ET<sub>0</sub>) is essential to better manage crop irrigation water consumption and improve crop water use efficiency. To effectively improve the accuracy of ET<sub>0</sub> simulated by machine learning models, five meteorological stations in Hailaer, Harbin, Hohhot, Changchun, and Dalian were taken as representative stations, daily and monthly ET<sub>0</sub> data from 1952 to 2020 were used, and empirical mode decomposition (EMD) and wavelet threshold denoising (WD) were considered. The convolutional neural network (CNN) and long short-term memory network (LSTM) models were improved, and two new hybrid neural network models (EMD–WD–CNN and EMD–WD–LSTM) were constructed. Using the ET<sub>0</sub> calculated using the FAO-56 Penman–Monteith (P–M) formula as the standard value, the applicability of the improved machine learning model was evaluated. Results showed the following: i) the daily ET<sub>0-PM</sub> minimum values of five stations were close to 0, the average values were not significantly increased, and the maximum values significantly fluctuated (the fluctuations in Hailaer and Hohhot showed an upward trend, and the fluctuations in Harbin, Changchun, and Dalian showed a downward trend). The annual average monthly ET<sub>0-PM</sub> varied seasonally, with the peak in June in the Hailaer station and May in all other stations (the peak in Hohhot was the largest, and the peak in Dalian was the smallest). ii) The daily and monthly ET<sub>0</sub> values predicted by the proposed EMD–WD–CNN and EMD–WD–LSTM models were highly consistent with the calculated results of the P–M model, showing high accuracy on the daily and monthly ET<sub>0</sub> of the simulated five stations (daily: mean absolute error (MAE), 0.30–0.41 mm/day; root mean square error (RMSE), 0.46–0.60 mm/day; R<sup>2</sup>, 0.86–0.95; monthly: MAE, 5.66–13.71 mm/month; RMSE, 8.97–18.04 mm/month; R<sup>2</sup>, 0.91–0.95). iii) The EMD–WD–CNN model was suitable for daily scale ET<sub>0</sub> simulation and prediction in Northeast China and monthly scale in Harbin, Changchun, and Hohhot. The EMD–WD–LSTM model was suitable for monthly ET<sub>0</sub> simulation and prediction in Hailaer and Dalian in Northeast China. The mixed models of EMD–WD–CNN and EMD–WD–CNN can effectively improve the prediction accuracy of ET<sub>0</sub> and can provide a new method for agricultural development and irrigation regulation in Northeast China.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132223"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.jhydrol.2024.132227
Dong-mei Xu , Yang-hao Hong , Wen-chuan Wang , Zong Li, Jun Wang
The development of artificial intelligence has introduced new perspectives to the field of hydrological forecasting. However, there is still a lack of research on efficiently identifying the physical characteristics of runoff sequences and developing prediction models that consider global and local sequence features. This study proposes a parallel computing prediction model called IMCAEN (Integrated Multi-Feature Causal Dilated Convolutional Attention Encoder Network) to address these issues. Unlike existing models, this model can monitor fluctuations and anomalies in time series. Incorporating the CDC-AA (Causal Dilated Convolutional Network with Aggregation Attention) and encoder structure captures both local sequence variations and global abrupt anomalies, allowing for comprehensive attention to sequence features. When predicting runoff data from three different hydrological conditions, the IMCAEN model achieved NSEC (Nash-Sutcliffe Efficiency Coefficient) values of 0.98, 0.97, and 0.88, respectively, and outperformed benchmark models in other evaluation indicators as well. Given the opacity of the feature distribution process in AI models, SHAP (SHapleyAdditive exPlanations) analysis and spatial expression of feature distribution are used to assess the contribution of each feature variable to the long-term trend of runoff and to verify the distribution of features trained in each module. The proposed IMCAEN model efficiently captures local and global information in the runoff evolution process through parallel computing and shared features, enabling accurate runoff forecasting and providing critical references for timely warnings and predictions.
{"title":"A novel daily runoff forecasting model based on global features and enhanced local feature interpretation","authors":"Dong-mei Xu , Yang-hao Hong , Wen-chuan Wang , Zong Li, Jun Wang","doi":"10.1016/j.jhydrol.2024.132227","DOIUrl":"10.1016/j.jhydrol.2024.132227","url":null,"abstract":"<div><div>The development of artificial intelligence has introduced new perspectives to the field of hydrological forecasting. However, there is still a lack of research on efficiently identifying the physical characteristics of runoff sequences and developing prediction models that consider global and local sequence features. This study proposes a parallel computing prediction model called IMCAEN (Integrated Multi-Feature Causal Dilated Convolutional Attention Encoder Network) to address these issues. Unlike existing models, this model can monitor fluctuations and anomalies in time series. Incorporating the CDC-AA (Causal Dilated Convolutional Network with Aggregation Attention) and encoder structure captures both local sequence variations and global abrupt anomalies, allowing for comprehensive attention to sequence features. When predicting runoff data from three different hydrological conditions, the IMCAEN model achieved NSEC (Nash-Sutcliffe Efficiency Coefficient) values of 0.98, 0.97, and 0.88, respectively, and outperformed benchmark models in other evaluation indicators as well. Given the opacity of the feature distribution process in AI models, SHAP (SHapleyAdditive exPlanations) analysis and spatial expression of feature distribution are used to assess the contribution of each feature variable to the long-term trend of runoff and to verify the distribution of features trained in each module. The proposed IMCAEN model efficiently captures local and global information in the runoff evolution process through parallel computing and shared features, enabling accurate runoff forecasting and providing critical references for timely warnings and predictions.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132227"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.jhydrol.2024.132196
Zhaoqiang Zhou , Ping Wang , Linqi Li , Qiang Fu , Yibo Ding , Peng Chen , Ping Xue , Tian Wang , Haiyun Shi
Drought is one of the most extensive natural disasters affecting human society. It spreads through land–atmosphere system and hydrological cycle, and evolves into different types of drought, such as hydrological drought, agricultural drought, socio-economic drought, groundwater drought and ecological drought. Extensive recent research has explored classifications, methods, characteristics, driving factors, but gaps remain in summarizing the latest concepts and research methods on drought propagation. Therefore, this study first introduces the types of drought propagation, summarizes the previous and latest drought propagation classifications, and supplements the reviews on the propagation of socioeconomic drought. Secondly, the research methods and the characteristic parameters of drought propagation are summarized, especially the spatial and internal propagation are extended. Thirdly, from the perspective of driving forces for drought propagation, this study summarizes the impact of natural factors (such as precipitation, evapotranspiration, snow, slope, vegetation, etc.) and human factors (such as reservoirs, irrigation, ecological project, urbanization, etc.) on drought propagation. Finally, the challenges of existing research and future research directions of drought propagation are summarized. This study expected to be useful for understanding the mechanism of drought propagation, so as to strengthen drought monitoring and forecasting, improve comprehensive drought resistance and improve water resources management.
{"title":"Recent development on drought propagation: A comprehensive review","authors":"Zhaoqiang Zhou , Ping Wang , Linqi Li , Qiang Fu , Yibo Ding , Peng Chen , Ping Xue , Tian Wang , Haiyun Shi","doi":"10.1016/j.jhydrol.2024.132196","DOIUrl":"10.1016/j.jhydrol.2024.132196","url":null,"abstract":"<div><div>Drought is one of the most extensive natural disasters affecting human society. It spreads through land–atmosphere system and hydrological cycle, and evolves into different types of drought, such as hydrological drought, agricultural drought, socio-economic drought, groundwater drought and ecological drought. Extensive recent research has explored classifications, methods, characteristics, driving factors, but gaps remain in summarizing the latest concepts and research methods on drought propagation. Therefore, this study first introduces the types of drought propagation, summarizes the previous and latest drought propagation classifications, and supplements the reviews on the propagation of socioeconomic drought. Secondly, the research methods and the characteristic parameters of drought propagation are summarized, especially the spatial and internal propagation are extended. Thirdly, from the perspective of driving forces for drought propagation, this study summarizes the impact of natural factors (such as precipitation, evapotranspiration, snow, slope, vegetation, etc.) and human factors (such as reservoirs, irrigation, ecological project, urbanization, etc.) on drought propagation. Finally, the challenges of existing research and future research directions of drought propagation are summarized. This study expected to be useful for understanding the mechanism of drought propagation, so as to strengthen drought monitoring and forecasting, improve comprehensive drought resistance and improve water resources management.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132196"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.jhydrol.2024.132211
Léa Laurent , Albin Ullmann , Thierry Castel
The significant increase in surface air temperature experienced by Western Europe over the last few decades has resulted in an abrupt warming in France, around 1987/1988 years. This climatic shift impacted hydrological cycle, particularly by reducing runoff in spring and summer. Evapotranspiration and precipitation have been identified as the main drivers of climatic water balance. But the impact of the 1987/1988 climatic shift on local water cycle over France has not been quantified yet. This study tries to assess the consequences of this rapid warming on the main climatic components of local water cycle. Climate variables linked to the water cycle extracted from a reanalysed observed climate database are analysed using robust Bayesian change points detection and mean comparison techniques. After the abrupt rise in surface air temperature and surface solar radiation, water demand increases significantly on almost the entire French territory in spring, summer and autumn. Our results show that, from March to May, the vegetation cover is able to respond to this increase by drawing from the soil water reservoirs. But in summer, most of the territory is facing a significant rise in water constraint (i.e. difference between potential and actual evapotranspiration), extending on the last decade over autumn. In spring and summer, the increase in potential evapotranspiration is the main driver of the intensification of water constraint. In the beginning of autumn, longer dry spell also plays a major role in the lengthening of periods of water constraint. This innovative study highlights the specific impact of a climatic shift on the main components of the atmospheric water balance at regional and local scale. As the observed changes in climate hazard linked to water cycle affect growth cycle of the majority of the vegetation covers and crops, this could lead to a worsening of hydric stress events. As such climatic shifts are expected to happen again in the future, their impacts on the local water cycle represent a major issue for natural terrestrial ecosystems as well as for agriculture.
{"title":"How have atmospheric components of the local water cycle changed around the abrupt climatic shift over France?","authors":"Léa Laurent , Albin Ullmann , Thierry Castel","doi":"10.1016/j.jhydrol.2024.132211","DOIUrl":"10.1016/j.jhydrol.2024.132211","url":null,"abstract":"<div><div>The significant increase in surface air temperature experienced by Western Europe over the last few decades has resulted in an abrupt warming in France, around 1987/1988 years. This climatic shift impacted hydrological cycle, particularly by reducing runoff in spring and summer. Evapotranspiration and precipitation have been identified as the main drivers of climatic water balance. But the impact of the 1987/1988 climatic shift on local water cycle over France has not been quantified yet. This study tries to assess the consequences of this rapid warming on the main climatic components of local water cycle. Climate variables linked to the water cycle extracted from a reanalysed observed climate database are analysed using robust Bayesian change points detection and mean comparison techniques. After the abrupt rise in surface air temperature and surface solar radiation, water demand increases significantly on almost the entire French territory in spring, summer and autumn. Our results show that, from March to May, the vegetation cover is able to respond to this increase by drawing from the soil water reservoirs. But in summer, most of the territory is facing a significant rise in water constraint (i.e. difference between potential and actual evapotranspiration), extending on the last decade over autumn. In spring and summer, the increase in potential evapotranspiration is the main driver of the intensification of water constraint. In the beginning of autumn, longer dry spell also plays a major role in the lengthening of periods of water constraint. This innovative study highlights the specific impact of a climatic shift on the main components of the atmospheric water balance at regional and local scale. As the observed changes in climate hazard linked to water cycle affect growth cycle of the majority of the vegetation covers and crops, this could lead to a worsening of hydric stress events. As such climatic shifts are expected to happen again in the future, their impacts on the local water cycle represent a major issue for natural terrestrial ecosystems as well as for agriculture.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132211"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.jhydrol.2024.132202
Thomas Pulka , Mathew Herrnegger , Caroline Ehrendorfer , Sophie Lücking , Francesco Avanzi , Herbert Formayer , Karsten Schulz , Franziska Koch
<div><div>Gridded meteorological data products often fall short in accurately capturing the amount of precipitation and its patterns in regions characterized by high elevations and complex topography. However, realistic precipitation data is crucial for high-alpine hydrological modeling. To address these discrepancies, we analyze possible corrections for solid, liquid and total precipitation of the 1 km<sup>2</sup> gridded meteorological INCA-product in the high-alpine catchment of the Kölnbrein hydropower reservoir operated by VERBUND Hydro Power GmbH in the Malta Valley in Austria. By leveraging information from a stereo-satellite-derived snow depth map with physically-based snowpack modeling with Alpine3D, we quantitatively adjust and spatially redistribute solid precipitation, complemented by a multiplicative, stepwise correction model for liquid precipitation. We compare and evaluate five approaches using the hydrological COSERO model to our <em>a</em>) baseline simulation with no corrections on INCA in contrast of correcting, <em>b</em>) the amount and distribution of solely solid precipitation, <em>c</em>) the amount of liquid and solid precipitation, <em>d</em>) the amount of liquid and solid precipitation and the spatial distribution of the latter, <em>e</em>) precipitation inversely by the inflow bias, and <em>f</em>) calibrating the precipitation correction factor. In evaluating these strategies to improve the accuracy of reservoir inflow predictions, we found that separately correcting solid and liquid precipitation yielded the best results (<em>c</em> & <em>d</em>), with a substantial increase of up to 65% over the study period (1.10.2015–30.9.2023), while the other correction variants ranged between 42 and 52%. The inflow predictions by COSERO showed an increase in Nash-Sutcliffe Efficiency (NSE) by 17% and in Kling-Gupta Efficiency by 57% and 59% for variants <em>c</em> and <em>d</em>, respectively, along with an almost complete elimination of model bias. The higher KGE values observed for variant <em>d</em> compared to <em>c</em> during spring, summer, and fall suggest that a more realistic snow distribution enhances the simulation of snowmelt-driven runoff dynamics. In contrast, using a global (i.e., spatially homogeneous) and uniform (i.e., not distinguishing between liquid and solid precipitation phase) correction factor, inversely derived from the inflow bias (<em>e</em>), or solely correcting solid precipitation (<em>b</em>), demonstrated less performance, with a KGE increase of 47% and 49%, respectively, compared to 59% for variant <em>d</em>. Conversely, the calibration of the global and uniform correction factor (<em>f</em>) resulted in significant performance metric improvements (17% NSE, 60% KGE and 90% pBias), similar to variant <em>d</em>, however also led to unrealistic simulations of evapotranspiration, sublimation and glacier net runoff. The simulated water balance components – evapotranspiration and sublimation
{"title":"Evaluating precipitation corrections to enhance high-alpine hydrological modeling","authors":"Thomas Pulka , Mathew Herrnegger , Caroline Ehrendorfer , Sophie Lücking , Francesco Avanzi , Herbert Formayer , Karsten Schulz , Franziska Koch","doi":"10.1016/j.jhydrol.2024.132202","DOIUrl":"10.1016/j.jhydrol.2024.132202","url":null,"abstract":"<div><div>Gridded meteorological data products often fall short in accurately capturing the amount of precipitation and its patterns in regions characterized by high elevations and complex topography. However, realistic precipitation data is crucial for high-alpine hydrological modeling. To address these discrepancies, we analyze possible corrections for solid, liquid and total precipitation of the 1 km<sup>2</sup> gridded meteorological INCA-product in the high-alpine catchment of the Kölnbrein hydropower reservoir operated by VERBUND Hydro Power GmbH in the Malta Valley in Austria. By leveraging information from a stereo-satellite-derived snow depth map with physically-based snowpack modeling with Alpine3D, we quantitatively adjust and spatially redistribute solid precipitation, complemented by a multiplicative, stepwise correction model for liquid precipitation. We compare and evaluate five approaches using the hydrological COSERO model to our <em>a</em>) baseline simulation with no corrections on INCA in contrast of correcting, <em>b</em>) the amount and distribution of solely solid precipitation, <em>c</em>) the amount of liquid and solid precipitation, <em>d</em>) the amount of liquid and solid precipitation and the spatial distribution of the latter, <em>e</em>) precipitation inversely by the inflow bias, and <em>f</em>) calibrating the precipitation correction factor. In evaluating these strategies to improve the accuracy of reservoir inflow predictions, we found that separately correcting solid and liquid precipitation yielded the best results (<em>c</em> & <em>d</em>), with a substantial increase of up to 65% over the study period (1.10.2015–30.9.2023), while the other correction variants ranged between 42 and 52%. The inflow predictions by COSERO showed an increase in Nash-Sutcliffe Efficiency (NSE) by 17% and in Kling-Gupta Efficiency by 57% and 59% for variants <em>c</em> and <em>d</em>, respectively, along with an almost complete elimination of model bias. The higher KGE values observed for variant <em>d</em> compared to <em>c</em> during spring, summer, and fall suggest that a more realistic snow distribution enhances the simulation of snowmelt-driven runoff dynamics. In contrast, using a global (i.e., spatially homogeneous) and uniform (i.e., not distinguishing between liquid and solid precipitation phase) correction factor, inversely derived from the inflow bias (<em>e</em>), or solely correcting solid precipitation (<em>b</em>), demonstrated less performance, with a KGE increase of 47% and 49%, respectively, compared to 59% for variant <em>d</em>. Conversely, the calibration of the global and uniform correction factor (<em>f</em>) resulted in significant performance metric improvements (17% NSE, 60% KGE and 90% pBias), similar to variant <em>d</em>, however also led to unrealistic simulations of evapotranspiration, sublimation and glacier net runoff. The simulated water balance components – evapotranspiration and sublimation","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132202"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.jhydrol.2024.132199
Ping-Cheng Hsieh, Ming-Chang Wu
This paper develops a comprehensive two-dimensional (2D) groundwater model that surpasses traditional one-dimensional approaches by incorporating extensive hydrological and geological data typically acquired through well drilling. The primary objective of this research is to explore and characterize the complex dynamics of groundwater flow within sloping heterogeneous aquifers subject to rainfall-induced recharge events. To achieve this, the study utilizes the 2D Boussinesq equation, which is enhanced with meticulously defined boundary conditions to more accurately reflect real-world scenarios. The Integral Transform Technique (ITT) is employed to derive an analytical solution that integrates the effects of variable rainfall recharge, aquifer inclination angles, and inherent heterogeneity. This analytical model effectively captures varying recharge dynamics, both spatially and temporally, contributing to a better understanding and management of groundwater systems. The paper presents a new analytical framework, offering deeper insights into how natural factors impact porous medium behavior, thereby providing strategic references for sustainable water resource management.
{"title":"Comprehensive two-dimensional analytical modeling of groundwater levels in bi-directional sloping heterogeneous aquifers under variable recharge conditions","authors":"Ping-Cheng Hsieh, Ming-Chang Wu","doi":"10.1016/j.jhydrol.2024.132199","DOIUrl":"10.1016/j.jhydrol.2024.132199","url":null,"abstract":"<div><div>This paper develops a comprehensive two-dimensional (2D) groundwater model that surpasses traditional one-dimensional approaches by incorporating extensive hydrological and geological data typically acquired through well drilling. The primary objective of this research is to explore and characterize the complex dynamics of groundwater flow within sloping heterogeneous aquifers subject to rainfall-induced recharge events. To achieve this, the study utilizes the 2D Boussinesq equation, which is enhanced with meticulously defined boundary conditions to more accurately reflect real-world scenarios. The Integral Transform Technique (ITT) is employed to derive an analytical solution that integrates the effects of variable rainfall recharge, aquifer inclination angles, and inherent heterogeneity. This analytical model effectively captures varying recharge dynamics, both spatially and temporally, contributing to a better understanding and management of groundwater systems. The paper presents a new analytical framework, offering deeper insights into how natural factors impact porous medium behavior, thereby providing strategic references for sustainable water resource management.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132199"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.jhydrol.2024.132209
C. Cammalleri , M.C. Anderson , C. Corbari , Y. Yang , C.R. Hain , P. Salamon , M. Mancini
Accurate estimations of actual evapotranspiration (ET) are key in a variety of water balance applications, but divergent results can be obtained due to the large range of available methodologies. The use of an ensemble approach is a suitable alternative, as it summarizes multiple sources in an optimized strategy. In this study, an expert-based multi-step collocation (MC) approach is tested to merge six ET datasets, with the aim of reconstructing a spatiotemporally-consistent monthly dataset for Italy in the climatological period 1991–2020. The merged products are: three water balance datasets (BIG BANG, LSA SAF, and LISFLOOD), two residual surface energy balance model datasets (SSEBop, and ALEXI), and the MODIS standard ET product. The merged product is analyzed for spatio-temporal consistency and evaluated using flux observations from 11 sites. On average, the merged product has higher accuracy (mean absolute difference = 0.47 ± 0.17 mm/d, relative difference = 27.9 ± 7.5 %) than any single base dataset, and it is characterized by limited bias (mean bias error = -0.17 ± 0.26 mm/d), high correlation (r = 0.83 ± 0.10), and more uniform performance across sites. The merged ET dataset is accompanied by an estimation of the ensemble spread, which highlights large differences in ET estimates in some areas and periods characterized by severe water stress, such as in southern Italy during the summer. This large spread seems to be mostly driven by systematic differences among datasets, which affect the estimation of the reference climatology, suggesting how inter-model spread can have a defining role in further improving the merging strategies.
{"title":"Evaluating a multi-step collocation approach for an ensemble climatological dataset of actual evapotranspiration over Italy","authors":"C. Cammalleri , M.C. Anderson , C. Corbari , Y. Yang , C.R. Hain , P. Salamon , M. Mancini","doi":"10.1016/j.jhydrol.2024.132209","DOIUrl":"10.1016/j.jhydrol.2024.132209","url":null,"abstract":"<div><div>Accurate estimations of actual evapotranspiration (ET) are key in a variety of water balance applications, but divergent results can be obtained due to the large range of available methodologies. The use of an ensemble approach is a suitable alternative, as it summarizes multiple sources in an optimized strategy. In this study, an expert-based multi-step collocation (MC) approach is tested to merge six ET datasets, with the aim of reconstructing a spatiotemporally-consistent monthly dataset for Italy in the climatological period 1991–2020. The merged products are: three water balance datasets (BIG BANG, LSA SAF, and LISFLOOD), two residual surface energy balance model datasets (SSEBop, and ALEXI), and the MODIS standard ET product. The merged product is analyzed for spatio-temporal consistency and evaluated using flux observations from 11 sites. On average, the merged product has higher accuracy (mean absolute difference = 0.47 ± 0.17 mm/d, relative difference = 27.9 ± 7.5 %) than any single base dataset, and it is characterized by limited bias (mean bias error = -0.17 ± 0.26 mm/d), high correlation (<em>r</em> = 0.83 ± 0.10), and more uniform performance across sites. The merged ET dataset is accompanied by an estimation of the ensemble spread, which highlights large differences in ET estimates in some areas and periods characterized by severe water stress, such as in southern Italy during the summer. This large spread seems to be mostly driven by systematic differences among datasets, which affect the estimation of the reference climatology, suggesting how inter-model spread can have a defining role in further improving the merging strategies.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132209"},"PeriodicalIF":5.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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