Pub Date : 2023-11-16DOI: 10.5194/hess-27-4151-2023
Daniel Camilo Roman Quintero, P. Marino, G. Santonastaso, Roberto Greco
Abstract. Soil and underground conditions prior to the initiation of rainfall events control the hydrological processes that occur in slopes, affecting the water exchange through their boundaries. The present study aims at identifying suitable variables to be monitored to predict the response of sloping soil to precipitation. The case of a pyroclastic coarse-grained soil mantle overlaying a karstic bedrock in the southern Apennines (Italy) is described. Field monitoring of stream level recordings, meteorological variables, and soil water content and suction has been carried out for a few years. To enrich the field dataset, a synthetic series of 1000 years has been generated with a physically based model coupled to a stochastic rainfall model. Machine learning techniques have been used to unwrap the non-linear cause–effect relationships linking the variables. The k-means clustering technique has been used for the identification of seasonally recurrent slope conditions in terms of soil moisture and groundwater level, and the random forest technique has been used to assess how the conditions at the onset of rainfall controlled the attitude of the soil mantle to retain much of the infiltrating rainwater. The results show that the response in terms of the fraction of rainwater remaining stored in the soil mantle at the end of rainfall events is controlled by soil moisture and groundwater level prior to the rainfall initiation, giving evidence of the activation of effective drainage processes.
{"title":"Understanding hydrologic controls of sloping soil response to precipitation through machine learning analysis applied to synthetic data","authors":"Daniel Camilo Roman Quintero, P. Marino, G. Santonastaso, Roberto Greco","doi":"10.5194/hess-27-4151-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4151-2023","url":null,"abstract":"Abstract. Soil and underground conditions prior to the initiation of rainfall events control the hydrological processes that occur in slopes, affecting the water exchange through their boundaries. The present study aims at identifying suitable variables to be monitored to predict the response of sloping soil to precipitation. The case of a pyroclastic coarse-grained soil mantle overlaying a karstic bedrock in the southern Apennines (Italy) is described. Field monitoring of stream level recordings, meteorological variables, and soil water content and suction has been carried out for a few years. To enrich the field dataset, a synthetic series of 1000 years has been generated with a physically based model coupled to a stochastic rainfall model. Machine learning techniques have been used to unwrap the non-linear cause–effect relationships linking the variables. The k-means clustering technique has been used for the identification of seasonally recurrent slope conditions in terms of soil moisture and groundwater level, and the random forest technique has been used to assess how the conditions at the onset of rainfall controlled the attitude of the soil mantle to retain much of the infiltrating rainwater. The results show that the response in terms of the fraction of rainwater remaining stored in the soil mantle at the end of rainfall events is controlled by soil moisture and groundwater level prior to the rainfall initiation, giving evidence of the activation of effective drainage processes.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"20 2","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139267359","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}
Abstract. Heavy rains and tropical storms often result in floods, which are expected to increase in frequency and intensity. Flood prediction models and inundation mapping tools provide decision-makers and emergency responders with crucial information to better prepare for these events. However, the performance of models relies on the accuracy and timeliness of data received from in situ gaging stations and remote sensing; each of these data sources has its limitations, especially when it comes to real-time monitoring of floods. This study presents a vision-based framework for measuring water levels and detecting floods using computer vision and deep learning (DL) techniques. The DL models use time-lapse images captured by surveillance cameras during storm events for the semantic segmentation of water extent in images. Three different DL-based approaches, namely PSPNet, TransUNet, and SegFormer, were applied and evaluated for semantic segmentation. The predicted masks are transformed into water level values by intersecting the extracted water edges, with the 2D representation of a point cloud generated by an Apple iPhone 13 Pro lidar sensor. The estimated water levels were compared to reference data collected by an ultrasonic sensor. The results showed that SegFormer outperformed other DL-based approaches by achieving 99.55 % and 99.81 % for intersection over union (IoU) and accuracy, respectively. Moreover, the highest correlations between reference data and the vision-based approach reached above 0.98 for both the coefficient of determination (R2) and Nash–Sutcliffe efficiency. This study demonstrates the potential of using surveillance cameras and artificial intelligence for hydrologic monitoring and their integration with existing surveillance infrastructure.
{"title":"Eye of Horus: a vision-based framework for real-time water level measurement","authors":"Seyed Mohammad, Hassan Erfani, Corinne Smith, Zhenyao Wu, Elyas Asadi, Farboud Khatami, Austin Downey, Jasim Imran, E. Goharian, Mohammad Erfani, Elyas Asadi Shamsabadi","doi":"10.5194/hess-27-4135-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4135-2023","url":null,"abstract":"Abstract. Heavy rains and tropical storms often result in floods, which are expected to increase in frequency and intensity. Flood prediction models and inundation mapping tools provide decision-makers and emergency responders with crucial information to better prepare for these events. However, the performance of models relies on the accuracy and timeliness of data received from in situ gaging stations and remote sensing; each of these data sources has its limitations, especially when it comes to real-time monitoring of floods. This study presents a vision-based framework for measuring water levels and detecting floods using computer vision and deep learning (DL) techniques. The DL models use time-lapse images captured by surveillance cameras during storm events for the semantic segmentation of water extent in images. Three different DL-based approaches, namely PSPNet, TransUNet, and SegFormer, were applied and evaluated for semantic segmentation. The predicted masks are transformed into water level values by intersecting the extracted water edges, with the 2D representation of a point cloud generated by an Apple iPhone 13 Pro lidar sensor. The estimated water levels were compared to reference data collected by an ultrasonic sensor. The results showed that SegFormer outperformed other DL-based approaches by achieving 99.55 % and 99.81 % for intersection over union (IoU) and accuracy, respectively. Moreover, the highest correlations between reference data and the vision-based approach reached above 0.98 for both the coefficient of determination (R2) and Nash–Sutcliffe efficiency. This study demonstrates the potential of using surveillance cameras and artificial intelligence for hydrologic monitoring and their integration with existing surveillance infrastructure.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"10 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139274621","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 : 2023-11-15DOI: 10.5194/hess-27-4115-2023
Lu Tian, Markus Disse, Jingshui Huang
Abstract. Drought is typically induced by the extreme water deficit stress that cascades through the atmosphere, hydrosphere, and biosphere. Cascading drought events could cause severe damage in multiple systems. However, identifying cascading drought connections considering the dynamic space–time progression remains challenging, which hinders further exploring the emergent patterns of drought cascades. This study proposes a novel framework for tracking drought cascades across multiple systems by utilizing dynamic space–time motion similarities. Our investigation focuses on the four primary drought types in Central Asia from 1980 to 2007, namely precipitation (PCP), evapotranspiration (ET), runoff, and root zone soil moisture (SM), representing the four systems of atmosphere, hydrosphere, biosphere, and soil layer respectively. A total of 503 cascading drought events are identified in this study, including the 261 four-system cascading drought events. Our results show a significant prevalence of the four-system cascading drought pattern in Central Asia with high systematic drought risk, mainly when seasonal PCP droughts with high severity/intensity and sizeable spatial extent are observed. As for the temporal order in the cascading drought events, ET droughts are likely to occur earlier than runoff droughts after PCP droughts, and SM droughts are more likely to occur at last, implying the integrated driven effect of the energy-limited and water-limited phases on the drought progression in Central Asia. Our proposed framework could attain precise internal spatial trajectories within each cascading drought event and enable the capture of space–time cascading connections across diverse drought systems and associated hazards. The identification of cascading drought patterns could provide a systematic understanding of the drought evolution across multiple systems under exacerbated global warming.
{"title":"Drought cascades across multiple systems in Central Asia identified based on the dynamic space–time motion approach","authors":"Lu Tian, Markus Disse, Jingshui Huang","doi":"10.5194/hess-27-4115-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4115-2023","url":null,"abstract":"Abstract. Drought is typically induced by the extreme water deficit stress that cascades through the atmosphere, hydrosphere, and biosphere. Cascading drought events could cause severe damage in multiple systems. However, identifying cascading drought connections considering the dynamic space–time progression remains challenging, which hinders further exploring the emergent patterns of drought cascades. This study proposes a novel framework for tracking drought cascades across multiple systems by utilizing dynamic space–time motion similarities. Our investigation focuses on the four primary drought types in Central Asia from 1980 to 2007, namely precipitation (PCP), evapotranspiration (ET), runoff, and root zone soil moisture (SM), representing the four systems of atmosphere, hydrosphere, biosphere, and soil layer respectively. A total of 503 cascading drought events are identified in this study, including the 261 four-system cascading drought events. Our results show a significant prevalence of the four-system cascading drought pattern in Central Asia with high systematic drought risk, mainly when seasonal PCP droughts with high severity/intensity and sizeable spatial extent are observed. As for the temporal order in the cascading drought events, ET droughts are likely to occur earlier than runoff droughts after PCP droughts, and SM droughts are more likely to occur at last, implying the integrated driven effect of the energy-limited and water-limited phases on the drought progression in Central Asia. Our proposed framework could attain precise internal spatial trajectories within each cascading drought event and enable the capture of space–time cascading connections across diverse drought systems and associated hazards. The identification of cascading drought patterns could provide a systematic understanding of the drought evolution across multiple systems under exacerbated global warming.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"31 6","pages":""},"PeriodicalIF":6.3,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139272546","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 : 2023-11-14DOI: 10.5194/hess-27-4087-2023
Samuel Scherrer, Gabriëlle De Lannoy, Zdenko Heyvaert, Michel Bechtold, Clement Albergel, Tarek S. El-Madany, Wouter Dorigo
Abstract. Data assimilation (DA) of remotely sensed leaf area index (LAI) can help to improve land surface model estimates of energy, water, and carbon variables. So far, most studies have used bias-blind LAI DA approaches, i.e. without correcting for biases between model forecasts and observations. This might hamper the performance of the DA algorithms in the case of large biases in observations or simulations or both. We perform bias-blind and bias-aware DA of Copernicus Global Land Service LAI into the Noah-MP land surface model forced by the ERA5 reanalysis over Europe in the 2002–2019 period, and we evaluate how the choice of bias correction affects estimates of gross primary productivity (GPP), evapotranspiration (ET), runoff, and soil moisture. In areas with a large LAI bias, the bias-blind LAI DA leads to a reduced bias between observed and modelled LAI, an improved agreement of GPP, ET, and runoff estimates with independent products, but a worse agreement of soil moisture estimates with the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product. While comparisons to in situ soil moisture in areas with weak bias indicate an improvement of the representation of soil moisture climatology, bias-blind LAI DA can lead to unrealistic shifts in soil moisture climatology in areas with strong bias. For example, when the assimilated LAI data in irrigated areas are much higher than those simulated without any irrigation activated, LAI will be increased and soil moisture will be depleted. Furthermore, the bias-blind LAI DA produces a pronounced sawtooth pattern due to model drift between DA updates, because each update pushes the Noah-MP leaf model to an unstable state. This model drift also propagates to short-term estimates of GPP and ET and to internal DA diagnostics that indicate a suboptimal DA system performance. The bias-aware approaches based on a priori rescaling of LAI observations to the model climatology avoid the negative effects of the bias-blind assimilation. They retain the improvements in GPP anomalies from the bias-blind DA but forego improvements in the root mean square deviations (RMSDs) of GPP, ET, and runoff. As an alternative to rescaling, we discuss the implications of our results for model calibration or joint parameter and state update DA, which has the potential to combine bias reduction with optimal DA system performance.
{"title":"Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe","authors":"Samuel Scherrer, Gabriëlle De Lannoy, Zdenko Heyvaert, Michel Bechtold, Clement Albergel, Tarek S. El-Madany, Wouter Dorigo","doi":"10.5194/hess-27-4087-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4087-2023","url":null,"abstract":"Abstract. Data assimilation (DA) of remotely sensed leaf area index (LAI) can help to improve land surface model estimates of energy, water, and carbon variables. So far, most studies have used bias-blind LAI DA approaches, i.e. without correcting for biases between model forecasts and observations. This might hamper the performance of the DA algorithms in the case of large biases in observations or simulations or both. We perform bias-blind and bias-aware DA of Copernicus Global Land Service LAI into the Noah-MP land surface model forced by the ERA5 reanalysis over Europe in the 2002–2019 period, and we evaluate how the choice of bias correction affects estimates of gross primary productivity (GPP), evapotranspiration (ET), runoff, and soil moisture. In areas with a large LAI bias, the bias-blind LAI DA leads to a reduced bias between observed and modelled LAI, an improved agreement of GPP, ET, and runoff estimates with independent products, but a worse agreement of soil moisture estimates with the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product. While comparisons to in situ soil moisture in areas with weak bias indicate an improvement of the representation of soil moisture climatology, bias-blind LAI DA can lead to unrealistic shifts in soil moisture climatology in areas with strong bias. For example, when the assimilated LAI data in irrigated areas are much higher than those simulated without any irrigation activated, LAI will be increased and soil moisture will be depleted. Furthermore, the bias-blind LAI DA produces a pronounced sawtooth pattern due to model drift between DA updates, because each update pushes the Noah-MP leaf model to an unstable state. This model drift also propagates to short-term estimates of GPP and ET and to internal DA diagnostics that indicate a suboptimal DA system performance. The bias-aware approaches based on a priori rescaling of LAI observations to the model climatology avoid the negative effects of the bias-blind assimilation. They retain the improvements in GPP anomalies from the bias-blind DA but forego improvements in the root mean square deviations (RMSDs) of GPP, ET, and runoff. As an alternative to rescaling, we discuss the implications of our results for model calibration or joint parameter and state update DA, which has the potential to combine bias reduction with optimal DA system performance.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"15 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134953855","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 : 2023-11-13DOI: 10.5194/hess-27-4057-2023
Awad M. Ali, Lieke A. Melsen, Adriaan J. Teuling
Abstract. The filling of the Grand Ethiopian Renaissance Dam (GERD) started in 2020, posing additional challenges for downstream water management in the Blue Nile River in the Republic of the Sudan, which is already struggling to cope with the effects of climate change. This is also the case for many transboundary rivers that are affected by a lack of cooperation and transparency during the filling and operation of new dams. Without information about water supply from neighboring countries, it is risky to manage downstream dams as usual, but operational information is needed to apply modifications. This study aims to develop a novel approach/framework that utilizes hydrological modeling in conjunction with remote-sensing data to retrieve reservoir filling strategies under limited-data-availability conditions. Firstly, five rainfall products (i.e., ARC2, CHIRPS, ERA5, GPCC, and PERSIANN-CDR; see Sect. 2.3 for more information) were evaluated against historical measured rainfall at 10 stations. Secondly, to account for input uncertainty, the three best-performing rainfall products were forced in the conceptual hydrological model HBV-light with potential evapotranspiration and temperature data from ERA5. The model was calibrated during the period from 2006 to 2019 and validated during the period from 1991 to 1996. Thirdly, the parameter sets that obtained very good performance (Nash–Sutcliffe efficiency, NSE, greater than 0.75) were utilized to predict the inflow of GERD during the operation period (2020–2022). Then, from the water balance of GERD, the daily storage was estimated and compared with the storage derived from Landsat and Sentinel imageries to evaluate the performance of the selected rainfall products and the reliability of the framework. Finally, 3 years of GERD filling strategies was retrieved using the best-performing simulation of CHIRPS with an RMSE of 1.7 ×109 and 1.52 ×109m3 and an NSE of 0.77 and 0.86 when compared with Landsat- and Sentinel-derived reservoir storage, respectively. It was found that GERD stored 14 % of the monthly inflow of July 2020; 41 % of July 2021; and 37 % and 32 % of July and August 2022, respectively. Annually, GERD retained 5.2 % and 7.4 % of the annual inflow in the first two filling phases and between 12.9 % and 13.7 % in the third phase. The results also revealed that the retrieval of filling strategies is more influenced by input uncertainty than parameter uncertainty. The retrieved daily change in GERD storage with the measured outflow to the Republic of the Sudan allowed further interpretation of the downstream impacts of GERD. The findings of this study provide systematic steps to retrieve filling strategies, which can serve as a base for future development in the field, especially for data-scarce regions. Locally, the analysis contributes significantly to the future water management of the Roseires and Sennar dams in the Republic of the Sudan.
{"title":"Inferring reservoir filling strategies under limited-data-availability conditions using hydrological modeling and Earth observations: the case of the Grand Ethiopian Renaissance Dam (GERD)","authors":"Awad M. Ali, Lieke A. Melsen, Adriaan J. Teuling","doi":"10.5194/hess-27-4057-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4057-2023","url":null,"abstract":"Abstract. The filling of the Grand Ethiopian Renaissance Dam (GERD) started in 2020, posing additional challenges for downstream water management in the Blue Nile River in the Republic of the Sudan, which is already struggling to cope with the effects of climate change. This is also the case for many transboundary rivers that are affected by a lack of cooperation and transparency during the filling and operation of new dams. Without information about water supply from neighboring countries, it is risky to manage downstream dams as usual, but operational information is needed to apply modifications. This study aims to develop a novel approach/framework that utilizes hydrological modeling in conjunction with remote-sensing data to retrieve reservoir filling strategies under limited-data-availability conditions. Firstly, five rainfall products (i.e., ARC2, CHIRPS, ERA5, GPCC, and PERSIANN-CDR; see Sect. 2.3 for more information) were evaluated against historical measured rainfall at 10 stations. Secondly, to account for input uncertainty, the three best-performing rainfall products were forced in the conceptual hydrological model HBV-light with potential evapotranspiration and temperature data from ERA5. The model was calibrated during the period from 2006 to 2019 and validated during the period from 1991 to 1996. Thirdly, the parameter sets that obtained very good performance (Nash–Sutcliffe efficiency, NSE, greater than 0.75) were utilized to predict the inflow of GERD during the operation period (2020–2022). Then, from the water balance of GERD, the daily storage was estimated and compared with the storage derived from Landsat and Sentinel imageries to evaluate the performance of the selected rainfall products and the reliability of the framework. Finally, 3 years of GERD filling strategies was retrieved using the best-performing simulation of CHIRPS with an RMSE of 1.7 ×109 and 1.52 ×109m3 and an NSE of 0.77 and 0.86 when compared with Landsat- and Sentinel-derived reservoir storage, respectively. It was found that GERD stored 14 % of the monthly inflow of July 2020; 41 % of July 2021; and 37 % and 32 % of July and August 2022, respectively. Annually, GERD retained 5.2 % and 7.4 % of the annual inflow in the first two filling phases and between 12.9 % and 13.7 % in the third phase. The results also revealed that the retrieval of filling strategies is more influenced by input uncertainty than parameter uncertainty. The retrieved daily change in GERD storage with the measured outflow to the Republic of the Sudan allowed further interpretation of the downstream impacts of GERD. The findings of this study provide systematic steps to retrieve filling strategies, which can serve as a base for future development in the field, especially for data-scarce regions. Locally, the analysis contributes significantly to the future water management of the Roseires and Sennar dams in the Republic of the Sudan.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"58 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136348442","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 : 2023-11-10DOI: 10.5194/hess-27-4039-2023
Eunsang Cho, Yonghwan Kwon, Sujay V. Kumar, Carrie M. Vuyovich
Abstract. An airborne gamma-ray remote-sensing technique provides a strong potential to estimate a reliable snow water equivalent (SWE) in forested environments where typical remote-sensing techniques have large uncertainties. This study explores the utility of assimilating the temporally (up to four measurements during a winter period) and spatially sparse airborne gamma SWE observations into a land surface model (LSM) to improve SWE estimates in forested areas in the northeastern US. Here, we demonstrate that the airborne gamma SWE observations add value to the SWE estimates from the Noah LSM with multiple parameterization options (Noah-MP) via assimilation despite the limited number of measurements. Improvements are witnessed during the snow accumulation period, while reduced skills are seen during the snowmelt period. The efficacy of the gamma data is greater for areas with lower vegetation cover fraction and topographic heterogeneity ranges, and it is still effective at reducing the SWE estimation errors for areas with higher topographic heterogeneity. The gamma SWE data assimilation (DA) also shows a potential to extend the impact of flight-line-based measurements to adjacent areas without observations by employing a localization approach. The localized DA reduces the modeled SWE estimation errors for adjacent grid cells up to 32 km distance from the flight lines. The enhanced performance of the gamma SWE DA is evident when the results are compared to those from assimilating the existing satellite-based SWE retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) for the same locations and time periods. Although there is still room for improvement, particularly for the melting period, this study shows that the gamma SWE DA is a promising method to improve the SWE estimates in forested areas.
{"title":"Assimilation of airborne gamma observations provides utility for snow estimation in forested environments","authors":"Eunsang Cho, Yonghwan Kwon, Sujay V. Kumar, Carrie M. Vuyovich","doi":"10.5194/hess-27-4039-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4039-2023","url":null,"abstract":"Abstract. An airborne gamma-ray remote-sensing technique provides a strong potential to estimate a reliable snow water equivalent (SWE) in forested environments where typical remote-sensing techniques have large uncertainties. This study explores the utility of assimilating the temporally (up to four measurements during a winter period) and spatially sparse airborne gamma SWE observations into a land surface model (LSM) to improve SWE estimates in forested areas in the northeastern US. Here, we demonstrate that the airborne gamma SWE observations add value to the SWE estimates from the Noah LSM with multiple parameterization options (Noah-MP) via assimilation despite the limited number of measurements. Improvements are witnessed during the snow accumulation period, while reduced skills are seen during the snowmelt period. The efficacy of the gamma data is greater for areas with lower vegetation cover fraction and topographic heterogeneity ranges, and it is still effective at reducing the SWE estimation errors for areas with higher topographic heterogeneity. The gamma SWE data assimilation (DA) also shows a potential to extend the impact of flight-line-based measurements to adjacent areas without observations by employing a localization approach. The localized DA reduces the modeled SWE estimation errors for adjacent grid cells up to 32 km distance from the flight lines. The enhanced performance of the gamma SWE DA is evident when the results are compared to those from assimilating the existing satellite-based SWE retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) for the same locations and time periods. Although there is still room for improvement, particularly for the melting period, this study shows that the gamma SWE DA is a promising method to improve the SWE estimates in forested areas.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":" 1263","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135186566","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}
Abstract. Climate change accelerates the global water cycle. However, the relationships between climate change and hydrological processes in the alpine arid regions remain elusive. We sampled surface water and groundwater at high spatial and temporal resolutions to investigate these relationships in the Qaidam Basin, an extremely arid area in the northeastern Tibetan Plateau. Stable H–O isotopes and radioactive 3H isotopes were combined with atmospheric simulations to examine hydrological processes and their response mechanisms to climate change. Contemporary climate processes and change dominate the spatial and temporal variations of surface water isotopes, specifically the westerlies moisture transport and the local temperature and precipitation regimes. The H–O isotopic compositions in the eastern Kunlun Mountains showed a gradually depleted eastward pattern, while a reverse pattern occurred in the Qilian Mountains water system. Precipitation contributed significantly more to river discharge in the eastern basin (approximately 45 %) than in the middle and western basins (10 %–15 %). Moreover, increasing precipitation and a shrinking cryosphere caused by current climate change have accelerated basin groundwater circulation. In the eastern and southwestern Qaidam Basin, precipitation and meltwater infiltrate along preferential flow paths, such as faults, volcanic channels, and fissures, permitting rapid seasonal groundwater recharge and enhanced terrestrial water storage. However, compensating for water loss due to long-term ice and snow melt will be a challenge under projected increasing precipitation in the southwestern Qaidam Basin, and the total water storage may show a trend of increasing before decreasing. Great uncertainty about water is a potential climate change risk facing the arid Qaidam Basin.
{"title":"Isotopic variations in surface waters and groundwaters of an extremely arid basin and their responses to climate change","authors":"Yu Zhang, Hongbing Tan, Peixin Cong, Dongping Shi, Wenbo Rao, Xiying Zhang","doi":"10.5194/hess-27-4019-2023","DOIUrl":"https://doi.org/10.5194/hess-27-4019-2023","url":null,"abstract":"Abstract. Climate change accelerates the global water cycle. However, the relationships between climate change and hydrological processes in the alpine arid regions remain elusive. We sampled surface water and groundwater at high spatial and temporal resolutions to investigate these relationships in the Qaidam Basin, an extremely arid area in the northeastern Tibetan Plateau. Stable H–O isotopes and radioactive 3H isotopes were combined with atmospheric simulations to examine hydrological processes and their response mechanisms to climate change. Contemporary climate processes and change dominate the spatial and temporal variations of surface water isotopes, specifically the westerlies moisture transport and the local temperature and precipitation regimes. The H–O isotopic compositions in the eastern Kunlun Mountains showed a gradually depleted eastward pattern, while a reverse pattern occurred in the Qilian Mountains water system. Precipitation contributed significantly more to river discharge in the eastern basin (approximately 45 %) than in the middle and western basins (10 %–15 %). Moreover, increasing precipitation and a shrinking cryosphere caused by current climate change have accelerated basin groundwater circulation. In the eastern and southwestern Qaidam Basin, precipitation and meltwater infiltrate along preferential flow paths, such as faults, volcanic channels, and fissures, permitting rapid seasonal groundwater recharge and enhanced terrestrial water storage. However, compensating for water loss due to long-term ice and snow melt will be a challenge under projected increasing precipitation in the southwestern Qaidam Basin, and the total water storage may show a trend of increasing before decreasing. Great uncertainty about water is a potential climate change risk facing the arid Qaidam Basin.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":" 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135240809","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 : 2023-11-09DOI: 10.5194/hess-27-3999-2023
En Ning Lai, Lan Wang-Erlandsson, Vili Virkki, Miina Porkka, Ruud J. van der Ent
Abstract. Root zone soil moisture is a key variable representing water cycle dynamics that strongly interact with ecohydrological, atmospheric, and biogeochemical processes. Recently, it was proposed as the control variable for the green water planetary boundary, suggesting that widespread and considerable deviations from baseline variability now predispose Earth system functions critical to an agriculture-based civilization to destabilization. However, the global extent and severity of root zone soil moisture changes under future scenarios remain to be scrutinized. Here, we analysed root zone soil moisture departures from the pre-industrial climate variability for a multi-model ensemble of 14 Earth system models (ESMs) in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in four climate scenarios as defined by the shared socioeconomic pathways (SSPs) SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5 between 2021 and 2100. The analyses were done for 43 ice-free climate reference regions used by the Intergovernmental Panel on Climate Change (IPCC). We defined “permanent departures” when a region's soil moisture exits the regional variability envelope of the pre-industrial climate and does not fall back into the range covered by the baseline envelope until 2100. Permanent dry departures (i.e. lower soil moisture than pre-industrial variability) were found to be most pronounced in Central America, southern Africa, the Mediterranean region, and most of South America, whereas permanent wet departures are most pronounced in south-eastern South America, northern Africa, and southern Asia. In the Mediterranean region, dry permanent departure may have already happened according to some models. By 2100, there are dry permanent departures in the Mediterranean in 70 % of the ESMs in SSP1–2.6, the most mitigated situation, and more than 90 % in SSP3–7.0 and SSP5–8.5, the medium–high and worst-case scenarios. North-eastern Africa is projected to experience wet permanent departures in 64 % of the ESMs under SSP1–2.6 and 93 % under SSP5–8.5. The percentage of ice-free land area with departures increases in all SSP scenarios as time goes by. Wet departures are more widespread than dry departures throughout the studied time frame, except in SSP1–2.6. In most regions, the severity of the departures increases with the severity of global warming. In 2050, permanent departures (ensemble median) occur in about 10 % of global ice-free land areas in SSP1–2.6 and in 25 % in SSP3–7.0. By the end of the 21st century, the occurrence of permanent departures in SSP1–2.6 increases to 34 % and, in SSP3–7.0, to 45 %. Our findings underscore the importance of mitigation to avoid further degrading the Earth system functions upheld by soil moisture.
{"title":"Root zone soil moisture in over 25 % of global land permanently beyond pre-industrial variability as early as 2050 without climate policy","authors":"En Ning Lai, Lan Wang-Erlandsson, Vili Virkki, Miina Porkka, Ruud J. van der Ent","doi":"10.5194/hess-27-3999-2023","DOIUrl":"https://doi.org/10.5194/hess-27-3999-2023","url":null,"abstract":"Abstract. Root zone soil moisture is a key variable representing water cycle dynamics that strongly interact with ecohydrological, atmospheric, and biogeochemical processes. Recently, it was proposed as the control variable for the green water planetary boundary, suggesting that widespread and considerable deviations from baseline variability now predispose Earth system functions critical to an agriculture-based civilization to destabilization. However, the global extent and severity of root zone soil moisture changes under future scenarios remain to be scrutinized. Here, we analysed root zone soil moisture departures from the pre-industrial climate variability for a multi-model ensemble of 14 Earth system models (ESMs) in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in four climate scenarios as defined by the shared socioeconomic pathways (SSPs) SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5 between 2021 and 2100. The analyses were done for 43 ice-free climate reference regions used by the Intergovernmental Panel on Climate Change (IPCC). We defined “permanent departures” when a region's soil moisture exits the regional variability envelope of the pre-industrial climate and does not fall back into the range covered by the baseline envelope until 2100. Permanent dry departures (i.e. lower soil moisture than pre-industrial variability) were found to be most pronounced in Central America, southern Africa, the Mediterranean region, and most of South America, whereas permanent wet departures are most pronounced in south-eastern South America, northern Africa, and southern Asia. In the Mediterranean region, dry permanent departure may have already happened according to some models. By 2100, there are dry permanent departures in the Mediterranean in 70 % of the ESMs in SSP1–2.6, the most mitigated situation, and more than 90 % in SSP3–7.0 and SSP5–8.5, the medium–high and worst-case scenarios. North-eastern Africa is projected to experience wet permanent departures in 64 % of the ESMs under SSP1–2.6 and 93 % under SSP5–8.5. The percentage of ice-free land area with departures increases in all SSP scenarios as time goes by. Wet departures are more widespread than dry departures throughout the studied time frame, except in SSP1–2.6. In most regions, the severity of the departures increases with the severity of global warming. In 2050, permanent departures (ensemble median) occur in about 10 % of global ice-free land areas in SSP1–2.6 and in 25 % in SSP3–7.0. By the end of the 21st century, the occurrence of permanent departures in SSP1–2.6 increases to 34 % and, in SSP3–7.0, to 45 %. Our findings underscore the importance of mitigation to avoid further degrading the Earth system functions upheld by soil moisture.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":" 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241946","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 : 2023-11-09DOI: 10.5194/hess-27-3977-2023
Pamela E. Tetford, Joseph R. Desloges
Abstract. A reliable flood frequency analysis (FFA) requires selection of an appropriate statistical distribution to model historical streamflow data and, where streamflow data are not available (ungauged sites), a regression-based regional flood frequency analysis (RFFA) often correlates well with downstream channel discharge to drainage area relations. However, the predictive strength of the accepted RFFA relies on an assumption of homogeneous watershed conditions. For glacially conditioned fluvial systems, inherited glacial landforms, sediments, and variable land use can alter flow paths and modify flow regimes. This study compares a multivariate RFFA that considers 28 explanatory variables to characterize variable watershed conditions (i.e., surficial geology, climate, topography, and land use) to an accepted power-law relationship between discharge and drainage area. Archived gauge data from southern Ontario, Canada, are used to test these ideas. Mathematical goodness-of-fit criteria best estimate flood discharge for a broad range of flood recurrence intervals, i.e., 1.25, 2, 5, 10, 25, 50, and 100 years. The log-normal, Gumbel, log-Pearson type III, and generalized extreme value distributions are found most appropriate in 42.5 %, 31.9 %, 21.7 %, and 3.9 % of cases, respectively, suggesting that systematic model selection criteria are required for FFA in heterogeneous landscapes. Multivariate regression of estimated flood quantiles with backward elimination of explanatory variables using principal component and discriminant analyses reveal that precipitation provides a greater predictive relationship for more frequent flood events, whereas surficial geology demonstrates more predictive ability for high-magnitude, less-frequent flood events. In this study, all seven flood quantiles identify a statistically significant two-predictor model that incorporates upstream drainage area and the percentage of naturalized landscape with 5 % improvement in predictive power over the commonly used single-variable drainage area model (p<2.2×10-16). Leave-one-out model testing and an analysis of variance (ANOVA) further support the parsimonious two-predictor model when estimating flood discharge in this low-relief landscape with pronounced glacial legacy effects and heterogeneous land use.
{"title":"Modelling flood frequency and magnitude in a glacially conditioned, heterogeneous landscape: testing the importance of land cover and land use","authors":"Pamela E. Tetford, Joseph R. Desloges","doi":"10.5194/hess-27-3977-2023","DOIUrl":"https://doi.org/10.5194/hess-27-3977-2023","url":null,"abstract":"Abstract. A reliable flood frequency analysis (FFA) requires selection of an appropriate statistical distribution to model historical streamflow data and, where streamflow data are not available (ungauged sites), a regression-based regional flood frequency analysis (RFFA) often correlates well with downstream channel discharge to drainage area relations. However, the predictive strength of the accepted RFFA relies on an assumption of homogeneous watershed conditions. For glacially conditioned fluvial systems, inherited glacial landforms, sediments, and variable land use can alter flow paths and modify flow regimes. This study compares a multivariate RFFA that considers 28 explanatory variables to characterize variable watershed conditions (i.e., surficial geology, climate, topography, and land use) to an accepted power-law relationship between discharge and drainage area. Archived gauge data from southern Ontario, Canada, are used to test these ideas. Mathematical goodness-of-fit criteria best estimate flood discharge for a broad range of flood recurrence intervals, i.e., 1.25, 2, 5, 10, 25, 50, and 100 years. The log-normal, Gumbel, log-Pearson type III, and generalized extreme value distributions are found most appropriate in 42.5 %, 31.9 %, 21.7 %, and 3.9 % of cases, respectively, suggesting that systematic model selection criteria are required for FFA in heterogeneous landscapes. Multivariate regression of estimated flood quantiles with backward elimination of explanatory variables using principal component and discriminant analyses reveal that precipitation provides a greater predictive relationship for more frequent flood events, whereas surficial geology demonstrates more predictive ability for high-magnitude, less-frequent flood events. In this study, all seven flood quantiles identify a statistically significant two-predictor model that incorporates upstream drainage area and the percentage of naturalized landscape with 5 % improvement in predictive power over the commonly used single-variable drainage area model (p<2.2×10-16). Leave-one-out model testing and an analysis of variance (ANOVA) further support the parsimonious two-predictor model when estimating flood discharge in this low-relief landscape with pronounced glacial legacy effects and heterogeneous land use.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":" 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135242433","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 : 2023-11-08DOI: 10.5194/hess-27-3957-2023
Ross Pidoto, Uwe Haberlandt
Abstract. Long continuous time series of meteorological variables (i.e. rainfall, temperature and radiation) are required for applications such as derived flood frequency analyses. However, observed time series are generally too short, too sparse in space or incomplete, especially at the sub-daily timestep. Stochastic weather generators overcome this problem by generating time series of arbitrary length. This study presents a major revision to an existing space–time hourly rainfall model based on a point alternating renewal process, now coupled to a k-NN resampling model for conditioned simulation of non-rainfall climate variables. The point-based rainfall model is extended into space by the resampling of simulated rainfall events via a simulated annealing optimisation approach. This approach enforces observed spatial dependency as described by three bivariate spatial rainfall criteria. A new non-sequential branched shuffling approach is introduced which allows the modelling of large station networks (N>50) with no significant loss in the spatial dependence structure. Modelling of non-rainfall climate variables, i.e. temperature, humidity and radiation, is achieved using a non-parametric k-nearest neighbour (k-NN) resampling approach, coupled to the space–time rainfall model via the daily catchment rainfall state. As input, a gridded daily observational dataset (HYRAS) was used. A final deterministic disaggregation step was then performed on all non-rainfall climate variables to achieve an hourly output temporal resolution. The proposed weather generator was tested on 400 catchments of varying size (50–20 000 km2) across Germany, comprising 699 sub-daily rainfall recording stations. Results indicate no major loss of model performance with increasing catchment size and a generally good reproduction of observed climate and rainfall statistics.
{"title":"A semi-parametric hourly space–time weather generator","authors":"Ross Pidoto, Uwe Haberlandt","doi":"10.5194/hess-27-3957-2023","DOIUrl":"https://doi.org/10.5194/hess-27-3957-2023","url":null,"abstract":"Abstract. Long continuous time series of meteorological variables (i.e. rainfall, temperature and radiation) are required for applications such as derived flood frequency analyses. However, observed time series are generally too short, too sparse in space or incomplete, especially at the sub-daily timestep. Stochastic weather generators overcome this problem by generating time series of arbitrary length. This study presents a major revision to an existing space–time hourly rainfall model based on a point alternating renewal process, now coupled to a k-NN resampling model for conditioned simulation of non-rainfall climate variables. The point-based rainfall model is extended into space by the resampling of simulated rainfall events via a simulated annealing optimisation approach. This approach enforces observed spatial dependency as described by three bivariate spatial rainfall criteria. A new non-sequential branched shuffling approach is introduced which allows the modelling of large station networks (N>50) with no significant loss in the spatial dependence structure. Modelling of non-rainfall climate variables, i.e. temperature, humidity and radiation, is achieved using a non-parametric k-nearest neighbour (k-NN) resampling approach, coupled to the space–time rainfall model via the daily catchment rainfall state. As input, a gridded daily observational dataset (HYRAS) was used. A final deterministic disaggregation step was then performed on all non-rainfall climate variables to achieve an hourly output temporal resolution. The proposed weather generator was tested on 400 catchments of varying size (50–20 000 km2) across Germany, comprising 699 sub-daily rainfall recording stations. Results indicate no major loss of model performance with increasing catchment size and a generally good reproduction of observed climate and rainfall statistics.","PeriodicalId":13143,"journal":{"name":"Hydrology and Earth System Sciences","volume":"111 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135345445","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}
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