Pub Date : 2024-03-07DOI: 10.5194/hess-28-1147-2024
Mohammed Abdallah, Ke Zhang, Chao Li, Abubaker Omer, Khalid Hassaballah, Kidane Welde Reda, Linxin Liu, Tolossa Lemma Tola, Omar M. Nour
Abstract. Precipitation is a vital key element in various studies of hydrology, flood prediction, drought monitoring, and water resource management. The main challenge in conducting studies over remote regions with rugged topography is that weather stations are usually scarce and unevenly distributed. However, open-source satellite-based precipitation products (SPPs) with a suitable resolution provide alternative options in these data-scarce regions, which are typically associated with high uncertainty. To reduce the uncertainty of individual satellite products, we have proposed a D-vine copula-based quantile regression (DVQR) model to merge multiple SPPs with rain gauges (RGs). The DVQR model was employed during the 2001–2017 summer monsoon seasons and compared with two other quantile regression methods based on the multivariate linear (MLQR) and the Bayesian model averaging (BMAQ) techniques, respectively, and with two traditional merging methods – the simple modeling average (SMA) and the one-outlier-removed average (OORA) – using descriptive and categorical statistics. Four SPPs have been considered in this study, namely, Tropical Applications of Meteorology using SATellite (TAMSAT v3.1), the Climate Prediction Center MORPHing Product Climate Data Record (CMORPH-CDR), Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG v06), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR). The bilinear (BIL) interpolation technique was applied to downscale SPPs from a coarse to a fine spatial resolution (1 km). The rugged-topography region of the upper Tekeze–Atbara Basin (UTAB) in Ethiopia was selected as the study area. The results indicate that the precipitation data estimates with the DVQR, MLQR, and BMAQ models and with traditional merging methods outperform the downscaled SPPs. Monthly evaluations reveal that all products perform better in July and September than in June and August due to precipitation variability. The DVQR, MLQR, and BMAQ models exhibit higher accuracy than the traditional merging methods over the UTAB. The DVQR model substantially improved all of the statistical metrics (CC = 0.80, NSE = 0.615, KGE = 0.785, MAE = 1.97 mm d−1, RMSE = 2.86 mm d−1, and PBIAS = 0.96 %) considered compared with the BMAQ and MLQR models. However, the DVQR model did not outperform the BMAQ and MLQR models with respect to the probability of detection (POD) and false-alarm ratio (FAR), although it had the best frequency bias index (FBI) and critical success index (CSI) among all of the employed models. Overall, the newly proposed merging approach improves the quality of SPPs and demonstrates the value of the proposed DVQR model in merging multiple SPPs over regions with rugged topography such as the UTAB.
{"title":"A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin","authors":"Mohammed Abdallah, Ke Zhang, Chao Li, Abubaker Omer, Khalid Hassaballah, Kidane Welde Reda, Linxin Liu, Tolossa Lemma Tola, Omar M. Nour","doi":"10.5194/hess-28-1147-2024","DOIUrl":"https://doi.org/10.5194/hess-28-1147-2024","url":null,"abstract":"Abstract. Precipitation is a vital key element in various studies of hydrology, flood prediction, drought monitoring, and water resource management. The main challenge in conducting studies over remote regions with rugged topography is that weather stations are usually scarce and unevenly distributed. However, open-source satellite-based precipitation products (SPPs) with a suitable resolution provide alternative options in these data-scarce regions, which are typically associated with high uncertainty. To reduce the uncertainty of individual satellite products, we have proposed a D-vine copula-based quantile regression (DVQR) model to merge multiple SPPs with rain gauges (RGs). The DVQR model was employed during the 2001–2017 summer monsoon seasons and compared with two other quantile regression methods based on the multivariate linear (MLQR) and the Bayesian model averaging (BMAQ) techniques, respectively, and with two traditional merging methods – the simple modeling average (SMA) and the one-outlier-removed average (OORA) – using descriptive and categorical statistics. Four SPPs have been considered in this study, namely, Tropical Applications of Meteorology using SATellite (TAMSAT v3.1), the Climate Prediction Center MORPHing Product Climate Data Record (CMORPH-CDR), Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG v06), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR). The bilinear (BIL) interpolation technique was applied to downscale SPPs from a coarse to a fine spatial resolution (1 km). The rugged-topography region of the upper Tekeze–Atbara Basin (UTAB) in Ethiopia was selected as the study area. The results indicate that the precipitation data estimates with the DVQR, MLQR, and BMAQ models and with traditional merging methods outperform the downscaled SPPs. Monthly evaluations reveal that all products perform better in July and September than in June and August due to precipitation variability. The DVQR, MLQR, and BMAQ models exhibit higher accuracy than the traditional merging methods over the UTAB. The DVQR model substantially improved all of the statistical metrics (CC = 0.80, NSE = 0.615, KGE = 0.785, MAE = 1.97 mm d−1, RMSE = 2.86 mm d−1, and PBIAS = 0.96 %) considered compared with the BMAQ and MLQR models. However, the DVQR model did not outperform the BMAQ and MLQR models with respect to the probability of detection (POD) and false-alarm ratio (FAR), although it had the best frequency bias index (FBI) and critical success index (CSI) among all of the employed models. Overall, the newly proposed merging approach improves the quality of SPPs and demonstrates the value of the proposed DVQR model in merging multiple SPPs over regions with rugged topography such as the UTAB.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140258699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-06DOI: 10.5194/hess-28-1127-2024
Dipti Tiwari, Mélanie Trudel, R. Leconte
Abstract. In northern cold-temperate countries, a large portion of annual streamflow is produced by spring snowmelt, which often triggers floods. It is important to have spatial information about snow variables such as snow water equivalent (SWE), which can be incorporated into hydrological models, making them more efficient tools for improved decision-making. The present research implements a unique spatial pattern metric in a multi-objective framework for calibration of hydrological models and attempts to determine whether raw SNODAS (SNOw Data Assimilation System) data can be utilized for hydrological model calibration. The spatial efficiency (SPAEF) metric is explored for spatially calibrating SWE. Different calibration experiments are performed combining Nash–Sutcliffe efficiency (NSE) for streamflow and root-mean-square error (RMSE) and SPAEF for SWE, using the Dynamically Dimensioned Search (DDS) and Pareto Archived Dynamically Dimensioned Search multi-objective optimization (PADDS) algorithms. Results of the study demonstrate that multi-objective calibration outperforms sequential calibration in terms of model performance (SWE and discharge simulations). Traditional model calibration involving only streamflow produced slightly higher NSE values; however, the spatial distribution of SWE could not be adequately maintained. This study indicates that utilizing SPAEF for spatial calibration of snow parameters improved streamflow prediction compared to the conventional practice of using RMSE for calibration. SPAEF is further implied to be a more effective metric than RMSE for both sequential and multi-objective calibration. During validation, the calibration experiment incorporating multi-objective SPAEF exhibits enhanced performance in terms of NSE and Kling–Gupta efficiency (KGE) compared to calibration experiment solely based on NSE. This observation supports the notion that incorporating SPAEF computed on raw SNODAS data within the calibration framework results in a more robust hydrological model. The novelty of this study is the implementation of SPAEF with respect to spatially distributed SWE for calibrating a distributed hydrological model.
{"title":"On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow","authors":"Dipti Tiwari, Mélanie Trudel, R. Leconte","doi":"10.5194/hess-28-1127-2024","DOIUrl":"https://doi.org/10.5194/hess-28-1127-2024","url":null,"abstract":"Abstract. In northern cold-temperate countries, a large portion of annual streamflow is produced by spring snowmelt, which often triggers floods. It is important to have spatial information about snow variables such as snow water equivalent (SWE), which can be incorporated into hydrological models, making them more efficient tools for improved decision-making. The present research implements a unique spatial pattern metric in a multi-objective framework for calibration of hydrological models and attempts to determine whether raw SNODAS (SNOw Data Assimilation System) data can be utilized for hydrological model calibration. The spatial efficiency (SPAEF) metric is explored for spatially calibrating SWE. Different calibration experiments are performed combining Nash–Sutcliffe efficiency (NSE) for streamflow and root-mean-square error (RMSE) and SPAEF for SWE, using the Dynamically Dimensioned Search (DDS) and Pareto Archived Dynamically Dimensioned Search multi-objective optimization (PADDS) algorithms. Results of the study demonstrate that multi-objective calibration outperforms sequential calibration in terms of model performance (SWE and discharge simulations). Traditional model calibration involving only streamflow produced slightly higher NSE values; however, the spatial distribution of SWE could not be adequately maintained. This study indicates that utilizing SPAEF for spatial calibration of snow parameters improved streamflow prediction compared to the conventional practice of using RMSE for calibration. SPAEF is further implied to be a more effective metric than RMSE for both sequential and multi-objective calibration. During validation, the calibration experiment incorporating multi-objective SPAEF exhibits enhanced performance in terms of NSE and Kling–Gupta efficiency (KGE) compared to calibration experiment solely based on NSE. This observation supports the notion that incorporating SPAEF computed on raw SNODAS data within the calibration framework results in a more robust hydrological model. The novelty of this study is the implementation of SPAEF with respect to spatially distributed SWE for calibrating a distributed hydrological model.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"34 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140262791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-20DOI: 10.5194/hess-28-761-2024
Qi Sun, Patrick Olschewski, Jianhui Wei, Zhan Tian, Laixiang Sun, H. Kunstmann, P. Laux
Abstract. There is evidence of an increased frequency of rapid intensification events of tropical cyclones (TCs) in global offshore regions. This will not only result in increased peak wind speeds but may lead to more intense heavy precipitation events, leading to flooding in coastal regions. Therefore, high impacts are expected for urban agglomerations in coastal regions such as the densely populated Pearl River Delta (PRD) in China. Regional climate models (RCMs) such as the Weather Research and Forecasting (WRF) model are state-of-the-art tools commonly applied to predict TCs. However, typhoon simulations are connected with high uncertainties due to the high number of parameterization schemes of relevant physical processes (including possible interactions between the parameterization schemes) such as cumulus (CU) and microphysics (MP), as well as other crucial model settings such as domain setup, initial times, and spectral nudging. Since previous studies mostly focus on either individual typhoon cases or individual parameterization schemes, in this study a more comprehensive analysis is provided by considering four different typhoons of different intensity categories with landfall near the PRD, i.e. Typhoon Neoguri (2008), Typhoon Hagupit (2008), Typhoon Hato (2017), and Typhoon Usagi (2013), as well as two different schemes for CU and MP, respectively. Moreover, the impact of the model initialization and the driving data is studied by using three different initial times and two spectral nudging settings. Compared with the best-track reference data, the results show that the four typhoons show some consistency. For track bias, nudging only horizontal wind has a positive effect on reducing the track distance bias; for intensity, compared with a model explicitly resolving cumulus convection, i.e. without cumulus parameterization (CuOFF; nudging potential temperature and horizontal wind; late initial time), using the Kain–Fritsch scheme (KF; nudging only horizontal wind; early initial time) configuration shows relatively lower minimum sea level pressures and higher maximum wind speeds, which means stronger typhoon intensity. Intensity shows less sensitivity to two MP schemes compared with the CuOFF, nudging, and initial time settings. Furthermore, we found that compared with the CuOFF, using the KF scheme shows a relatively larger latent heat flux and higher equivalent potential temperature, providing more energy to typhoon development and inducing stronger TCs. This study could be used as a reference to configure WRF with the model's different combinations of schemes for historical and future TC simulations and also contributes to a better understanding of the performance of principal TC structures.
{"title":"Key ingredients in regional climate modelling for improving the representation of typhoon tracks and intensities","authors":"Qi Sun, Patrick Olschewski, Jianhui Wei, Zhan Tian, Laixiang Sun, H. Kunstmann, P. Laux","doi":"10.5194/hess-28-761-2024","DOIUrl":"https://doi.org/10.5194/hess-28-761-2024","url":null,"abstract":"Abstract. There is evidence of an increased frequency of rapid intensification events of tropical cyclones (TCs) in global offshore regions. This will not only result in increased peak wind speeds but may lead to more intense heavy precipitation events, leading to flooding in coastal regions. Therefore, high impacts are expected for urban agglomerations in coastal regions such as the densely populated Pearl River Delta (PRD) in China. Regional climate models (RCMs) such as the Weather Research and Forecasting (WRF) model are state-of-the-art tools commonly applied to predict TCs. However, typhoon simulations are connected with high uncertainties due to the high number of parameterization schemes of relevant physical processes (including possible interactions between the parameterization schemes) such as cumulus (CU) and microphysics (MP), as well as other crucial model settings such as domain setup, initial times, and spectral nudging. Since previous studies mostly focus on either individual typhoon cases or individual parameterization schemes, in this study a more comprehensive analysis is provided by considering four different typhoons of different intensity categories with landfall near the PRD, i.e. Typhoon Neoguri (2008), Typhoon Hagupit (2008), Typhoon Hato (2017), and Typhoon Usagi (2013), as well as two different schemes for CU and MP, respectively. Moreover, the impact of the model initialization and the driving data is studied by using three different initial times and two spectral nudging settings. Compared with the best-track reference data, the results show that the four typhoons show some consistency. For track bias, nudging only horizontal wind has a positive effect on reducing the track distance bias; for intensity, compared with a model explicitly resolving cumulus convection, i.e. without cumulus parameterization (CuOFF; nudging potential temperature and horizontal wind; late initial time), using the Kain–Fritsch scheme (KF; nudging only horizontal wind; early initial time) configuration shows relatively lower minimum sea level pressures and higher maximum wind speeds, which means stronger typhoon intensity. Intensity shows less sensitivity to two MP schemes compared with the CuOFF, nudging, and initial time settings. Furthermore, we found that compared with the CuOFF, using the KF scheme shows a relatively larger latent heat flux and higher equivalent potential temperature, providing more energy to typhoon development and inducing stronger TCs. This study could be used as a reference to configure WRF with the model's different combinations of schemes for historical and future TC simulations and also contributes to a better understanding of the performance of principal TC structures.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"7 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139958086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-16DOI: 10.5194/hess-28-719-2024
Zongxing Li, Juan Gui, Qiao Cui, Jian Xue, Fa Du, Lanping Si
Abstract. Amid global warming, the timely supplementation of soil water is crucial for the effective restoration and protection of the ecosystem. It is therefore of great importance to understand the temporal and spatial variations of soil water sources. The research collected 2451 samples of soil water, precipitation, river water, ground ice, supra-permafrost water, and glacier snow meltwater in June, August, and September 2020. The goal was to quantify the contribution of various water sources to soil water in the Three-Rivers Headwater Region (China) during different ablation periods. The findings revealed that precipitation, ground ice, and snow meltwater constituted approximately 72 %, 20 %, and 8 % of soil water during the early ablation period. The snow is fully liquefied during the latter part of the ablation period, with precipitation contributing approximately 90 % and 94 % of soil water, respectively. These recharges also varied markedly with altitude and vegetation type. The study identified several influencing factors on soil water sources, including temperature, precipitation, vegetation, evapotranspiration, and the freeze–thaw cycle. However, soil water loss will further exacerbate vegetation degradation and pose a significant threat to the ecological security of the “Chinese Water Tower”. It emphasizes the importance of monitoring soil water, addressing vegetation degradation related to soil water loss, and determining reasonable soil and water conservation and vegetation restoration models.
{"title":"Soil water sources and their implications for vegetation restoration in the Three-Rivers Headwater Region during different ablation periods","authors":"Zongxing Li, Juan Gui, Qiao Cui, Jian Xue, Fa Du, Lanping Si","doi":"10.5194/hess-28-719-2024","DOIUrl":"https://doi.org/10.5194/hess-28-719-2024","url":null,"abstract":"Abstract. Amid global warming, the timely supplementation of soil water is crucial for the effective restoration and protection of the ecosystem. It is therefore of great importance to understand the temporal and spatial variations of soil water sources. The research collected 2451 samples of soil water, precipitation, river water, ground ice, supra-permafrost water, and glacier snow meltwater in June, August, and September 2020. The goal was to quantify the contribution of various water sources to soil water in the Three-Rivers Headwater Region (China) during different ablation periods. The findings revealed that precipitation, ground ice, and snow meltwater constituted approximately 72 %, 20 %, and 8 % of soil water during the early ablation period. The snow is fully liquefied during the latter part of the ablation period, with precipitation contributing approximately 90 % and 94 % of soil water, respectively. These recharges also varied markedly with altitude and vegetation type. The study identified several influencing factors on soil water sources, including temperature, precipitation, vegetation, evapotranspiration, and the freeze–thaw cycle. However, soil water loss will further exacerbate vegetation degradation and pose a significant threat to the ecological security of the “Chinese Water Tower”. It emphasizes the importance of monitoring soil water, addressing vegetation degradation related to soil water loss, and determining reasonable soil and water conservation and vegetation restoration models.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"41 35","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139961939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-16DOI: 10.5194/hess-28-691-2024
Claire Kouba, Thomas Harter
Abstract. In undammed watersheds in Mediterranean climates, the timing and abruptness of the transition from the dry season to the wet season have major implications for aquatic ecosystems. Of particular concern in many coastal areas is whether this transition can provide sufficient flows at the right time to allow passage for spawning anadromous fish, which is determined by dry season baseflow rates and the timing of the onset of the rainy season. In (semi-) ephemeral watershed systems, these functional flows also dictate the timing of full reconnection of the stream system. In this study, we propose methods to predict, approximately 5 months in advance, two key hydrologic metrics in the undammed rural Scott River watershed in northern California. The two metrics are intended to characterize (1) the severity of a dry year and (2) the relative timing of the transition from the dry to the wet season. The ability to predict these metrics in advance could support seasonal adaptive management. The first metric is the minimum 30 d dry season baseflow volume, Vmin, which occurs at the end of the dry season (September–October) in this Mediterranean climate. The second metric is the cumulative precipitation, starting 1 September, necessary to bring the watershed to a “full” or “spilling” condition (i.e., initiate the onset of wet season storm- or baseflows) after the end of the dry season, referred to here as Pspill. As potential predictors of these two metrics, we assess maximum snowpack, cumulative precipitation, the timing of the snowpack and precipitation, spring groundwater levels, spring river flows, reference evapotranspiration, and a subset of these metrics from the previous water year. Though many of these predictors are correlated with the two metrics of interest, we find that the best prediction for both metrics is a linear combination of the maximum snowpack water content and total October–April precipitation. These two linear models could reproduce historical values of Vmin and Pspill with an average model error (RMSE) of 1.4 Mm3 per 30 d (19.4 cfs) and 25.4 mm (1 in.), corresponding to 49 % and 37 % of mean observed values, respectively. Although these predictive indices could be used by governance entities to support local water management, careful consideration of baseline conditions used as a basis for prediction is necessary.
{"title":"Seasonal prediction of end-of-dry-season watershed behavior in a highly interconnected alluvial watershed in northern California","authors":"Claire Kouba, Thomas Harter","doi":"10.5194/hess-28-691-2024","DOIUrl":"https://doi.org/10.5194/hess-28-691-2024","url":null,"abstract":"Abstract. In undammed watersheds in Mediterranean climates, the timing and abruptness of the transition from the dry season to the wet season have major implications for aquatic ecosystems. Of particular concern in many coastal areas is whether this transition can provide sufficient flows at the right time to allow passage for spawning anadromous fish, which is determined by dry season baseflow rates and the timing of the onset of the rainy season. In (semi-) ephemeral watershed systems, these functional flows also dictate the timing of full reconnection of the stream system. In this study, we propose methods to predict, approximately 5 months in advance, two key hydrologic metrics in the undammed rural Scott River watershed in northern California. The two metrics are intended to characterize (1) the severity of a dry year and (2) the relative timing of the transition from the dry to the wet season. The ability to predict these metrics in advance could support seasonal adaptive management. The first metric is the minimum 30 d dry season baseflow volume, Vmin, which occurs at the end of the dry season (September–October) in this Mediterranean climate. The second metric is the cumulative precipitation, starting 1 September, necessary to bring the watershed to a “full” or “spilling” condition (i.e., initiate the onset of wet season storm- or baseflows) after the end of the dry season, referred to here as Pspill. As potential predictors of these two metrics, we assess maximum snowpack, cumulative precipitation, the timing of the snowpack and precipitation, spring groundwater levels, spring river flows, reference evapotranspiration, and a subset of these metrics from the previous water year. Though many of these predictors are correlated with the two metrics of interest, we find that the best prediction for both metrics is a linear combination of the maximum snowpack water content and total October–April precipitation. These two linear models could reproduce historical values of Vmin and Pspill with an average model error (RMSE) of 1.4 Mm3 per 30 d (19.4 cfs) and 25.4 mm (1 in.), corresponding to 49 % and 37 % of mean observed values, respectively. Although these predictive indices could be used by governance entities to support local water management, careful consideration of baseline conditions used as a basis for prediction is necessary.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"12 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139961017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Saline lakes on the Qinghai–Tibet Plateau (QTP) affect the regional climate and water cycle through water loss (E, evaporation under ice-free conditions and sublimation under ice-covered conditions). Due to the observational difficulty over lakes, E and its underlying driving forces are seldom studied when targeting saline lakes on the QTP, particularly during ice-covered periods (ICP). In this study, the E of Qinghai Lake (QHL) and its influencing factors during ice-free periods (IFP) and ICP were first quantified based on 6 years of observations. Subsequently, three models were calibrated and compared in simulating E during the IFP and ICP from 2003 to 2017. The annual E sum of QHL is 768.58±28.73 mm, and the E sum during the ICP reaches 175.22±45.98 mm, accounting for 23 % of the annual E sum. E is mainly controlled by the wind speed, vapor pressure difference, and air pressure during the IFP but is driven by the net radiation, the difference between the air and lake surface temperatures, the wind speed, and the ice coverage during the ICP. The mass transfer model simulates lake E well during the IFP, and the model based on energy achieves a good simulation during the ICP. Moreover, wind speed weakening resulted in an 7.56 % decrease in E during the ICP of 2003–2017. Our results highlight the importance of E in ICP, provide new insights into saline lake E in alpine regions, and can be used as a reference to further improve hydrological models of alpine lakes.
摘要青藏高原（QTP）的盐湖通过失水（E，无冰条件下的蒸发和冰盖条件下的升华）影响区域气候和水循环。由于湖泊观测困难，针对青藏高原盐湖，特别是冰封期（ICP）的 E 及其内在驱动力的研究很少。在本研究中，首先根据 6 年的观测资料量化了青海湖（QHL）在无冰期（IFP）和有冰期（ICP）的 E 值及其影响因素。随后，校核并比较了 2003 至 2017 年无冰期和有冰期的三个模拟模型。结果表明，QHL 的年径流总和为（768.58±28.73）毫米，ICP 期间的径流总和为（175.22±45.98）毫米，占全年径流总和的 23%。E 主要受 IFP 期间的风速、水汽压差和气压控制，但受 ICP 期间的净辐射、气温与湖面温差、风速和冰覆盖率的影响。传质模型可以很好地模拟 IFP 期间的湖泊 E，而基于能量的模型可以很好地模拟 ICP 期间的湖泊 E。此外，在 2003-2017 年的国际比较方案期间，风速减弱导致 E 值下降了 7.56%。我们的研究结果凸显了 E 在 ICP 中的重要性，为高寒地区盐湖 E 的研究提供了新的视角，可为进一步改进高寒湖泊水文模型提供参考。
{"title":"Evaporation and sublimation measurement and modeling of an alpine saline lake influenced by freeze–thaw on the Qinghai–Tibet Plateau","authors":"F. Shi, Xiaoyan Li, Shaojie Zhao, Yujun Ma, Junqi Wei, Qiwen Liao, Deliang Chen","doi":"10.5194/hess-28-163-2024","DOIUrl":"https://doi.org/10.5194/hess-28-163-2024","url":null,"abstract":"Abstract. Saline lakes on the Qinghai–Tibet Plateau (QTP) affect the regional climate and water cycle through water loss (E, evaporation under ice-free conditions and sublimation under ice-covered conditions). Due to the observational difficulty over lakes, E and its underlying driving forces are seldom studied when targeting saline lakes on the QTP, particularly during ice-covered periods (ICP). In this study, the E of Qinghai Lake (QHL) and its influencing factors during ice-free periods (IFP) and ICP were first quantified based on 6 years of observations. Subsequently, three models were calibrated and compared in simulating E during the IFP and ICP from 2003 to 2017. The annual E sum of QHL is 768.58±28.73 mm, and the E sum during the ICP reaches 175.22±45.98 mm, accounting for 23 % of the annual E sum. E is mainly controlled by the wind speed, vapor pressure difference, and air pressure during the IFP but is driven by the net radiation, the difference between the air and lake surface temperatures, the wind speed, and the ice coverage during the ICP. The mass transfer model simulates lake E well during the IFP, and the model based on energy achieves a good simulation during the ICP. Moreover, wind speed weakening resulted in an 7.56 % decrease in E during the ICP of 2003–2017. Our results highlight the importance of E in ICP, provide new insights into saline lake E in alpine regions, and can be used as a reference to further improve hydrological models of alpine lakes.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"71 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139440681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-09DOI: 10.5194/hess-28-139-2024
L. Schmidt, T. Francke, Peter Martin Grosse, A. Bronstert
Abstract. Future changes in suspended sediment export from deglaciating high-alpine catchments affect downstream hydropower reservoirs, flood hazard, ecosystems and water quality. Yet, quantitative projections of future sediment export have so far been hindered by the lack of process-based models that can take into account all relevant processes within the complex systems determining sediment dynamics at the catchment scale. As a promising alternative, machine-learning (ML) approaches have recently been successfully applied to modeling suspended sediment yields (SSYs). This study is the first, to our knowledge, exploring a machine-learning approach to derive sediment export projections until the year 2100. We employ quantile regression forest (QRF), which proved to be a powerful method to model past SSYs in previous studies, for two nested glaciated high-alpine catchments in the Ötztal, Austria, above gauge Vent (98.1 km2) and gauge Vernagt (11.4 km2). As predictors, we use temperature and precipitation projections (EURO-CORDEX) and discharge projections (AMUNDSEN physically based hydroclimatological and snow model) for the two gauges. We address uncertainties associated with the known limitation of QRF that underestimates can be expected if values in the projection period exceed the range represented in the training data (out-of-observation-range days, OOOR). For this, we assess the frequency and extent of these exceedances and the sensitivity of the resulting mean annual suspended sediment concentration (SSC) estimates. We examine the resulting SSY projections for trends, the estimated timing of peak sediment and changes in the seasonal distribution. Our results show that the uncertainties associated with the OOOR data points are small before 2070 (max. 3 % change in estimated mean annual SSC). Results after 2070 have to be treated more cautiously as OOOR data points occur more frequently, and glaciers are projected to have (nearly) vanished by then in some projections, which likely substantially alters sediment dynamics in the area. The resulting projections suggest decreasing sediment export at both gauges in the coming decades, regardless of the emission scenario, which implies that peak sediment has already passed or is underway. This is linked to substantial decreases in discharge volumes, especially during the glacier melt phase in late summer, as a result of increasing temperatures and thus shrinking glaciers. Nevertheless, high(er) annual yields can occur in response to heavy summer precipitation, and both developments would need to be considered in managing sediments, as well as e.g., flood hazard. While we chose the predictors to act as proxies for sediment-relevant processes, future studies are encouraged to try and include geomorphological changes more explicitly, e.g., changes in connectivity, landsliding, rockfalls or vegetation colonization, as these could improve the reliability of the projections.
{"title":"Projecting sediment export from two highly glacierized alpine catchments under climate change: exploring non-parametric regression as an analysis tool","authors":"L. Schmidt, T. Francke, Peter Martin Grosse, A. Bronstert","doi":"10.5194/hess-28-139-2024","DOIUrl":"https://doi.org/10.5194/hess-28-139-2024","url":null,"abstract":"Abstract. Future changes in suspended sediment export from deglaciating high-alpine catchments affect downstream hydropower reservoirs, flood hazard, ecosystems and water quality. Yet, quantitative projections of future sediment export have so far been hindered by the lack of process-based models that can take into account all relevant processes within the complex systems determining sediment dynamics at the catchment scale. As a promising alternative, machine-learning (ML) approaches have recently been successfully applied to modeling suspended sediment yields (SSYs). This study is the first, to our knowledge, exploring a machine-learning approach to derive sediment export projections until the year 2100. We employ quantile regression forest (QRF), which proved to be a powerful method to model past SSYs in previous studies, for two nested glaciated high-alpine catchments in the Ötztal, Austria, above gauge Vent (98.1 km2) and gauge Vernagt (11.4 km2). As predictors, we use temperature and precipitation projections (EURO-CORDEX) and discharge projections (AMUNDSEN physically based hydroclimatological and snow model) for the two gauges. We address uncertainties associated with the known limitation of QRF that underestimates can be expected if values in the projection period exceed the range represented in the training data (out-of-observation-range days, OOOR). For this, we assess the frequency and extent of these exceedances and the sensitivity of the resulting mean annual suspended sediment concentration (SSC) estimates. We examine the resulting SSY projections for trends, the estimated timing of peak sediment and changes in the seasonal distribution. Our results show that the uncertainties associated with the OOOR data points are small before 2070 (max. 3 % change in estimated mean annual SSC). Results after 2070 have to be treated more cautiously as OOOR data points occur more frequently, and glaciers are projected to have (nearly) vanished by then in some projections, which likely substantially alters sediment dynamics in the area. The resulting projections suggest decreasing sediment export at both gauges in the coming decades, regardless of the emission scenario, which implies that peak sediment has already passed or is underway. This is linked to substantial decreases in discharge volumes, especially during the glacier melt phase in late summer, as a result of increasing temperatures and thus shrinking glaciers. Nevertheless, high(er) annual yields can occur in response to heavy summer precipitation, and both developments would need to be considered in managing sediments, as well as e.g., flood hazard. While we chose the predictors to act as proxies for sediment-relevant processes, future studies are encouraged to try and include geomorphological changes more explicitly, e.g., changes in connectivity, landsliding, rockfalls or vegetation colonization, as these could improve the reliability of the projections.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"18 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139443504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-08DOI: 10.5194/hess-28-117-2024
Celray James Chawanda, Albert Nkwasa, W. Thiery, A. van Griensven
Abstract. Africa depends on its water resources for hydroelectricity, inland fisheries and water supply for domestic, industrial and agricultural operations. Anthropogenic climate change (CC) has changed the state of these water resources. Land use and land cover have also undergone significant changes due to the need to provide resources to a growing population. Yet, the impact of the land-use and land cover change (LULCC) in addition to CC on the water resources of Africa is underexplored. Here we investigate how precipitation, evapotranspiration (ET) and river flow respond to both CC and LULCC scenarios across the entire African continent. We set up a Soil and Water Assessment Tool (SWAT+) model for Africa and calibrated it using the hydrological mass balance calibration (HMBC) methodology detailed in Chawanda et al. (2020a). The model was subsequently driven by an ensemble of bias-adjusted global climate models to simulate the hydrological cycle under a range of CC and LULCC scenarios. The results indicate that the Zambezi and the Congo River basins are likely to experience reduced river flows under CC with an up to 7 % decrease, while the Limpopo River will likely have higher river flows. The Niger River basin is likely to experience the largest decrease in river flows in all of Africa due to CC. The Congo River basin has the largest difference in river flows between scenarios with (over 18 % increase) and without LULCC (over 20 % decrease). The projected changes have implications for the agriculture and energy sectors and hence the livelihood of people on the continent. Our results highlight the need to adopt policies to halt global greenhouse gas emissions and to combat the current trend of deforestation to avoid the high combined impact of CC and LULCC on water resources in Africa.
摘要非洲的水力发电、内陆渔业以及家庭、工业和农业用水都依赖于水资源。人为气候变化（CC）改变了这些水资源的状况。由于需要为不断增长的人口提供资源，土地利用和土地覆盖也发生了重大变化。然而，除了气候变化之外，土地利用和土地覆被变化（LULCC）对非洲水资源的影响还未得到充分探索。在此，我们研究了整个非洲大陆的降水量、蒸散量（ET）和河流流量是如何对 CC 和 LULCC 情景做出反应的。我们为非洲建立了水土评估工具（SWAT+）模型，并使用 Chawanda 等人（2020a）详述的水文质量平衡校准（HMBC）方法对其进行了校准。随后，该模型由一系列经过偏差调整的全球气候模型驱动，模拟了一系列 CC 和 LULCC 情景下的水文循环。结果表明，在 CC 条件下，赞比西河和刚果河流域的河水流量可能会减少，降幅可达 7%，而林波波河的河水流量可能会增加。尼日尔河流域可能是整个非洲因气候变化而导致河水流量减少最多的地区。刚果河流域在有 LULCC 的情况下（增加超过 18%）和没有 LULCC 的情况下（减少超过 20%）河水流量的差异最大。预计的变化会对农业和能源部门产生影响，进而影响非洲大陆人民的生计。我们的研究结果突出表明，有必要采取政策阻止全球温室气体排放，并遏制当前的森林砍伐趋势，以避免气候变化和土地利用、土地利用的变化和碳循环对非洲水资源的综合影响。
{"title":"Combined impacts of climate and land-use change on future water resources in Africa","authors":"Celray James Chawanda, Albert Nkwasa, W. Thiery, A. van Griensven","doi":"10.5194/hess-28-117-2024","DOIUrl":"https://doi.org/10.5194/hess-28-117-2024","url":null,"abstract":"Abstract. Africa depends on its water resources for hydroelectricity, inland fisheries and water supply for domestic, industrial and agricultural operations. Anthropogenic climate change (CC) has changed the state of these water resources. Land use and land cover have also undergone significant changes due to the need to provide resources to a growing population. Yet, the impact of the land-use and land cover change (LULCC) in addition to CC on the water resources of Africa is underexplored. Here we investigate how precipitation, evapotranspiration (ET) and river flow respond to both CC and LULCC scenarios across the entire African continent. We set up a Soil and Water Assessment Tool (SWAT+) model for Africa and calibrated it using the hydrological mass balance calibration (HMBC) methodology detailed in Chawanda et al. (2020a). The model was subsequently driven by an ensemble of bias-adjusted global climate models to simulate the hydrological cycle under a range of CC and LULCC scenarios. The results indicate that the Zambezi and the Congo River basins are likely to experience reduced river flows under CC with an up to 7 % decrease, while the Limpopo River will likely have higher river flows. The Niger River basin is likely to experience the largest decrease in river flows in all of Africa due to CC. The Congo River basin has the largest difference in river flows between scenarios with (over 18 % increase) and without LULCC (over 20 % decrease). The projected changes have implications for the agriculture and energy sectors and hence the livelihood of people on the continent. Our results highlight the need to adopt policies to halt global greenhouse gas emissions and to combat the current trend of deforestation to avoid the high combined impact of CC and LULCC on water resources in Africa.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"2 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139446827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elisabeth Brochet, Youen Grusson, S. Sauvage, Ludovic Lhuissier, V. Demarez
Abstract. In agricultural areas, the downstream flow can be highly influenced by human activities during low-flow periods, especially during dam releases and irrigation withdrawals. Irrigation is indeed the major use of freshwater in the world. This study aims at precisely taking these factors into account in a watershed model. The Soil and Water Assessment Tool (SWAT+) agro-hydrological model was chosen for its capacity to model crop dynamics and management. Two different crop models were compared in terms of their ability to estimate water needs and actual irrigation. The first crop model is based on air temperature as the main determining factor for growth, whereas the second relies on high-resolution data from the Sentinel-2 satellite to monitor plant growth. Both are applied at the plot scale in a watershed of 800 km2 that is characterized by irrigation withdrawals. Results show that including remote sensing data leads to more realistic modeled emergence dates for summer crops. However, both approaches have proven to be able to reproduce the evolution of daily irrigation withdrawals throughout the year. As a result, both approaches allowed us to simulate the downstream flow with a good daily accuracy, especially during low-flow periods.
{"title":"How to account for irrigation withdrawals in a watershed model","authors":"Elisabeth Brochet, Youen Grusson, S. Sauvage, Ludovic Lhuissier, V. Demarez","doi":"10.5194/hess-28-49-2024","DOIUrl":"https://doi.org/10.5194/hess-28-49-2024","url":null,"abstract":"Abstract. In agricultural areas, the downstream flow can be highly influenced by human activities during low-flow periods, especially during dam releases and irrigation withdrawals. Irrigation is indeed the major use of freshwater in the world. This study aims at precisely taking these factors into account in a watershed model. The Soil and Water Assessment Tool (SWAT+) agro-hydrological model was chosen for its capacity to model crop dynamics and management. Two different crop models were compared in terms of their ability to estimate water needs and actual irrigation. The first crop model is based on air temperature as the main determining factor for growth, whereas the second relies on high-resolution data from the Sentinel-2 satellite to monitor plant growth. Both are applied at the plot scale in a watershed of 800 km2 that is characterized by irrigation withdrawals. Results show that including remote sensing data leads to more realistic modeled emergence dates for summer crops. However, both approaches have proven to be able to reproduce the evolution of daily irrigation withdrawals throughout the year. As a result, both approaches allowed us to simulate the downstream flow with a good daily accuracy, especially during low-flow periods.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"17 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139450912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eliot Sicaud, David H. Fortier, J. Dedieu, J. Franssen
Abstract. For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Landscape hydrology provides useful approaches for the indirect assessment of the hydrological characteristics of watersheds through analysis of landscape properties. In this study, we used unsupervised geographic object-based image analysis (GeOBIA) paired with the fuzzy c-means (FCM) clustering algorithm to produce seven high-resolution territorial classifications of key remotely sensed hydro-geomorphic metrics for the 1985–2019 time period, each with a frequency of 5 years. Our study site is the George River watershed (GRW), a 42 000 km2 watershed located in Nunavik, northern Quebec (Canada). The subwatersheds within the GRW, used as the objects of the GeOBIA, were classified as a function of their hydrological similarities. Classification results for the period 2015–2019 showed that the GRW is composed of two main types of subwatersheds distributed along a latitudinal gradient, which indicates broad-scale differences in hydrological regimes and water balances across the GRW. Six classifications were computed for the period 1985–2014 to investigate past changes in hydrological regime. The time series of seven classifications showed a homogenization of subwatershed types associated with increases in vegetation productivity and in water contents in soil and vegetation, mostly concentrated in the northern half of the GRW, which were the major changes occurring in the land cover metrics of the GRW. An increase in vegetation productivity likely contributed to an augmentation in evaporation and may be a primary driver of fundamental shifts in the GRW water balance, potentially explaining a measured decline of about 1 % (∼ 0.16 km3 yr−1) in the George River’s discharge since the mid-1970s. Permafrost degradation over the study period also likely affected the hydrological regime and water balance of the GRW. However, the shifts in permafrost extent and active layer thickness remain difficult to detect using remote-sensing-based approaches, particularly in areas of discontinuous and sporadic permafrost.
{"title":"Pairing remote sensing and clustering in landscape hydrology for large-scale change identification: an application to the subarctic watershed of the George River (Nunavik, Canada)","authors":"Eliot Sicaud, David H. Fortier, J. Dedieu, J. Franssen","doi":"10.5194/hess-28-65-2024","DOIUrl":"https://doi.org/10.5194/hess-28-65-2024","url":null,"abstract":"Abstract. For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Landscape hydrology provides useful approaches for the indirect assessment of the hydrological characteristics of watersheds through analysis of landscape properties. In this study, we used unsupervised geographic object-based image analysis (GeOBIA) paired with the fuzzy c-means (FCM) clustering algorithm to produce seven high-resolution territorial classifications of key remotely sensed hydro-geomorphic metrics for the 1985–2019 time period, each with a frequency of 5 years. Our study site is the George River watershed (GRW), a 42 000 km2 watershed located in Nunavik, northern Quebec (Canada). The subwatersheds within the GRW, used as the objects of the GeOBIA, were classified as a function of their hydrological similarities. Classification results for the period 2015–2019 showed that the GRW is composed of two main types of subwatersheds distributed along a latitudinal gradient, which indicates broad-scale differences in hydrological regimes and water balances across the GRW. Six classifications were computed for the period 1985–2014 to investigate past changes in hydrological regime. The time series of seven classifications showed a homogenization of subwatershed types associated with increases in vegetation productivity and in water contents in soil and vegetation, mostly concentrated in the northern half of the GRW, which were the major changes occurring in the land cover metrics of the GRW. An increase in vegetation productivity likely contributed to an augmentation in evaporation and may be a primary driver of fundamental shifts in the GRW water balance, potentially explaining a measured decline of about 1 % (∼ 0.16 km3 yr−1) in the George River’s discharge since the mid-1970s. Permafrost degradation over the study period also likely affected the hydrological regime and water balance of the GRW. However, the shifts in permafrost extent and active layer thickness remain difficult to detect using remote-sensing-based approaches, particularly in areas of discontinuous and sporadic permafrost.\u0000","PeriodicalId":507846,"journal":{"name":"Hydrology and Earth System Sciences","volume":"63 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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