Pub Date : 2025-01-01DOI: 10.1016/j.hydroa.2024.100198
Abhinav Gupta , Sean A. McKenna
This study examines streamflow simulations using deep learning (DL) to understand the information extraction capability of global DL models trained on multiple watersheds. The study separately examined the entire streamflow time series and recession flow predictions. It introduces a global–local (GL) modeling strategy, where the global model outputs are fed as input to a locally trained model, with the hypothesis that the local model can leverage watershed-specific information that the global model may miss. The GL models demonstrate enhanced accuracy in recession flow prediction for 20-30% of the watersheds compared to the global and local models. However, considering the entire hydrograph, the GL models often perform worse than the global model. Further, the DL models were trained on two different objective functions. The performance of the global model in a watershed depended strongly upon the objective function used. These results suggest that the performance of global models is affected by watershed uniqueness, suggesting that even a global DL model should be tailored to individual watersheds for optimal performance.
{"title":"Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions","authors":"Abhinav Gupta , Sean A. McKenna","doi":"10.1016/j.hydroa.2024.100198","DOIUrl":"10.1016/j.hydroa.2024.100198","url":null,"abstract":"<div><div>This study examines streamflow simulations using deep learning (DL) to understand the information extraction capability of global DL models trained on multiple watersheds. The study separately examined the entire streamflow time series and recession flow predictions. It introduces a global–local (GL) modeling strategy, where the global model outputs are fed as input to a locally trained model, with the hypothesis that the local model can leverage watershed-specific information that the global model may miss. The GL models demonstrate enhanced accuracy in recession flow prediction for 20-30% of the watersheds compared to the global and local models. However, considering the entire hydrograph, the GL models often perform worse than the global model. Further, the DL models were trained on two different objective functions. The performance of the global model in a watershed depended strongly upon the objective function used. These results suggest that the performance of global models is affected by watershed uniqueness, suggesting that even a global DL model should be tailored to individual watersheds for optimal performance.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"26 ","pages":"Article 100198"},"PeriodicalIF":3.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multivariate bias correction (BC) models are well-known to correct more statistical attributes in climate model simulations. However, their inherent complexity and excessive parameters can introduce higher uncertainty into future climate simulations. In contrast, univariate BC models, with fewer parameters, are limited to correcting certain attributes. An issue that has not been investigated in-depth is the impact of an increased number of variables in the multivariate BC has on the bias-corrected climate models’ stability. This study compares the performance of a multivariate BC approach, Multivariate Recursive Nested Bias Correction (MRNBC), and a univariate BC approach, Continuous Wavelet-based Bias Correction (CWBC), as the number of variables to be corrected increases, known as the “curse of dimensionality” (CoD). The analysis uses high-resolution climate model outputs for both current and future simulations of sea surface temperature and precipitation in the Niño 3.4 region. Results show both BC models effectively correct current climate biases. As the number of variables increases, CWBC remains robust and produces sensible future simulations, while MRNBC’s complexity leads to deterioration in standard deviations and spatial cross-correlation. CWBC, based on univariate correction, is relatively unaffected by the CoD.
众所周知，多变量偏差校正（BC）模型可以校正气候模型模拟中的更多统计属性。然而，其固有的复杂性和过多的参数会给未来气候模拟带来更高的不确定性。相比之下，单变量 BC 模型参数较少，仅限于修正某些属性。一个尚未深入研究的问题是，多元 BC 中变量数量的增加对偏差校正气候模式稳定性的影响。本研究比较了多变量偏差校正方法--多变量递归嵌套偏差校正（MRNBC）和单变量偏差校正方法--基于连续小波的偏差校正（CWBC）在需要校正的变量数量增加（即 "维度诅咒"（CoD））时的性能。分析使用了高分辨率气候模式输出，对 3.4 尼诺地区当前和未来的海面温度和降水量进行了模拟。结果表明，两种 BC 模式都能有效纠正当前的气候偏差。随着变量数量的增加，CWBC 仍然保持稳健，并产生了合理的未来模拟，而 MRNBC 的复杂性导致标准偏差和空间交叉相关性恶化。基于单变量校正的 CWBC 相对不受 CoD 的影响。
{"title":"Revisiting model complexity: Space-time correction of high dimensional variable sets in climate model simulations","authors":"Cilcia Kusumastuti , Rajeshwar Mehrotra , Ashish Sharma","doi":"10.1016/j.hydroa.2024.100193","DOIUrl":"10.1016/j.hydroa.2024.100193","url":null,"abstract":"<div><div>Multivariate bias correction (BC) models are well-known to correct more statistical attributes in climate model simulations. However, their inherent complexity and excessive parameters can introduce higher uncertainty into future climate simulations. In contrast, univariate BC models, with fewer parameters, are limited to correcting certain attributes. An issue that has not been investigated in-depth is the impact of<!--> <!-->an increased number of variables in the multivariate BC has on the bias-corrected climate models’ stability. This study compares the performance of a multivariate BC approach, Multivariate Recursive Nested Bias Correction (MRNBC), and a univariate BC approach, Continuous Wavelet-based Bias Correction (CWBC), as the number of variables to be corrected increases, known as the “curse of dimensionality” (CoD). The analysis uses high-resolution climate model outputs for both current and future simulations of sea surface temperature and precipitation in the Niño 3.4 region. Results show both BC models effectively correct current climate biases. As the number of variables increases, CWBC remains robust and produces sensible future simulations, while MRNBC’s complexity leads to deterioration in standard deviations and spatial cross-correlation. CWBC, based on univariate correction, is<!--> <!-->relatively unaffected by the CoD.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"25 ","pages":"Article 100193"},"PeriodicalIF":3.1,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Severe flood events are deemed more frequent in the near future with a changing climate. Headwater catchments, especially when karstified, exhibit a pronounced susceptibility to swift and substantial responses to precipitation events, leading to flooding. In this study, a karstified headwater catchment in SW Germany is investigated, focusing on gaining insights into the key processes controlling its discharge behavior. Intensive fieldwork was conducted and a variety of field data were collected and analyzed to determine the general system behavior during low flow and flood events. Field insights reveal a groundwater borne streamflow generation with a subsurface catchment largely differing from the surface catchment. Episodic and sporadic springs were identified as crucial contributors to stream flow generation.
The study was undertaken to evaluate the viability of simulating streamflow for flood warning using a lumped modeling approach at a sub-daily temporal scale, since lumped models are widely used for karst spring discharge modeling. Based on field data observations, a comparative analysis of different model structures was undertaken, aiming at assessing the required degree of model complexity for representing catchment runoff generation as well as the relevant system features and properties. In order to find an adequate model structure, a total of 21 models with varying degree of complexity were set up and run. Both, subsurface and surface catchment limits were considered. Results show that the hydrograph of the whole catchment can be represented by a rather simple lumped model in the present case under two prerequisites: (1) input needs to represent the groundwater catchment emphasizing the groundwater borne nature of flow and (2) the models need to allow for direct runoff, as the sporadic springs observed in the field contribute significant discharge to streamflow during flood events. It is revealed that it seems valid to start modeling with a relatively simple storage model as long as key processes in the catchment are represented. The general feasibility of such a simple modeling approach in this complex catchment encourages its feasibility in other headwater catchments.
{"title":"Effects of model complexity on karst catchment runoff modeling for flood warning systems","authors":"Paul Knöll , Ferry Schiperski , Antonia Roesrath , Traugott Scheytt","doi":"10.1016/j.hydroa.2024.100194","DOIUrl":"10.1016/j.hydroa.2024.100194","url":null,"abstract":"<div><div>Severe flood events are deemed more frequent in the near future with a changing climate. Headwater catchments, especially when karstified, exhibit a pronounced susceptibility to swift and substantial responses to precipitation events, leading to flooding. In this study, a karstified headwater catchment in SW Germany is investigated, focusing on gaining insights into the key processes controlling its discharge behavior. Intensive fieldwork was conducted and a variety of field data were collected and analyzed to determine the general system behavior during low flow and flood events. Field insights reveal a groundwater borne streamflow generation with a subsurface catchment largely differing from the surface catchment. Episodic and sporadic springs were identified as crucial contributors to stream flow generation.</div><div>The study was undertaken to evaluate the viability of simulating streamflow for flood warning using a lumped modeling approach at a sub-daily temporal scale, since lumped models are widely used for karst spring discharge modeling. Based on field data observations, a comparative analysis of different model structures was undertaken, aiming at assessing the required degree of model complexity for representing catchment runoff generation as well as the relevant system features and properties. In order to find an adequate model structure, a total of 21 models with varying degree of complexity were set up and run. Both, subsurface and surface catchment limits were considered. Results show that the hydrograph of the whole catchment can be represented by a rather simple lumped model in the present case under two prerequisites: (1) input needs to represent the groundwater catchment emphasizing the groundwater borne nature of flow and (2) the models need to allow for direct runoff, as the sporadic springs observed in the field contribute significant discharge to streamflow during flood events. It is revealed that it seems valid to start modeling with a relatively simple storage model as long as key processes in the catchment are represented. The general feasibility of such a simple modeling approach in this complex catchment encourages its feasibility in other headwater catchments.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"26 ","pages":"Article 100194"},"PeriodicalIF":3.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-10DOI: 10.1016/j.hydroa.2024.100192
Kush Thakar , Neil Macdonald , Karyn Morrissey
This study reports the results of a Choice Experiment to quantify households’ willingness-to-pay for river gauging programmes in Scotland. The hydrometric network is operated and maintained by the Scottish Environment Protection Agency (SEPA), Scotland’s principal environment regulator, a non-department public body of the Scottish Government. Results from mixed logit and latent class modelling show that most households (‘Hydrometric Maximisers’ − around 70 %) have significant, positive willingness-to-pay values for river gauging programmes, but a minority (‘Hydrometric Satisficers’ − around 30 %) do not view this as a major public policy priority. On average, hydrometric data collection delivers non-market benefits worth £84,625,562 to the Scottish economy, with a minimum economic Benefit-to-Cost ratio of 25:1. This is in addition to the infrastructure value and any private returns made by commercial users of the data. The findings demonstrate that traditional approaches to assessing the benefits of hydrometric networks often underestimate their value. The research also highlights the importance of public information campaigns and household engagement initiatives to increase awareness of hydro-meteorological services, and to develop the business case more fully for public investment in environmental observation networks.
{"title":"Quantifying the economic value of a national hydrometric network for households","authors":"Kush Thakar , Neil Macdonald , Karyn Morrissey","doi":"10.1016/j.hydroa.2024.100192","DOIUrl":"10.1016/j.hydroa.2024.100192","url":null,"abstract":"<div><div>This study reports the results of a Choice Experiment to quantify households’ willingness-to-pay for river gauging programmes in Scotland. The hydrometric network is operated and maintained by the Scottish Environment Protection Agency (SEPA), Scotland’s principal environment regulator, a non-department public body of the Scottish Government. Results from mixed logit and latent class modelling show that most households (‘Hydrometric Maximisers’ − around 70 %) have significant, positive willingness-to-pay values for river gauging programmes, but a minority (‘Hydrometric Satisficers’ − around 30 %) do not view this as a major public policy priority. On average, hydrometric data collection delivers non-market benefits worth £84,625,562 to the Scottish economy, with a minimum economic Benefit-to-Cost ratio of 25:1. This is in addition to the infrastructure value and any private returns made by commercial users of the data. The findings demonstrate that traditional approaches to assessing the benefits of hydrometric networks often underestimate their value. The research also highlights the importance of public information campaigns and household engagement initiatives to increase awareness of hydro-meteorological services, and to develop the business case more fully for public investment in environmental observation networks.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"25 ","pages":"Article 100192"},"PeriodicalIF":3.1,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1016/j.hydroa.2024.100191
Kay Khaing Kyaw , Emma Baietti , Cristian Lussana , Valerio Luzzi , Paolo Mazzoli , Stefano Bagli , Attilio Castellarin
Cloudbursts and extreme rainstorms pose a growing threat to urban areas. Accurate rainfall data is essential for predicting inundations and urban flooding. Private weather stations are becoming increasingly common, and their spatial distribution roughly follows population density. This makes them a valuable source of crowdsourced data for high-resolution rainfall fields in urban areas. We evaluated the performance of private rain gauges in two recent pluvial flood events in Oslo. We also explored the potential use of private rain gauge data in inundation models. Our results indicate that private sensors have excellent rain detection capabilities, but they tend to underestimate the reference value on average by approximately 25%. However, bias-corrected crowdsourced rainfall data can produce significantly more accurate inundation maps than those generated from official rain gauges, if compared with maps resulting from bias-corrected weather radar. Overall, our study highlights the potential of utilizing crowdsourced rainfall data from private sensors for accurately representing pluvial flooding in urban areas. These findings have significant implications for improving flood prediction and mitigation strategies in vulnerable urban settings.
{"title":"Private sensors and crowdsourced rainfall data: Accuracy and potential for modelling pluvial flooding in urban areas of Oslo, Norway","authors":"Kay Khaing Kyaw , Emma Baietti , Cristian Lussana , Valerio Luzzi , Paolo Mazzoli , Stefano Bagli , Attilio Castellarin","doi":"10.1016/j.hydroa.2024.100191","DOIUrl":"10.1016/j.hydroa.2024.100191","url":null,"abstract":"<div><div>Cloudbursts and extreme rainstorms pose a growing threat to urban areas. Accurate rainfall data is essential for predicting inundations and urban flooding. Private weather stations are becoming increasingly common, and their spatial distribution roughly follows population density. This makes them a valuable source of crowdsourced data for high-resolution rainfall fields in urban areas. We evaluated the performance of private rain gauges in two recent pluvial flood events in Oslo. We also explored the potential use of private rain gauge data in inundation models. Our results indicate that private sensors have excellent rain detection capabilities, but they tend to underestimate the reference value on average by approximately 25%. However, bias-corrected crowdsourced rainfall data can produce significantly more accurate inundation maps than those generated from official rain gauges, if compared with maps resulting from bias-corrected weather radar. Overall, our study highlights the potential of utilizing crowdsourced rainfall data from private sensors for accurately representing pluvial flooding in urban areas. These findings have significant implications for improving flood prediction and mitigation strategies in vulnerable urban settings.</div></div>","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"25 ","pages":"Article 100191"},"PeriodicalIF":3.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<div><div>Our understanding of the impact of climate change on water availability and natural hazards in high-mountain regions is limited due to the spatial and temporal scarcity of ground observations of precipitation and snow. Freely available, satellite-based information about the snowpack is currently mainly limited to indirect measurements of snow-covered area or very coarse-scale snow water equivalent (SWE), but only for flat areas in lowlands without vegetation cover. Novel space-based laser altimeters, such as ICESat-2, have the potential to provide high-resolution snow depth data in worldwide mountain regions where no ground observations exist. However, these space-based laser altimeters come with spatial gaps between ground tracks, obtained without repetition at a give location. To overcome these drawbacks, here, we present a combined probabilistic data assimilation and deep learning approach to reconstruct spatio-temporal SWE from observations of snow depth along ground tracks, imitating ICESat-2 tracks in view of a potential future global application.</div><div>Our approach is based on assimilating SWE and snow cover information in a degree-day model with an iterative ensemble smoother (IES) which allows temporally reconstructing SWE along hypothetical ground tracks separated by 3 km. As input, the degree-day model uses daily precipitation and downscaled air temperature from the ERA5 reanalysis. A feedforward neural network (FNN) is then used for spatial propagation of the daily mean and standard deviation of the updated SWE ensemble members obtained from the IES. The combined IES-FNN approach provides uncertainty-aware spatio-temporally continuous estimates of SWE.</div><div>We tested our approach in the alpine Dischma valley (Switzerland) using high-resolution snow depth maps obtained from photogrammetric techniques mounted on airplanes and unmanned aerial system observations. Our results show that the IES-FNN model provides reliable estimates at a resolution of approximately 100 m. Even assimilating only one SWE observation during the year (combined with satellite-based melt-out date estimates) produces satisfying results when evaluating the IES-FNN SWE reconstructions on independent dates and smaller (<span><math><mrow><mo><</mo></mrow></math></span>4 km<sup>2</sup>) areas: mean absolute error of 86 mm (78 mm) at Schürlialp (Latschüelfurgga) for average SWE of 180 mm (254 mm), and average spatial linear correlation with the reference SWE of 0.51 (0.48). However, the assimilated SWE observation must not be too early in the accumulation season or too late in the melt season when the snowpack is starting or ending to accumulate or melt, respectively. Smaller distances between ground tracks (1500 m and 500 m) show improved performance of the IES-FNN approach in space, with no significant improvement in terms of temporal reconstruction.</div><div>Applying the IES-FNN approach to e.g., real ICESat-2 data, remains challenging due to t
由于缺乏对降水和积雪的时空地面观测，我们对气候变化对高山地区水资源供应和自然灾害的影响的了解十分有限。目前，基于卫星的免费积雪信息主要限于间接测量积雪覆盖面积或非常粗略的雪水当量（SWE），但仅限于低地无植被覆盖的平坦区域。新型天基激光测高仪（如 ICESat-2）有可能在没有地面观测数据的全球山区提供高分辨率雪深数据。然而，这些天基激光测高仪的地面轨迹之间存在空间差距，在特定地点获得的数据不重复。为了克服这些缺点，我们在此提出了一种概率数据同化和深度学习相结合的方法，以模仿 ICESat-2 的轨迹，根据沿地面轨迹的雪深观测数据重建时空 SWE，从而在未来实现潜在的全球应用。我们的方法基于将 SWE 和雪盖信息同化到一个度日模型中，并使用迭代集合平滑器（IES），从而可以沿相距 3 公里的假定地面轨迹重建 SWE。度日模型使用ERA5再分析的日降水量和降尺度气温作为输入。然后使用前馈神经网络（FNN）对从 IES 中获得的 SWE 更新集合成员的日平均值和标准偏差进行空间传播。我们使用安装在飞机上的摄影测量技术和无人机系统观测所获得的高分辨率雪深图，在瑞士高山迪施玛山谷测试了我们的方法。结果表明，IES-FNN 模型可在约 100 米的分辨率范围内提供可靠的估计值。在评估独立日期和较小（4 平方公里）区域的 IES-FNN SWE 重建时，即使只同化一年中的一次 SWE 观测（结合基于卫星的融化日期估计），也能得出令人满意的结果：平均 SWE 为 180 毫米（254 毫米）时，Schürlialp（Latschüelfurgga）的平均绝对误差为 86 毫米（78 毫米），与参考 SWE 的平均空间线性相关为 0.51（0.48）。不过，同化的 SWE 观测值不能在积雪开始或结束积雪或融化的季节过早或过晚进行。地面轨迹之间的距离越小（1500 米和 500 米），IES-FNN 方法的空间性能就越好，但在时间重建方面没有明显改善。将 IES-FNN 方法应用于 ICESat-2 等真实数据仍具有挑战性，因为这些数据的不确定性更高。不过，我们提出的方法仍有可能非常有助于解决高山地区降水和降雪地面观测资料匮乏的问题。
{"title":"A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks","authors":"Matteo Guidicelli , Kristoffer Aalstad , Désirée Treichler , Nadine Salzmann","doi":"10.1016/j.hydroa.2024.100190","DOIUrl":"10.1016/j.hydroa.2024.100190","url":null,"abstract":"<div><div>Our understanding of the impact of climate change on water availability and natural hazards in high-mountain regions is limited due to the spatial and temporal scarcity of ground observations of precipitation and snow. Freely available, satellite-based information about the snowpack is currently mainly limited to indirect measurements of snow-covered area or very coarse-scale snow water equivalent (SWE), but only for flat areas in lowlands without vegetation cover. Novel space-based laser altimeters, such as ICESat-2, have the potential to provide high-resolution snow depth data in worldwide mountain regions where no ground observations exist. However, these space-based laser altimeters come with spatial gaps between ground tracks, obtained without repetition at a give location. To overcome these drawbacks, here, we present a combined probabilistic data assimilation and deep learning approach to reconstruct spatio-temporal SWE from observations of snow depth along ground tracks, imitating ICESat-2 tracks in view of a potential future global application.</div><div>Our approach is based on assimilating SWE and snow cover information in a degree-day model with an iterative ensemble smoother (IES) which allows temporally reconstructing SWE along hypothetical ground tracks separated by 3 km. As input, the degree-day model uses daily precipitation and downscaled air temperature from the ERA5 reanalysis. A feedforward neural network (FNN) is then used for spatial propagation of the daily mean and standard deviation of the updated SWE ensemble members obtained from the IES. The combined IES-FNN approach provides uncertainty-aware spatio-temporally continuous estimates of SWE.</div><div>We tested our approach in the alpine Dischma valley (Switzerland) using high-resolution snow depth maps obtained from photogrammetric techniques mounted on airplanes and unmanned aerial system observations. Our results show that the IES-FNN model provides reliable estimates at a resolution of approximately 100 m. Even assimilating only one SWE observation during the year (combined with satellite-based melt-out date estimates) produces satisfying results when evaluating the IES-FNN SWE reconstructions on independent dates and smaller (<span><math><mrow><mo><</mo></mrow></math></span>4 km<sup>2</sup>) areas: mean absolute error of 86 mm (78 mm) at Schürlialp (Latschüelfurgga) for average SWE of 180 mm (254 mm), and average spatial linear correlation with the reference SWE of 0.51 (0.48). However, the assimilated SWE observation must not be too early in the accumulation season or too late in the melt season when the snowpack is starting or ending to accumulate or melt, respectively. Smaller distances between ground tracks (1500 m and 500 m) show improved performance of the IES-FNN approach in space, with no significant improvement in terms of temporal reconstruction.</div><div>Applying the IES-FNN approach to e.g., real ICESat-2 data, remains challenging due to t","PeriodicalId":36948,"journal":{"name":"Journal of Hydrology X","volume":"25 ","pages":"Article 100190"},"PeriodicalIF":3.1,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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