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Global groundwater modeling: Proof-of-concept of 3D variably saturated flow simulation at kilometer resolution 全球地下水模型:千米分辨率的三维变饱和流模拟的概念验证
IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-12-18 DOI: 10.1016/j.hydroa.2025.100213
Stefan Kollet , Alexandre Belleflamme , Laura Condon , Muhammad Fahad , Klaus Goergen , Reed Maxwell , Bibi Naz
The proof-of-concept study presents, for the first time, the results of integrated numerical hydrological simulations at kilometer resolution on a global scale. Using available datasets and applying significant simplifications to the real terrestrial system, the model was informed with hydrofacies, soil texture and topographic slopes, and effective recharge at the upper boundary. A steady-state spin-up was performed, resulting in a 3D pressure head distribution of the water continuum from 60 m deep variably saturated groundwater to surface water. Relative saturation and diagnostic water table depth were examined, resolving variability over several orders of magnitude. In our opinion, the added value of the partial differential equation (PDE) based simulations outweighs the computational resources required, which are considerable. These simulations are possible, because of the advent of massively parallel, accelerator based supercomputer architectures and performance portable scientific software. While the current simulation results may not be reliable from the perspective of stakeholders at this stage of model development, the study demonstrates the feasibility of prognostic groundwater simulation at the global scale, and will stimulate future model improvements, including the quantification of uncertainties. Simultaneously, the study opens new avenues for future research in the context of hyper-resolution global Earth system modelling.
概念验证研究首次展示了全球尺度千米分辨率的综合数值水文模拟结果。利用现有的数据集,并对实际陆地系统进行了显著的简化,该模型包含了水文相、土壤质地、地形坡度以及上边界的有效补给。通过稳态自旋,得到了从60m深的可变饱和地下水到地表水的连续水的三维压头分布。研究了相对饱和度和诊断地下水位深度,解决了几个数量级的变异性。在我们看来,基于偏微分方程(PDE)的模拟的附加价值超过了所需的计算资源,这是相当可观的。这些模拟是可能的,因为大规模并行,基于加速器的超级计算机架构和性能便携式科学软件的出现。虽然从利益相关者的角度来看,目前的模拟结果在模型开发的这个阶段可能并不可靠,但该研究证明了在全球尺度上预测地下水模拟的可行性,并将刺激未来模型的改进,包括不确定性的量化。同时,该研究为未来在超分辨率全球地球系统建模背景下的研究开辟了新的途径。
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引用次数: 0
Coherent changes in global high and low flows 全球高流量和低流量的一致变化
IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-12-06 DOI: 10.1016/j.hydroa.2025.100212
Qi Huang , Jan Seibert , Yongqiang Zhang
Streamflow shifts threaten water security under climate change. While gauged basins have been extensively studied, ungauged regions remain poorly characterized. Using high- and low-flow trends from 2,213 unregulated catchments, we predict four types of streamflow changes globally. From 1982 to 2018, 81.1% of studied regions exhibited coherent trends: 49.3% showed wetting (increased high and low flows), and 31.8% showed drying (decreased flows). The remaining areas experienced diverging (6.2%) or equalizing (12.6%) changes. Wetting dominated arid, snow, and polar climate regions, whereas drying prevailed in equatorial and temperate zones. Streamflow trends aligned with precipitation changes in 58.3% of areas, while 39.6% were also driven by non-precipitation factors, highlighting complex hydroclimatic interactions. Ongoing shifts demand enhanced water management and hazard adaptation.
气候变化下的水流变化威胁着水安全。虽然对测量盆地进行了广泛的研究,但未测量区域的特征仍然很差。利用2213个无管制集水区的高流量和低流量趋势,我们预测了全球四种类型的流量变化。1982 - 2018年,81.1%的研究区域表现出一致的趋势,其中49.3%表现为湿润(高流量和低流量增加),31.8%表现为干燥(流量减少)。其余地区经历了分化(6.2%)或平衡(12.6%)的变化。干旱、多雪和极地气候地区多雨,而赤道和温带地区多雨。58.3%的地区的径流趋势与降水变化一致,39.6%的地区也受非降水因素的影响,突出了复杂的水文气候相互作用。持续的转变需要加强水资源管理和适应灾害。
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引用次数: 0
Charting uncertain waters: Insights into objective function formulations under future uncertainty 绘制不确定的水域:对未来不确定性下目标函数公式的见解
IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-09-27 DOI: 10.1016/j.hydroa.2025.100209
Jiajia Huang , Wenyan Wu , Michael Leonard , Ye Wang
Optimal management of water resources is challenging due to uncertainty in future conditions. One promising approach is to directly incorporate future uncertainty into objective function formulations of optimization problems, enabling system performance evaluation across multiple potential conditions. However, this creates additional uncertainties as both the choice of objective function formulation and the plausible future conditions included in optimization are subjective. Given the inherent uncertainty in plausible future conditions, it is highly unlikely that future conditions included in optimization can cover all conditions that might occur. Therefore, identifying objective function formulations that perform well regardless of future uncertainties is crucial; however, it has not been formally explored. In this study, the performance of different objective function formulations under both expected (i.e., similar to conditions used in optimization) and unexpected (i.e., vastly different from conditions used in optimization) future conditions is investigated using a real-world case study. Results reveal that percentile and expected-value-based formulations generally perform consistently under both expected and unexpected conditions, whereas extreme-case-based formulations can lead to highly variable results depending on the actual conditions that will be realized in the future. Finally, variance-based formulations offer the greatest consistency across all conditions but may lead to compromised performance under favorable conditions.
由于未来条件的不确定性,水资源的最佳管理具有挑战性。一种有希望的方法是直接将未来的不确定性纳入优化问题的目标函数公式,从而实现跨多种潜在条件的系统性能评估。然而,这产生了额外的不确定性,因为目标函数公式的选择和优化中包含的可能的未来条件都是主观的。考虑到在可能的未来条件中固有的不确定性,优化中包含的未来条件几乎不可能涵盖所有可能发生的条件。因此,无论未来的不确定性如何,确定表现良好的目标函数公式至关重要;然而,它还没有被正式探讨。在这项研究中,不同的目标函数公式在预期(即,类似于优化中使用的条件)和意外(即,与优化中使用的条件大不相同)的未来条件下的性能通过现实世界的案例研究进行了调查。结果表明,在预期和意外条件下,百分位数和期望值的公式通常表现一致,而基于极端情况的公式可能导致高度可变的结果,这取决于未来将实现的实际条件。最后,基于方差的配方在所有条件下都提供了最大的一致性,但在有利条件下可能导致性能受损。
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引用次数: 0
Fuzzy-based input method for uncertainty quantification in a deterministic model comparison with ChatGPT for peak flow prediction 基于模糊输入的不确定性量化方法在确定性模型中与ChatGPT进行峰值流量预测的比较
IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-09-24 DOI: 10.1016/j.hydroa.2025.100208
Zhonghao Zhang, Caterina Valeo
ChatGPT, a generative AI, is applied and compared to the PCSWMM hydrological model for modelling peak flow in a small watershed in the runoff period of April to September. A new approach for fuzzy mathematical representation of rainfall and peak-flow errors was developed to lead to a fuzzy based GPT model and fuzzy based PCSWMM model. This led to fuzzy output for both models and a more appropriate application of both models given data errors and large language model structure. Training and validation were conducted with an approximately 25/75 split of the data and again using a 75/25 data split. Evaluation metrics were used to compare model performance under the different data-split scenarios. Calibrated and validated PCSWMM outperformed GPT in the 25/75 data split but ChatGPT 4o mini’s generation outperformed PCSWMM in the 75/25 split and with comparable validation metrics and an application that was less onerous than when using PCSWMM. The fuzzy-based error analysis showed that for both models, a fuzzy-based approach produced more interpretable and reasonable results than either original model. Moreover, the trade-off between coverage (uncertainty range) and precision for GPT‑4o mini model’s fuzzy output at high membership levels (∝-cut) demonstrated enhanced predictive performance under data‑scarce conditions.
ChatGPT是一种生成式人工智能,用于模拟4 - 9月径流期小流域的峰值流量,并与PCSWMM水文模型进行了比较。提出了一种降雨和峰流误差的模糊数学表示方法,得到了基于模糊的GPT模型和基于模糊的PCSWMM模型。这导致两种模型的输出都是模糊的,并且在数据错误和大型语言模型结构的情况下,两种模型的应用都更合适。训练和验证以大约25/75的数据分割进行,再次使用75/25的数据分割。评估指标用于比较不同数据分割场景下的模型性能。经过校准和验证的PCSWMM在25/75数据分割中优于GPT,但ChatGPT 40 mini一代在75/25数据分割中优于PCSWMM,并且具有可比的验证指标,并且应用程序比使用PCSWMM时更轻松。基于模糊的误差分析表明,对于两个模型,基于模糊的方法产生的结果比原始模型更具有可解释性和合理性。此外,GPT - 40迷你模型在高隶属度水平(∝-cut)下的模糊输出的覆盖范围(不确定范围)和精度之间的权衡表明,在数据稀缺条件下,预测性能得到增强。
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引用次数: 0
Reservoir storage flash droughts in India are driven by human interventions 印度的水库储水突发性干旱是由人为干预造成的
IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-08-10 DOI: 10.1016/j.hydroa.2025.100207
Rajesh Singh , Vimal Mishra
Reservoir storage flash droughts (RFDs), characterized by the rapid decline in reservoir storage, and conventional (long-term) reservoir storage droughts (RDs) impact water availability, hydropower generation, and agricultural activities. However, the mechanism and drivers of flash and conventional reservoir storage droughts in India remain unexplored. Using daily observations of reservoir storage, we identify RFDs and RDs in 81 major reservoirs in India during the 2000–2023 period. 46 out of 81 reservoirs are dominated by upstream climate as reservoir storage trends are driven by changes and variability in upstream precipitation, while the remaining 35 reservoirs are identified as human-dominating reservoirs. RFDs occur more frequently in human-dominating reservoirs than climate-dominating, especially in small reservoirs. About 70 % of RFDs in climate and human-dominating reservoirs are caused by sudden release to meet increased water demands in the downstream regions. Additionally, upstream precipitation deficit and downstream water demand control RDs, while downstream water demands can solely drive RFDs. Unlike reservoir storage trends, reservoir storage droughts are mostly linked with downstream water demands. We highlight the role of climate and human interventions in reservoir storage/droughts in India.
以库容迅速减少为特征的突发性水库干旱(rfd)和常规(长期)库容干旱(RDs)影响着水的可利用性、水力发电和农业活动。然而,印度突发性干旱和常规水库干旱的机制和驱动因素仍未得到探索。通过对水库储水量的日常观测,我们确定了2000-2023年期间印度81个主要水库的rfd和RDs。81个水库中有46个受上游气候影响,因为水库蓄水量趋势受上游降水变化和变率的驱动,其余35个水库被确定为人类主导水库。在以人为主导的水库中,特别是在小型水库中,rfd发生的频率高于以气候为主导的水库。在气候和人为主导的水库中,大约70%的rfd是由于下游地区为满足增加的用水需求而突然释放造成的。上游降水亏缺和下游需水量控制着rfd,下游需水量仅能驱动rfd。与水库蓄水趋势不同,水库蓄水干旱主要与下游用水需求有关。我们强调了气候和人为干预在印度水库蓄水/干旱中的作用。
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引用次数: 0
Adapting to future changes using smart stormwater storage systems to preserve flow regimes 使用智能雨水储存系统来适应未来的变化,以保持水流状态
IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-06-14 DOI: 10.1016/j.hydroa.2025.100206
Ruijie Liang , Mark A. Thyer , Holger R. Maier , Graeme C. Dandy , Emily Z. Berglund
Worldwide, stormwater systems are increasingly stressed due to increased rainfall and runoff caused by climate change and urbanization. Traditional static strategies for addressing these challenges, including increasing infrastructure capacity, are often inadequate as they are not suited to dealing with large uncertainties. In contrast, adaptive strategies, such as smart real-time control (RTC), are suited to dealing with such uncertainties, as they are able to respond to future changes as they occur. However, existing RTC approaches are not truly adaptive, as they require information on future rainfall. In this paper, we modify an existing RTC approach that does not require such information so that it is able to match desired outflow hydrographs in the face of changing inflow hydrographs. The utility of the proposed Target Flow Control for Hydrographs (TFC-H) approach is demonstrated by simulating its ability to achieve desired target flow hydrographs for multiple future worlds of a simplified lot-scale system, in which peak flows increase from 7 % to 95 % and storm volumes increase from 25 % to 57 %. The results show that use of the TFC-H approach effectively maintains the desired target outflow hydrograph with less than 5 % error for this wide range of “future worlds”. Importantly, unlike other RTC approaches, the TFC-H approach is able to adapt without any knowledge/predictions of future rainfall/inflow hydrographs. This clearly demonstrates the potential of the TFC-H approach to enable existing stormwater systems to adapt to future changes.
在世界范围内,由于气候变化和城市化导致的降雨和径流增加,雨水系统的压力越来越大。应对这些挑战的传统静态战略,包括增加基础设施容量,往往是不够的,因为它们不适合处理巨大的不确定性。相比之下,自适应策略,如智能实时控制(RTC),适合于处理这种不确定性,因为它们能够对未来发生的变化做出反应。然而,现有的RTC方法并不真正具有适应性,因为它们需要关于未来降雨量的信息。在本文中,我们修改了现有的不需要这些信息的RTC方法,以便它能够在面对变化的流入水线时匹配所需的流出水线。提出的目标流量控制水文(TFC-H)方法的实用性通过模拟其在简化的批量系统的多个未来世界中实现所需目标流量水文的能力来证明,其中峰值流量从7%增加到95%,风暴量从25%增加到57%。结果表明,对于这种大范围的“未来世界”,使用TFC-H方法有效地保持了期望的目标流出线,误差小于5%。重要的是,与其他RTC方法不同,TFC-H方法能够在不了解或预测未来降雨量/流入水文曲线的情况下进行调整。这清楚地显示了TFC-H方法在使现有雨水系统适应未来变化方面的潜力。
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引用次数: 0
Toward an integrated sustainability assessment of Water-Energy-Food nexus indicators 对水-能源-粮食关系指标进行综合可持续性评价
IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-06-07 DOI: 10.1016/j.hydroa.2025.100205
Adrija Roy , Hamid Moradkhani
The interdependence of crucial resources and the imperative for ensuring sustainability through integrated management approaches is underscored by the Water-Energy-Food (WEF) Nexus. The current study focuses on Alabama, Arkansas, Louisiana, Mississippi, and Tennessee in the Deep South USA to analyze the trade-offs and synergies in WEF Nexus. We propose an Integrated WEF Sustainability Index (IWSI) to provide a quantitative assessment of sustainability across these states. The IWSI is constructed by integrating standardized indicators across the water, energy, and food sectors, with weights derived from inter-sectoral economic interactions, to capture both trade-offs and synergies in a single composite score to provide an aggregated sustainability assessment. USA has an IWSI value of 1.62. Tennessee has an IWSI value of 2.34, characterized by efficient water utilization, substantial contributions from renewable sources, and robust agricultural productivity. Conversely, Louisiana and Arkansas encounter notable sustainability challenges, respectively, primarily attributable to low energy and water efficiency, reliance on fossil fuels, high emissions, and large water footprints. Arkansas demonstrates a significant water footprint in agriculture, well above the national average, highlighting its heavy reliance on irrigation. There is variation in hydropower conditions across states, with Tennessee leading in renewable energy use. The study underscores regional disparities in sustainability and emphasizes the need for tailored strategies to enhance resource efficiency and renewable energy adoption. A global assessment using datasets from the World Bank and Our World in Data highlights disparities across regions, providing insights into region-specific opportunities and challenges.
水-能源-粮食关系强调了关键资源的相互依存关系和通过综合管理办法确保可持续性的必要性。目前的研究重点是美国南部的阿拉巴马州、阿肯色州、路易斯安那州、密西西比州和田纳西州,以分析世界经济论坛Nexus的权衡和协同效应。我们提出了一个综合的世界经济论坛可持续发展指数(IWSI)来对这些州的可持续性进行定量评估。IWSI是通过整合水、能源和粮食部门的标准化指标,以及部门间经济相互作用得出的权重来构建的,以便在单一的综合得分中捕捉权衡和协同效应,从而提供综合的可持续性评估。美国的IWSI值为1.62。田纳西州的IWSI值为2.34,其特点是水资源利用效率高,可再生能源贡献大,农业生产率高。相反,路易斯安那州和阿肯色州分别面临着显著的可持续性挑战,主要原因是能源和水效率低、依赖化石燃料、高排放和大水足迹。阿肯色州在农业方面的水足迹显著,远高于全国平均水平,凸显了该州对灌溉的严重依赖。各州的水力发电条件各不相同,田纳西州在可再生能源使用方面处于领先地位。该研究强调了可持续性方面的区域差异,并强调需要制定有针对性的战略来提高资源效率和可再生能源的采用。利用世界银行和《我们的数据世界》的数据集进行的全球评估凸显了地区之间的差异,为了解地区特有的机遇和挑战提供了见解。
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引用次数: 0
Flooding from Hurricane Helene and associated impacts: A historical perspective 飓风“海伦”造成的洪水及其影响:一个历史的视角
IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-05-01 DOI: 10.1016/j.hydroa.2025.100204
Renato Amorim , Gabriele Villarini , Jeffrey Czajkowski , James Smith
During September 2024, Hurricane Helene devasted large areas of western North Carolina and eastern Tennessee, causing extensive loss of life and widespread damage due to heavy rainfall and extreme flooding. Despite the impacts of this storm, Helene’s heavy rainfall and resulting floods were not entirely unprecedented, as the region experienced several floods linked to tropical cyclones in the past, including multiple storms during the 2004 hurricane season. To make matters worse, this is an area with historically low market penetration by the National Flood Insurance Program, highlighting a strong asymmetry with respect to the coastal areas: while roughly 14% of the buildings in the eastern third of North Carolina were insured against floods, inland areas had less than a tenth of that coverage. Therefore, to improve resiliency and reduce the residual flood losses, it is critical to reconcile perceived versus actual flood risk and expand insurance coverage in hurricane-prone areas.
2024年9月,飓风“海伦”摧毁了北卡罗来纳州西部和田纳西州东部的大片地区,由于暴雨和极端洪水,造成大量人员死亡和广泛破坏。尽管这场风暴的影响很大,但海伦的强降雨和由此引发的洪水并非完全是前所未有的,因为该地区过去曾经历过几次与热带气旋有关的洪水,包括2004年飓风季节的多次风暴。更糟糕的是,这是一个历史上国家洪水保险计划的市场渗透率较低的地区,突出了沿海地区的强烈不对称:北卡罗来纳州东部三分之一的建筑物中约有14%投保了洪水保险,而内陆地区的覆盖率不到十分之一。因此,为了提高抗灾能力和减少剩余洪水损失,协调感知和实际洪水风险并扩大飓风易发地区的保险覆盖范围至关重要。
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引用次数: 0
Decadal drought prediction via spectral transformation of projected Sea Surface Temperatures 通过预测海面温度的光谱变换进行十年干旱预测
IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-03-20 DOI: 10.1016/j.hydroa.2025.100203
Ze Jiang, Ashish Sharma
Knowledge of impending drought can help significantly with water planning and management. This study introduces a novel forecasting framework for decadal drought projection which relies on climate model projections of Sea Surface Temperature Anomaly (SSTA) indices over the next decade and a spectral transformation methodology to maximise forecast skill. Decadal SSTA projections from the Decadal Climate Prediction Project (DCPP) undergo spectral transformation using Wavelet System Prediction (WASP). WASP modulates the frequency spectrum of predictor variables to better mimic the response spectrum of drought indices. The transformed SSTA indices are then used in a multiple linear regression (MLR) model to forecast drought indices across multiple time scales. This framework significantly improves drought forecasting skills, especially for lead times exceeding 24 months. While demonstrated for Australia, the MLR-WASP framework is transferable to other regions, offering a reliable tool for long-term water resource management by projecting drought risk over the coming decade. The implications of this research extend beyond hydroclimatology, impacting environmental science and engineering, sustainable planning, and adaptation efforts to climate change.

Plain language summary

Projecting drought risk over the next decade is essential for effective long-term water resources management. This study presents a new framework that reliably projects drought conditions up to 10 years ahead by optimizing decadal climate model data. It uses a spectral transformation technique to adjust predictors like Sea Surface Temperature Anomalies to better match drought patterns. These transformed predictors are then integrated into a regression model to forecast drought indices. When applied to Australia, this approach significantly outperformed existing methods, especially for 2-year forecasts. By combining advanced climate predictions with prediction-oriented data transformation, this framework enables reliable drought risk projections a decade out, offering invaluable insights for proactive planning in drought-prone regions worldwide.
了解即将到来的干旱对水资源规划和管理有很大帮助。本文介绍了一种新的年代际干旱预测框架,该框架依赖于气候模式对未来十年的海表温度异常(SSTA)指数的预测和光谱变换方法,以最大限度地提高预测技能。年代际气候预测项目(DCPP)的年代际海温预估采用小波系统预测(WASP)进行光谱变换。WASP通过调节预测变量的频谱,更好地模拟干旱指数的响应谱。然后将转换后的SSTA指数用于多元线性回归(MLR)模型中,对多个时间尺度的干旱指数进行预测。这一框架大大提高了干旱预测技能,特别是提前期超过24个月的预测技能。虽然在澳大利亚得到了示范,但MLR-WASP框架可转移到其他地区,通过预测未来十年的干旱风险,为长期水资源管理提供可靠的工具。这项研究的意义超出了水文气候学,影响了环境科学与工程、可持续规划和适应气候变化的努力。预测未来十年的干旱风险对于有效的长期水资源管理至关重要。这项研究提出了一个新的框架,通过优化年代际气候模型数据,可靠地预测未来10年的干旱条件。它使用光谱转换技术来调整海面温度异常等预测指标,以更好地匹配干旱模式。然后将这些转换后的预测因子整合到回归模型中来预测干旱指数。当应用于澳大利亚时,该方法明显优于现有方法,特别是对于2年预测。通过将先进的气候预测与以预测为导向的数据转换相结合,该框架能够实现可靠的十年后干旱风险预测,为全球干旱易发地区的主动规划提供宝贵的见解。
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引用次数: 0
Improving prediction of class-imbalanced time series through curation of training data: A case study of frozen ground prediction 通过训练数据管理改进类不平衡时间序列的预测:以冻土预测为例
IF 3.1 Q2 GEOSCIENCES, MULTIDISCIPLINARY Pub Date : 2025-03-05 DOI: 10.1016/j.hydroa.2025.100201
Mousumi Ghosh , Aatish Anshuman , Mukesh Kumar
The field of geosciences is replete with problems where the target variable to be predicted is inherently class-imbalanced, meaning the events of interest are rare and infrequent. Examples include predicting landslides, ice jam breakups, preferential flow, and frozen ground. Such imbalance poses substantial challenges for modeling approaches. Using frozen ground prediction as a case study, this research examines how the frequency of event occurrence influences its prediction performance and proposes a data curation strategy to improve predictability. To this end, a data-driven approach utilizing a Long Short-Term Memory neural network is first implemented to predict soil temperature and determine frozen periods. Application of this approach at 25 gaging sites in Michigan reveals model underperformance, particularly at sites where the frozen data fraction (FDF) or the ratio of the frozen period to the total observation period, is low. The. study further demonstrates that under-sampling of more prevalent non-frozen period in training data improves detection of frozen periods. Greater improvements are experienced at sites with lower FDFs. However, performance peaks after a threshold FDF, plateauing or declining thereafter due to increased class imbalance and reduced training data length. The presented training data curation approach can be used for predictions of other class-imbalanced time series.
地球科学领域充满了要预测的目标变量本质上是类不平衡的问题,这意味着感兴趣的事件是罕见和不频繁的。例子包括预测滑坡、冰塞破裂、优先流和冻土。这种不平衡对建模方法提出了实质性的挑战。本研究以冻土预测为例,探讨了事件发生频率对其预测性能的影响,并提出了一种提高可预测性的数据管理策略。为此,首先实现了利用长短期记忆神经网络的数据驱动方法来预测土壤温度并确定冻结期。该方法在密歇根州25个测量点的应用表明,模型表现不佳,特别是在冻结数据分数(FDF)或冻结期与总观测期之比较低的地点。的。研究进一步表明,训练数据中更普遍的非冻结期的欠采样提高了冻结期的检测。在fdf较低的地点有更大的改善。然而,性能在阈值FDF之后达到峰值,此后由于类不平衡增加和训练数据长度减少而趋于平稳或下降。本文提出的训练数据管理方法可用于其他类不平衡时间序列的预测。
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引用次数: 0
期刊
Journal of Hydrology X
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