Chapter 5 论文写作
5.1 IMRaD
IMRaD是学术论文和学术讨论的基本框架。无论是写学术论文、毕业论文、毕业答辩、组会报告、给同行讲解，都请不断练习这种表达方式。
IMRaD即Introduction, Method Results and Discussion。中文为：引言、方法、结果、讨论。 尽管，论文写作中，通常在结尾加入Conclusion一节，但Conclusion是对前面所述内容的总结，而IMRaD则是相互不重复的内容。
IMRaD的基本结构如下：
- 引言（Introduction）
- 研究的背景：具体研究问题之后的大背景。
- 研究的意义：该研究对于人类认识、生产生活、其他研究的价值。
- 前人研究：重点关注5个要点：
- 前人的研究对象；
- 前人的研究方法；
- 前人研究出来的结论；
- 这些研究对于本次研究的借鉴价值或提供的思路/数据/背景；
- 前人研究的局限性/不足，或尚未回答的问题。从此引出本次研究的科学问题————即，在前人研究基础之上，我在本次研究哦中打算回答的问题，或称为零假设（Null Hypothesis）。
- 简要地阐明本论文的组织结构。
- 方法（Method）
- 研究对象。我们的研究对象通常为研究区。这里需要对研究区的基本地理背景做简要说明。包括研究区的面积、所属行政区、海拔、气候、人类活动情况等等。
- 方法：模型介绍、计算公式、统计方法、分析工具等等。
- 结果（Results）
- 方法验证： 通常为模型的校准、方法的初步结果，论证方法的有效性。
- 直接结果：方法结算出来的结果
- 间接结果：基于数据间的关系和逻辑
- 新发现/新结论：对以上结果的分析，此部分内容应该分散在个结果分析之中。
- 讨论（Discussion）
- 不确定性讨论：对模型、数据、参数、方法等可能得不确定性讨论和分析。
- 当前研究可能存在的不足：大方承认研究的尚未全面考虑的方面。这部分往往是受制于客观条件。
- 后人或者今后研究可能改进和进一步论证的方向和可能性。
- 研究的局限性：对研究结果的局限性进行讨论。包括空间、时间、方法、数据等方面的局限性。
- 结论（Conclusion）
将以上IMRaD的内容总结成三段话：
- 背景和意义，一两句话。
- 方法，一句话。
- 结果，分条目列举文章的新发现。
- 讨论，总结一句最重要的不足和局限性。
- 展望和价值，本研究的发现对于科研或生产生活有何指导意义。
5.2 题目（Title）
题目是影响读者第一印象的重要因素。作为地理学家和水文学家，在选择题目时需要注意以下几点：
- 精准定位核心贡献
- 包含：研究区域（如流域/地貌单元）+ 核心方法（如SWAT模型/遥感反演）+ 关键结论（如”径流响应机制”）
- 例：青藏高原冰川融水对三江源区径流变化的贡献度研究（对比：“水循环研究”）
- 关键词前置原则
- 将学科关键词置于标题前部，提高检索可见度
- 推荐结构：【研究方法】+【研究对象】+【研究区域】
- 例：基于InSAR技术的黄河三角洲地面沉降监测（对比：地面沉降监测新方法）
- 长度控制与信息密度
- 严格控制在12-20个汉字/英文8-15个实词
- 中文标题建议结构：主标题（核心结论）+ 副标题（方法补充）
- 例：城市化进程中的地表径流响应机制——以深圳河流域多尺度水文模拟为例
- 学科术语规范
- 使用本领域标准术语（如DEM、NDVI、SWMM等通用缩写）
- 避免自创缩写（如将”分布式水文模型”简写为DFHM）
- 问题导向吸引力
- 在严谨性基础上突出科学问题
- 例：气候变化如何影响干旱区内陆河流域的水-生态-经济耦合系统？（对比：某流域水系统研究）
要简明扼要地写一个好题目，首先要明确研究的核心重点。确保题目能够传达以下三个关键元素中的至少一个：研究主题、研究方法、或者研究结果。可以使用副标题进一步细化。
应该避免的错误
- 冗长和模糊：太长的题目让人难以捉摸关键点，如“关于某些方面的初步研究”这样模糊不清的表达应当尽量避免。
- 专业术语过多：虽然使用行业关键词很重要，但过多复杂术语会吓跑非专业读者。
- 吸引注意但内容无关：题目应与研究内容高度相关，避免吸引眼球的浮夸表达却与实际内容不符。
举例
- 主题：城市化对水资源管理的影响
- 好题目：“城市化影响下的区域水资源管理：XX城市的实证研究” 优点：简洁明了，且指出了研究地点和方法，提供了明确的研究背景。
- 坏题目：“我们如何应对城市化导致的水资源问题” 缺点：使用问句，范围太宽泛而不具体，没有明确表示研究范围或对象。
5.3 摘要（Abstract）
摘要是论文的缩影，通常是读者首先接触到的部分，
摘要的作用： - 吸引读者：好的摘要能够吸引读者深入阅读整篇论文。 - 提供背景信息：帮助读者快速了解研究的背景、方法、结果和结论。 - 提高可见度：在数据库中，摘要是检索的主要依据之一，好的摘要能够提高论文的可见度。
- 研究背景与目的（1-2句）
- 阐明研究的科学问题及其重要性 例：“青藏高原冰川消融对亚洲水塔的影响是当前全球变化研究的核心问题”
- 研究方法（2-3句）
- 明确技术路线：实验设计/数值模拟/野外观测手段/模拟方法 注明关键参数：如”利用Landsat 8 OLI数据（30m分辨率）和MODIS积雪产品（500m）” 说明时间空间尺度：“基于2010-2020年青藏高原78个气象站观测数据”
- 核心结果（3-4句）
- 定量化呈现主要发现 例：“观测到冰川面积年均退缩率1.2±0.3%，显著高于前十年（0.7±0.2%）” 突出创新性发现：“首次发现印度季风与西风环流耦合对高原降水相变的调控作用”
- 科学意义（1-2句）
- 阐明理论贡献或应用价值 例：“本研究为评估第三极地区水循环演变提供了新的观测约束”
5.3.1 技术规范
- 篇幅控制
- 传统期刊：150-400词（如Nature子刊严格限制150词）
- 结构式摘要：300词左右（背景、方法、结果、结论分段）
- 语体特征
- 使用现在时态陈述结论，过去时态描述方法
- 专业术语首次出现需全称（如ENSO: El Niño-Southern Oscillation）
- 信息密度控制
- 关键数据保留2-3个核心数值
- 方法学仅保留最具特征性的技术（如”通过LA-ICP-MS锆石U-Pb定年”）
- 空间信息标注
- 研究区域应给出地理范围：“羌塘盆地”
- 涉及模式模拟需说明分辨率：“WRF模式3km嵌套网格”
- 时间尺度表征
- 地质年代使用国际地层年代表（如晚更新世：126-11.7 ka BP）
- 现代过程研究需注明观测时段：“2016-2020年季风期（6-9月）”
5.3.2 自检清单
- □ 是否包含研究的关键四要素：背景，方法/内容，结果，结论？
- □ 地理范围/时间尺度是否明确？
- □ 摘要中无需参考文献
- □ 术语规范，如避免混淆地质年代单位（如误用”纪/世”）
- □ 缩略规范，避免非标准缩写（如自创”TPM”代指青藏高原）
- □ 避免使用缩写和符号，除非是常用的缩写和符号。
- □ 内容应当与正文一致，避免使用与正文不一致的术语和概念。
- □ 关键词是否与主题高度匹配？
- □ 长度是否控制在期刊要求±10%？
5.3.3 摘要案例
以下是一些不同领域的研究摘要案例，供参考。
Kratzert, F., Gauch, M., Klotz, D., and Nearing, G.: HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin, Hydrol. Earth Syst. Sci., 28, 4187–4201, https://doi.org/10.5194/hess-28-4187-2024, 2024.
- Machine learning (ML) has played an increasing role in the hydrological sciences. In particular, Long Short-Term Memory (LSTM) networks are popular for rainfall–runoff modeling（研究背景）. A large majority of studies that use this type of model do not follow best practices, and there is one mistake in particular that is common: training deep learning models on small, homogeneous data sets, typically data from only a single hydrological basin（研究结论）. In this position paper, we show that LSTM rainfall–runoff models are best when trained with data from a large number of basins（研究方法, 结果）.
Sun, H., Yao, T., Su, F., Yang, W., and Chen, D.: Spatiotemporal responses of runoff to climate change in the southern Tibetan Plateau, Hydrol. Earth Syst. Sci., 28, 4361–4381, https://doi.org/10.5194/hess-28-4361-2024, 2024. A comprehensive understanding of spatiotemporal runoff changes in the Yarlung Zangbo (YZ) basin in the southern Tibetan Plateau (TP) at a sub-basin scale（研究区）, amidst varying climatic and cryospheric conditions, is imperative for effective water resources management（研究背景）. However, spatiotemporal differences of runoff composition and change and their attribution within the YZ basin have not been extensively explored, primarily due to the lack of hydrometeorological observations, especially in the downstream region（研究问题和局限）. In this study, we investigated historical and future evolution of annual and seasonal total water availability, as well as glacier runoff and snowmelt contributions across six sub-basins of the YZ, with a particular focus on the comparison between the upstream Nuxia (NX) basin and the downstream Nuxia–Pasighat (NX-BXK) basin, based on a newly generated precipitation dataset and a well-validated model with streamflow, glacier mass, and snow cover observations（研究内容和方法）.Our findings revealed that large spatiotemporal differences in changes exist within the YZ basin for 1971–2020. Firstly, runoff generation was dominated by rainfall runoff throughout the YZ basin, with glacier runoff playing a more important role in the annual total runoff (19 %) in the NX-BXK sub-basin compared to other sub-basins. Notably, glacier runoff contributed 52 % of the total runoff at the Pasighat outlet of the YZ basin. Secondly, annual runoff exhibited an increasing trend in the NX basin but a decreasing trend in the NX-BXK, primarily attributed to rainfall runoff changes influenced by atmospheric moisture. Glacier runoff enhanced water supply by offsetting the decreasing contribution from rainfall. Total runoff will consistently increase (27–100 mm (10 yr)−1) across the sub-basins through the 21st century, resulting from increased rainfall runoff and a minor effect of increased snowmelt and glacier runoff（研究结果与结论）.
Farmani, M. A., Behrangi, A., Gupta, A., Tavakoly, A., Geheran, M., and Niu, G.-Y.: Do land models miss key soil hydrological processes controlling soil moisture memory?, Hydrol. Earth Syst. Sci., 29, 547–566, https://doi.org/10.5194/hess-29-547-2025, 2025. Soil moisture memory (SMM), which refers to how long a perturbation in soil moisture (SM) can last, is critical for understanding climatic, hydrological, and ecosystem interactions. Most land surface models (LSMs) tend to overestimate surface soil moisture and its persistency (or SMM), sustaining spuriously large soil surface evaporation during dry-down periods（研究背景）. We attempt to answer a question: do LSMs miss or misrepresent key hydrological processes controlling SMM?（科学问题） We use a version of Noah-MP with advanced hydrology that explicitly represents preferential flow and surface ponding and provides optional schemes of soil hydraulics. We test the effects of these processes, which are generally missed by most LSMs in SMM. We compare SMMs computed from various Noah-MP configurations against that derived from the Soil Moisture Active Passive (SMAP) L3 soil moisture and in situ measurements from the International Soil Moisture Network (ISMN) from the years 2015 to 2019 over the contiguous United States (CONUS)（研究方法）. The results suggest that (1) soil hydraulics plays a dominant role and the Van Genuchten hydraulic scheme reduces the overestimation of the long-term surface SMM produced by the Brooks–Corey scheme, which is commonly used in LSMs; (2) explicitly representing surface ponding enhances SMM for both the surface layer and the root zone; and (3) representing preferential flow improves the overall representation of soil moisture dynamics. The combination of these missing schemes can significantly improve the long-term memory overestimation and short-term memory underestimation issues in LSMs（研究结果及发现）. We suggest that LSMs for use in seasonal-to-subseasonal climate prediction should, at least, adopt the Van Genuchten hydraulic scheme（研究结论）.
Sadri, S., Famiglietti, J. S., Pan, M., Beck, H. E., Berg, A., and Wood, E. F.: FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms, Hydrol. Earth Syst. Sci., 26, 5373–5390, https://doi.org/10.5194/hess-26-5373-2022, 2022.
In the coming decades, a changing climate, the loss of high-quality land, the slowing in the annual yield of cereals, and increasing fertilizer use indicate that better agricultural water management strategies are needed（研究背景）. In this study, we designed FarmCan, a novel, robust remote sensing and machine learning (ML) framework to forecast farms’ needed daily crop water quantity or needed irrigation (NI)（研究内容）. We used a diverse set of simulated and observed near-real-time (NRT) remote sensing data coupled with a random forest (RF) algorithm and inputs about farm-specific situations to predict the amount and timing of evapotranspiration (ET), potential ET (PET), soil moisture (SM), and root zone soil moisture (RZSM)（研究方法）. Our case study of four farms in the Canadian Prairies Ecozone (CPE) shows that 8 d composite precipitation (P) has the highest correlation with changes (Δ) of RZSM and SM. In contrast, 8 d PET and 8 d ET do not offer a strong correlation with 8 d P. Using R2, root mean square error (RMSE), and Kling–Gupta efficiency (KGE) indicators, our algorithm could reasonably calculate daily NI up to 14 d in advance. From 2015 to 2020, the R2 values between predicted and observed 8 d ET and 8 d PET were the highest (80 % and 54 %, respectively). The 8 d NI also had an average R2 of 68%. The KGE of the 8 d ET and 8 d PET in four study farms showed an average of 0.71 and 0.50, respectively, with an average KGE of 0.62（研究结果）. FarmCan can be used in any region of the world to help stakeholders make decisions during prolonged periods of drought or waterlogged conditions, schedule cropping and fertilization, and address local government policy concerns（研究结论）.
Shen, H., Tolson, B. A., & Mai, J. (2022). Time to update the split-sample approach in hydrological model calibration. Water Resources Research, 58, e2021WR031523. https://doi.org/10.1029/2021WR031523 Model calibration and validation are critical in hydrological model robustness assessment. Unfortunately, the commonly used split-sample test (SST) framework for data splitting requires modelers to make subjective decisions without clear guidelines（研究背景及问题）. This large-sample SST assessment study empirically assesses how different data splitting methods influence post-validation model testing period performance, thereby identifying optimal data splitting methods under different conditions（研究内容）. This study investigates the performance of two lumped conceptual hydrological models calibrated and tested in 463 catchments across the United States using 50 different data splitting schemes. These schemes are established regarding the data availability, length and data recentness of continuous calibration sub-periods (CSPs). A full-period CSP is also included in the experiment, which skips model validation. The assessment approach is novel in multiple ways including how model building decisions are framed as a decision tree problem and viewing the model building process as a formal testing period classification problem, aiming to accurately predict model success/failure in the testing period（研究方法）. Results span different climate and catchment conditions across a 35-year period with available data, making conclusions quite generalizable. Calibrating to older data and then validating models on newer data produces inferior model testing period performance in every single analysis conducted and should be avoided. Calibrating to the full available data and skipping model validation entirely is the most robust split-sample decision. Experimental findings remain consistent no matter how model building factors (i.e., catchments, model types, data availability, and testing periods) are varied（研究结果）. Results strongly support revising the traditional split-sample approach in hydrological modeling（研究结论）.
Kunadi, A. S., Lardner, T., Silberstein, R. P., Leopold, M., Callow, N., Veneklaas, E., et al. (2024). Introducing pour points: Characteristics and hydrological significance of a rainfall-concentrating mechanism in a water-limited woodland ecosystem. Water Resources Research, 60, e2023WR035458. https://doi.org/10.1029/2023WR035458 The interception of rainfall by plant canopies alters the depth and spatial distribution of water arriving at the soil surface, and thus the location, volume, and depth of infiltration（研究背景）. Mechanisms like stemflow are known to concentrate rainfall and route it deep into the soil, yet other mechanisms of flow concentration are poorly understood（科学问题）. This study characterizes pour points, formed by the detachment of water flowing under a branch, using a combination of field observations in Western Australian banksia woodlands and rainfall simulation experiments on Banksia menziesii branches. We aim to establish the hydrological significance of pour points in a water-limited woodland ecosystem, along with the features of the canopy structure and rainfall that influence pour point formation and fluxes. Pour points were common in the woodland and could be identified by visually inspecting trees. Throughfall depths at pour points were up to 15 times greater than rainfall and generally comparable to or greater than stemflow. Soil water content beneath pour points was greater than in adjacent controls, with 20%–30% of the seasonal rainfall volume infiltrated into the top 1 m of soil beneath pour points, compared to 5% in controls（研究方法）. Rainfall simulations showed that pour points amplified the spatial heterogeneity of throughfall, violating assumptions used to close the water balance（研究结果）. The simulation experiments demonstrated that pour point fluxes depend on the interaction of branch angle and foliation for a given branch architecture. Pour points can play a significant part in the water balance, depending on their density and rainfall concentration ability（研究结论）.
Wi, S., & Steinschneider, S. (2022). Assessing the physical realism of deep learning hydrologic model projections under climate change. Water Resources Research, 58, e2022WR032123. https://doi.org/10.1029/2022WR032123 This study examines whether deep learning models can produce reliable future projections of streamflow under warming（科学问题）. We train a regional long short-term memory network (LSTM) to daily streamflow in 15 watersheds in California and develop three process models (HYMOD, SAC-SMA, and VIC) as benchmarks. We force all models with scenarios of warming and assess their hydrologic response, including shifts in the hydrograph and total runoff ratio（研究方法）. All process models show a shift to more winter runoff, reduced summer runoff, and a decline in the runoff ratio due to increased evapotranspiration. The LSTM predicts similar hydrograph shifts but in some watersheds predicts an unrealistic increase in the runoff ratio（研究结果）. We then test two alternative versions of the LSTM in which process model outputs are used as either additional training targets (i.e., multi-output LSTM) or input features（研究方法）. Results indicate that the multi-output LSTM does not correct the unrealistic streamflow projections under warming. The hybrid LSTM using estimates of evapotranspiration from SAC-SMA as an additional input feature produces more realistic streamflow projections, but this does not hold for VIC or HYMOD（研究结果）. This suggests that the hybrid method depends on the fidelity of the process model（研究结论）. Finally, we test climate change responses under an LSTM trained to over 500 watersheds across the United States and find more realistic streamflow projections under warming（研究方法）. Ultimately, this work suggests that hybrid modeling may support the use of LSTMs for hydrologic projections under climate change, but so may training LSTMs to a large, diverse set of watersheds（研究结论）.
Lee, R. M., McGuire, K. J., Strahm, B. D., Knoepp, J. D., Jackson, C. R., & Stewart, R. D. [2020]. Revisiting the Hewlett and Hibbert (1963) Hillslope Drainage Experiment and Modeling Effects of Decadal Pedogenic Processes and Leaky Soil Boundary Conditions. Water Resources Research, 56, e2019WR025090. https://doi.org/10.1029/2019WR025090 Subsurface flow dominates water movement from hillslopes to streams in most forested headwater catchments. Hewlett and Hibbert (1963, https://doi.org/10.1029/JZ068i004p01081) constructed an idealized hillslope model (0.91 × 0.91 × 15.0 m; 21.8°) using reconstituted C horizon soil to investigate importance of interflow, a type of subsurface flow. They saturated the model, covered it to prevent evaporation, and allowed free drainage for 145 days. The resulting recession drainage curve suggested two phases: fast drainage of saturated soil in the first 1.5 days and then slow drainage of unsaturated soil. Hydrologists interpreted the latter as evidence interflow could sustain baseflow, even during extended drought. Since that experiment, typical forest vegetation grew in the model, providing root and litter inputs for 55 years（研究背景）. We removed all aboveground live biomass and repeated the experiment physically and numerically (HYDRUS-2D)（研究方法）, hypothesizing that pedogenesis would change the drainage curve and further elucidate the role of unsaturated flow from hillslopes（科学零假设）. Contrary to this hypothesis, drainage curves in our twice-repeated physical experiments and numerical simulation were unchanged for the first ~10 days（结果）, indicating pedogenesis and biological processes had not largely altered bulk hydraulic conductivities or soil moisture release characteristics（研究结论）. However, drainage unexpectedly ceased after about 2 weeks (14.3 ± 2.5 days), an order of magnitude sooner than in the original experiment, due to an apparent leak in the hillslope analogous to commonly observed bedrock fractures in natural systems（结果）. Thus, our results are a more natural recession behavior that highlight how incorporation of alternative hydrologic outputs can reduce drainage duration and volume from soils to baseflow（研究结论）.
Illuminating water cycle modifications and Earth system resilience in the Anthropocene Fresh water—the bloodstream of the biosphere—is at the center of the planetary drama of the Anthropocene. Water fluxes and stores regulate the Earth’s climate and are essential for thriving aquatic and terrestrial ecosystems, as well as water, food, and energy security. But the water cycle is also being modified by humans at an unprecedented scale and rate. A holistic understanding of freshwater’s role for Earth system resilience and the detection and monitoring of anthropogenic water cycle modifications across scales is urgent, yet existing methods and frameworks are not well suited for this（研究背景）. In this paper we highlight four core Earth system functions of water (hydroclimatic regulation, hydroecological regulation, storage, and transport) and key related processes. Building on systems and resilience theory, we review the evidence of regional-scale regime shifts and disruptions of the Earth system functions of water. We then propose a framework for detecting, monitoring, and establishing safe limits to water cycle modifications and identify four possible spatially explicit methods for their quantification（研究方法）. In sum, this paper presents an ambitious scientific and policy grand challenge that could substantially improve our understanding of the role of water in the Earth system and cross-scale management of water cycle modifications that would be a complementary approach to existing water management tools（研究结论）.
MERIT Hydro: A High-Resolution Global Hydrography Map Based on Latest Topography Dataset Dai Yamazaki, Daiki Ikeshima, Jeison Sosa, Paul D. Bates, George H. Allen, Tamlin M. Pavelsky High-resolution raster hydrography maps are a fundamental data source for many geoscience applications（研究背景）. Here we introduce MERIT Hydro, a new global flow direction map at 3-arc sec resolution (~90 m at the equator) derived from the latest elevation data (MERIT DEM) and water body data sets (G1WBM, Global Surface Water Occurrence, and OpenStreetMap)（研究内容）. We developed a new algorithm to extract river networks near automatically by separating actual inland basins from dummy depressions caused by the errors in input elevation data. After a minimum amount of hand editing, the constructed hydrography map shows good agreement with existing quality-controlled river network data sets in terms of flow accumulation area and river basin shape. The location of river streamlines was realistically aligned with existing satellite-based global river channel data. Relative error in the drainage area was <0.05 for 90% of Global Runoff Data Center (GRDC) gauges, confirming the accuracy of the delineated global river networks. Discrepancies in flow accumulation area were found mostly in arid river basins containing depressions that are occasionally connected at high water levels and thus resulting in uncertain watershed boundaries（研究结果）. MERIT Hydro improves on existing global hydrography data sets in terms of spatial coverage (between N90 and S60) and representation of small streams, mainly due to increased availability of high-quality baseline geospatial data sets. The new flow direction and flow accumulation maps, along with accompanying supplementary layers on hydrologically adjusted elevation and channel width, will advance geoscience studies related to river hydrology at both global and local scales（研究意义）.
Herrera‐Estrada, J. E., Diffenbaugh, N. S. [2020]. Landfalling droughts: Global tracking of moisture deficits from the oceans onto land. Water Resources Research, 56, e2019WR026877. https://doi.org/10.1029/2019WR026877 Droughts threaten food, energy, and water security, causing death and displacement of millions of people and billions of dollars in damages. However, there are still important gaps in the understanding of drought mechanisms and behaviors, inhibiting the accuracy of early-warning systems designed to protect communities worldwide（研究背景）. We use an object-tracking algorithm to track clusters of precipitation-minus-evaporation moisture deficits across land and ocean areas of the globe from 1981–2018（研究方法）. This analysis reveals a new type of “landfalling drought” that originates over the ocean and “migrates” onto land. We find that 16% of droughts that affected the continents worldwide from 1981–2018 were landfalling droughts. These droughts were significantly larger (220–425%) and more intense (4–30%)—and grew (253–285%) and intensified (9–28%) faster—than droughts that developed solely over the land or ocean（研究结果）. To identify potential underlying mechanisms, we analyze moisture transport associated with landfalling droughts over western North America. We find that landfalling droughts in this region are associated with anomalously anticyclonic atmospheric pressure patterns that reduce moisture fluxes over the Pacific Ocean toward the continent（研究结论）. By advancing understanding of the spatiotemporal evolution of droughts, our findings offer the potential to improve seasonal-scale prediction and long-term projection of global drought risks（研究价值）.
Global Investigation of Soil Moisture and Latent Heat Flux Coupling Strength As a key variable in the climate system, soil moisture (SM) plays a central role in the Earth’s terrestrial water, energy, and biogeochemical cycles through its coupling with surface latent heat flux (LH). Despite the need to accurately represent SM/LH coupling in Earth system models, we currently lack quantitative, observation-based, and unbiased estimates of its strength（研究背景）. Here we utilize the triple-collocation (TC) approach introduced in Crow et al. (2015) to SM and LH products obtained from multiple satellite remote sensing platforms and land surface models (LSMs) to obtain unbiased global maps of SM/LH coupling strength（研究方法）. Results demonstrate that relative to coupling strength estimates acquired directly from remote sensing-based data sets, the application of TC generally enhances estimates of warm-season SM/LH coupling, especially in the western United States, the Sahel, central Asia, and Australia. However, relative to triple-collocation estimates, LSMs (still) overpredict SM/LH coupling strength along transitional climate regimes between wet and dry climates, such as the central Great Plains of North America, India, and coastal Australia. Specific climate zones with biased relations in LSMs are identified to geographically focus the reexamination of LSM parameterizations（研究结果）. TC-based coupling strength estimates are robust to our choice of LSM contributing SM and LH products to the TC analysis. Given their robustness, TC-based coupling strength estimates can serve as an objective benchmark for investigating model-predicted SM/LH coupling（研究结论）.
If Precipitation Extremes Are Increasing, Why Aren’t Floods? Despite evidence of increasing precipitation extremes, corresponding evidence for increases in flooding remains elusive. If anything, flood magnitudes are decreasing despite widespread claims by the climate community that if precipitation extremes increase, floods must also（研究背景与问题）. In this commentary we suggest reasons why increases in extreme rainfall are not resulting in corresponding increases in flooding. Among the possible mechanisms responsible, we identify decreases in antecedent soil moisture, decreasing storm extent, and decreases in snowmelt. We argue that understanding the link between changes in precipitation and changes in flooding is a grand challenge for the hydrologic community and is deserving of increased attention（研究结论）.