The Reynolds Creek Long-Term Dataset: A long-term meteorological dataset derived from station observations in the Reynolds Creek Experimental Watershed

<b>Abstract</b>A forty-year gridded meteorological forcing dataset spanning the water years 1984 to 2023 (October 1^st to September 30^th) has been compiled for the Reynolds Creek Experimental Watershed (RCEW) in southwest Idaho, USA. This Reynold Creek Long-Term (RCLT) dataset consists of hourly, 10-meter resolution grids of air temperature, vapor pressure, precipitation mass and phase, incoming shortwave and longwave radiation, and wind speed and direction. These variables are foundational for many ecological and hydrological Land Surface Models (LSMs) used in research and operational applications and have been interpolated and calculated from hourly measurements from the dense meteorological station network within the RCEW. The elevation relief of the 240 km² RCEW spans the historical winter rain-to-snow transition, so a research application example is presented in which we show how the snow-dominated area of the basin has evolved over the forty-year data record. This dataset, stored in cloud-optimized Zarr format, enables future model development, benchmarking, and uncertainty analyses of existing models, independent validation of gridded atmospheric reanalysis datasets, and novel investigations of hydroclimatic variability across snow-dominated semi-arid environments.<b>1 Site Description</b>In 1960, the United States Congress allocated funding for an experimental research watershed to advance hydrologic research in western U.S. rangelands. Since then, the USDA Agricultural Research Service (ARS) Northwest Watershed Research Center (NWRC) has managed the scientific infrastructure in the Reynolds Creek Experimental Watershed (RCEW) in Southwest Idaho (43.205°, -116.75°). At approximately 240 km², the RCEW is characteristic of semiarid snow-dominated environments found throughout the Great Basin and spans an elevation gradient of 1100 to 2244 meters above sea level. Characteristic of mountain climatology, a significant elevational and directional precipitation gradient exists due to the prevailing northeast-trending storms during the winter and spring, when most of the annual precipitation occurs (Hanson, 2001). Since 1984, average annual precipitation ranged from 228 mm at site RC.057 site in the low northeast elevations to 1,086 mm at site RC.163 in the highest southwest elevations of the watershed. The NWRC has a long history of publishing station-based hydrometeorological datasets, including Slaughter et al. (2001), Reba et al. (2011) and Godsey et al. (2018). This dataset updates the gridded temperature, humidity, and precipitation dataset reported by Kormos et al. (2018), includes additional wind and radiation data, and appends nine more recent years of conditions (2015-2023) that have experienced a wider range of weather variability (Monteiro &amp; Morin, 2023).<b>2 Instrumentation and Variable Distribution</b>The Spatial Modeling for Resources Framework (SMRF; Havens et al. (2017)) was employed to distribute each of the ten land-surface meteorological variables to a 10-meter regular grid. Each forcing variable has either been empirically derived or directly interpolated from hourly station measurements across the catchment domain. Owing to the considerable length of time encompassed by the dataset, many different sensors have been deployed in the watershed over the forty-year data record with differing levels of accuracy, which are not reported here. Invalid data were preliminarily removed for all measured variables besides precipitation (which necessitated a unique approach detailed in subsection 2.6), and temporal interpolation was performed for data gaps of two hours or less. Gaps lasting longer than two hours were left empty with the foreknowledge that spatial interpolation from nearby sites in the high-density network would act as data surrogates. It is worth noting that the NWRC has always employed a full-time staff of technicians tasked with the calibration and servicing of each sensor deployed in the RCEW. The gridded interpolation methods for all ten modeled variables from the six measured variables are described in Hedrick et al. (2018) and further elaborated upon in the following subsections.<b>3.1 Air Temperature</b>Hourly measurements of air temperature (T_a) were made at 35 individual sites over the 40-year period (Figure 2), with the number of sites significantly increasing after water year 2000. Measurements are currently made using various incarnations of the widely used Vaisala HMP series of temperature and humidity sensors with ventilated radiation shields.A modified inverse distance weighting (IDW) approach was used to distribute T_a across the 10-meter grid. In this process, the elevational trend is calculated at each time step, constrained to be negative due to the general relationship between elevation and T_a, then subtracted from the station measurements to produce a temperature residual. These residuals are distributed using standard IDW and added to each grid cell’s position on the elevation gradient slope line. This approach for distributing T_a improves station representativeness for areas with complex local topography.<b>2.2 Vapor Pressure</b>Gridded actual vapor pressure (e_a) values were interpolated from measurements of relative humidity (RH) at the same 35 sites as T_a over the 40-year data record, using the same Vaisala HMP instruments referred to in subsection 2.1. The empirical Tetens equation was used for deriving e_a from RH and T_a:e_a= RH × 0.6108 × e ^ (17.3 × T_a/ 237.3 + T_a). (1)Station-derived e_a was then distributed to the 10-meter grid using the same modified IDW detrending approach described in subsection 2.1. Dew point temperature was also calculated for the wet bulb temperature calculation (subsection 2.6.2 below) but was not stored in the RCLT dataset because it can be calculated from the vapor pressure and air temperature using the existing empirical relationships (e.g., the Clausius-Clapeyron equation).<b>2.3 Wind</b>Wind speed (u_s) and direction (u_dir) were measured at a total of 29 sites over the data record, though only three sites were available prior to 1994 and four sites prior to 2002. The sparseness of wind measurements through the early years of this dataset is likely a source of uncertainty in the distributed wind grids for that period, though we should note that the pre-2002 measurements captured the full elevation gradient in RCEW at low (RC.076), mid (RC.127), and high elevation sites (RC.176).Station measurements of wind were distributed to the 10-meter grid using the maximum upwind slope (maxus) terrain parameter described in Winstral, et al. (2002) and Winstral et al. (2009). In short, the underlying digital elevation model (DEM) is used to calculate a maxus value (in degrees) over a user-defined upwind distance (here 300 meters) for all possible upwind directions (0˚ to 360˚) in 5˚ increments. The resulting 72 layers of maxus grids are stored in a lookup library. Then, for each station the measured wind speed is adjusted to simulate what the wind speed would have been on a flat surface (‘flatwind’) using the maxus value for the measured wind direction at the site. Once the adjusted ‘flatwind’ speeds and wind direction components have been distributed across the entire grid using standard IDW, the distributed wind directions are used to find the maxus value for each grid cell and the distributed ‘flatwind’ speeds are converted back to actual wind speeds.For the gridded dataset, wind speed and direction were converted into U- and V-components to match the conventions of NWP models such as the WRF and HRRR models. The U-component represents the East-West wind speed, with positive values indicating wind out of the west, while the V-component represents the North-South wind speed, positive value indicating wind out of the south.<b>2.4 Shortwave Radiation</b>Gridded incoming shortwave radiation (S_in) has been measured at 23 sites across the RCEW but cannot be directly spatially interpolated from measurements due to the complex terrain and the variable vegetation canopy present across the catchment. Instead, a three-step process produced the hourly gridded S_in product.Hourly clear sky atmospheric S_in was modeled, then corrected for surrounding terrain in each 10-meter grid cell following Dozier (1980) and Dubayah (1994), respectively.Station measurements of S_in were divided by clear sky radiation to derive a cloud factor (C_fac) at each station pixel (C_fac =1 represents cloud-free conditions), which was then distributed using standard IDW across the domain.Canopy-corrected S_in values were estimated using empirical relationships presented by Link and Marks (1999), where direct beam shortwave radiation under canopy (R_b) can be represented by:R_b= S_b,in × e ^ (-μh / cos(⁡θ)).In this equation, S_b,in is the above canopy cloud corrected direct beam radiation, μ is a canopy extinction coefficient, h is the height of the canopy, and θ is the solar zenith angle. Diffuse shortwave radiation under canopy (R_d) is computed by adjusting the above canopy cloud corrected diffuse radiation (S_d,in) by the canopy optical transmissivity (τ):R_d= τ × S_d,in.The terrain-, cloud-, and canopy-corrected S_i_n presented in this dataset is then the sum of R_b and R_d.When a comparison was performed between modeled and measured S_in across the 40-year record, we discovered that the values for τ and μ presented in Link and Marks (1999), which were derived in the Canadian Boreal forests, led to an overestimation of S_in at open sites by up to 40%, and an underestimation of S_in at forested sites by 30% or more. Therefore, a manual adjustment of the τ, μ, and height parameters was performed to produce S_in values that more closely matched the station observations. Importantly, the scale difference between point measurements of S_in and averaged S_in across a 10-meter by 10-meter grid cell precludes direct agreement since shortwave radiation at the ground surface varies over very short length scales. However, the general trends in modeled S_in magnitude as a function of cloud cover were well-represented in the spatial dataset.Calculating net shortwave radiation from the RCLT incoming solar product requires an estimate of land surface reflectance, or albedo, but there are many ways to derive an albedo product from both models and remote sensing products. Users of the RCLT dataset can use their own methods, but for simplicity we also include here visible and infrared bands of snow albedo for when snow is present. These albedo estimates use a time decay approach to capture albedo change as a function of springtime snow metamorphism, terrain factors, and solar zenith angle (Marshall and Warren, 1987). Importantly, these albedo estimates do not apply for snow-free conditions and should be masked for applications involving land surface models.<b>3.5 Longwave Radiation</b>Due to a lack of continuous upward looking pyrgeometers, or sensors measuring incoming longwave radiation (L_in), across the dataset time domain, incoming longwave radiation was not directly interpolated from measurements. Instead, clear sky L_in was empirically estimated from distributed air temperature and humidity using the methods of Brutsaert (1975), then corrected for surrounding terrain using equations presented in Marks and Dozier (1979). Next, cloud-corrected L_in was computed using the empirical relationship described by Garen and Marks (2005). The final step was to adjust the cloud-corrected L_in using vegetation maps derived from the LANDFIRE 2016 dataset (LANDFIRE, 2016) and empirically derived transmissivity (τ) reported by Link and Marks (1999) then adjusted as described in subsection 2.5.<b>2.6 Precipitation</b>To satisfy the standard input requirements of an energy and mass balance snow model, The RCLT dataset contains four distinct variables related to precipitation in the basin. These variables of precipitation mass, temperature of the falling hydrometeor, initial density of newly fallen snow, and the snow proportion of precipitation are described in the following subsections.<b>2.6.1 Precipitation Mass</b>Despite being the foundation upon which hydrologic models rely, precipitation measurements are often the largest source of predictive hydrologic uncertainty (Bárdossy et al., 2022). Across the wide spectrum of snow-dominated watersheds in the Western U.S., the majority of in situ measurements are made with weighing buckets fitted with alter shields that reduce wind speeds above the bucket orifice and thus increase the gauge catch efficiency (CE), or the ratio of measured precipitation to a “true” value (Thériault et al., 2021). However, many sites lack co-located wind speed measurements for applying the necessary World Meteorological Organization (WMO) transfer functions for undercatch correction (Kochendorfer et al., 2018), which can lead to low biases in regional and basin estimates of precipitation. Additionally, precipitation exhibits high spatial heterogeneity in complex terrain, which cannot be captured by a single measurement site in a large mountain basin.To overcome the issue of spatial representativeness, the RCEW measurement network was initially planned to contain one gauge for every square mile of the watershed (n=110). By the beginning of this dataset in water year 1984, the number of sites had been reduced to the 25 stations (~one measurement per 10 km²) used here to produce the hourly gridded precipitation fields.To address the undercatch issue, a unique dual-gauge approach (Hamon, 1970) was used for all but one of the sites in the RCEW. This method requires two co-located Belfort-type weighing buckets with one existing unshielded and the other fitted with a single-alter shield. The combination of the shielded and unshielded measurements allows an empirical extrapolation of more accurate ‘actual’ precipitation data compared with single shielded gauges employing a WMO transfer function (Hanson et al., 2004). Site RC.124B is the only single shielded gauge in the basin and was here corrected using the WMO transfer function. Hourly measurements of precipitation mass were distributed across the 10-meter grid using a Detrended Kriging interpolation method (Garen, 1995) identical to the approach by Kormos et al. (2018).<b>3.6.2 Precipitation Temperature</b>The precipitation temperature variable is represented by the hourly computed ice or wet bulb temperature calculated with a widely used Newton-Raphson iterative solution to the psychrometric equation (Campbell and Norman, 1998). This approach requires air temperature (subsection 2.1), dew point temperature (from calculated vapor pressure in subsection 2.2) and estimated atmospheric pressure from elevation. Prior work has demonstrated that wet bulb temperature is the most suitable method for partitioning snow from rain in a semiarid watershed such as the RCEW (Marks et al., 2013).<b>3.6.3 Density of New Snow</b>Hourly estimated of new accumulated snow density is included in the RCLT dataset for energy and mass balance snow models that may require it. For this long-term application, we computed new snow density from a lookup table based on previous work (Susong et al., 1999) using the calculated precipitation temperature (see subsection 2.6.2) and precipitation mass (see subsection 2.6.1) in each grid cell.<b>3.6.4 Snow Fraction of Precipitation</b>In addition to new snow density, the lookup table from Susong et al. (1999) was also used to determine the amount of precipitation that fell as snow across the catchment. When time step precipitation temperatures fell between -0.5°C and +0.5°C, the precipitation was defined as mixed phase, while colder temperatures resulted in 100% snowfall, and warmer temperatures were 100% rainfall.<br>

<b>摘要</b>本研究针对美国爱达荷州西南部的雷诺溪实验流域（Reynolds Creek Experimental Watershed, RCEW），构建了一套时长40年的网格化气象强迫数据集，涵盖1984至2023水文年（即每年10月1日至次年9月30日）。本雷诺溪长期数据集（Reynold Creek Long-Term, RCLT）包含空气温度、水汽压、降水质量与相态、入射短波与长波辐射、风速与风向的逐小时10米分辨率网格化数据。这些变量是众多用于科研与业务应用的生态、水文陆面模型（Land Surface Models, LSMs）的核心输入项，其数据由RCEW内部密集气象站网的逐小时观测资料通过插值与计算生成。该240平方公里的RCEW区域的高程起伏跨越了历史上冬季雨转雪的过渡带，因此本文给出了一个研究应用案例，展示了该流域以降雪为主的区域在40年数据记录中的演变过程。本数据集采用云优化Zarr格式存储，可为未来模型开发、模型基准测试与现有模型不确定性分析、网格化大气再分析数据集的独立验证，以及以降雪为主的半干旱区域水文气候变化的创新性研究提供支撑。
<b>1 场地概况</b>1960年，美国国会拨款设立实验研究流域，以推进美国西部牧场的水文研究。此后，美国农业部农业研究服务局（USDA Agricultural Research Service, ARS）西北流域研究中心（Northwest Watershed Research Center, NWRC）负责管理位于爱达荷州西南部（北纬43.205°，西经116.75°）的雷诺溪实验流域（RCEW）的科学基础设施。RCEW总面积约240平方公里，具有大盆地地区广泛分布的以降雪为主的半干旱环境特征，海拔跨度为1100至2244米。作为山地气候的典型特征，由于冬春季节盛行东北向风暴（该时段为全年主要降水期），区域存在显著的高程与方位降水梯度（Hanson, 2001）。自1984年以来，流域年均降水量从东北部低海拔区域的RC.057站点的228毫米，到西南部最高海拔区域的RC.163站点的1086毫米不等。NWRC长期发布基于站点的水文气象数据集，相关成果包括Slaughter等（2001）、Reba等（2011）以及Godsey等（2018）的研究。本数据集更新了Kormos等（2018）发布的网格化温度、湿度与降水数据集，新增了风速与辐射数据，并补充了2015至2023年共9年的观测资料——该时段经历了更广泛的天气变率（Monteiro & Morin, 2023）。
<b>2 仪器布设与变量空间分布</b>本研究采用资源空间建模框架（Spatial Modeling for Resources Framework, SMRF; Havens等, 2017），将10个陆面气象变量插值至10米规则网格。每个气象强迫变量均通过经验推导，或从流域范围内的逐小时站点观测资料直接插值得到。由于本数据集时长跨度较大，40年的观测期间流域内布设了多种不同精度的传感器，本文未详细记录其具体参数。除降水变量（需采用2.6小节详述的专属处理方法）外，其余所有观测变量的无效数据均已初步剔除；对于时长不超过2小时的资料间隙，采用时间插值进行填补。对于时长超过2小时的资料间隙，本文保留空缺，原因在于高密度站网中邻近站点的空间插值可作为数据替代项。值得注意的是，NWRC始终配备专职技术人员，负责RCEW内所有布设传感器的校准与维护工作。Hedrick等（2018）已详述了基于6个观测变量插值得到10个模拟变量的网格化方法，后续小节将对此进行进一步阐释。
<b>3.1 空气温度</b>40年期间，共有35个站点开展空气温度（T_a）的逐小时观测（图2），2000水文年之后站点数量显著增加。目前采用多款广泛使用的维萨拉HMP系列温湿度传感器开展观测，传感器配备通风辐射防护罩。本研究采用改进的反距离权重法（inverse distance weighting, IDW）将T_a插值至10米网格。该方法首先在每个时间步长计算高程趋势，由于高程与气温的普遍负相关关系，将该趋势约束为负值，随后从站点观测值中减去该趋势以得到气温残差。采用标准IDW方法对残差进行空间插值，随后将插值结果叠加至每个网格单元在高程梯度斜率线上的对应位置。该气温插值方法可提升复杂局地地形区域的站点代表性。
<b>2.2 水汽压</b>在40年的观测期间，与T_a相同的35个站点开展了相对湿度（RH）观测，本研究基于这些观测资料插值得到网格化实际水汽压（e_a），所用仪器与2.1小节所述的维萨拉HMP系列传感器一致。采用经验特滕斯方程（Tetens equation）由RH与T_a推导e_a：$$e_a = RH imes 0.6108 imes expleft(frac{17.3 T_a}{237.3 + T_a}
ight) ag{1}$$。随后采用2.1小节所述的改进IDW去趋势方法，将站点-derived的e_a插值至10米网格。本研究还计算了露点温度，用于湿球温度计算（详见2.6.2小节），但未将其存入RCLT数据集，因为露点温度可通过现有经验关系（如克劳修斯-克拉佩龙方程）由水汽压与气温推导得到。
<b>2.3 风场</b>观测期间，共有29个站点开展风速（u_s）与风向（u_dir）观测，1994年之前仅有3个站点，2002年之前仅有4个站点。本数据集早期时段的风场观测站点较为稀疏，这可能是该时段网格化风场数据的不确定性来源之一；但需指出，2002年之前的观测覆盖了RCEW的全高程梯度，包括低海拔（RC.076）、中海拔（RC.127）与高海拔（RC.176）站点。本研究采用Winstral等（2002）与Winstral等（2009）提出的最大上风坡度（maximum upwind slope, maxus）地形参数，将站点风场观测插值至10米网格。简言之，本研究基于基础数字高程模型（digital elevation model, DEM），以5°为增量对所有可能的上风方向（0°至360°）计算用户定义的上风距离（此处为300米）内的maxus值（单位为度）。最终生成的72层maxus网格数据存储于查找库中。随后，针对每个站点，利用该站点观测风向对应的maxus值，将观测风速调整为平坦地表上的风速（‘flatwind’）。采用标准IDW方法将调整后的‘flatwind’风速与风向分量插值至全流域网格后，利用插值得到的风向为每个网格单元获取对应的maxus值，再将‘flatwind’风速转换为实际风速。为匹配数值天气预报模式（如WRF与HRRR模式）的惯例，网格化风场数据的风速与风向被转换为U、V分量。其中U分量代表东西向风速，正值表示西风（风从西向东吹）；V分量代表南北向风速，正值表示南风（风从南向北吹）。
<b>2.4 短波辐射</b>RCEW内共有23个站点开展入射短波辐射（S_in）观测，但由于流域内地形复杂、植被冠层多变，无法直接由站点观测资料进行空间插值。为此，本研究采用三步流程生成逐小时网格化S_in数据。首先逐小时模拟晴空大气入射短波辐射，随后分别按照Dozier（1980）与Dubayah（1994）的方法，对每个10米网格单元的周边地形进行校正。将站点观测的S_in除以晴空辐射，得到每个站点像素的云量因子（C_fac，C_fac=1代表无云条件），随后采用标准IDW方法将该因子插值至全流域。采用Link与Marks（1999）提出的经验关系估算冠层校正后的S_in值，其中冠层下的直接短波辐射（R_b）可表示为：$$R_b = S_{b,in} imes e^{-frac{mu h}{cos heta}} ag{2}$$。式中，S_{b,in}为冠层上方经云量校正的直接辐射，μ为冠层消光系数，h为冠层高度，θ为太阳天顶角。冠层下的散射短波辐射（R_d）通过冠层光学透射率（τ）对冠层上方经云量校正的散射辐射（S_{d,in}）进行调整得到：$$R_d = au imes S_{d,in} ag{3}$$。本数据集提供的经过地形、云量与冠层校正的S_in即为R_b与R_d之和。在对40年的模拟与观测S_in数据进行对比后，本研究发现，Link与Marks（1999）在加拿大北方森林中推导得到的τ与μ参数，会导致开阔站点的S_in被高估最多达40%，而森林站点的S_in被低估30%以上。因此，本研究手动调整了τ、μ与冠层高度参数，使生成的S_in值更贴近站点观测结果。需特别指出的是，单点S_in观测与10米×10米网格平均S_in之间的尺度差异，导致二者无法直接匹配，因为地表短波辐射在极短的空间尺度上就存在显著变化。但空间数据集能够较好地反映S_in大小随云量变化的总体趋势。由RCLT入射太阳辐射数据计算净短波辐射需要估算地表反照率，而从模型或遥感产品中获取反照率数据的方法众多。RCLT数据集的使用者可采用自定义方法，为简化流程，本数据集还提供了积雪存在时的可见光与红外波段积雪反照率数据。这些反照率估算值采用时间衰减方法，以反映春季积雪变质、地形因子与太阳天顶角对反照率的影响（Marshall & Warren, 1987）。需特别注意，这些反照率估算值不适用于无雪条件，在陆面模型应用中需对其进行掩膜处理。
<b>3.5 长波辐射</b>由于本数据集时段内缺乏连续的上行式长波辐射计（即测量入射长波辐射L_in的传感器）观测资料，无法直接由站点观测插值得到入射长波辐射。为此，本研究采用Brutsaert（1975）提出的方法，由网格化气温与湿度经验估算晴空L_in，随后按照Marks与Dozier（1979）提出的方程对周边地形进行校正。随后，采用Garen与Marks（2005）提出的经验关系计算云量校正后的L_in。最后，采用由LANDFIRE 2016数据集（LANDFIRE, 2016）得到的植被图，以及Link与Marks（1999）提出并经2.5小节调整的经验透射率（τ），对云量校正后的L_in进行调整。
<b>2.6 降水</b>为满足能量与质量平衡积雪模型的标准输入要求，RCLT数据集包含了与流域降水相关的4个独立变量。以下小节将分别介绍降水质量、降水质点温度、新降雪初始密度以及降水降雪占比这四个变量。
<b>2.6.1 降水质量</b>尽管降水是水文模型的核心输入项，但降水观测通常是水文预测不确定性的最大来源（Bárdossy等, 2022）。在美国西部众多以降雪为主的流域中，多数原位观测采用配备防风罩的称重雨量筒，防风罩可降低雨量筒口上方的风速，从而提升雨量筒捕获效率（CE，即实测降水量与‘真实’降水量的比值）（Thériault等, 2021）。但许多站点缺乏同步的风速观测资料，无法应用世界气象组织（WMO）推荐的用于修正雨量筒少测的传递函数（Kochendorfer等, 2018），这会导致区域与流域降水估算值偏低。此外，复杂地形区域的降水具有高度空间异质性，大型山地流域的单个观测站点无法捕捉这种异质性。为解决空间代表性问题，RCEW观测网最初规划为每平方英里布设1个雨量筒（共110个）。至1984水文年本数据集启动时，站点数量缩减至25个（约每10平方公里1个观测点），本研究基于这些站点生成逐小时网格化降水场。为解决少测问题，RCEW内除1个站点外，其余所有站点均采用独特的双雨量筒方法（Hamon, 1970）。该方法需在同一位置布设2个Belfort型称重雨量筒，其中1个无防风罩，另1个配备单层防风罩。与采用WMO传递函数的单防风罩雨量筒相比，结合防风与无防风观测结果可通过经验推导得到更准确的‘真实’降水数据（Hanson等, 2004）。RC.124B站点是流域内唯一的单防风罩雨量筒，本研究采用WMO传递函数对其观测数据进行校正。本研究采用与Kormos等（2018）一致的去趋势克里金插值方法（Garen, 1995），将逐小时降水质量观测值插值至10米网格。
<b>3.6.2 降水温度</b>降水温度变量采用逐小时计算的冰点温度或湿球温度表示，该计算基于广泛使用的干湿球方程牛顿-拉夫逊迭代解法（Campbell & Norman, 1998）。该计算方法需要用到气温（2.1小节）、露点温度（由2.2小节计算的水汽压推导得到）以及由高程估算的大气压强。已有研究表明，湿球温度是在RCEW这类半干旱流域中区分降雪与降雨的最适宜方法（Marks等, 2013）。
<b>3.6.3 新降雪密度</b>RCLT数据集包含逐小时估算的新积雪密度，以满足可能需要该参数的能量与质量平衡积雪模型。针对本长期数据集的应用需求，本研究基于Susong等（1999）的研究成果构建查找表，利用每个网格单元的降水温度（2.6.2小节）与降水质量（2.6.1小节）计算新降雪密度。
<b>3.6.4 降水降雪占比</b>除新降雪密度外，本研究还采用Susong等（1999）的查找表确定流域内以降雪形式降落的降水量。当逐时降水温度介于-0.5℃至+0.5℃之间时，降水被定义为混合相；温度低于-0.5℃时为100%降雪，温度高于+0.5℃时为100%降雨。
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