Empirical downscaling of daily minimum air temperature at very fine resolutions in complex terrain

Available air temperature models do not adequately account for the influence of terrain on nocturnal air temperatures. An empirical model for night time air temperatures was developed using a network of one hundred and forty inexpensive temperature sensors deployed across the Bitterroot National Forest, Montana. A principle component analysis (PCA) on minimum temperatures showed that 98% of the spatiotemporal variability could be accounted for using the first two modes which described the coupling and decoupling of surface temperature from free air temperatures, respectively. The spatial character of these modes were strongly correlated with terrain variables and were then modeled to topographic variables derived from a 30 m digital elevation model. PCA scores were modeled using independent predictors from in situ observations and regional reanalysis that incorporate temperature, solar radiation and relative humidity. By applying modeled PC scores back to predicted loading surfaces, nighttime minimum temperatures were predicted at fine spatial resolution (30 m) for novel locations across a broad (∼45,000 km²), topographically complex landscape. Our results suggest that this modeling approach can be used with retrospective and projected predictors to model fine scale temperature variation across time in regions of complex terrain.     

基于GWR模型降尺度模拟蒙古高原地区TRMM降水数据

以多年月平均NDVI值高于0.1为阈值,对蒙古高原进行“植被区”与“非植被区”的子区域划分。在分析“植被区”植被对降水响应时滞性和“非植被区”陆地表面温度不同数据值与降水量相关性的基础上,子区域分别构建了TRMM3B43降水数据与海拔、坡度、坡向数据、归一化植被指数（NDVI）/陆地表面温度（LST）数据的地理加权回归（GWR）模型,得到区内2006-2015年每年5-10月1km空间分辨率的月降水量降尺度模拟数据,并利用区内141个气象站点数据对降尺度模拟数据进行精度验证。结果表明：（1）蒙古高原“植被区”植被对降水响应存在时滞性,约为一个月;“非植被区”多数月份白天与夜晚陆地表面温度差（LST＿D＿N）与降水量的相关性最显著。（2）降尺度模拟数据与气象站点数据具有较好的一致性,月尺度相关系数为0.83,各站点相关系数介于0.42～0.98。（3）在生长季、月平均尺度上,降尺度模拟数据具有较高精度,其中9月和10月数据精度优于TRMM 3B43数据。降尺度模拟数据整体精度较高,加之对原始数据在50°N以上未覆盖地区的填补以及空间分辨率的提高,可为区内水循环变化、农牧业生产、干旱监测等...     

Implications of climate model biases and downscaling on crop model simulated climate change impacts

In estimating responses of crops to future climate realisations, it is necessary to understand and differentiate sources of uncertainty. This paper considers the specific aspect of input weather data quality from a Regional Climate Model (RCM) leading to differences in estimates made by three crop models. The availability of hindcast RCM estimates enables comparison of crop model outputs derived from observed and modelled weather data. Errors in estimating the past climate implies biases in future projections, and thus affect modelled crop responses. We investigate the complexities in using climate model projections representing different spatial scales within climate change impacts and adaptation studies. This is illustrated by simulating spring barley with three crop models run using site-specific observed (12 UK sites), original (50 × 50 km) and bias corrected downscaled (site-specific) hindcast (1960–1990) weather data from the HadRM3 RCM. Though the bias correction downscaling method improved the match between observed and hindcast data, this did not always translate into better matching of crop model estimates. At four sites the original HadRM3 data produced near identical mean simulated yield values as from the observed weather data, despite evaluated (observed-hindcast) differences. This is likely due to compensating errors in the input weather data and non-linearity in the crop models processes, making interpretation of results problematic. Understanding how biases in climate data manifest themselves in individual crop models gives greater confidence in the utility of the estimates produced using downscaled future climate projections and crop model ensembles. The results have implications on how future projections of climate change impacts are interpreted. Fundamentally, considerable care is required in determining the impact weather data sources have in climate change impact and adaptation studies, whether from individual models or ensembles.     

Seasonal climate change impacts on evapotranspiration, precipitation deficit and crop yield in Puerto Rico

The purpose of this study was to estimate precipitation (P), reference evapotranspiration (ET_o), precipitation deficit (PD = P − ET_o) and relative crop yield reduction (YR) for a generic crop under climate change conditions for three locations in Puerto Rico: Adjuntas, Mayagüez, and Lajas. Reference evapotranspiration was estimated by the Penman-Monteith method. Precipitation and temperature data were statistically downscaled and evaluated using the DOE/NCAR PCM global circulation model projections for the B1 (low), A2 (mid-high) and A1fi (high) emission scenarios of the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios. Relative crop yield reduction was estimated from a water stress factor, which is a function of soil moisture content. Average soil moisture content for the three locations was determined by means of a simple water balance approach.Results from the analysis indicate that the rainy season will become wetter and the dry season will become drier. The 20-year average September precipitation excess (i.e., PD > 0) increased for all scenarios and locations from 121 to 321 mm between 2000 and 2090. Conversely, the 20-year average February precipitation deficit (i.e., PD < 0) changed from −27 to −77 mm between 2000 and 2090. The results suggest that additional water could be saved during the wet months to offset increased irrigation requirements during the dry months. The 20-year average relative crop yield reduction for all scenarios decreased on average from 12% to 6% between 2000 and 2090 during September, but increased on average from 51% to 64% during February. Information related to the components of the hydrologic water budget (i.e., actual evapotranspiration, surface runoff, aquifer recharge and soil moisture storage) is also presented. This study provides important information that may be useful for future water resource planning in Puerto Rico.     

A new approach for rescaling land cover data

The resolution of satellite imagery must often be increased or decreased to fill data gaps or match preexisting project requirements. It is well known that a change in resolution introduces systematic errors of size, shape, location and amount of contiguous land cover types. Nevertheless, robust methods for rescaling landscape data are frequently required to assess patterns of landscape change through time and over large areas. We developed a new method for rescaling spatial data that allows map resolution (grain size) to be either increased or decreased while holding the total proportion of land cover types constant. The method uses a weighted sampling net of variable resolution to sample an existing map and then randomly selects from the frequency of cover types derived from this sample to assign the cover type for the corresponding location in the rescaled map. The properties of the sampling net had a variable effect on measures of landscape pattern with the characteristic patch size (S) the most robust metric and the number of clusters (A) the most variable. A comparison of up-scaled and down-scaled maps showed that this process is not symmetrical, producing different errors for increases versus decreases in grain size. Rescaling Landsat (30 m) imagery to the 10 m resolution of SPOT imagery for four National Park units within Maryland and Virginia resulted in errors due to rescaling that were small (1–2%) relative to the total error (∼11%) associated with these images. The new rescaling method is general because it provides a single method for increasing or decreasing resolution, can be applied to maps with multiple land cover types, allows grid geometry to be transformed (i.e., square to hexagonal grids), and provide a more consistent basis for landscape comparisons when maps must be derived from multiple sources of classified imagery.     

利用降尺度技术建立降水量预测方程的研究

[目的]研究一种利用多层次的模式环流输出场建立的降水预测模型。[方法]对安徽省业务运行的降尺度预测方法进行了改进，在模式多个层次的环流场中寻找与降水的高相关区；并利用最优子集回归模型对预报因子进行筛选和组合，形成了月降水量的预测方程；最后分别将2005—2009年的实况环流场和模式环流场代八方程，比较2种资料方案的预测评分，分析各月评分的高低，考察其业务运行的可行性。[结果l与传统降尺度方法相比，利用多层次的模式环流输出场建立的降水预测模型资料内容更为丰富；从预测效果来看，平均距平符号一致率为63％，Ps评分为75分，不仅高于业务运行的降尺度方法，也高于业务发布预测的评分；此外，该方法对典型涝月的预测效果更好，表明该方法对异常值具有较好的预测能力。[结论]该研究为丰富降尺度技术方案提供参考。     

辽宁省土壤墒情预测方法研究

从气象服务需求出发,基于前期研究建立的辽西、辽东地区4、5月土壤相对湿度预测模型,通过降尺度分析,定量化预测辽宁省18个站点4月、5月土壤相对湿度。以2014年为例,通过模型应用与预测结果验证发现,4月预测模型各站点平均相对误差为18%,5月预测模型各站点平均相对误差为13%,总体预测结论基本符合实际情况,模型精度较高。     

基于微波遥感的土壤水分反演估算研究进展

土壤水分是地表和大气水热过程交换的重要纽带，对于农业生产、生态规划、水资源管理等具有十分重要的意义。微波遥感具有基本不受天气条件影响，具有较好探测植被覆盖下的土壤信息和土壤水分变化趋势等优势，成为目前遥感精确反演土壤水分的热点。本文整理了现有全球尺度的基于微波遥感的土壤水分产品；分析比较了土壤水分反演中主动微波遥感、被动微波遥感、主被动微波协同技术的原理、特点、适用范围和关键技术进展：主动微波遥感和被动微波遥感的 优势分别在于高空间分辨率和高时间分辨率，高空间分辨率可以很好捕捉地表细微的空间信息特征，但囿于土壤水分与后向散射系数之间的复杂关系，特别是植被、地表粗糙度等对雷达后向散射系数的干扰，使得反演土壤水分的精度不高，因而根据现实情况选取不同散射模型以及利用多源数据协同是目前改善精度的研究热点。而高时间分辨率可以实现全球及大尺度下的土壤水分监测，但是很难满足小尺度或者小区域范围的实际研究需求，为了能使实测数据在空间上得以较好匹配，提出多种降尺度方法。结合以上两种微波遥感方式的优劣，依托更为丰富的数据源、相对成熟的观测技术来对两者进行融合以提取更多的水分信息，以提升反演精度或者获得长时间序列数据。在目前的方法中，土壤水分反演在小尺度下表现出良好的性能，但在全球尺度上会出现数据缺失、适用性不强、反演精度不高以及反演过程过于复杂等诸多问题，可以借助多种观测方式（多极化、多角度、多波段）、多时相重复观测、在原有模型上引入新的算法以及数据同化等方面着手进行改进，同时全球卫星导航系统（Global Navigation Satellite System, GNSS） 中长期稳定、高时空分辨率的L波段微波信号在陆面遥感领域的快速发展也为我国北斗卫星导航系统（BeiDou Navigation Satellite System, BDS）的发展提供了借鉴，展现出在土壤水分反演方面的巨大潜力。