Complex Topography

[Florian Peitz]

AbstractFractional snow-covered area (SCA) is a key parameter in large-scale hydrological, meteorological and regional climate models. Since SCA affects albedos and surface energy balance fluxes, it is especially of interest over mountainous terrain where generally a reduced SCA is observed in large grid cells. Temporal and spatial snow distributions are, however, difficult to measure over complex topography. We therefore present a parameterization of SCA based on a new subgrid parameterization for the standard deviation of snow depth over complex topography. Highly resolved snow depth data at the peak of winter were used from two distinct climatic regions, in eastern Switzerland and in the Spanish Pyrenees. Topographic scaling parameters are derived assuming Gaussian slope characteristics. We use computationally cheap terrain parameters, namely, the correlation length of subgrid topographic features and the mean squared slope. A scale dependent analysis was performed by randomly aggregating the alpine catchments in domain sizes ranging from 50 m to 3 km. For the larger domain sizes, snow depth was predominantly normally distributed. Trends between terrain parameters and standard deviation of snow depth were similar for both climatic regions, allowing one to parameterize the standard deviation of snow depth based on terrain parameters. To make the parameterization widely applicable, we introduced the mean snow depth as a climate indicator. Assuming a normal snow distribution and spatially homogeneous melt, snow-cover depletion (SCD) curves were derived for a broad range of coefficients of variations. The most accurate closed form fit resembled an existing fractional SCA parameterization. By including the subgrid parameterization for the standard deviation of snow depth, we extended the fractional SCA parameterization for topographic influences. For all domain sizes we obtained errors lower than 10% between measured and parameterized SCA.

The accuracy and reliability of any hydrologic study, whether related to flood forecasting, drought monitoring, water resources management, or climate change impact assessment, depend heavily on the availability of good-quality precipitation estimates. Rain gauges provide direct physical measurement of the surface precipitation; however, they are susceptible to certain errors arising from location, spatial scale (point), wind, mechanical errors, and density (Groisman and Legates 1994). Especially in remote parts of the world and in developing countries, ground-based precipitation measurements, such as rain gauge and radar networks, are either sparse or nonexistent, mainly because of the high cost of establishing and maintaining the infrastructure. This situation is further exacerbated in regions with complex topography, where precipitation is characterized by high spatiotemporal variability. In these regions, rain gauges are generally located in lowlands because of accessibility considerations, thus underrepresenting the precipitation occurring in highlands. Satellite-based precipitation (SBP) products are perhaps the only source to fill this important gap.

SBP products are available with quasi-global coverage. However, their performance largely depends on the hydroclimatic characteristics of the region (Yilmaz et al. 2005), and thus, evaluation of these products in different regions will provide the expected error characteristics to the end users and feedback to the algorithm developers. There is an increasing number of studies focusing on the evaluation of the performance of SBP products (Ebert et al. 2007; Sapiano and Arkin 2009; Tian et al. 2007; Kidd et al. 2012). However, studies evaluating the performance of these algorithms over complex topography are still very limited.

The regions characterized by complex topography are among the most challenging environments for SBP estimation because of high spatiotemporal variability of precipitation controlled by the orography. SBP products that utilize information from a combination of IR and PMW sensors are faced with challenges over complex topography. The challenge for IR retrievals is mainly attributed to warm orographic rain, which cannot be detected by the IR retrievals that use cloud-top temperature, hence leading to an underestimation of orographic rains (Dinku et al. 2008) and a failure to capture light-precipitation events (Hong et al. 2007). The underestimation by PMW retrievals over mountainous regions is attributed to warm orographic clouds without ice particles that produce heavy rain (Dinku et al. 2010). The overestimation by PMW retrievals over mountains can be related to the classification of cold land surface and ice covers as rain clouds (Dinku et al. 2007; Gebregiorgis and Hossain 2013). Because of this uncertainty associated with the land surface background emissivity, SBP algorithms are sensitized toward estimating liquid precipitation rather than frozen hydrometeors. SBP algorithms are prone to all these errors in mountainous regions and should therefore be evaluated in detail. Despite the importance of SBP products over complex topography, there are only a few studies that focus on evaluation of these products over mountainous regions. Hirpa et al. (2010) found that the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) near-real-time product (3B42RT; Huffman et al. 2007) and the Climate Prediction Center (CPC) morphing technique (CMORPH; Joyce et al. 2004) SBP products have similar performances at lower elevations; however, over higher elevations both products suffer from elevation-dependent bias. Dinku et al. (2010) compared CMORPH, TMPA 3B42, and TMPA 3B42RT over two mountainous regions that are characterized by complex topography. They found that both products have low correlations and underestimated the occurrence and amount of precipitation. Another study by Stampoulis and Anagnostou (2012) indicated that over mountainous regions in Europe, both CMORPH and TMPA 3B42V6 products significantly overestimate precipitation in the cold season because of snow/cold surface contamination and that CMORPH shows higher accuracy in winter relative to TMPA 3B42V6. Moreover, they noted that the error variance of the SBP products is seasonally dependent and generally higher over mountains. In summary, performances of SBP products vary significantly over topographically complex regions and are complicated by significant elevation change, seasonality, and snow cover.

The paper is organized as follows: the details of the study area and datasets are given in section 2. Evaluation methodology is presented in section 3. The results of the evaluation are presented in section 4, and the summary, conclusions, and recommendations are offered in section 5.

The rain gauge dataset was provided by the Turkish State Meteorological Service (TSMS). TSMS operates two types of meteorological stations in the study region: Automated Weather Observing Systems (AWOS) and pluviometer-type stations (Table 1). Data from the AWOS stations were available at hourly time scales, whereas the pluviometer-type stations report data three times a day. All pluviometer stations are collocated with an AWOS station, thus providing an opportunity for the quality control of the data. In the quality-control step, the consistency between the daily records of the collocated stations was first checked through graphical (double mass curves, time series, and scatterplot) and statistical (such as bias and correlation coefficient) methods. Later, these corrected AWOS stations were used to quality control similar stations nearby by using the correlation weighting method (Westerberg et al. 2010). The daily precipitation data from the quality-controlled AWOS stations were used in this study (Fig. 1).

The MPE algorithm estimates near-real-time precipitation rates by blending measurements from SSM/I with brightness temperatures from the IR channel of the Meteosat geostationary satellites (Meteosat-7, Meteosat-8, and Meteosat-9). SSM/I and Meteosat measurements are temporally and spatially coregistered to derive lookup tables (LUTs). LUTs describe the rain rate as a function of the Meteosat IR brightness temperature. The product is generated over the regions up to 60 longitude and latitude from the nominal subsatellite points of three satellites. Since MPE is produced on the assumption that cold clouds produce the most rain, product estimation is most effective for convective precipitation. Moreover, precipitation at warm fronts and orographically induced precipitation is usually detected but could be mislocated by up to 100 km (Heinemann et al. 2002). For this study, MPE product having a 15-min temporal and 4 km 4 km spatial resolution is used.

The optimized PRISM parameters were then used to interpolate the precipitation values for each 0.05 0.05 grid within the study area at the daily time scale, assuming monthly PRISM parameters are also valid for the daily time scale. The PRISM interpolated grids were further coarsened to 0.25 resolution via box-averaging technique.

The primary objective of this study was to evaluate the performance of various SBP products over complex topography using a rain gauge network. The evaluation was performed at various spatial scales. First, point-scale precipitation measurements from the rain gauge network were compared with the collocated grid-scale precipitation estimates (0.25 0.25) from SBP algorithms. Second, the RGP product has been further utilized in the evaluation of the SBP products.

To examine the influence of orography on the performance of the SBP products, cross-section lines were taken along and perpendicular to the mountain ranges (Fig. 1). Figure 3 shows the annual precipitation from rain gauges and collocated SBP grids along cross-section line 1 together with the topographic elevations. Note that cross-section line 1 is perpendicular to the shore line; station BRT is in region 1 (on the coastal, windward side of the mountains) and other two stations are located in region 2 (on the drier, leeward side of the mountains). Cross-section lines 2 and 3 are taken along the coastal region (region 1) and inland region (region 2), respectively. For the sake of brevity, we summarize the results from all cross-section lines below using cross-section line 1. The influence of the orography on the precipitation distribution is clearly seen in Fig. 3, with station BRT receiving significantly more mean annual precipitation (850.5 mm) compared to stations KRA (408.1 mm) and CRK (344.2 mm) located inland in region 2. Along the coastal region (region 1), all SBP products underestimate observed precipitation. In this region, TMPA-7A performs better than other products with slight underestimation, possibly because of the monthly rain gauge correction procedure. CMORPH consistently and significantly underestimates the precipitation compared to rain gauges along the coast. MPE, on the other hand, shows underestimation with a wide range of scatter between years. TMPA-7RT underestimates along the coast, however, with less annual bias compared to CMORPH.

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