Uncertainty analysis of radar rainfall estimates over two different climates in Iran

Accuracy of rainfall quantification is one of the most important concerns in meteorological and hydrological modelling. Satellites and weather radars can provide meteorological information with higher temporal and spatial resolution than ground stations. Rain gauges measure rain rate directly; however, weather radars estimate rain rate by converting radar reflectivity aloft to rain rate at ground level. This conversion with a power law relation between radar reflectivity and rain rate could be altered from place to place or in various precipitation types. This variety may be the source of errors and uncertainty of radar rainfall estimates. One way to assess the uncertainty of radar rainfall is simulation of rainfall fields. In this article, after calibrating two radars located in the south-western and northern parts of Iran, uncertainty of rainfall estimates of these radars has been analysed using the Gaussian Copula model. Reliability of this model was examined for 10% of the rainfall events that were not included in the simulation process. Obtained results of the current research indicate that recalibration of radars can considerably reduce bias and root mean error. In addition, the Copula-based model can generate rainfall fields with similarly spatial structures to those of observed rainfall data.     

Identifying and analysing uncertainty structures in the TRMM microwave imager precipitation product over tropical ocean basins

Despite continuous improvements in microwave sensors and retrieval algorithms, our understanding of precipitation uncertainty is quite limited, due primarily to inconsistent findings in studies that compare satellite estimates to in situ observations over different parts of the world. This study seeks to characterize the temporal and spatial properties of uncertainty in the Tropical Rainfall Measuring Mission Microwave Imager surface rainfall product over tropical ocean basins. Two uncertainty analysis frameworks are introduced to qualitatively evaluate the properties of uncertainty under a hierarchy of spatiotemporal data resolutions. The first framework (i.e. ‘climate method’) demonstrates that, apart from random errors and regionally dependent biases, a large component of the overall precipitation uncertainty is manifested in cyclical patterns that are closely related to large-scale atmospheric modes of variability. By estimating the magnitudes of major uncertainty sources independently, the climate method is able to explain 45–88% of the monthly uncertainty variability. The percentage is largely resolution dependent (with the lowest percentage explained associated with a 1° × 1° spatial/1 month temporal resolution, and highest associated with a 3° × 3° spatial/3 month temporal resolution). The second framework (i.e. ‘weather method’) explains regional mean precipitation uncertainty as a summation of uncertainties associated with individual precipitation systems. By further assuming that self-similar recurring precipitation systems yield qualitatively comparable precipitation uncertainties, the weather method can consistently resolve about 50% of the daily uncertainty variability, with only limited dependence on the regions of interest.     

Comparison of TRMM multisatellite precipitation analysis (TMPA) products and daily-monthly gauge data over Bali

Research has been conducted to compare daily, monthly and seasonal rain rates derived from Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis (TMPA) using rain gauge analysis from 1998 to 2002. Three rain gauges in the Bali islands were employed. Statistical analysis was used to analyse the relationship of the TMPA product with the rain gauge data. Resulting statistical measures consisted of the linear correlation coefficient (r), the mean bias error (MBE), the root mean square error (RMSE) and the mean absolute error (MAE). The results of these analyses indicate that satellite data have lower values than the gauge estimation values. The validation analysis showed a very good relationship with the gauge data on monthly timescales. However, a poor relationship was found between the gauge data and the daily data analysis from the TMPA. The 3B42 and 3B43 products showed the same levels of relationship during the wet season and dry season. The correlation in the dry season was better than during the wet season. Statistical error levels during the wet season were better than in the dry season. The 3B43 showed slight improvement in these values when compared with the 3B42 (both the random error measurement and the scatter of the estimates were reduced). In general, the data from TMPA are potentially usable to replace rain gauge data, especially to replace the monthly data, if inconsistencies and errors are taken into account.     

Evaluating image-based estimates of leaf area index in boreal conifer stands over a range of scales using high-resolution CASI imagery

Leaf area index (LAI) is an important surface biophysical parameter as a measure of vegetation cover, vegetation productivity, and as an input to ecosystem process models. Recently, a number of coarse-scale (1-km) LAI maps have been generated over large regions including the Canadian boreal forest. This study focuses on the production of fine-scale (≤30-m) LAI maps using the forest light interaction model-clustering (FLIM-CLUS) algorithm over selected boreal conifer stands and the subsequent comparison of the fine-scale maps to coarse-scale LAI maps synthesized from Landsat TM imagery. The fine-scale estimates are validated using surface LAI measurements to give relative root mean square errors of under 7% for jack pine sites and under 14% for black spruce sites. In contrast, finer scale site mean LAI ranges between 49% and 86% of the mean of surface estimates covering only part of the sites and 54% to 110% of coarse-scale site mean LAI. Correlations between fine-scale and coarse-scale estimates range from near 0.5 for 30-m coarse-scale images to under 0.3 to 1-km coarse-scale images but increase to near 0.90 after imposing fine-scale zero LAI areas in coarse-scale estimates. The increase suggests that coarse-scale image-based LAI estimates require consideration of sub-pixel open areas. Both FLIM-CLUS and coarse-scale site mean LAI are substantially lower than surface estimates over northern sites. The assumption of spatially random residuals in regression-based estimates of LAI may not be valid and may therefore add to local bias errors in estimating LAI remotely. Differences between fine-scale airborne LAI maps and 30-m-scale Landsat TM LAI maps suggests that, for sparse boreal conifer stands, LAI maps produced from Landsat TM alone may not always be sufficient for validation of coarser scale LAI maps. In addition, previous studies may have used biased LAI estimates over the study site. Fine-scale spatial LAI maps offer one means of assessing and correcting for effects of sub-pixel open area patches and for characterising the spatial pattern of residuals in coarse-scale LAI estimates in comparison to the true distribution of LAI on the surface.     

Comparison of PERSIANN and V7 TRMM Multi-satellite Precipitation Analysis (TMPA) products with rain gauge data over Iran

The objective of this research is to evaluate daily rain rates derived from three widely used high-resolution satellite precipitation products (PERSIANN, TMPA-3B42V7, and TMPA-3B42RT) using rain gauge observations over the entire country of Iran. Evaluations are implemented for 47 comprehensive daily rainfall events during the winter and spring seasons from 2003 to 2006. These events are selected because each encompasses more than 50% of the country’s area. In this study, daily rainfall observations derived from 1180 rain gauges distributed throughout the country are employed as reference surface data. Six statistical indices: bias, multiplicative bias (MBias), relative bias (RBias), mean absolute error (MAE), root mean square error (RMSE), and linear correlation coefficient (CC), as well as a contingency table are applied to evaluate the satellite rainfall estimates qualitatively. The spatially averaged results over the entire country indicate that 3B42V7, with an average bias value of –1.47 mmd^?1, RBias of –13.6%, MAE of 4.5 mmd^?1, RMSE of 6.5 mmd^?1, and CC of 0.61, leads to better estimates of daily precipitation than those of PERSIANN and 3B42RT. Furthermore, PERSIANN with an average MBias value of 0.56 tends to underestimate precipitation, while 3B42V7 and 3B42RT with average MBias values of 0.86 and 1.02, respectively, demonstrate a reasonable agreement in regard to rainfall estimations with the rain gauge data. With respect to the categorical verification technique implemented in this study, PERSIANN exhibits better results associated with the probability of detection of rainfall events; however, its false alarm ratio is worse than that of 3B42V7 and 3B42RT.     

An evaluative study on ESO and SIMP for optimising a cantilever tie—beam

We examine both the evolutionary structural optimisation (ESO) and solid isotropic microstructure with penalisation (SIMP) methodologies by investigating a cantilever tie–beam. Initially, both ESO and SIMP produce designs with higher objective function values relative to a previously published ‘intuitive’ design. However, after careful investigation of the numerical parameters such as the initial design domain and the mesh size, both methods obtain designs that have lower objective function values relative to the intuitive design. Thus, a clearer understanding of the numerical parame- ters and their influence on optimisation methods is achieved.     

A methodological approach for spatial downscaling of TRMM precipitation data in North China

Accurate precipitation data with high spatial resolution are crucial for many applications in water and land management. Tropical Rainfall Monitoring Mission (TRMM) data, with accurate, high spatial resolution are crucial for improving our understanding of temporal and spatial variations of precipitation. However, when used in the Three-North Shelter Forest Programme of China, the spatial resolution of TRMM data is too coarse. In this study, we presented a hybrid method, i.e. a regression model with residual correction method, for downscaling annual TRMM 3B43 from 0.25° to 1 km grids from 2000 to 2009. The regression model was applied to construct the relationship among TRMM 3B43 data, continentality (CON), and the normalized difference vegetation index (NDVI) under five different scales (0.25°, 0.50°, 0.75°, 1.00°, and 1.25°). In the residual correction, three spatial interpolation techniques, i.e. inverse distance weighting (IDW), ordinary kriging, and tension spline, were employed. The 1 km monthly precipitation was disaggregated from 1 km annual precipitation by using monthly fractions. Analysis shows that (1) CON was a good variable for precipitation modelling at large-scale regions; (2) the optimum relationship between precipitation, NDVI, and CON was found at a scale of 1.25°; (3) the most feasible option for residual correction was IDW; and (4) the final annual/monthly downscaled precipitation (1 km) not only improved the spatial resolution but also agreed well with data from 220 rain gauge stations (average R² = 0.82, slope = 1.09, RRMSE = 18.30%, and RMSE = 51.91 mm for annual downscaled precipitation; average R² = 0.41, slope = 0.79, RRMSE = 76.88%, and RMSE = 15.09 mm for monthly downscaled precipitation).     

Testing LiSK BRDF models over a semi-arid grassland region with visible and near-infrared ATSR-2 and AVHRR data

This paper assesses the capability of the Roujean and LiSparse-MODIS-RossThin linear semi-empirical kernel-driven (LiSK) bidirectional reflectance distribution function (BRDF) models to predict bidirectional reflectance at geometries other than those of the observations used to invert the model, when the models are inverted against a sparse set of angular samples from 21 orbits (3-19 August 1996) of the operational Advanced Very High Resolution Radiometers (AVHRRs) on NOAA TIROS series AM (morning) and PM (evening) satellites. Red ('visible') and near-infrared (NIR) spectral reflectance estimates acquired at 4:40 GMT on 14 August 1996 by the Along-Track Scanning Radiometer-2 (ATSR-2) sensor flown on the European Space Agency's ERS-2 satellite are used as reference data. The test area is a semi-arid grassland region in Inner Mongolia, P.R. China, bounded by 42.84°-44.71° N and 112.40°-116.05° E. The results show that in spite of the difficulties posed by such a task, LiSK models can be inverted against multiangular AVHRR observations to predict bidirectional reflectance at the acquisition geometry of the ATSR-2 with reasonable accuracy: the rms. error of the reflectance predictions made by both models is less than 4% for the nadir views and less than or equal to 6% in the forward views. These error values are less than one-half those provided by a 13 August 1996 AM AVHRR scene in the 0.65 w m channel and about one-seventh of those for the AVHRR scene in the 0.87 w m (NIR) channel, in both nadir and forward views.     

Multi-scale evaluation of six high-resolution satellite monthly rainfall estimates over a humid region in China with dense rain gauges

This study evaluates and compares the performance of six high-resolution monthly satellite rainfall estimates (SREs), which include TRMM 3B43V6, TRMM 3B42RTV6, CMORPH, GSMaP MWR+, GSMaP MVK+, and PERSIANN, with dense ground rain gauges located in Ganjiang River Basin. The performance was evaluated at multiple spatial scales: the 0.25° × 0.25° grid, sub-catchment, and the whole basin. It was observed that 3B43V6 generally performed well and was able to capture the ground benchmark rainfall with slight overestimation, whereas all of the other SREs suffered large underestimation in the study area. Among the five pure satellite-derived products, 3B42RTV6 and CMORPH performed better, whereas PERSIANN performed the worst. All of the SREs except 3B43V6 showed a strong seasonal signature with much better performance in the wet season than in the dry season. The results also indicate that SREs performed better in the southeast and central regions, whereas poor performance was observed in the western mountains and in the northern plains. Furthermore, the spatial patterns of SREs errors are influenced mainly by the local terrain. The performance of SREs improved when the spatial scale was increased, whereas the performance reduced when the temporal scale was increased from month to year.     

Integrated modeling of aerosol,cloud, precipitation and land processes at satellite-resolved scales

With support from NASA's Modeling and Analysis Program, we have recently developed the NASA Unified-Weather Research and Forecasting model (NU-WRF). NU-WRF is an observation-driven integrated modeling system that represents aerosol, cloud, precipitation and land processes at satellite-resolved scales. “Satellite-resolved” scales (roughly 1–25 km), bridge the continuum between local (microscale), regional (mesoscale) and global (synoptic) processes. NU-WRF is a superset of the National Center for Atmospheric Research (NCAR) Advanced Research WRF (ARW) dynamical core model, achieved by fully integrating the GSFC Land Information System (LIS, already coupled to WRF), the WRF/Chem enabled version of the GOddard Chemistry Aerosols Radiation Transport (GOCART) model, the Goddard Satellite Data Simulation Unit (G-SDSU), and custom boundary/initial condition preprocessors into a single software release, with source code available by agreement with NASA/GSFC. Full coupling between aerosol, cloud, precipitation and land processes is critical for predicting local and regional water and energy cycles.     

Estimating spatially downscaled rainfall by regression kriging using TRMM precipitation and elevation in Zhejiang Province,southeast China

Estimating regional daily rainfall accurately is of prime importance for many environmental applications, such as hydrology, meteorology, and ecology. The rainfall product from the Tropical Rainfall Monitoring Mission (TRMM) satellite is better able to estimate rainfall than rain gauge interpolation in some regions with coarse rain gauge spatial resolution. In the present article, analyses were made at 1379 rain gauge stations in Zhejiang Province, China, during January 2011 to July 2012 (536 days). A good relationship was found between the rain gauge data and the data analysis from the TRMM, especially for the precipitation that was between 2 and 10 mm day^–1. However, gaps exist between TRMM products and rain gauge records, which could be considered as uncertainty. To predict rainfall more precisely, four categories of daily rainfall and three regression kriging (RK) models were selected for analysis. TRMM and elevation data were used as auxiliary variables to construct RK₁. The auxiliary variable in RK₂ and RK₃ was TRMM and elevation data, respectively. Residuals (four rainfall categories × three RK models) of RK models showed spatial auto-correlation. Compared with RK₂, which has a 0.25° resolution, RK₁ and RK₃ are predicted at a finer 1 km spatial resolution. However, RK₁ has the best performance in rainfall prediction according to validation statistics. The root mean square error was decreased from 0.667 to 0.437 and the mean of error was improved from –0.250 to –0.007 in the prediction of mean daily rainfall. RK₁ may facilitate easy downscaling of precipitation and capture the trends in daily rainfall variability.     

Use of TRMM in determining the climatic characteristics of rainfall over Bangladesh

Five years of data from 1998 to 2002 of TRMM-3B42 version 5 (V5), 3B43 V5, 3B42 version 6 (V6), 3B43 V6, and the Bangladesh Meteorological Department rain-gauge network were analyzed to understand the climatic characteristics of rainfall over Bangladesh. TRMM-PR 2A25 data were used to obtain the precipitation field of the convection events. Daily rainfall measured by TRMM V5 3B42 was compared to that of rain-gauge values from pre-monsoon to post-monsoon months (March-November). The time sequence patterns of the daily rainfall determined by the V5 3B42 and those from rain gauges were remarkably similar. The spatial and temporal averages of rainfall revealed good estimations of rainfall: during March to November, the V5 3B42- and rain gauge-estimated daily rainfall was 8.12 and 8.34 mm, respectively. In annual scale, TRMM V5 3B42-, V5 3B43-, V6 3B42-, V6 3B43- and rain-gauge estimated rainfall was 6.9, 6.4, 6.6, 6.8 and 7.1 mm/day, respectively. The average percentage of rainy days determined by V5 3B42 data with respect to the rain-gauge value was 96%. TRMM is useful for estimating the average values of rainfall in Bangladesh. The prominent difference between rainfall estimated by rain-gauge and V5 3B42 was found to be period- and location-dependent. The V5 3B42 overestimated the rainfall during the pre-monsoon period and in dry regions but underestimated it during the monsoon period and in wet regions. The reason for the differences according to season and locations is considered to be the vertical cross section of convection obtained by TRMM-PR 2A25 data. The rainfall overestimation in pre-monsoon and underestimation in monsoon period measured by V5 3B42 is reduced to reasonable amount by V6 3B42 and V6 3B43. In this manner, the merit of using TRMM data for climatological studies of rainfall over Bangladesh is shown.     

An integrated approach to hydro-geological lineament mapping of a semi-arid region of West Africa using Radarsat-1 and GIS

In the 21st century, water resource management will be a major socioeconomic issue and an essential component of progress in semi-arid regions. The Soudano-Sahelian region of Africa suffers from a chronic lack of reliable hydro-geological maps for local water resource managers. To provide better tools for groundwater exploration, this paper focuses on hydro-geological lineament mapping using remote sensing data. The objectives were (1) evaluate the potential of multi-angular and multi-temporal Radarsat-1 images for extracting lineaments in semi-arid regions and (2) provide map of potential hydro-geological lineaments. No significant relationship was found between lineament yield and incidence angle of Radarsat-1 images, while lineament spatial distribution was in good agreement with the land use and geology of the study area. The high scores observed for NNE-SSW and NNW-SSE orientations match the results of local lineament studies of the region. Radarsat-1 image acquired on May 01, 2001 offers the greatest potential, due to the reduced effect of vegetation during this region's dry season. Potential hydro-geological lineaments were mapped using the weighting of well location, presence of green vegetation during the dry season, preferential lineament orientations and presence of cross-points. The mapping revealed five classes (Very low, Low, Moderate, High and Very high) of potential hydro-geological lineaments with high and very high potential poorly represented. Results also reveal that most wells are far enough from lineaments or cross-points and hence the inefficiency of existing drilling programs.     

Satellite- and ground-based measurements of CO2 over the Indian region: its seasonal dependencies,spatial variability,and model estimates

ABSTRACT

The present study demonstrates the distribution of carbon dioxide (CO₂) concentration over the Indian region and the surrounding oceanic regions during 2009–2012, using measurements from satellites viz., Greenhouse Gases Observing Satellite (GOSAT) and Atmospheric Infrared Sounder, Carbon Tracker (CT) model simulations and flask measurements from two Indian stations Sinhagad (SNG) (73°45′ E, 18°21′36″ N) and Cape Rama (CRI) (73°54′ E, 15°6′ N). The concentration of CO₂ is observed to be maximum during pre-monsoon and shows a decreasing phase during the post-monsoon season. In a regional scale, it is found that Indo-Gangetic Plain and northern India have relatively higher concentrations compared to the other regions. The probability distribution of the concentration differences shows that for most of the time, the differences lie between ±3 ppmv between GOSAT and CT. The comparison between the CO₂ flask measurements over SNG and CRI with respect to that of GOSAT and CT clearly reveals that the differences in CO₂ are as high as 10 ppmv between the ground- and satellite-based measurements. Further, we utilized the Lagrangian model FLEXible PARTicle (FLEXPART) to understand the source?receptor relationship over CRI, SNG, and over the equatorial Indian Ocean (IO). The source contributions from the northern and eastern continental regions of the Indian region are found to be more influential over SNG compared to CRI. It is also found from simulations that the equatorial IO has less influence from the continental source and therefore has a reduced seasonal variability compared to the other regions considered in the present study.     

Comprehensive comparison of daily IMERG and GSMaP satellite precipitation products in Ardabil Province,Iran

The measurement of precipitation is essential for most environmental studies such as drought monitoring, watershed operations, water hazard management, etc. Development of satellite products has improved their applicability in environmental modelling and could proffer an alternative to gauge-based precipitation data, particularly in areas where there is no sufficient number of gauges or poor gauge distribution but they should be evaluated in different areas using ground-based data as references. In the present study, daily Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM- IMERG- Final (Version 5)) and Global Satellite Mapping of Precipitation-Moving Vector with Kalman filter (GSMaP-MVK (Version 7)) precipitation products were evaluated in comparison with gauges observations in Ardabil province, north-west of Iran, from 1 January 2016 to 21 October 2017. Several statistical indices including linear correlation coefficient, Bias (B), Multiplicative Bias (B_m), Relative Bias (B_r), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Probability of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI) were used for evaluation. The results showed that the correlation between GSMaP estimates and gauge observations is higher than that of IMERG (0.42 and 0.33, respectively). On the other hand, GSMaP tends to overestimate precipitation substantially, while IMERG is involved in both under and overestimation slightly. Although these products could not show very high accuracy in precipitation estimation, the estimated precipitation values by IMERG were relatively closer to gauge records and can be used as a replacement for gauge observation in the study area where there is lack of weather stations.     

Modelling the impacts of river stage manipulation on a complex river-floodplain system in a semi-arid region

This paper investigates the complex interaction between a river and a saline floodplain in a semi-arid environment strongly influenced by groundwater lowering using a fully integrated physically-based numerical model. The main objective is to quantify the impacts of river stage manipulation on freshening of the shallow floodplain groundwater through bank storage. It is shown that river stage rises produce a relatively less saline floodplain aquifer with a larger freshwater lens. First, an increase in river stage reduces saline groundwater recharge to the floodplain. Second, the enhanced bank storage is able to freshen the groundwater near the river banks during high-flow pulses by mixing fresh water with saline groundwater. Overall, it was found that river stage manipulation may be considered as a short term salt management technique. However, if longer term strategies are required, it may be possible to implement these salt interception measures periodically.     

Ground water potential in a semi-arid region of Andhra Pradesh - a geographical information system approach

Continuous and adequate supplies of potable water from ground reservoirs are important for sustained agriculture, industry and domestic use throughout huge semi-arid regions of India. The present paper describes an approach to investigating groundwater potential over extensive geographical areas and illustrates its potential with reference to watershed planning in the large Varaha River Basin (VRB), Andhra Pradesh, India. The method involves the creation of a systematic database of information from satellite data for reconnaissance survey before going for field exploration. Colour composite images from Landsat Thematic Mapper and Indian Remote Sensing (IRS) satellite were used to interpret various thematic maps of the Varaha river basin. SPOT 1 MLA data of band 3 on a 1:250 000 scale was used for improving the accuracy of interpretation of topographic units due to its higher resolution and stereo coverage. Slope and other coverages were derived from topographic maps. The thematic and topographic information was digitized and ERDAS Imagine GIS software was used to analyse this information. Groundwater potential zones were delineated through subjective weights assigned to interpreted thematic and derived topographic units according to their likely infiltration capacities. Seven categories of groundwater potential ranging from very good to poor were derived automatically. Field measurements were then made within a selection of these categories to check the groundwater potential at selected sites. The validity and effectiveness of using remote sensing and GIS techniques for improving the targeting of field observations for groundwater for a huge river basin is shown by comparing the inferred groundwater potential with the field measurements.     

TRMM derived sea surface temperature in the wake of a cyclonic storm over the central Bay of Bengal

Sea surface cooling associated with a cyclone in the Bay of Bengal was investigated using the data derived from TRMM Microwave Imager (TMI) onboard Tropical Rainfall Measuring Mission (TRMM) spacecraft. Though the TRMM/TMI sensor has all weather capabilities, sea surface temperature (SSTs) can not be obtained during heavy rain conditions. Hence, to overcome the problem of having no observations during the cyclone day, weekly analysis was carried out during the cyclone week (27 March–2 April 2000) and pre‐cyclone week (20–26 March 2000). To compute the magnitude of SST cooling in the cyclone track, weekly SSTs of the cyclone period were subtracted from the pre‐cyclone period. Similar analysis was carried out during non‐cyclonic periods of 20–26 March and 27 March–2 April of 2001, 2002. The analysis indicated that the TMI SST was reduced by maximum of 1.57°C along the cyclone track during the passage of cyclonic storm. Such an activity was not observed during 27 March–2 April 2001 and 2002, indicating that the cooling observed in 27 March–2 April 2000 was due to the cyclonic storm. On the other hand, SST anomalies are positive during 27 March–2 April of 2001, 2002 over these regions. TRMM observations shows higher wind speed and precipitation rate associated with the storm and are responsible for decrease in SST. Analysis of Pathfinder Advanced Very High Resolution Radiometer (AVHRR) SST showed the cyclone induced cooling but the SSTs measurement was blocked by clouds during the cyclone period (27 March–2 April 2000). In the same time, Reynolds SSTs was unable to detect the cooled sea surface. In these circumstances, the cyclone induced sea surface cooling was well captured by TRMM/TMI and had distinct advantage of providing SSTs in presence of cloud as compared to infrared SSTs measurement like those from pathfinder SSTs.     

Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed

Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soil properties from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating within a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed.In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soil information using simple parameter estimation with few but appropriately timed remote sensing retrievals.     

A new approach for obtaining precipitation estimates with a finer spatial resolution on a daily scale based on TMPA V7 data over the Tibetan Plateau

ABSTRACT

Precipitation plays vital roles in the worldwide hydrological cycles. Gridded precipitation estimates with finer spatio-temporal resolutions are critical in various application fields. In this study, we focused on obtaining downscaled precipitation estimates (approximately1 km) at daily scale over the Tibetan Plateau (TP), which was considered as a great challenge in previous downscaling studies. To meet this challenge, a new approach, incorporating geographically ratio analysis (GRA) and spatially weighted moving window technique, was proposed. The performances of the downscaled results and those of TRMM Multisatellite Precipitation Analysis (TMPA) data were evaluated against point-based ground observations. The results indicated that: (1) the monthly downscaled results (R² around 0.70, bias around 10%) outperformed TMPA 3B43 data (R² around 0.55, bias around 25%) against ground observations; (2) the performances of the daily downscaled results (R² around 0.65, bias around 10%) were better than those of the TMPA 3B42 data (R² around 0.50, bias around 28%); and (3) the anomalies in the TMPA data did not exist in the downscaled results based on the proposed approach. Therefore, the proposed approach was suitable for obtaining both monthly and daily downscaled results based on TMPA data over the TP.