Purpose – The purpose of this paper is to propose a new temporal disaggregation method for time series based on the accumulated and inverse accumulated generating operations in grey modeling and the interpolation method. Design/methodology/approach – This disaggregation method includes three main steps, including accumulation, interpolation, and differentiation (AID). First, a low frequency flow series is transformed to the corresponding stock series through accumulated generating operation. Then, values of the stock series at unobserved time is estimated through appropriate interpolation method. And finally, the disaggregated stock series is transformed back to high frequency flow series through inverse accumulated generating operation. Findings – The AID method is tested with a sales series. Results shows that the disaggregated sales data are satisfactory and reliable compared with the original data and disaggregated data using a time series model. The AID method is applicable to both long time series and grey series with insufficient information. Practical implications – The AID method can be easily used to disaggregate low frequency flow series. Originality/value – The AID method is a combination of grey modeling technique and interpolation method. Compared with other disaggregation methods, the AID method is simple, and does not require auxiliary information or plausible minimizing criterion required by other disaggregation methods.
Flood irrigation is widely applied in harvested croplands of arid regions and can be classified as autumn/winter irrigation (AI) depending on the time of application. Due to its high water consumption, real-time monitoring of the AI extent is crucial to improve its scheduling. We proposed a remote sensing-based long short-term memory (LSTM) model for near-real-time monitoring of AI extent at sub-pixel scale. The model loosely coupled MODIS data with Sentinel-2 data to solve the mixed pixel issue of MODIS data, and calibrated Sentinel-2 thresholds for AI identification by a random forest (RF) module to extract large-scale reference data with high temporal frequencies. The variable importance estimated by RF is used as a reference for feature screening in the LSTM model. As Sentinel-2 images are not available daily, LSTM models trained with incomplete sequences were validated using multiple validation approaches. The model was applied to the Hetao Irrigation District, the largest irrigation district in arid region of China, and delivered reasonable performance with coefficient of determination of over 0.82 and mean absolute error of around 10.7%. Classification of irrigation patterns using simulated time series of irrigation area fractions revealed eight different irrigation patterns from 2010 to 2020 in the study region. Results indicate that the maximum fraction of AI area upstream is closely related to the cropland distribution pattern. The AI patterns changed significantly over the 11 years, with a more pronounced reduction in irrigation duration for individual pixels in the downstream area. These changes are associated with two important land policies implemented in many regions of China, land consolidation and land transfer. The proposed model demonstrates the potential for near-real-time monitoring of autumn irrigation extent within large irrigation districts, which can aid in AI scheduling and provide insight into irrigation patterns and practices.
Abstract Merged multisatellite precipitation datasets (MMPDs) combine the advantages of individual satellite precipitation products (SPPs), have a tendency to reduce uncertainties, and provide higher potentials to hydrological applications. This study applied a dynamic clustered Bayesian model averaging (DCBA) algorithm to merge four SPPs across Pakistan. The DCBA algorithm produced dynamic weights to different SPPs varying both spatially and temporally to accommodate the spatiotemporal differences of SPP performances. The MMPD is developed at daily temporal scale from 2000 to 2015 with spatial resolution of 0.25° using extensively evaluated SPPs and a global atmospheric reanalysis–precipitation dataset: Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Climate Prediction Center morphing technique (CMORPH), and ERA-Interim. The DCBA algorithm is evaluated across four distinct climate regions of Pakistan over 102 ground precipitation gauges (GPGs). DCBA forecasting outperformed all four SPPs with average Theil’s U of 0.49, 0.38, 0.37, and 0.36 in glacial, humid, arid, and hyperarid regions, respectively. The average mean bias error (MBE), mean error (MAE), root-mean-square error (RMSE), correlation coefficient (CC), and standard deviation (SD) of DCBA over all of Pakistan are 0.54, 1.40, 4.94, 0.77, and 5.17 mm day −1 , respectively. Seasonal evaluation revealed a dependency of DCBA performance on precipitation magnitude/intensity and elevation. Relatively poor DCBA performance is observed in premonsoon/monsoon seasons and at high/mild elevated regions. Average improvements of DCBA in comparison with TMPA are 59.56% (MBE), 49.37% (MAE), 45.89% (RMSE), 19.48% (CC), 46.7% (SD), and 18.66% (Theil’s U ). Furthermore, DCBA efficiently captured extreme precipitation trends (premonsoon/monsoon seasons).
Abstract Quantifying carbon fluxes at large spatial scales has attracted considerable scientific attentions. In this study, a novel approach was proposed to estimate the terrestrial ecosystem gross primary production (GPP) using imagery from the satellite‐borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The new model (named Temperature and Greenness Rectangle, TGR) uses a combination of MODIS Enhanced Vegetation Index and Land Surface Temperature products as well as in situ measurement of photosynthetically active radiation to estimate GPP at a 16 day interval. Three major advantages are included in the model: (1) the model follows strictly the logic of the light use efficiency model and each parameter has physical meaning; (2) the model reduces the dependency on ground‐based meteorological measurements; and (3) the overlap of information in correlated explanatory variables is avoided. The model was calibrated with data from 17 sites within the Ameriflux network and validated at another 13 sites, covering a wide range of climates and eight major vegetation types. Results show that the TGR model explains reasonably well the tower‐based measurements of GPP for all vegetation types, except for the evergreen broadleaf forest, with the coefficient of determination in a range from 0.67 to 0.91 and the root mean square error from 9.0 to 31.9 g C/m 2 /16 days. Comparisons with other two models (the TG and GR model) show that the TGR model generally gives better GPP estimates in nearly all vegetation types, especially under dry climate conditions. These results indicate that the TGR model can be potentially used to estimate GPP at regional scale.
Statistical hydrological forecasting models have been widely used for the medium and long term hydrological forecasting.One major concern for these models is how to choose an appropriate one with less input and higher precision.The Charnes,Cooper and Rhodes (CCR) model for data envelopment analysis is used here for the multi-attributes evaluation of these forecasting models.In the CCR model,the inputs include indexes for forecasting factors and model parameters,the outputs include precision indexes,and the evaluation result is the relative efficiency of each model compared with other models.This method is used to evaluate 20 models of radial basis function networks for forecasting reference evapotranspiration.Results show that the CCR model is feasible for this kind of multi-attributes evaluation of forecasting models.Key factors for forecasting reference evapotranspiration are identified,which are maximum and minimum temperature,wind speed and sunshine hours.Moreover,the forecasting precision and relative efficiency of a network could be improved significantly if the date was added as an input of the network.
Precise assessment of drought and its impact on the natural ecosystem is an arduous task in regions with limited climatic observations due to sparsely distributed in situ stations, especially in the hyper-arid region of Kingdom of Saudi Arabia (KSA). Therefore, this study investigates the application of remote sensing techniques to monitor drought and compare the remote sensing-retrieved drought indices (RSDIs) with the standardized meteorological drought index (Standardized Precipitation Evapotranspiration Index, SPEI) during 2001–2020. The computed RSDIs include Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI), which are derived using multi-temporal Landsat 7 ETM+, Landsat 8 OLI/TIRS satellites, and the Google Earth Engine (GEE) platform. Pearson correlation coefficient (CC) is used to find the extent of agreement between the SPEI and RSDIs. The comparison showed CC values of 0.74, 0.67, 0.57, and 0.47 observed for VHI/SPEI-12, VHI/SPEI-6, VHI/SPEI-3, and VHI/SPEI-1, respectively. Comparatively low agreement was observed between TCI and SPEI with CC values of 0.60, 0.61, 0.42, and 0.37 observed for TCI/SPEI-12, TCI/SPEI-6, TCI/SPEI-3, and TCI/SPEI-1. A lower correlation with CC values of 0.53, 0.45, 0.33 and 0.24 was observed for VCI/SPEI-12, VCI/SPEI-6, VCI/SPEI-3, and VCI/SPEI-1, respectively. Overall, the results suggest that VHI and SPEI are better correlated drought indices and are suitable for drought monitoring in the data-scarce hyper-arid regions. This research will help to improve our understanding of the relationships between meteorological and remote sensing drought indices.
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