Water stress or more specifically drought assessment plays a key role in water management, especially in extreme climate conditions. Basically, globally gridded satellite-based precipitation products ...are potential sources of data as alternatives for ground-based measurements. However, for a reliable application, they should be evaluated in different regions. In this paper, two satellite-based rainfall products, namely Modern-Era Retrospective Analysis for Research and Applications (MERRA)-Land and Global Land Data Assimilation System-2 (GLDAS-2), have been evaluated against ground-based observations in terms of precipitation and their application for drought analysis. At first, the coarse-resolution MERRA-Land is downscaled to the finer resolution of interest for better comparison. After comparison of these datasets against ground-based observations in terms of precipitation, it is concluded that MERRA-Land can better estimate precipitation. Then, the nonparametric SPIs at various timescales are derived to analyze how well MERRA-Land performs in drought monitoring. Different categorical and statistical error indices are used to assess the efficiency of MERRA-Land in capturing drought events. The results revealed that the downscaled MERRA-Land data can properly detect short-term and mid-term drought events known as agricultural and meteorological droughts throughout the study area, respectively. In addition, drought maps show that the majority of lands experience mid-term scale drought which are in agreement with ground-based observations. The methodology adopted in this study can be applied in areas lacking in rain-gauge stations which significantly extend current capabilities for drought monitoring and early warning systems.
In situ rainfall data play a significant role in drought assessment studies. However, they are not available with reliable spatiotemporal coverage. With the advancements in satellite rainfall ...estimates (SREs), monitoring hydrological events in ungauged basins is possible. Additionally, the evaluation of newly released SREs such as CHIRPS, with a long-term record and comparably high resolution (0.05°), in the assessment of extreme hydrological events (dry/wet spells) has scarcely been carried out, which is the most novel motivation of this study. Moreover, evaluation of CHIRPS in developing copula-based multivariate severity-duration-frequency curves based on the severity and duration of the occurred events in 1988-2019 over the Zayandehroud basin (a critical central basin of Iran), is innovatively appraised. An evaluation of CHIRPS in drought assessment shows its acceptable performance, with slight underestimation, in assessing the severity and duration of dry spells. In contrast, an overestimation is identified for wet spells.
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•A new Spatially Promoted SVM (SP-SVM) Model proposed to downscale GRACE from 0.5° to 0.25°.•The most influential fifteen variables, i.e., satellite- and gauged-based, are considered ...in GRACE downscaling.•The proposed SP-SVM model was compared with a commonly used statistical SVM.
Satellite-based terrestrial water storage changes have been recorded using the Gravity Recovery and Climate Experiment (GRACE) satellite which causing it an important dataset in hydrology and other related fields. GRACE dataset is widely utilized in many studies, but its coarse spatial resolution is a limiting drawback. Machine-learning approaches (e.g., ANN and SVM) are commonly applied in spatially downscaling. However, their input formation, which is in vector form, is a limitation of considering neighbor relations between the gridded-based inputs, specifically in spatial downscaling. Thus, developing an appropriate, simple, fast, and novel model to spatially downscale GRACE resolution is initially necessary for its utilizations. In this study, a Spatially Promoted Support Vector Machine (SP-SVM) model is innovatively proposed for GRACE downscaling from 0.5° to 0.25°. This promotion is investigated utilizing the distances between the unknown target points (with 0.25°) and their surrounding GRACE-valued points (0.5°), called their Distance Effect Coefficient (DEC), as the SP-SVM model input. In addition, the efficiencies of different in-situ and satellite-based datasets (fifteen variables from May 2005 to August 2020) are evaluated as the inputs of the GRACE downscaling models. After finding the most influential datasets, showing the best correlation with the GRACE, their best combinations in GRACE downscaling are identified. Based on the results, the set of PERSIANN-CDR without delay, the in-situ evaporation with a 1-month delay, and the soil moisture in 0–10 cm depth with a 1-month delay show the best performance in GRACE downscaling. The results of GRACE downscaling by the SP-SVM approach are also compared with the ones based on a usual statistical SVM (S-SVM) model, consisting of an intermediate bias interpolation to improve the estimations through a bias correction step. The results show that the SP-SVM model outperforms the common statistical SVM-based. Thus, compared with the usual S-SVM approach, the proposed SP-SVM (linear) model could be used as a simpler and more accurate model for downscaling any variable in a hierarchical process.
•Performance of SREs was evaluated for spatio-temporal drought analysis.•SREs may not perform adequately for extreme drought events.•Drought frequency curves can vary based on the climatic ...zones.•Performance of SREs can vary with temporal resolution of drought indices.
Satellite Rainfall Estimates (SREs) can provide rainfall information at finer spatial and temporal resolutions, however their performance varies with respect to gauged precipitation data in different climatic regions. A limited number of studies investigated the performance of SREs for spatio-temporal (regional) drought analysis, which is a key component for developing tools for regional drought planning and management. In this study, the performance of two recent SREs (data length > 30 years), which includes Artificial Neural Networks Climate Data Record (PERSIANN-CDR) and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) are selected for spatio-temporal drought assessment over different climatic regions located in Iran. Firstly, the accuracy of SREs was evaluated for deriving standardized precipitation index (SPI) at different time scales (1, 3, 6, 9 and 12 months) for four climatic regions during the period of 1983–2012. Secondly, the performance of SREs was evaluated for regional drought assessment based on the concept of the Severity-Areal-Frequency (SAF) curves. It was observed that the performance of SREs can be different with respect to gauge data in terms of quantifying drought characteristics (e.g., severity, duration, and frequency), identification of major historical droughts, and a significant difference can be observed based on the SAF analysis. For example, the number of drought events based on shorter time scales (SPI-1 and 3) found to be greater for SREs in comparison to gauge information for all climatic regions. While investigating the major historical droughts, discrepancies can be observed between these two types of data sets. For example, gauge data suggests wetness (i.e., SPI-3 > 0.5) near southern Iran, whereas, SREs show droughts (SPI < -1.0) in the same spatial domain. The performance of SREs with respect to gauge data varies largely in terms of quantifying the frequency component embedded in the SAF curves for selected climatic regions located in Iran. Our research findings can be useful for drought assessment in ungagged basins, as well as to develop regional drought management plans to improve water security by integrating multivariate nature of drought events.
ABSTRACT
In situ rainfall data observed by gauges is the most important data in water resources management. However, these data have some limitations both spatially and temporally. With the ...advancements in satellite rainfall products, it is now possible to evaluate whether these products can capture the climatology of known rainfall characteristics. In this study, five satellite rainfall estimates (SREs) were evaluated against gauge data based on different rainfall regimes over Iran. The evaluated SREs are Climate Prediction Center Morphing Technique, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Tropical Rainfall Measuring Mission (TRMM), PERSIANN Climate Data Record (PERSIANN‐CDR) and the most recently available Multi‐Source Weighted‐Ensemble Precipitation (MSWEP) data. The performance of these five SREs is evaluated with respect to gauge data (total: 958 stations) in eight different climatic zones at daily, monthly, and wet/dry spells during a ten‐year period (2003–2012). Performance of SREs was evaluated using metrics of comparison based on correlation coefficient (CC), root mean square error, and relative error. The study shows that MSWEP has the highest CC (0.72) followed by TRMM (0.46) and PERSIANN‐CDR (0.43) at daily time scale. The performance of SREs varies with respect to climatic regimes, for example, the best correlation was observed in the south, the shore of Persian Gulf with ‘very hot and humid’ climate with CC values of 0.72, 0.70, and 0.82 for MSWEP, TRMM and PERSIANN‐CDR, respectively. Further, the performance of SREs was evaluated using the categorical statistics to capture the rainfall pattern based on different groups (e.g. light, moderate and heavy rainfall events). Results show that MSWEP, PERSIANN‐CDR, and TRMM performed well to distinguish rain from no‐rain condition, whereas for higher rainfall rates, PERSIANN‐CDR outperforms the other SREs.
Iran as a country with various geographical features (sea shores, high mountains, and vast deserts) could be a good climatological case study. The exist of mountains on north and west, beside deserts at the center of the country are the reason of insufficient both temporal and spatial distribution of gauge stations. Satellite rainfall estimated (SRE) datasets could be the other sources for climatological studies. In this study, the evaluation of five different SREs (CMORPH, PERSIANN, PERSIANN‐CDR, TRMM, and MSWEP) with comparison to gauge dataset is considered over Iran.
In situ rainfall data plays a vital role in drought assessment. However, adequate in situ data are not available in many parts of the world, and they do not provide the proper spatial coverage for ...drought assessment. With the advacements in satellite rainfall estimates (SREs), it is possible to monitor droughts in ungauged basins. However, the applications of SREs for drought forecasting are not widely explored due to the inherent uncertainties associated with these products.In this study, we evaluated two long‐term SREs for drought forecasting in the Zayandehrood basin, a critical region in the central plateau of Iran. The performance of two SREs, including Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Climate Data Record (PERSIANN‐CDR), and Multi‐Source Weighted‐Ensemble Precipitation (MSWEP) are compared with observations during 1983–2015. The overall results indicate that utilizing MSWEP data in the forecasting model can slightly overestimate the probability of spring drought based on winter drought (with the highest error of 8.5%). In comparison, the PERSIANN‐CDR underestimated the probabilities (with the lowest error being −44%). The performance of copula models and SREs can vary based on the thresholds for drought severity. For example, the performance of MSWEP datasets for predicting moderate to severe droughts during the Spring season is closer to the predicted values by gauge datasets. It is concluded that the MSWEP may be considered more reliable in drought forecasting than the PERSIANN‐CDR. Our results highlight the potential application of copula‐based forecasting models for seasonal drought forecasting using SREs datasets. Such models can be implemented for global‐scale drought predictions, especially in ungagged basins.
This research is an attempt to find the evaluation of PERSIANN‐CDR and MSWEP in drought forecasting. Zayandehrood basin which is located on central Iran and suffers from severe droughts is selected as a case study. Two different probabilistic approaches are developed by copula joint functions and their results are compared. Results show that MSWEP shows better performance in drought forecasting.
