Search

Precipitation, as an important component of the Earth's water cycle, plays a determinant role in various socio-economic practices. Consequently, having access to high-quality and reliable precipitation datasets is highly demanded. Although Gridded Precipitation Products (GPPs) have been widely employed in different applications, the lack of quantitative assessment of GPPs is a critical concern that should be addressed. This is because the inherent errors in GPPs would propagate into any models in which precipitation values are incorporated, introducing uncertainties into the final results. This paper aims to quantify the capability of six well-known GPPs (TMPA, CHIRPS, PERSIANN, GSMaP, IMERG, and ERA5) at multiple time scales (daily, monthly, and yearly) using in situ observations (over 1.7 million) throughout Iran over the past two decades (2000–2020). Both continuous and categorical metrics were implemented for precipitation intensity and occurrence assessment based on the point-to-pixel comparison approach. Although all metrics did not support the superior performance of any specific GPP, taking all investigations into account, the findings suggested the better performance of the Global Satellite Mapping of Precipitation (GSMaP) in estimating daily precipitation (CC = 0.599, RMSE = 3.48 mm/day, and CSI = 0.454). Based on the obtained continuous metrics, all the GPPs had better performances in dry months, while this did not hold for the categorical metrics. The validation at the station level was also carried out to present the spatial characteristics of errors throughout Iran, indicating higher overestimation/underestimation in regions with higher precipitation rates. The validation analysis over the last two decades illustrated that the GPPs had stable performances, and no improvement was seen, except for the GSMaP, in which its bias error was significantly reduced. The comparisons on monthly and yearly time scales suggested the higher accuracy of monthly and yearly averaged precipitation values than accumulated values. Our study provides valuable guidance to the selection and application of GPPs in Iran and also offers beneficial feedback for further improving these products. [ABSTRACT FROM AUTHOR]

Palmer Drought Severity Index (PDSI) is known as a robust agricultural drought index since it considers the water balance conditions in the soil. It has been widely used as a reference index for monitoring agricultural drought. In this study, the PDSI time series were calculated for nine synoptic stations to monitor agricultural drought in semi-arid region located at Zagros mountains of Iran. Autoregressive Moving Average (ARMA) was used as the stochastic model while Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM) were applied as Machine Learning (ML)-based techniques. According to the time series analysis of PDSI, for the driest months the most PDSI drought events are normal drought and mild drought conditions. As an innovation, Dragonfly Algorithm (DA) was used in this study to optimize the SVM's parameters, called as the hybrid SVM-DA model. It is worthy to mention that the hybrid SVM-DA is developed as a meta-innovative model for the first time in hydrological studies. The novel hybrid SVM-DA paradigm could improve the SVM's accuracy up to 29% in predicting PDSI and therefore was found as the superior model. The best statistics for this model were obtained as Root Mean Squared Error (RMSE) = 0.817, Normalized RMSE (NRMSE) = 0.097, Wilmott Index (WI) = 0.940, and R = 0.889. The Mean Absolute Error values of the PDSI predictions via the novel SVM-DA model were under 0.6 for incipient drought, under 0.7 for mild and moderate droughts. In general, the error values in severe and extreme droughts were more than the other classes; however, the hybrid SVM-DA was the best-performing model in most of the cases. [ABSTRACT FROM AUTHOR]

Solar radiation plays a pivotal role in the energy balance at the Earth's surface, evaporation, snow melting, water requirements of plants, and hydrological control of catchments. In this work, performance of ERA-Interim (a reanalysis dataset) was examined to estimate solar radiation at Ahvaz, BandarAbbas, and Kermanshah weather stations representing the even spatial distribution over Iran using eight empirical models and an artificial intelligence-based model (SVM: Support Vector Machine). In the calibration set, SVM exhibited the best performance with RMSEs of 249, 299 and 437 J.cm−2.day−1 at the aforementioned stations, respectively. In validation set, SVM reduced the errors in the estimates of solar radiation by 2.5 and 7.3 percent compared to the best empirical model at Ahvaz station (Abdallah model, RMSE = 242 J.cm−2.day−1) and Kermanshah station (Angstrom-Prescott model, RMSE = 315 J.cm−2.day−1), respectively. During the validation at BandarAbbas station, Bahel and Abdallah model (RMSE = 309 J.cm−2.day−1), Angstrom-Prescott model (RMSE = 310 J.cm−2.day−1) and SVM (RMSE = 312 J.cm−2.day−1) showed a relatively similar performance. The results also showed that the ERA-Interim dataset can be a comparatively suitable alternative to some of the empirical models, where radiation or the input parameters of empirical models are not directly measured, with RMSEs ​​of 382.81, 320.82 and 414.1 J.cm−2.day−1 at Ahvaz, BandarAbbas, and Kermanshah stations, respectively (in validation phase); although its error rates are significant compared with the SVM model, and substituting it for artificial intelligence-based models is not recommended. [ABSTRACT FROM AUTHOR]

An exhaustive evaluation of water resources is a prerequisite for evidence-informed planning and implementing sustainable management strategies. However, the lack of sufficient information on water supply and consumption, alongside the technical limitations on comprehensive accounting for inter-relations and interactions between the subsystems, has resulted in decisions with often long-lasting outcomes and undesirable consequences. Water accounting is a tool for systematic acquisition, analysis, and reporting of water-related information to fill the existing knowledge gap on water flows and fluxes. In this study, Water Accounting Plus (WA+) framework is applied to the western region of Lake Urmia, a dying hyper-saline lake, to assess water use and crop water productivity (CWP) from 2010 to 2016. Remotely sensed information along with a distributed hydrological model (SPHY) is used to fill the information gap on water resources and inform effective policy actions. Our analysis reveals that the agricultural sector is neither productive nor efficient while there is a considerable scope to ameliorate water productivity and beneficial water use by adopting proper water management practices. Average CWP values for wheat, sugarbeet, vineyard, and apple vary between 0.38 and 0.55, 5.1–5.6, 1.5–1.7 and 1.9–2.3 (kg/m3), respectively while storage changes show consistent depletion, especially during dry year, up to 117 MCM. The results indicate that a 40% reduction in blue water use is achievable to supply additional water to revive Lake Urmia. This study highlights the importance of water accounting and information flow for decision-makers, practitioners, and farming communities to define practical targets and enhance productivity in water-scarce regions. [Display omitted] • WA+ was applied to the western region of Lake Urmia using RS and SPHY model. • Water accounts facilitate decision making and sustainable water management. • Water supply, availability, and productivity was analyzed. • There is significant potential to close CWP gap and reduce blue water use. • The proposed approach is valuable in data-scarce regions. [ABSTRACT FROM AUTHOR]

Timely and accurate detection of drought events using reliable data with continuous spatial coverage is of great importance to adapt efficient mitigation strategies. Despite several studies that have been carried out to validate satellite precipitation products in Iran, the performance of satellite precipitation products in precipitation amount estimation and drought events detection has not been assessed yet simultaneously. It is unclear if good estimates of mean precipitation values by satellites can also provide good performance in detecting drought events. Here, we used a comprehensive framework to evaluate the accuracy of the satellite-based precipitation and their performance in detecting drought events against ground-based data. We evaluated three satellite precipitation products, namely, TRMM TMPA 3B43 v7, CHIRPS, and PERSIANN-CDR using multiple evaluation metrics, and three different drought indices (Standardized Precipitation Index-SPI, Standardized Precipitation Evapotranspiration Index-SPEI, and Multivariate Standardized Precipitation Index-MSPI) over different climate zones of Iran during 1983–2017. Our results indicated that TRMM and PERSIANN-CDR had mainly overestimation while CHIRPS had mostly underestimation in precipitation over the different climates of Iran. The precipitation overestimation (underestimation) in TRMM, PERSIANN-CDR, and CHIRPS were observed in 80.0% (20.0%), 71.4% (28.6%), and 45.7% (54.3%) of stations, respectively. The most overestimation in satellite products was found to be in the arid-related climate regions, while the most underestimation was detected in humid-related climate regions. The average correlation coefficient values of PERSIANN-CDR (0.6) and TRMM (0.6) products against ground observations were higher than CHIRPS (0.4) dataset. Further, we found a lower correlation coefficient (0.3) between monthly satellite-based and ground-based precipitation in arid-related regions (with Root Mean Square Error, RMSE, ~ 10 mm per month) of the country but a high correlation coefficient (0.6) in humid-related climates (with RMSE ~100 mm per month). The results of the drought assessment indicated that the most severe meteorological drought events identified by SPI, SPEI, and MSPI were in 1998–2001, 2008–2009, 2010–2012, and 2016–2017 in all climate zones. The most intense drought events were in arid and semi-arid climate zones. Overall, in terms of the statistical metrics at multiple timescales, TRMM and PERSIANN-CDR outperformed CHIRPS, when compared against ground-based observations, for drought events detection. Also, TRMM and PERSIANN-CDR with higher scores for Probability of Detection (POD) and Critical Success Index (CSI) and lower scores for False Alarm Ratio (FAR) showed a good ability to detect drought events in arid and semi-arid climates compared to other climate zones. Although the CHIRPS estimated precipitation with acceptable accuracy, it did not detect drought events as the TRMM and PERSIANN-CDR did. [Display omitted] • Developing a comprehensive framework to detect accurate precipitation and drought events. • CHIRPS did not detect drought events as good as the TRMM and PERSIANN-CDR. • Satellite products detected more accurate drought events in arid-related climates. [ABSTRACT FROM AUTHOR]