This study investigates the application of precipitation estimation from remote sensing information using artificial neural networks (PERSIANN) for hydrological modeling over the Russian River catchment in California in the United States as a case study. We evaluate two new PERSIANN products including the PERSIANN-Cloud Classification System–Climate Data Record (CCS–CDR), a climatology dataset, and PERSIANN–Dynamic Infrared Rain Rate (PDIR), a near-real-time precipitation dataset. We also include older PERSIANN products, PERSIANN-Climate Data Record (CDR) and PERSIANN-Cloud Classification System (CCS) as the benchmarks. First, we evaluate these PERSIANN datasets against observations from the Climate Prediction Center (CPC) dataset as a reference. The results showed that CCS–CDR has the least bias among all PERSIANN family datasets. Comparing the two near-real-time datasets, PDIR performs significantly more accurately than CCS. In simulating streamflow using the nontransformed calibration process, E_KGE values (Kling–Gupta efficiency) for CCS–CDR (CDR) during the calibration and validation periods were 0.42 (0.34) and 0.45 (0.24), respectively. In the second calibration process, PDIR was considerably better than CCS (E_KGE for calibration and validation periods ~ 0.83, 0.82 for PDIR vs. 0.12 and 0.14 for CCS). The results demonstrate the capability of the two newly developed datasets (CCS–CDR and PDIR) of accurately estimating precipitation as well as hydrological simulations.

Lithological studies and geological unit mappings are generally applicable to many fields of natural resource management. Relatively suitable aquifers have been formed in complex formations in northwest Shahrood due to the presence of carbonate rocks as well as erosion and tectonic forces in the region. This study aims to identify and separate the calcareous formations that can form karst aquifers in the study area. As a result of erosion and tectonic forces, the rocks of the region exhibit spectral fluctuations, making it difficult for mapping geological formations using multispectral images. Therefore, Landsat 8 satellite-based images were processed by adopting the minimum noise fraction (MNF), independent component analysis (ICA), and band ratio (BR). The indices of calcareous formations and shale formations were created by the BR through the spectral behavior of pure pixels. Moreover, the support vector machine (SVM) and maximum likelihood (ML) were employed for classification. The SVM classifier proved more capable of classification than the ML classifier, and the transform ICA outperformed the MNF in the separation of formations. The lithological maps were extracted using the SVM with an overall accuracy (OA) of 68.04%. Furthermore, a method decision tree (DT) was employed to improve the classification accuracy. The DT classifier was then utilized to reclassify lithological maps that were classified by SVM and ML through morphological characteristics and indices of formations The DT classifier improved the lithological map accuracy by 10%. The boundaries of calcareous formations were extracted from non-calcareous formations with an accuracy of 93%, and the regional constructions were separated with an accuracy of ∼80 % . Finally, the lithological map was developed with a kappa of 0.734 and an OA of 78.59%.

Hydrological modeling is essential for runoff simulations in line with climate studies, especially in remote areas with data scarcity. Advancements in climatic precipitation datasets have improved the accuracy of hydrological modeling. This research aims to evaluate the APHRODITE, PERSIANN-CDR, and ERA5-Land climatic precipitation datasets for the Hablehroud watershed in Iran. The datasets were compared with interpolated ground station precipitation data using the inverse distance weighted (IDW) method. The variable infiltration capacity (VIC) model was utilized to simulate runoff from 1992 to 1996. The results revealed that the APHRODITE and PERSIANN-CDR datasets demonstrated the highest and lowest accuracy, respectively. The sensitivity of the model was analyzed using each precipitation dataset, and model calibration was performed using the Kling–Gupta efficiency (KGE). The evaluation of daily runoff simulation based on observed precipitation indicated a KGE value of 0.78 and 0.76 during the calibration and validation periods, respectively. The KGE values at the daily time scale were 0.64 and 0.77 for PERSIANN-CDR data, 0.62 and 0.75 for APHRODITE precipitation data, 0.50 and 0.66 for ERA5-Land precipitation data during the calibration and validation periods, respectively. These results indicate that despite varying sensitivity, climatic precipitation datasets present satisfactory performance, particularly in poorly gauged basins with infrequent historical datasets.

This study evaluates the performance of physically-based and deep learning models in simulating runoff and estimating terrestrial water storage (TWS) in the Hablehroud River Basin, a semi-arid watershed in northern Iran with increasing irrigation demands. Two semi-distributed and physically-based models, including SWAT (Soil and Water Assessment Tool), VIC (Variable Infiltration Capacity), and lumped and semi-distributed configurations of Bidirectional Long Short-Term Memory (BLSTM-L and BLSTM-S), were applied using daily meteorological and hydrometric data. The GLEAM v4.2 (Global Land Evaporation Amsterdam Model) dataset was used to estimate evapotranspiration, and a water balance method was used to determine monthly TWS. The monthly TWS results from each model varied considerably, especially during the growing season, but the annual storage estimates from each model exhibited a similar bias. The BLSTM-S model showed excellent consistency in monthly TWS estimation and the highest accuracy in streamflow simulation (NSE = 0.87, KGE = 0.91). According to observational analysis, BLSTM-S best represented the seasonal pattern of water being withdrawn during the agricultural months and primarily stored in the winter and early spring (often as snow in mountainous regions). These results suggest that in areas affected by irrigation, monthly TWS is a more sensitive indicator of model performance. Although physically-based models offer process transparency, their higher monthly biases can reduce their reliability in short-term water allocation. The study highlights the added value of deep learning, particularly semi-distributed BLSTM, in improving both runoff simulation and seasonal water storage representation for operational water management.

In hydrologic modeling, forecasting peak floods is a complex and high‐priority task, particularly in regions with limited in situ observations. This research investigates the potential of utilizing satellite‐based precipitation products with near real‐time updates (NRT‐SbPP), together with deep learning, to improve peak flow estimation and flood forecasting. Using Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (IMERG), the Final version as a reference, four NRT‐SbPPs were evaluated: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)‐Cloud Classification System (CCS), PERSIANN‐Dynamic‐Infrared–RainRate (PDIR), the IMERG‐Early, and IMERG‐Late. This study evaluated the performance of these datasets for streamflow simulation using the Hydrologic Modeling System (HEC‐HMS) in a sub‐basin of the Russian River watershed. Bias correction was performed using Long Short‐Term Memory (LSTM) networks, with IMERG‐Final serving as the reference precipitation dataset. This correction improved the quality of the input rainfall data and led to more accurate streamflow simulations. For example, the Root Mean Square Error of IMERG‐Late decreased from 1.98 to 1.48 mm with LSTM, resulting in performance metrics similar to those of observed discharge (Nash‐Sutcliffe Efficiency: 0.82). Most importantly, PDIR exhibited significant enhancement, with a 36% correlation coefficient increase (from 0.52 to 0.81), as well as high rates for extreme event detection. The further findings establish the potential of employing LSTM methods and using IMERG‐Final as a reference to incorporate NRT‐SbPPs within real‐time flood forecasting and early warning frameworks. This method leverages the new technologies of satellite‐based meteorological data and offers an efficient, cost‐effective option to enhance flood prediction and disaster risk management, especially in data‐scarce regions.