The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land–atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical—direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction.

Accurate accounting of spatiotemporal variability of precipitation is essential for understanding the changing climate. Among the available precipitation estimates, the Global Precipitation Measurement (GPM) is an international satellite network providing advanced global precipitation estimates. The integrated multi-satellite retrievals for GPM (IMERG) algorithm combines information from the GPM satellite constellation to estimate precipitation and yields a better performance in detecting precipitation events and spatial resolution. Here, we used twenty years (2001–2020) of IMERG Final data over the entire Nepal to analyze the spatial and temporal distribution of precipitation. This study evaluates the dynamic characteristics of the precipitation amounts, intensities, frequencies, and other relevant data across Nepal, using four IMERG datasets: (i) microwave only, (ii) infrared only, (iii) multi satellites gauge uncalibrated, and (iv) multi satellites gauge calibrated. A total of 28 precipitation indices was computed: threshold-based counts, consecutive days, precipitation amounts and extremes, precipitation intensity, percentile-based extremities, proportion-based indices, and additional seasonal indices. Results show that all four IMERG datasets are promising in capturing spatial details. The frequency of wet days corresponds with ground-based precipitation. Still, most indices, including consecutive wet days, annual and monsoon precipitation, and days when precipitation equaled or exceeded 20 and 50 mm, were substantially underestimated. In addition, the microwave-only dataset highly underestimated the precipitation amount. Notably, a substantial proportion of false alarms is a problem for all four IMERG datasets. Moreover, our results demonstrate that the IMERG uncalibrated dataset tends to overestimate precipitation during heavy precipitation events. These advantages and shortcomings of IMERG datasets over the rugged terrain of Nepal can provide useful feedback for sensor and algorithm developers to overcome limitations and improve retrieval algorithms. The study findings are helpful to the broader data users and practitioners for effective water decision applications.

The Indus Water Treaty allocated the water of the Ravi River to India, and India constructed the Thein Dam on the Ravi River. This study investigates the water availability of the Ravi Riverfront for both pre-dam and post-dam scenarios augmented with pre-flood, flood, and post-flood sub-scenarios. The study also investigates river water availability for low and high magnitudes (Flow Duration Curves) and its linkages with climate change. The modified Mann–Kendall, Sen’s slope estimator, and Pearson correlation were used to investigate the river flows. It was found that there is a remarkable decrease in the river water by −36% of annual mean flows as compared to the pre-dam scenario. However, during the flood season, it was −32% at the riverfront upstream (Ravi Syphon Gauge). The reduction in water volume was found as 2.13 Million Acre Feet (MAF) and 1.03 MAF for maximum and mean, respectively, in the Rabi (Winter) season, and 4.07 MAF and 2.76 MAF for max and mean, respectively, in the Kharif (Summer) season. It was also revealed that 180–750 cusecs of water would be available or exceeded for 90% to 99% of the time at Ravi Riverfront during the flood season. The high flows were mainly controlled by temperature in the pre-dam scenario; presently, this water is stored in the Thein Dam reservoir. In contrast, the precipitation role is significant in the post-dam scenario, which means that the flows in the Ravi River are mainly due to base flow contributions and precipitation. This study is the first step in analyzing the river water availability of the Ravi Riverfront, which will ultimately address the associated problems and their solutions to decision-makers. Additionally, implementing an eco-friendly riverfront promotes urban sustainability in developed urban areas, such as Lahore City, and will lead to a comfortable and healthy lifestyle; this will only be possible with water availability in the Ravi Riverfront reach.

Merging multi-source satellite precipitation data through Triple Collocation (TC) is an established method for enhancing precipitation estimates without ground observations. However, the effectiveness of TC is limited by three constraints: the need for three data sources, the sum of weights equalling one, and no error cross-correlation among datasets. These limitations restrict applications to areas with three distinct data sources, may result in sub-optimal combinations, and can introduce biases. We explored a new framework, Signal to Noise Ratio optimisation (SNR-opt), which alleviates these restrictions. Applying TC and SNR-opt to datasets in the Himalayas, including three satellite-based (IMERG Final, CMORPH, PERSIANN), one soil moisture-based (SM2RAIN-ASCAT), and one reanalysis dataset (ERA5), we assessed their performance. The flexibility of SNR-opt allowed for creating products suited to varying rainfall conditions and reconstructing extreme precipitation products for High Mountain Asia (HMA). The results show that (i) the data triplet containing IMERG-final was the best performing among all merging products (ii) the merging method significantly enhanced dataset accuracy compared to the parent datasets, with SNR-opt proving superior to TC in all selected precipitation indices evaluated; and, (iii) SNR-opt had the flexibility of estimating both low and heavy rainfall, with a reduced bias for extreme precipitation, using fewer inputs (two) compared to TC. These advantages were found to be especially notable for the high-mountain region in focus, given the significant variability and limited observational data available.

Gridded precipitation datasets (GPDs) have complemented gauge-based measurements in global hydrology by providing spatiotemporally continuous rainfall estimates for streamflow prediction. However, these datasets suffer from uncertainties in space and time, particularly in complex terrains like the Himalayas. Merging multi-GPDs offers a potential solution to reduce such uncertainties, but the actual contribution of the merged product to hydrological modeling remains underexplored in data-scarce and topographically complex regions. Here, we applied a gauge-independent merging technique called Signal-to-Noise Ratio optimization (SNR-opt) to merge three precipitation products: ERA5, SM2RAIN, and IMERG-late. The resulting Merged Gridded Precipitation Dataset (MGPD) was evaluated using the hydrological model (HYMOD) across three major river basins in the Central Himalayas (Koshi, Narayani, and Karnali). The results show that MGPD significantly outperforms the individual GPDs in streamflow simulation. This is evidenced by higher Nash–Sutcliffe Efficiency (NSE) values, 0.87 (Narayani) and 0.86 (Karnali), compared to ERA5 (0.83, 0.82), SM2RAIN (0.83, 0.85), and IMERG-Late (0.82, 0.78). In Koshi, the merged product (NSE = 0.80) showed slightly lower performance than SM2RAIN (NSE = 0.82) and ERA5 (NSE = 0.81), likely due to the poor performance of IMERG-Late (NSE = 0.69) in this basin. These findings underscore the value of merging precipitation datasets to enhance the accuracy and reliability of hydrological modeling, especially in ungauged or data-scarce mountainous regions, offering important implications for water resource management and forecasting.