Understanding the interaction between land use and climate variability in regulating the hydrology of tropical watersheds remains a significant scientific and policy challenge, particularly in regions with limited data. This study applied the InVEST Annual Water Yield model to assess hydrological dynamics in the Asa watershed, Nigeria, over the period 1991–2020, using three decades of precipitation and land-use/land-cover (LULC) data, along with uncertainty quantification. The results revealed a non-linear trend in water yield, with total annual yield increasing by 6.89% between 2000 and 2010, despite declining precipitation and rising evaporative demand, primarily driven by land-use modifications. Between 2010 and 2020, yield declined by 5.39% under further precipitation reduction, where precipitation sensitivity increased eightfold, marking a shift from land-use-dominated to precipitation-dominated hydrological controls. Surrogate modeling further confirmed precipitation as the dominant driver after 2010, highlighting that cumulative land degradation weakened the watershed’s natural buffering capacity and amplified climatic responses. These findings establish a threshold at which cumulative land degradation transforms watershed hydrology from land-use-dominated to climate-sensitive regimes, providing a transferable framework for identifying vulnerability thresholds in data-scarce African tropical watersheds.

High-resolution soil moisture data is a critical component for understanding the hydrological cycle and establishing climate adaptation strategies, particularly in the complex mountainous terrains of the Far East Asian region. Recognizing the significance of this data within the southern part of the Korean Peninsula, the Korea Institute of Hydrological Survey operates in-situ soil moisture monitoring networks to provide standardized, high-quality hydrological data. Located in mountainous regions with long-term operational history, these networks are co-located with evapotranspiration and streamflow stations, facilitating efficient and integrated water balance studies.To ensure high data reliability for global research applications, KIHS implements a multi-stage quality control (QC) framework for its SM datasets. We have developed an automated outlier detection system based on the International Soil Moisture Network (ISMN) protocols to identify and filter physical anomalies such as spike, break and constant values. Furthermore, to provide continuous data, KIHS utilizes a hybrid framework of statistical methods and machine learning algorithms for gap-filling. This framework integrates CDF Matching, Kalman Filter, and SARIMAX with non-linear models like Random Forest and KNN, ensuring robust and continuous time-series data even under challenging field conditions.These high-quality datasets are shared internationally through ISMN and are highly recommended for the calibration and validation of satellite products such as SMAP and Sentinel, particularly during the non-frozen period from April to November. The objective of this presentation is to present KIHS's soil moisture monitoring networks and QC methodologies and to demonstrate the academic significance of soil moisture observation stations in the Korean Peninsula.keywords : soil moisture, the Korea Peninsula, mountainous terrain, monitoring networks, long-term operation, QC frameworks

Complete daily streamflow time series are essential for sustainable water resources management and reliable hydrological modelling; however, even short data gaps can substantially reduce the usability of streamflow records. Recurrent missing data may lead to inefficient model calibration, decreased reliability of peak and low-flow estimates, and biased hydrological statistics. Therefore, rather than leaving missing values unfilled, it can be beneficial to infill daily streamflow using appropriate methods and to provide flags indicating imputed periods. In South Korea, streamflow monitoring prior to 2008 primarily focused on flood-related observations, resulting in relatively limited daily streamflow records; since then, the production of continuous daily streamflow data for water resources management has expanded. As of 2024, daily streamflow records from more than 420 gauging stations are managed and disseminated, yet a non-negligible number of stations still contain missing values due to various causes such as river works and uncertainties in stage–discharge relationships associated with the operation of hydraulic structures. This study comparatively evaluates gap-filling techniques using paired upstream–downstream gauging stations located in basins with diverse rainfall regimes and hydrological characteristics. We assess conventional methods widely used in practice (scaling, linear regression, and equi-percentile/quantile-based approaches) under different missing-data conditions and benchmark them against an extended long short-term memory (extended LSTM) time-series model designed for streamflow infilling. Performance is evaluated using the Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and percent bias (PBIAS). In addition, flow duration curves (FDCs) are compared to examine each method’s ability to reproduce the post-infilling flow regime distribution. The outcomes are expected to support condition-dependent selection of gap-filling strategies and to improve the reliability of daily streamflow datasets with explicit quality flags.