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Investigating systematic meteorological–agricultural–hydrological drought propagation within an integrated watershed framework is crucial yet challenging for advancing robust early warning systems and targeted resilience strategies. To address this gap, this study employs the standardized precipitation evapotranspiration index (SPEI), standardized soil moisture index (SSMI), and nonlinear joint hydrological drought index (NJHDI) to characterize meteorological, agricultural, and hydrological drought, respectively. Utilizing the strongest correlation method, variations in the propagation time along moisture movement pathways within the system are quantified using the Soil and Water Assessment Tool (SWAT) model. The Yellow River basin (YRB) is selected as the case study area. Key results reveal distinct seasonal patterns in meteorological–agricultural propagation across the basin, with escalated spring and summer timescales. However, weaker correlations and limited seasonality emerge for agricultural–hydrological linkages due to sustaining baseflow buffers. Specifically, the arid and semiarid region exhibited shorter propagation time with 1–5 months, while the semiarid and semihumid region displayed longer timescales with 7–12 months. These findings provide valuable scientific references for enhancing real-time early warning systems tailored to coupled watershed systems. The integrated methodology underscores the importance of unraveling fine-scale spatiotemporal propagation variability for localized drought resilience.

Hydropower plant operation reorganizes the temporal and spatial distribution of water resources to promote the comprehensive utilization of water resources in the basin. However, a lot of uncertainties were brought to light concerning cascade hydropower plant operation with the introduction of the stochastic process of incoming runoff. Therefore, it is of guiding significance for the practice operation to investigate the stochastic operation of cascade hydropower plants while considering runoff uncertainty. The runoff simulation model was constructed by taking the cascade hydropower plants in the lower reaches of the Lancang River as the research object, and combining their data with the copula joint function and Gibbs method, and a Markov chain was adopted to construct the transfer matrix of runoff between adjacent months. With consideration for the uncertainty of inflow runoff, the stochastic optimal operation model of cascade hydropower plants was constructed and solved by the SDP algorithm. The results showed that 71.12% of the simulated monthly inflow of 5000 groups in the Nuozhadu hydropower plant drop into the reasonable range. Due to the insufficiency of measured runoff, there were too many 0 values in the derived transfer probability, but after the simulated runoff series were introduced, the results significantly improved. Taking the transfer probability matrix of simulated runoff as the input of the stochastic optimal operation model of the cascade hydropower plants, the operation diagram containing the future-period incoming water information was obtained, which could directly provide a reference for the optimal operation of the Nuozhadu hydropower plant. In addition, taking the incoming runoff process in a normal year as the standard, the annual mean power generation based on stochastic dynamic programming was similar to that based on dynamic programming (respectively 305.97 × 108 kW⋅h and 306.91 × 108 kW⋅h), which proved that the operation diagram constructed in this study was reasonable.

The composition and change of runoff are closely related to climate change and human activities. To design effective watershed water resources management measures, there is a need for a clear understanding of the impact of climate change and human activities on baseflow and surface runoff. The purpose of this essay is to quantify their impact on the annual total stream flow, surface runoff, and base flow in the Weihe River Basin (WRB) using a two-stage annual precipitation partitioning method, wherein the surface runoff and base flow are separated from the measured total flow by using a one-parameter digital filter method for which the common filter parameter value is 0.925. The stream flow records were split into two periods: 1960–1970 (pre-change period) and 1971–2005 (post-change period) based on the hydrological breakpoints detected. We found that climate change and human activities have different impacts on base flow and surface runoff. We attributed the decrease in surface runoff due to climate change accounting for 76–78%, while we determined that human activities were responsible to the decrease in base flow accounting for 59–73% of the total observed change. We concluded that both climate change and human beings contributed to the hydrologic change through different hydrological processes: climate change dominated the surface runoff change, while human influences controlled the base flow change. To achieve the expected goals of ecological restoration, appropriate measures must be taken by watershed management in the WRB to mitigate the likely impacts of climate change on water hydrology.

Previous studies that quantified variations in runoff have mainly focused on the combined impacts of climate and human activities or climate and land cover change. Few have separated land cover change from human activities, which is critical for effective management of water resources. This study aims to investigate the impact of changing environmental conditions on runoff using the Soil and Water Assessment Tool (SWAT) model; we examined three categories: climate, land cover change and direct human activities. The study area was the Wei River Basin, a typical arid to semi-arid basin that was divided into five sub-zones (UZ, MZ, DZ, JZ and BZ). Our results showed the following: (1) the calibrated SWAT model produced satisfactory monthly flow processes over the baseline period from 1978 to 1986; (2) compared to the baseline period, the impact of climatic variations decreased and the impact of direct human activities increased from the 1990s to the 2000s, while the impact of land cover change was generally stable; and (3) climatic variations were the main cause of runoff declines over the entire basin during the 1990s and in the UZ, MZ and JZ areas during the 2000s, while direct human activities were most important in the DZ and BZ areas during the 2000s.