Introduction

Coastal zones, serving as vital interfaces between land and the ocean, have become focal points for dynamic socio-economic development and have exhibited increased sensitivity to the effects of climate change1,2. Extreme weather events, like storms, tsunamis, and coastal flooding, can rapidly alter the structure and function of coastal ecosystems, resulting in severe ecological damage and substantial economic losses when they arrive3,4,5. However, the influence of extreme weather events in remote inland areas on the ecological environment and aquaculture industry of coastal zones is still rarely reported, and the underlying transference mechanisms remain poorly understood.

Kelps serve as one of the most important aquaculture species and function as foundation species in coastal ecosystems, providing habitat, nursery ground, and food for thousands of organisms6. China boasts the world’s largest kelp cultivation industry. The aquaculture area and yield of fresh kelp in Rongcheng City on the eastern coast of the Shandong Peninsula both rank first in China7,8 (Fig. 1). However, in November 2021, Rongcheng City experienced the worst kelp mortality on record, accompanied by severe red tide blooms9. Consequently, the kelp (Saccharina Japonica) yield in Rongcheng City was almost extinct in 2022, resulting in an estimated direct economic loss of nearly 200 million Chinese Yuan10. The 2021–2022 kelp mortality event has dealt a devastating blow to local aquaculture industry. Investigations conducted in situ post-event have determined that this ecological disaster was attributed to abnormally high water transparency and a severe depletion of phosphate in seawater10. Nevertheless, the reasons for the increase in water transparency and nutrient deficiency remain unclear. Event attribution and underlying mechanism elucidation of this kelp mortality event on larger spatiotemporal scales remain to be further studied.

Fig. 1: Geographic and hydroclimate context of the study area.
figure 1

a Horizontal distribution of the rainfall anomalies of each province in China in 2021. The magenta lines indicate the large rivers. The black solid box indicates the study area in e. b The location of Yellow River watershed and Hai River watershed. c The location of Huang-Huai-Hai plain. d Horizontal distribution of the population (million) of each province in China. e Bathymetric map of the Bohai Sea and North Yellow Sea with the locations of the survey stations (black solid triangles). The magenta circles indicate the cities of Shandong Province. The green rhombus indicates the gauge station of Li**. The blue solid line indicates the Yellow River, Hai River, and Liao River. The dashed box indicates the Rongcheng offshore area where the MODIS imageries are averaged in Fig. 8. The arrows indicate the ocean current of North Shandong Coastal Current (NSCC), Yellow Sea Warm Current (YSWC) and Liaonan Coastal Current (LNCC). The Roman numerals indicate the location of I Bohai Bay, II Laizhou Bay, and III Liaodong Bay.

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Previous studies revealed that, wind-driven waves caused bottom sediment resuspension and the release of nutrients carried by sediment particles during the winter half-year, which promoted the growth of plankton along coast of the Shandong Peninsula11,12. The weakening of the wind speed was considered a primary factor in the reduction of suspended sediment concentration (SSC) and thus higher water transparency in the coastal area of Bohai and North Yellow Sea12,13, and the proportion of plankton in sediment particles was negligible11. However, no abnormalities were observed in the wind field during the autumn of 2021. The underlying cause of this anomalous environmental fluctuation remains elusive. It is worth noting that two months before the kelp mortality event, continuous extreme rainfall was strikingly recorded over North China in the autumn of 2021, causing an unexpected autumn flood in the Yellow River. Does the autumn extreme rainfall event in North China have a causal relationship with the kelp mortality event so far away, and what are the intrinsic mechanisms involved? Here, we aim to combine field observations (Supplementary Fig. S1) with contemporaneous satellite remote sensing data to unveil the relationship and underlying transference mechanism between remote inland extreme rainfall events and coastal kelp mortality events thousands of kilometers away. This provides a unique opportunity to examine the teleconnection of global climate change on regional marine ecosystems through a comprehensive assessment of physical, chemical, and biological cascading effects.

Results and discussion

Inland extreme rainfall events led to autumn flood of the Yellow River and phosphorus limitation of the Bohai Sea

In summer, the East Asian climate is regulated by a high-pressure system, called Western North Pacific Subtropical High (WNPSH)14. The WNPSH transports water vapor subtropical western North Pacific into East Asia through southerly wind, and anchors the rain belt on its northwestern periphery where a convergence of moist southerly winds and cold air masses occurs15. Anomalous intrusion or deficiency of the WNPSH can lead to extreme weather events such as floods, droughts, and heat waves in East Asia50. The model included eight major harmonic constituents (M2, S2, N2, K2, K1, O1, P1, Q1) on a 1/30° resolution grid. MATLAB package Tide Model Driver was utilized to extract tidal currents. Tidal amplitudes at gauge stations Yantai and Weihai were compared with tidal heights provided by the Tide Tables 2021 (Supplementary Fig. S9), edited by the National Marine Data and Information Service51. The quality and credibility of the two datasets were confirmed by their good consistency. Field observations and model results indicated that the tidal currents in the study area were dominated by barotropic tidal currents, with the energy of baroclinic tidal currents accounting for only 5% on the northern coast of the Shandong Peninsula52. To eliminate the influence of tidal currents, the residual current was derived by subtracting Tide Model Driver derived total barotropic tidal velocities from the original shipboard ADCP measurements based on time and coordinate. The water-mass volume backscattering strength (\({S}_{v}\); dB) recorded by the ADCP was used to represent the SSC53,54. On 2 November 2021, the current velocity and \({S}_{v}\) recorded by the ADCP were presented to check the hydrodynamic characteristics of the NSCC along the northern coast of the Shandong Peninsula (Fig. 6).

Open-access data

Open-access reanalysis datasets were employed to identify the long-term trends of sea surface properties and monitor red tide blooms over the study area (Supplementary Table S1). The monthly average wind data were derived from the NCEP/NCAR Reanalysis 1 dataset (https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html) provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA. High- resolution LANDSAT-8 images were downloaded from the US Geological Survey (USGS) database through the Global Visualization Viewer (http://glovis.usgs.gov/) to track variations in water properties.

The daily and monthly average SST, Remote Sensing Reflectance at 555 nm (Rrs555), PAR, and chl-a products (2002–2022) were derived from the NASA MODIS level 3 products (https://oceancolor.gsfc.nasa.gov). The Level 3 products featured a spatial resolution of 4.63 km. Then, the position and strength of the temperature fronts were determined55:

$${SSTG}=\sqrt{{\left(\frac{\partial T}{\partial x}\right)}^{2}+{\left(\frac{\partial T}{\partial y}\right)}^{2}}$$
(1)

\({SSTG}\) represented sea surface temperature gradient (°C km−1). T represents the \({SST}\), and x and y indicate the direction of east and north, respectively. The Rrs555 was usually used as a substitute index of water turbidity because their exponential relationship56,57,58. Here, we converted the Rrs555 to SSC based on the result of Liu et al.58 (SSC = Exp (101.8 × Rrs555) × 1.301). In addition, the chl-a concentration was used to track traces of red tides since there was a strong relationship between them59. Satellite-derived chl-a concentrations were generally overestimated in turbid coastal waters, due to the influence of dissolved organic matter on remote sensing reflectance60,61. However, it was found the overestimation had an upper limit value (10 mg m−3) in Chinese coastal seas57, which could be used as a threshold to identify phytoplankton blooms to avoid false blooms caused by turbid waters. Here, we took it as the standard for identifying phytoplankton blooms.

Statistical data from official releases helped us to clarify regional climate change and hydrological variability characteristics. The aquaculture area and yield of kelp in Shandong province were mainly from the China Fisheries Statistical Yearbook. The yearly rainfall data were derived from China Water Resources Bulletin and China Climate Bulletin. The river discharge and sediment flux recorded at gauging stations of Li** and Haihezha were supplied by the Yellow River Conservancy Commission (http://www.yrcc.gov.cn/) and the Bulletin of Chinese River Sediment compiled by the Ministry of Water Resources of the People’s Republic of China. In addition, the monthly concentration of riverine TDN and TDP for 2021 at gauging stations of Li** were obtained from the China National Environmental Monitoring Centre (http://www.cnemc.cn/en/). The TDN and TDP were measured by the alkaline potassium sulfate method62. The monthly nutrient fluxes of 2021 were obtained by multiplying the concentrations of TDN and TDP by the river discharge. Although the riverine nitrogen-to-phosphorus ratio (TDN/TDP) was different from the nitrogen-to-phosphorus ratio from the cruise survey (DIN/DIP), it still reflected the impact of extreme rainfall events on the nutrient status of rivers. The distribution and time of duration of red tide blooms during 2021 were supplied by the Bulletin of China Marine Disaster (2021) compiled by the Ministry of Natural Resources of the People’s Republic of China. Economic loss statistics resulted from extreme rainfall events were derived from Ministry of Emergency Management of the People’s Republic of China. The distribution of agricultural regions and river basins were derived from Resource and Environment Science and Data Center at the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences (https://www.resdc.cn). The population data of each province was from the National Bureau of Statistics according to the Seventh National Population Census (http://www.stats.gov.cn).

In this study, the climate mean state of each parameter was computed by averaging the multi-year data (2002–2022). The anomalies indicated the difference between the individual values and the climate mean state values. In addition, the juvenile kelp was generally transformed from the hatchery to the sea during the autumn season, and subsequently harvested in the spring through to the summer of the subsequent year63. Considering that the rapid increase in kelp unit yield after 2014 may be related to the innovation of farming technology, we conducted a lag-correlation analysis between hydrological environmental parameters (including wind speed, SST, PAR, SSC, chl-a and Yellow River discharge) and unit yield of kelp from 2014 to 2021, i.e., the relationship between environmental parameters in November each year and unit yield of kelp in the next year (Fig. 8g–l).