Influence of land use on spatial distribution of primary productivity in aquatic environment in the Weihe River Basin, China

doi:10.1007/s40333-025-0095-6

Journal of Arid Land

2025, Vol. 17

Issue (3): 304-323 DOI: 10.1007/s40333-025-0095-6 CSTR: 32276.14.JAL.02500956

Research article

Influence of land use on spatial distribution of primary productivity in aquatic environment in the Weihe River Basin, China

ZHANG Haoying¹, LI Nan¹^,²^,^*(

), SONG Jinxi¹^,³, WANG Fei², TANG Bin¹, GUAN Mengdan¹, ZHANG Chaosong¹, ZHANG Yuchen¹

¹Xi'an Key Laboratory of Environmental Simulation and Ecological Health in the Yellow River Basin, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China
²Shaanxi Key Laboratory of Environmental Monitoring and Forewarning of Trace Pollutants, Shaanxi Environmental Monitoring Center, Xi'an 710054, China
³Yellow River Institute of Shaanxi Province, Northwest University, Xi'an 710127, China

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Abstract

Increasing concerns regarding aquatic ecological health and eutrophication driven by urbanization and human activities have highlighted the need to understand primary productivity (PP) dynamics in aquatic ecosystems. This study investigated the spatial distribution of PP across the Weihe River Basin, China using inverse distance weighting and analyzed the influence of different land uses and water physical-chemical parameters on PP using Mantel test and Spearman analysis. Significantly spatial heterogeneity in PP concentrations, ranging from 0.458 to 3262.807 mg C/(m²•d), was observed with high-PP sites clustered in the middle-lower reaches dominated by farmland-construction land mosaics. Core drivers included light availability (Secchi depth and sunlight duration) and phytoplankton biomass (chlorophyll-a (Chl-a)), while water temperature exhibited threshold-dependent effects. Total organic carbon played dual roles, promoting PP concentrations in low-Chl-a regions, but suppressing it under high-Chl-a regions. Dual-scale buffer analysis (500 and 1000 m buffer zones) revealed PP heterogeneity stemed from interactive land use configurations, rather than isolated types. Balanced construction land-to-farmland ratio (0.467-2.890) elevated PP concentrations in human-dominated basins (the main stem of the Weihe River and Jinghe River), whereas excessive agricultural homogenization reduced PP likely due to fertilizer saturation and algal self-shading. Ecologically sensitive basins (the Beiluohe River Basin) demonstrated distinct patterns, in which PP concentration was regulated through natural-agricultural synergies. These results deepened the understanding of land use effects on aquatic PP, providing a theoretical basis for optimizing land use strategies to reconcile eutrophication control with ecological productivity in human-stressed basins.

Key words： chlorophyll-a water physical-chemical parameters land use proportions spatial heterogeneity Mantel test Spearman analysis inverse distance weighting

Received: 01 November 2024 Published: 31 March 2025

Corresponding Authors: ^*LI Nan (linan0635@nwu.edu.cn)

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	ZHANG Haoying
	LI Nan
	SONG Jinxi
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	TANG Bin
	GUAN Mengdan
	ZHANG Chaosong
	ZHANG Yuchen

Cite this article:

ZHANG Haoying, LI Nan, SONG Jinxi, WANG Fei, TANG Bin, GUAN Mengdan, ZHANG Chaosong, ZHANG Yuchen. Influence of land use on spatial distribution of primary productivity in aquatic environment in the Weihe River Basin, China. Journal of Arid Land, 2025, 17(3): 304-323.

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http://jal.xjegi.com/10.1007/s40333-025-0095-6 OR http://jal.xjegi.com/Y2025/V17/I3/304

Fig. 1 Overview of study area and the positions of sample sites. The image is sourced from the Data Center for Resources and Environmental Sciences of the Chinese Academy of Sciences (http://www.resdc.cn).

Fig. 2 Normalized concentrations of chlorophyll-a (Chl-a) and primary productivity (PP) of each sample site in the Weihe River Basin. (a), main stem of the Weihe River; (b), Jinghe River; (c), Beiluohe River.

Table 1 Concentrations of chlorophyll-a (Chl-a) and primary productivity (PP) of three rivers in the Weihe River Basin

Fig. 3 Spatial distribution of PP in the Weihe River Basin in April and May 2023

Table S1 Physical-chemical property of water of each river in the Weihe River Basin

Fig. 4 Spearman analysis of relationships between water physical-chemical parameters and PP in the Weihe River Basin. (a), main stem of the Weihe River; (b), Jinghe River; (c), Beiluohe River. SUN, sunlight duration; SD, Secchi depth; WT, water temperature; DO, dissolved oxygen; TOC, total organic carbon; TN, total nitrogen; TP, total phosphorus; TN/TP, ratio of total nitrogen to total phosphorus. ^*, P<0.05 level; ^**, P<0.01 level.

Fig. 5 Distribution of land use types in the Weihe River Basin

Fig. S1 Area proportion of each land use type in the Weihe River Basin

Fig. 6 Relationship between area proportion of land use types and PP in the 500 (a, c, and e) and 1000 (b, d, and f) m buffer zones in the Weihe River Basin

Fig. 7 Mantel test between water physical-chemical parameters and land use types in the 500 (a, c, and e) and 1000 (b, d, and f) m buffer zones in the Weihe River Basin


[1]	Akinnawo S O. 2023. Eutrophication: Causes, consequences, physical, chemical and biological techniques for mitigation strategies. Environmental Challenges, 12: 100733, doi: 10.1016/j.envc.2023.100733.

[2]	Bai X L, Gu X H, Yang L Y. 2006. Analyses on water quality and its protection in east Lake Taihu. Scientia Limnological Sinica, 18(1): 91-96. (in Chinese)

[3]	Bell C D, Tague C L, McMillan S K. 2019. Modeling runoff and nitrogen loads from a watershed at different levels of impervious surface coverage and connectivity to storm water control measures. Water Resources Research, 55(4): 2690-2707.

[4]	Bian G D, Wang G Q, Chen J, et al. 2021. Spatial and seasonal variations of hydrological responses to climate and land use changes in a highly urbanized basin of southeastern China. Hydrology Research, 52(2): 506-522.

[5]	Bonsdorff E. 2021. Eutrophication: early warning signals, ecosystem-level and societal responses, and ways forward. Ambio, 50: 753-758.

[6]	Booeshaghi A S, Pachter L. 2021. Normalization of single-cell RNA-seq counts by log(x+1) or log(1+x). Bioinformatics, 37(15): 2223-2224. doi: 10.1093/bioinformatics/btab085 pmid: 33676365

[7]	Bratt A R, Finlay J C, Welter J R, et al. 2020. Co-limitation by N and P characterizes phytoplankton communities across nutrient availability and land use. Ecosystems, 23: 1121-1137. doi: 10.1007/s10021-019-00459-6

[8]	Cadée G C. 1975. Primary production off the Guyana coast. Netherlands Journal of Sea Research, 9(1): 128-143.

[9]	Chang S, Wei Y Q, Dai Z Z, et al. 2024. Landscape ecological risk assessment and its driving factors in the Weihe River Basin, China. Journal of Arid Land, 16(5): 603-614. doi: 10.1007/s40333-024-0013-3

[10]	Chinfak N, Sompongchaiyakul P, Charoenpong C, et al. 2023. Riverine and submarine groundwater nutrients fuel high primary production in a tropical bay. Science of the Total Environment, 877: 162896, doi: 10.1016/j.scitotenv.2023.162896.

[11]	Choi J, Min J O, Choi B, et al. 2020. Key factors controlling primary production and cyanobacterial harmful algal blooms (cHABs) in a continuous weir system in the Nakdong River, Korea. Sustainability, 12(15): 6224, doi: 10.3390/su12156224.

[12]	Cui G Y, Wang B L, Xiao J, et al. 2021. Water column stability driving the succession of phytoplankton functional groups in karst hydroelectric reservoirs. Journal of Hydrology, 592: 125607, doi: 10.1016/j.jhydrol.2020.125607.

[13]	Dokulil M T, Qian K M. 2021. Photosynthesis, carbon acquisition and primary productivity of phytoplankton: a review dedicated to Colin Reynolds. Hydrobiologia, 848: 77-94.

[14]	Doubek J P, Carey C C, Cardinale B J. 2015. Anthropogenic land use is associated with N-fixing cyanobacterial dominance in lakes across the continental United States. Aquatic Sciences, 77: 681-694.

[15]	Du J, Yang X, Xu P, et al. 2024. Linking water quality indicators in stable reservoir ecosystems: correlation analysis and ecohydrological implications. Water, 16(24): 3600, doi: 10.3390/w16243600.

[16]	Elser J J, Kyle M, Steger L, et al. 2009. Nutrient availability and phytoplankton nutrient limitation across a gradient of atmospheric nitrogen deposition. Ecology, 90(11): 3062-3073. pmid: 19967862

[17]	Fried S, Mackie B, Nothwehr E. 2003. Nitrate and phosphate levels positively affect the growth of algae species found in Perry Pond. Tillers, 4: 21-24.

[18]	Gove N E, Edwards R T, Conquest L L. 2001. Effects of scale on land use and water quality relationships: a longitudinal basin‐wide perspective. Journal of the American Water Resources Association, 37(6): 1721-1734.

[19]	Haddout S, Priya K L, Hoguane A M, et al. 2022. Relationship of salinity, temperature, pH, and transparency to dissolved oxygen in the Bouregreg estuary (Morocco): First results. Water Practice and Technology, 17(12): 2654-2663.

[20]	Harmon L J, Glor R E. 2010. Poor statistical performance of the mantel test in phylogenetic comparative analyses. Evolution, 64(7): 2173-2178. doi: 10.1111/j.1558-5646.2010.00973.x pmid: 20163450

[21]	Harvey E T, Walve J, Andersson A, et al. 2019. The effect of optical properties on Secchi depth and implications for eutrophication management. Frontiers in Marine Science, 5: 496, doi: 10.3389/fmars.2018.00496.

[22]	Helsel D R, Hirsch R M, Ryberg K R, et al. 2020. Statistical methods in water resources. In: U.S. Geological Survey Techniques and Methods, Book 4, Chapter 8. Reston: U.S. Geological Survey, 210-219.

[23]	Huang P, Zhao X Q, Pu J W, et al. 2024. Defining the land use area threshold and optimizing its structure to improve supply-demand balance state of ecosystem services. Journal of Geographical Sciences, 34(5): 891-920. doi: 10.1007/s11442-024-2232-0

[24]	Isles P D F, Creed I F, Jonsson A, et al. 2021. Trade-offs between light and nutrient availability across gradients of dissolved organic carbon lead to spatially and temporally variable responses of lake phytoplankton biomass to browning. Ecosystems, 24: 1837-1852.

[25]	Jia J J, Gao Y, Song X W, et al. 2019. Characteristics of phytoplankton community and water net primary productivity response to the nutrient status of the Poyang Lake and Gan River, China. Ecohydrology, 12(7): e2136, doi: 10.1002/eco.2136.

[26]	Kang J H, Huang H, Li W W, et al. 2018. Study of monthly variations in primary production and their relationships with environmental factors in the Daya Bay based on a general additive model. Acta Oceanologica Sinica, 37: 107-117.

[27]	Katsiapi M, Mazaris A D, Charalampous E, et al. 2012. Watershed land use types as drivers of freshwater phytoplankton structure. Hydrobiologia, 698: 121-131.

[28]	Knight C A. 2021. Causes and consequences of eutrophication, which are leading to water pollution. Journal of Preventive Medicine, 6(10): 118, doi: 10.36648/2572-5483.6.10.118.

[29]	Lee Y J, Ha S Y, Park H K, et al. 2015. Identification of key factors influencing primary productivity in two river-type reservoirs by using principal component regression analysis. Environmental Monitoring and Assessment, 187: 213, doi: 10.1007/s10661-015-4438-1. pmid: 25813033

[30]	Li B J, Song J X, Guan M C, et al. 2024. With spatial distribution, risk evaluation of heavy metals and microplastics to emphasize the composite mechanism in hyporheic sediments of Beiluo River. Journal of Hazardous Materials, 462: 132784, doi: 10.1016/j.jhazmat.2023.132784.

[31]	Li H H, Huang Y M, Guo W, et al. 2022. Influence of land use and land cover patterns on water quality at different spatio-temporal scales in Hehuang Valley. Environmental Science, 43(8): 4042-4053. (in Chinese)

[32]	Li X Y, Sun H L, Sun X L, et al. 2012. Temporal and spatial distribution of total organic carbon in Liusha Bay and the influence factors investigation. Marine Sciences, 36(7): 61-69. (in Chinese)

[33]	Liao J Q, Zhao L, Cao X F, et al. 2016. Cyanobacteria in lakes on Yungui Plateau, China are assembled via niche processes driven by water physicochemical property, lake morphology and watershed land-use. Scientific Reports, 6: 36357, doi: 10.1038/srep36357. pmid: 27819304

[34]	Liu E C, Lai Z N, Liu Q F, et al. 2022. The spatial-temporal variation characteristics and influencing factors of primary productivity in the mainstream of the Pearl River. Chinese Fishery Quality and Standards, 12(1): 12-21. (in Chinese)

[35]	Liu S S, Qiu Y, Fu R, et al. 2023. Identifying the water quality variation characteristics and their main driving factors from 2008 to 2020 in the Yellow River Basin, China. Environmental Science and Pollution Research, 30: 66753-66766.

[36]	Locke K A. 2024. Impacts of land use/land cover on water quality: A contemporary review for researchers and policymakers. Water Quality Research Journal, 59(2): 89-106.

[37]	Lü J Q, Zhang H H, Huang Y J, et al. 2024. Evolution characteristics and risk assessment on nonpoint source pollution in the Weihe River Basin, China. Remote Sensing, 16(23): 4605, doi: 10.3390/rs16234605.

[38]	Lu W Q, Zhang S Y, Zhou Z Z, et al. 2023. Effects of land use and physicochemical factors on phytoplankton community structure: The case of two fluvial lakes in the lower reach of the Yangtze River, China. Global Ecology and Conservation, 15(2): 180, doi: 10.3390/d15020180.

[39]	MacFarland T W, Yates J M. 2016. Introduction to Nonparametric Statistics for the Biological Sciences Using R. New York: Springer International Publishing, 249-297.

[40]	Ministry of Ecology and Environment of the People's Republic of China. 1990. Water Quality-Determination of Total Phosphorus-Ammonium Molybdate Spectrophotometric Method (GB11893-89). [2024-09-28]. https://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/jcffbz/199007/t19900701_67131.shtml. (in Chinese)

[41]	Ministry of Ecology and Environment of the People's Republic of China. 2012. Water Quality-Determination of Total Nitrogen-Alkaline Potassium Persulfate Digestion UV Spectrophotometric Method (HJ 636-2012). [2024-09-16]. https://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/jcffbz/201203/t20120307_224383.shtml. (in Chinese)

[42]	Miranda L E, Andrews C S, Kröger R. 2014. Connectedness of land use, nutrients, primary production, and fish assemblages in oxbow lakes. Aquatic Sciences, 76(1): 41-50.

[43]	Naipal V, Ciais P, Wang Y L, et al. 2018. Global soil organic carbon removal by water erosion under climate change and land use change during AD 1850-2005. Biogeosciences, 15(4): 4459-4480.

[44]	Oleksy I A, Solomon C T, Jones S E, et al. 2024. Controls on lake pelagic primary productivity: Formalizing the nutrient‐color paradigm. Journal of Geophysical Research: Biogeosciences, 129(12): e2024JG008140, doi: 10.1029/2024JG008140.

[45]	Olson C R, Jones S E. 2022. Chlorophyll-total phosphorus relationships emerge from multiscale interactions from algae to catchments. Limnology and Oceanography Letters, 7(6): 483-491.

[46]	Parker A E, Dugdale R C, Wilkerson F P. 2012. Elevated ammonium concentrations from wastewater discharge depress primary productivity in the Sacramento River and the Northern San Francisco Estuary. Marine Pollution Bulletin, 64(3): 574-586. doi: 10.1016/j.marpolbul.2011.12.016 pmid: 22236959

[47]	Peng X, Zhang L, Li Y, et al. 2021. The changing characteristics of phytoplankton community and biomass in subtropical shallow lakes: Coupling effects of land use patterns and lake morphology. Water Research, 200: 117235, doi: 10.1016/j.watres.2021.117235.

[48]	Phileze P O. 2010. Variability of soil properties related to vegetation cover in a tropical rainforest landscape. Journal of Geography and Regional Planning, 3(7): 177-184.

[49]	Pilla R M, Griffiths N A. 2023. Integrating reservoirs into the dissolved organic matter versus primary production paradigm: How does chlorophyll-a change across dissolved organic carbon concentrations in reservoirs? Ecosystems, 27: 137-150.

[50]	Pramanik S. 2023. Assessment of spatial heterogeneity of landscape ecology of the Subarnarekha River Basin, India. International Journal of Ecology and Environmental Sciences, 49(5): 501, doi: 10.55863/ijees.v49i5.2813.

[51]	Preisner M, Neverova D E, Kowalewski Z. 2021. Mitigation of eutrophication caused by wastewater discharge: A simulation-based approach. Ambio, 50: 413-424.

[52]	Qu Y M, Wu N C, Guse B, et al. 2022. Distinct indicators of land use and hydrology characterize different aspects of riverine phytoplankton communities. Science of the Total Environment, 851: 158209, doi: 10.1016/j.scitotenv.2022.158209.

[53]	Quan J L, Xu Y X, Ma T, et al. 2022. Improving surface water quality of the Yellow River Basin due to anthropogenic changes. Science of the Total Environment, 836: 155607, doi: 10.1016/j.scitotenv.2022.155607.

[54]	Ren X F, Li P Y, He X D, et al. 2021. Hydrogeochemical processes affecting groundwater chemistry in the central part of the Guanzhong Basin, China. Archives of Environmental Contamination and Toxicology, 80: 74-91. doi: 10.1007/s00244-020-00772-5 pmid: 33146757

[55]	Sarkar S, Kumar S. 2024. Water stagnancy and wastewater input enhance primary productivity in an engineered river system. River, 3(2): 191-198.

[56]	Shiomoto A, Sasaki H, Nomura D. 2023. Size-fractionated phytoplankton biomass and primary production in the eastern Indian sector of the Southern Ocean in the austral summer 2018/2019. Progress in Oceanography, 218: 103119, doi: 10.1016/j.pocean.2023.103119.

[57]	Sinsomboonthong S. 2022. Performance comparison of new adjusted min-max with decimal scaling and statistical column normalization methods for artificial neural network classification. International Journal of Mathematics and Mathematical Sciences, 2022: 3584406, doi: 10.1155/2022/3584406.

[58]	Siqueira T D S, Pessoa L A, Vieira L, et al. 2023. Evaluating land use impacts on water quality: perspectives for watershed management. Sustainable Water Resources Management, 9(6): 192, doi: 10.1007/s40899-023-00968-2.

[59]	State Environmental Protection Administration of the People's Republic of China. 2002. Water and Wastewater Monitoring and Analysis Method (4^th ed.). Beijing: China Environmental Science Press, 670-671. (in Chinese)

[60]	Sun J Z, Wang T F, Huang R P, et al. 2022. Enhancement of diatom growth and phytoplankton productivity with reduced O₂ availability is moderated by rising CO₂. Communications Biology, 5(1): 54, doi: 10.1038/s42003-022-03006-7.

[61]	Sun K, Deng W Q, Jia J J, et al. 2023. Spatiotemporal patterns and drivers of phytoplankton primary productivity in China's lakes and reservoirs at a national scale. Global and Planetary Change, 228: 104215, doi: 10.1016/j.gloplacha.2023.104215.

[62]	Tu J. 2011. Spatially varying relationships between land use and water quality across an urbanization gradient explored by geographically weighted regression. Applied Geography, 31(1): 376-392.

[63]	Wu J Y, Luo J G, Zhang H, et al. 2023. Driving forces behind the spatiotemporal heterogeneity of land-use and land-cover change: A case study of the Weihe River Basin, China. Journal of Arid Land, 15(3): 253-273. doi: 10.1007/s40333-023-0052-1

[64]	Wu Q T, Li Y J, Shi X P, et al. 2022. Primary productivity assessment of typical reservoirs in Guangdong Province based on chlorophyll measurement. Aquaculture, 43(10): 4-8. (in Chinese)

[65]	Xie G H, Chen Y F, Zhou Y, et al. 2024. Steady-state transformation of phytoplankton community structure and its influencing factors in Honghu Lake. Wetland Science, 22(2): 264-272. (in Chinese)

[66]	Xu C, Jiang Y N, Su Z H, et al. 2022. Assessing the impacts of grain-for-green programme on ecosystem services in Jinghe River Basin, China. Ecological Indicators, 137: 108757, doi: 10.1016/j.ecolind.2022.108757.

[67]	Yamaguchi H, Koga N, Ichimi K, et al. 2020. Seasonal variations in phytoplankton productivity in a shallow cove in the eastern Seto Inland Sea, Japan. Fisheries Science, 86: 1067-1078.

[68]	Yan Z X, Li P, Li Z B, et al. 2023. Effects of land use and slope on water quality at multi-spatial scales: A case study of the Weihe River Basin. Environmental Science and Pollution Research, 30: 57599-57616.

[69]	Yang L Q, He X T, Ru S G, et al. 2024. Herbicide leakage into seawater impacts primary productivity and zooplankton globally. Nature Communications, 15(1): 1783, doi: 10.1038/s41467-024-46059-4.

[70]	Ye H B, Chen C Q, Sun Z H, et al. 2015. Estimation of the primary productivity in Pearl River estuary using MODIS data. Estuaries and Coasts, 38: 506-518.

[71]	Yu H F, Shi X H, Zhao S N, et al. 2022. Primary productivity of phytoplankton and its influencing factors in cold and arid regions: A case study of Wuliangsuhai Lake, China. Ecological Indicators, 144: 109545, doi: 10.1016/j.ecolind.2022.109545.

[72]	Zhang C, Li J, Zhou Z X, et al. 2021. Application of ecosystem service flows model in water security assessment: A case study in Weihe River Basin, China. Ecological Indicators, 120: 106974, doi: 10.1016/j.ecolind.2020.106974.

[73]	Zhang T, Su X L, Zhang G X, et al. 2022. Evaluation of the impacts of human activities on propagation from meteorological drought to hydrological drought in the Weihe River Basin, China. Science of the Total Environment, 819: 153030, doi: 10.1016/j.scitotenv.2022.153030.

[74]	Zhang X Q, Qi Y, Li H Y, et al. 2024. Assessing the response of non-point source nitrogen pollution to land use change based on SWAT model. Ecological Indicators, 158: 111391, doi: 10.1016/j.ecolind.2023.111391.

[75]	Zhao A Z, Zhu X F, Liu X F, et al. 2016. Impacts of land use change and climate variability on green and blue water resources in the Weihe River Basin of Northwest China. CATENA, 137: 318-327.

[76]	Zhao N, Zhang G L, Zhang S, et al. 2019. Temporal-spatial distribution of chlorophyll-a and impacts of environmental factors in the Bohai Sea and Yellow Sea. IEEE Access, 7: 160947-160960. doi: 10.1109/ACCESS.2019.2950833

[77]	Zhao X Z, Li F M, Mo F, et al. 2012. Integrated conservation solutions for the endangered Loess Plateau of Northwest China. Pakistan Journal of Botany. 44: 77-83.

[78]	Zhong J, Wallin M B, Wang W F, et al. 2021. Synchronous evaporation and aquatic primary production in tropical river networks. Water Research, 200: 117272, doi: 10.1016/j.watres.2021.117272.

[79]	Zuo D P, Bi Y Q, Song Y H, et al. 2023. The response of non-point source pollution to land use change and risk assessment based on model simulation and grey water footprint theory in an agricultural river basin of Yangtze River, China. Ecological Indicators, 154: 110581, doi: 10.1029/2024JG008.

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