Home / Current Issue / Paper 1724529
Micro-Catchment Typology for Integrated Catchment Management
Subject area: Physical Sciences and Environment · Area of research: Environmental Science
Abstract
Catchment management plans in data-scarce regions usually treat erosion, flooding, runoff and water harvesting separately, so interventions are prioritised hazard by hazard rather than by the combined condition of each land unit. This study developed a multivariate typology of 163 micro-catchments in the 1,641.7 km2 Idah Catchment, north-central Nigeria. Nine indicators derived from a 12.5 m digital elevation model and open global datasets were reduced with principal component analysis and grouped with k-means clustering. The first component (57.6% of variance) described a gradient from wooded plateau units to cultivated, flood-prone lowland units. Four management zones were identified: wooded plateau headwaters (50.1% of the area), steep, flashy plateau margins (21.7%), cultivated erosion–runoff hotspots (10.4%) and flood-prone floodplain (17.8%). The zones agreed with Ward clustering (adjusted Rand index 0.69), were reproduced by a random forest with 92% out-of-bag accuracy and were spatially coherent (65% of neighbouring units shared a zone, against 32% expected by chance). The hotspot and floodplain zones occupied 28.2% of the area but generated 54.9% of the soil loss and 58.3% of the 25-year runoff, contained 26 of the 28 significant soil-loss hotspots and had the highest groundwater recharge potential. Scenario analysis indicated that conservation agriculture with contour bunds on cropland in these two zones would reduce soil loss by 31.8%, whereas clearing 20% of headwater tree cover would increase it by 89.6%. Check dams outside the floodplain could retain up to 18.7% of the 25-year runoff. Annual rainfall declined significantly in the lowland zones. The typology concentrates management effort where hazards coincide and offers a transferable framework for integrated planning in ungauged tropical catchments.
Keywords
catchment management; cluster analysis; flood susceptibility; principal component analysis; soil erosion
References
[1] Global Water Partnership. Integrated water resources management. TAC Background Paper No. 4. Stockholm: Global Water Partnership; 2000.
[2] Wagener T, Sivapalan M, Troch P, Woods R. Catchment classification and hydrologic similarity. Geography Compass. 2007;1(4):901–931.
[3] Jolliffe IT, Cadima J. Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A. 2016;374(2065):20150202.
[4] Biswas S, Sudhakar S, Desai VR. Prioritisation of subwatersheds based on morphometric analysis of drainage basin: A remote sensing and GIS approach. Journal of the Indian Society of Remote Sensing. 1999;27(3):155–166.
[5] Aladejana OO, Ebijuoworih EJ. Flood risk assessment in Kogi State Nigeria through the integration of hazard and vulnerability factors. Discover Geoscience. 2024;2:31. https://doi.org/10.1007/s44288-024-00036-y
[6] Obaje NG. Geology and mineral resources of Nigeria. Berlin: Springer; 2009.
[7] Nwajide CS. Geology of Nigeria's sedimentary basins. Lagos: CSS Bookshops; 2013.
[8] Zanaga D, Van De Kerchove R, Daems D, De Keersmaecker W, Brockmann C, Kirches G, et al. ESA WorldCover 10 m 2021 v200 [dataset]. Zenodo; 2022. https://doi.org/10.5281/zenodo.7254221
[9] Runfola D, et al. geoBoundaries: A global database of political administrative boundaries. PLOS ONE. 2020;15(4):e0231866.
[10] Natural Earth. Free vector and raster map data at 1:10m, 1:50m, and 1:110m scales [Internet]. Available from: https://www.naturalearthdata.com
[11] ASF DAAC. ALOS PALSAR radiometric terrain corrected high resolution DEM [dataset]. Fairbanks (AK): Alaska Satellite Facility; 2015. Includes material © JAXA/METI 2007. https://doi.org/10.5067/Z97HFCNKR6VA
[12] Lindsay JB. Efficient hybrid breaching-filling sink removal methods for flow path enforcement in digital elevation models. Hydrological Processes. 2016;30(6):846–857.
[13] Funk C, et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data. 2015;2:150066.
[14] Poggio L, et al. SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL. 2021;7(1):217–240.
[15] Tatem AJ. WorldPop, open data for spatial demography. Scientific Data. 2017;4:170004.
[16] Pesaresi M, Politis P. GHS-BUILT-S R2023A: GHS built-up surface grid, derived from Sentinel-2 composite and Landsat, multitemporal (1975–2030) [dataset]. Ispra: European Commission, Joint Research Centre; 2023.
[17] Hansen MC, et al. High-resolution global maps of 21st-century forest cover change. Science. 2013;342(6160):850–853.
[18] Strahler AN. Hypsometric (area-altitude) analysis of erosional topography. Geological Society of America Bulletin. 1952;63(11):1117–1142.
[19] Renard KG, Foster GR, Weesies GA, McCool DK, Yoder DC. Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). Agriculture Handbook No. 703. Washington (DC): USDA; 1997.
[20] USDA-NRCS. Urban hydrology for small watersheds. Technical Release 55. Washington (DC): United States Department of Agriculture; 1986.
[21] Saaty TL. The analytic hierarchy process. New York: McGraw-Hill; 1980.
[22] Nobre AD, et al. Height Above the Nearest Drainage—a hydrologically relevant new terrain model. Journal of Hydrology. 2011;404(1–2):13–29.
[23] Hartigan JA, Wong MA. Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society, Series C (Applied Statistics). 1979;28(1):100–108.
[24] Rousseeuw PJ. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics. 1987;20:53–65.
[25] Hubert L, Arabie P. Comparing partitions. Journal of Classification. 1985;2(1):193–218.
[26] Ward JH. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association. 1963;58(301):236–244.
[27] Hennig C. Cluster-wise assessment of cluster stability. Computational Statistics & Data Analysis. 2007;52(1):258–271.
[28] Breiman L. Random forests. Machine Learning. 2001;45(1):5–32.
[29] Moran PAP. Notes on continuous stochastic phenomena. Biometrika. 1950;37(1/2):17–23.
[30] Anselin L. Local indicators of spatial association—LISA. Geographical Analysis. 1995;27(2):93–115.
[31] Rey SJ, Anselin L. PySAL: A Python library of spatial analytical methods. Review of Regional Studies. 2007;37(1):5–27.
[32] Mann HB. Nonparametric tests against trend. Econometrica. 1945;13(3):245–259.
[33] Kendall MG. Rank correlation methods. 4th ed. London: Charles Griffin; 1975.
[34] Sen PK. Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association. 1968;63(324):1379–1389.
[35] Panagos P, et al. Estimating the soil erosion cover-management factor at the European scale. Land Use Policy. 2015;48:38–50.
[36] Wischmeier WH, Smith DD. Predicting rainfall erosion losses: a guide to conservation planning. Agriculture Handbook No. 537. Washington (DC): USDA; 1978.
How to cite this paper
@article{1724529,
author = {Samuel Achonu Ojih, Hyelduku Aliyu Mwada, Elijah Saleh Usman, Aminu Fahad},
title = {Micro-Catchment Typology for Integrated Catchment Management},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {5},
pages = {310-328},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724529.pdf},
abstract = {Catchment management plans in data-scarce regions usually treat erosion, flooding, runoff and water harvesting separately, so interventions are prioritised hazard by hazard rather than by the combined condition of each land unit. This study developed a multivariate typology of 163 micro-catchments in the 1,641.7 km2 Idah Catchment, north-central Nigeria. Nine indicators derived from a 12.5 m digital elevation model and open global datasets were reduced with principal component analysis and grouped with k-means clustering. The first component (57.6% of variance) described a gradient from wooded plateau units to cultivated, flood-prone lowland units. Four management zones were identified: wooded plateau headwaters (50.1% of the area), steep, flashy plateau margins (21.7%), cultivated erosion–runoff hotspots (10.4%) and flood-prone floodplain (17.8%). The zones agreed with Ward clustering (adjusted Rand index 0.69), were reproduced by a random forest with 92% out-of-bag accuracy and were spatially coherent (65% of neighbouring units shared a zone, against 32% expected by chance). The hotspot and floodplain zones occupied 28.2% of the area but generated 54.9% of the soil loss and 58.3% of the 25-year runoff, contained 26 of the 28 significant soil-loss hotspots and had the highest groundwater recharge potential. Scenario analysis indicated that conservation agriculture with contour bunds on cropland in these two zones would reduce soil loss by 31.8%, whereas clearing 20% of headwater tree cover would increase it by 89.6%. Check dams outside the floodplain could retain up to 18.7% of the 25-year runoff. Annual rainfall declined significantly in the lowland zones. The typology concentrates management effort where hazards coincide and offers a transferable framework for integrated planning in ungauged tropical catchments.},
keywords = {catchment management; cluster analysis; flood susceptibility; principal component analysis; soil erosion},
month = {November},
}