Some non-uniformity patterns spread over the lower Mahanadi River Basin, India

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Purpose The purpose of the study is to assess the non-uniformity in precipitation patterns spread over the lower Mahanadi River basin (LMB). Methodology/approach Characterization of precipitation patterns was studied using an Eigen-based spatial pattern analysis resembling PCA (Principal component analysis). Pattern identification using DBSCAN (Density-based spatial clustering of applications with noise) implementation, and the results were confirmed and accomplished using intra-cluster and inter-cluster distance (silhouette index). Homogeneity characterization of the identified patterns, accomplished using L-moment and Probability weighted moment (PWMs) based heterogeneity measures. Research motivation/gap Improvement over spatial clustering and attribution of non-uniformity present in precipitation over different timeslot segments. Findings DBSCAN implementation results in a combination of Epsilon (ε)=4.1, and minPts = 6 with 4groups (170, 7, 11, 6) and 36 noise points. The measure of spread for LMB with large variations in precipitation magnitude is identical in small seized clusters. Heterogeneity (H1) assessment; cluster-2 with H1 − 1.21 (Possibly Heterogeneous), cluster-3 with H1 − 0.22 (acceptably homogeneous), and cluster-4 with H1 –−1.77 (Possibly Heterogeneous). Cluster-4 experienced more extreme events during segments-1 (1901–1939) and 2 (1940–1978). Cluster-4 had > 46% of precipitation above the mean-annual precipitation of the Mahanadi basin (>1572 mm) during segment-3 (1979–2017). A noticeable decrease in the spatial pattern variability of the 2nd component, i.e., 36.07% to 28.09% during segments 1 and 3. Similarly, segment-2 identified three components sharing a good amount of variability (30.35%, 31.36%, and 15.86%). Implications The identified sub-regions of the precipitation obtained using DBSCAN approaches have separate applications and uses. The future prediction of rainfall can be simplified for water resource management in regions with known precipitation variability. Similarly, regions with known precipitation regimes are helpful in activities, including agricultural planning, farming calendar, planting time of different crops, and rain-fed and dry farming practices. Importance The findings are practical and scientific towards water resource planning and management.