Investigating spring flow dynamics towards solving water management issues in the Indian Himalayan region.

A decline in spring flows has been observed in the Indian Himalayas due to changes in landuse and rainfall variability (Matheswaran et al., 2019). Consequently, the Himalayan communities face water scarcity issues, as springs remain a significant source of drinking and agriculture. To ensure water and livelihood security, management of these springs through landscape interventions and source protection is critical. But lack of fundamental understanding of factors influencing its flow regime limits the impact of management approaches (Vashisht and Bam, 2013) in hydrologically complex and ungauged Himalayan watersheds. To increase the flows, aquifer recharge is aided through interventions. Aquifers recharge is a function of hydrogeology, landuse and rainfall, and conventional hands-on management approaches (conceptual mapping, digging contour trenches, recharge area protection, vegetative measures) for improving spring flows are partially effective (Tarafdar et al., 2019). As the mitigating measures don’t incorporate aquifer recharge functions. Hence, understanding the flow behavior is a prerequisite for instituting a best-suited management practice.

In this study, four springs (A₁, P₁, P₂, P₃) were instrumented for high-resolution monitoring in two pilot watersheds in Almora and Pauri region, Uttarakhand, India. Hydrograph analysis, including Recession and Flow durations curves (FDC), facilitated the assessment of spring hydrodynamics. In addition, autocorrelation and cross-correlation functions (ACF and CCF) aided in understanding the memory of the system and the interdependence of rainfall and spring discharge. Results showed that spring A₁ in Almora has intricate flow networks and slow flow velocities while P₁, P₂, P₃ spring clusters in Pauri show characteristics of transmissive fractures. This is Indicative of better storage capacity and homogeneity of underlying geology for A₁ compared to P₁, P₂, P₃. Recession coefficient ‘α’ for A₁, P₁, P₂, P₃ was calculated as 0.038, 0.109, 0.088 and 0.081 respectively. The low value of α for A₁ depicts diffused fracture system compared to P₁, P₂, P_3, which indicate rapid emptying of the aquifer, the shallow spatial extent of the recharge area and a well-interconnected flow network. Steep FDC for P₁, P₂, P₃ indicates high variability in springflows, while A₁ has a gradually flattening curve attributed to the presence of storage. ACF for A₁ shows a steady decline of r_xx(k) value till 0.4, a high memory for more than 120 lag days, while P₁, P₂, P₃ have r_xx(k) value rapidly declining below 0.2 (significance threshold) in 50, 60 and 70 lag days exhibiting shorter system memory and poor drainage of the extensive flow network.

Such a multi-approach analysis of spring flow systems aids in spring flow characterization, assessment of response lags and flow regimes. The combined usage of such techniques permits detailed process understanding and limits erroneous interpretations (Torresan et al., 2020). Policymakers can extend the results across the Indian Himalayas to design site-specific management frameworks.

Keywords : Springshed management, memory effect, high-resolution dataset, spring aquifer, Himalayas