Hydro-climatic extremes in the Himalayan watersheds: a case of the Marshyangdi Watershed, Nepal

Abstract

Climate change/variability and subsequent exacerbation of extremes are affecting human and ecological health across the globe. This study aims at unpacking hydro-climatic extremes in a snow-fed Marshyangdi watershed, which has a potential for water infrastructure development, located in Central Nepal. Bias-corrected projected future climate for near (2014–2033) and mid-future (2034–2053) under moderate and pessimistic scenarios were developed based on multiple regional climate models. Historical (1983–2013) and future trends of selected climatic extreme indices were calculated using RClimDex and hydrological extremes using Indicators of Hydrologic Alteration tool. Results show that historical trends in precipitation extremes such as number of heavy and very heavy precipitation days and maximum 1-day precipitation are decreasing while the temperature-related extremes have both increasing and decreasing trends (e.g., warm spell duration index, warm days and summer days are increasing whereas cold spell duration index, cool days and warm nights are decreasing). These results indicate drier and hotter conditions over the historical period. The projected future temperature indices (hot nights, warm days) reveal increasing trend for both the scenarios in contrast with decreasing trends in some of the extreme precipitation indices such as consecutive dry and wet days and maximum 5-day precipitation. Furthermore, the watershed has low mean hydrological alterations (27.9%) in the natural flow regime. These results indicate continuation of wetter and hotter future in the Marshyangdi watershed with likely impacts on future water availability and associated conflicts for water allocation, and therefore affect the river health conditions.

Appendices

Appendix 1. Formula for calculating hydrological alteration

The percentage of deviation degree of each hydrological alteration of streamflow regime is calculated as (Timpe and Kaplan 2017; Xue et al.2017):

$$ {P}_i\ \left(\%\right)\frac{\left(M\mathrm{post}-M\mathrm{pre}\right)}{M\mathrm{pre}}\ast 100 $$

(1)

where M_post is the median for the post-impact period and M_pre is the median for the pre-impact period. After calculation of percentage of deviation degree, these values were then averaged by parameter groups and across all parameters. A positive P_i value indicates an increased median value in the post-impacted period compared with the pre-impacted period while a negative P_i suggests a decreased median value in the post-impacted period compared with the pre-impacted period.

Degree of hydrological alteration of a flow regime can be further calculated for each indicator according to the following equation (Ritcher et al.1998)

$$ {D}_i=\mid \frac{\mathrm{OF}-\mathrm{EF}}{\mathrm{EF}}\mid \times 100 $$

(2)

where OF is the observed number of post-impacted years for which the value of the indicator falls within the RVA target range, from 25th percentile to 75th percentile, as suggested by Richter et al. (1998); EF is the expected number of post-impacted years for which the value of indicator falls within the targeted range and can be estimated by r × NT (r is percentage of pre-impacted years for which the value of an indicator falls within the RVA target range and NT is total number of post-impacted years). As different hydrological indices may show different variabilities in flow regime, hence, an overall degree (OD) of hydrological alteration of all indices may be computed as:

$$ {D}_i=\sqrt{\frac{\sum_{i=1}^{32}{D}_i^2}{32}} $$

(3)

Appendix 2. Relevant tables referred in the manuscript

Table 6 Climatic models and scenarios used in climate change-related studies in Nepal

Table 7 Details of the hydro-meteorological stations used in this study

Table 8 Projected changes in future climatic extreme indices (based on ensemble time series) for RCP4.5 scenarios across four stations in the Marshyangdi watershed

Table 9 Change-point analysis for hydro-meteorological variables in the Marshyangdi watershed

Table 10 Summary of hydrologic parameters used in the indicators of hydrologic alteration

Table 11 Trends in selected hydrologic extreme indices

Appendix 3. Relevant figures referred in the manuscript

Fig. 6

Trends in consecutive dry days (CDD) across the stations

Fig. 7

Trends in consecutive wet days (CWD) across the stations

Fig. 8

Spatial distribution in warm days (TX90p) trends

Fig. 9

Spatial distribution in cold nights (TN10p) trends

Fig. 10

Extremely wet days (R99p) for different future scenarios

Fig. 11

Very wet days (R95p) for different future scenarios

Fig. 12

Monthly median flows (group 1 parameters)—flow value (left) and degree of deviation (right)

Fig. 13

Pulse count and duration (left and degree of deviation on those values (right))

Fig. 14

Anomaly of selected hydro-climatic extreme indices