Management of water resources and low flow estimation for the Himalayan basins of Nepal

Chalise, S. R.; Kansakar, Sunil R.; Rees, Gwyn; Croker, K.M.; Zaidman, M. D.

doi:10.1016/s0022-1694(03)00250-6

Cited by 56 publications

(31 citation statements)

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“…The annual and seasonal precipitation patterns found in the current study resemble the results of previous studies (e.g., Chalise et al, 2003;Kansakar et al, 2004). However, this is the first study to reveal a local maximum precipitation over western Nepal in winter.…”

Section: Spatial Distributionsupporting

confidence: 89%

Precipitation in Nepal between 1987 and 1996

Ichiyanagi

Yamanaka

Muraji

et al. 2007

Intl Journal of Climatology

106

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Abstract:Rain gauge station data from 1987 to 1996 were used to investigate spatial and temporal variability in monthly precipitation and annual and seasonal precipitation patterns over Nepal. Maximum annual precipitation increased with altitude for elevations below 2000 m but decreased for elevations of 2000-3500 m. The data revealed a negative relationship between annual precipitation and elevation only in western Nepal. Annual precipitation averaged on a 0.25°grid exceeded 3000 mm/yr in central Nepal but was less than 1000 mm/yr over Nepal's northwestern mountains. Only winter precipitation over western Nepal was heavier than precipitation over central and eastern Nepal. A time series of standardized precipitation anomalies averaged over Nepal revealed no significant long-term trends. Further, almost no stations exhibited significant long-term trends by Kendall's rank correlation analysis. A correlation analysis between summer monsoon precipitation and the All Indian Rainfall (AIR) index revealed positive and negative correlations in western and eastern Nepal, respectively. This analysis also revealed a positive correlation, but no negative correlation, between summer monsoon precipitation and the Southern Oscillation Index (SOI) in western and eastern Nepal. Composite differences in temperature, 850-hPa winds, outgoing longwave radiation (OLR), and precipitation rates between low and high AIR phases revealed that moist air from the Arabian Sea supported precipitation over western Nepal, whereas cold dry air from the Tibetan Plateau suppressed precipitation over eastern Nepal. However, composite differences in precipitation between low and high SOI phases revealed no anomalies for Nepal.

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Section: Spatial Distributionsupporting

confidence: 89%

Precipitation in Nepal between 1987 and 1996

Ichiyanagi

Yamanaka

Muraji

et al. 2007

Intl Journal of Climatology

106

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“…Moreover, the data collection, fieldwork, calibration, and validation for the setup of detailed, catchment-specific water balance models may be long and expensive; this poses practical constraints to application in many mountainous areas of the world, where hydrologic information is scarce and sparse, sites are difficult to access, and the operational management of a model for decision support may be difficult due to economic, organizational, and technical limitations (as discussed in e.g., [5]). …”

Section: Introductionmentioning

confidence: 99%

Seasonal River Discharge Forecasting Using Support Vector Regression: A Case Study in the Italian Alps

Callegari

Mazzoli

Gregorio

et al. 2015

Water

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Abstract:In this contribution we analyze the performance of a monthly river discharge forecasting model with a Support Vector Regression (SVR) technique in a European alpine area. We considered as predictors the discharges of the antecedent months, snow-covered area (SCA), and meteorological and climatic variables for 14 catchments in South Tyrol (Northern Italy), as well as the long-term average discharge of the month of prediction, also regarded as a benchmark. Forecasts at a six-month lead time tend to perform no better than the benchmark, with an average 33% relative root mean square error (RMSE%) on test samples. However, at one month lead time, RMSE% was 22%, a non-negligible improvement over the benchmark; moreover, the SVR model reduces the frequency of higher errors associated with anomalous months. Predictions with a lead time of three months show an intermediate performance between those at one and six months lead time. Among the considered predictors, OPEN ACCESSWater 2015, 7 2495SCA alone reduces RMSE% to 6% and 5% compared to using monthly discharges only, for a lead time equal to one and three months, respectively, whereas meteorological parameters bring only minor improvements. The model also outperformed a simpler linear autoregressive model, and yielded the lowest volume error in forecasting with one month lead time, while at longer lead times the differences compared to the benchmarks are negligible. Our results suggest that although an SVR model may deliver better forecasts than its simpler linear alternatives, long lead-time hydrological forecasting in Alpine catchments remains a challenge. Catchment state variables may play a bigger role than catchment input variables; hence a focus on characterizing seasonal catchment storage-Rather than seasonal weather forecasting-Could be key for improving our predictive capacity.

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“…8%) [58,59]. Recent estimates carry a potential for hydropower nearby 40-60 GW [60][61][62], with ca. 600 MW produced nowadays, mostly run-of-river.…”

Section: Need For Large Scale Planning and Developing Of Irrigation Smentioning

confidence: 99%

Agriculture and Food Security under Climate Change in Nepal

Bocchiola¹

2017

APAR

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Management of water resources and low flow estimation for the Himalayan basins of Nepal

Cited by 56 publications

References 1 publication

Precipitation in Nepal between 1987 and 1996

Precipitation in Nepal between 1987 and 1996

Seasonal River Discharge Forecasting Using Support Vector Regression: A Case Study in the Italian Alps

Agriculture and Food Security under Climate Change in Nepal

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