Simulating Water Resource Availability under Data Scarcity—A Case Study for the Ferghana Valley (Central Asia)

Abstract

Glaciers and snowmelt supply the Naryn and Karadarya rivers, and about 70% of the water available for the irrigated agriculture in the Ferghana Valley. Nineteen smaller catchments contribute the remaining water mainly from annual precipitation. The latter will gain importance if glaciers retreat as predicted. Hydrological models can visualize such climate change impacts on water resources. However, poor data availability often hampers simulating the contributions of smaller catchments. We tested several data pre-processing methods (gap filling, MODAWEC (MOnthly to DAily WEather Converter), lapse rate) and their effect on the performance of the HBV (Hydrologiska Byråns Vattenavdelning)-light model. Monte Carlo simulations were used to define parameter uncertainties and ensembles of behavioral model runs. Model performances were evaluated by constrained measures of goodness-of-fit criteria (cumulative bias, coefficient of determination, model efficiency coefficients (NSE) for high flow and log-transformed flow). The developed data pre-processing arrangement can utilize data of relatively poor quality (only monthly means or daily data with gaps) but still provide model results with NSE between 0.50 and 0.88. Some of these may not be accurate enough to directly guide water management applications. However, the pre-processing supports producing key information that may initiate rigging OPEN ACCESSWater 2014, 6 3271 of monitoring facilities, and enable water management to respond to fundamentally changing water availability.

Hydrological Analysis of Extreme Rain Events in a Medium-Sized Basin

“…The initial model, Hydrologiska Byråns Vattenbalansavdelning (HBV) was primarily developed by the Swedish Meteorological and Hydrological Institute (SMHI) [55,56]. Although the HBV-light model is a relatively recent version, the research community already uses it widely for flood forecasting, and producing valid output results [57][58][59][60][61][62]. The HBV-light version uses a warming-up period so that the initial state values progress to their proper values based on the meteorological data and parameter calibration [54,63].…”