Investigating Alternative Climate Data Sources for Hydrological Simulations in the Upstream of the Amu Darya River

Ayetiguli Sidike,Yue Huang,Khaydar Durdiev,Xi Chen,Tie Liu

doi:10.3390/w8100441

Abstract

The main objective of this study is to investigate alternative climate data sources for long-term hydrological modeling. To accomplish this goal, one weather station data set (WSD) and three grid-based data sets including three types of precipitation data and two types of temperature data were selected according to their spatial and temporal details. An accuracy assessment of the grid-based data sets was performed using WSD. Then, the performances of corrected data combination and non-corrected grid-based precipitation and temperature data combinations from multiple sources on simulating river flow in the upstream portion of the Amu Darya River Basin (ADRB) were analyzed using a Soil and Water Assessment Tool (SWAT) model. The results of the accuracy assessments indicated that all the grid-based data sets underestimated precipitation. The Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE) precipitation data provided the highest accuracy (correlation coefficients (CF) > 0.89, root mean square error (RMSE) < 41.6 mm), followed by the CRUNCEP reanalysis data (a combination of the CRU TS.3.2 data and the National Centers for Environmental Prediction (NCEP) reanalysis data) (CF > 0.5, RMSE < 58.1 mm) and Princeton’s Global Meteorological Forcing Dataset (PGMFD) precipitation data (CF > 0.46, RMSE < 62.8 mm). The PGMFD temperature data exhibited a higher accuracy (CF > 0.98, RMSE < 7.1 °C) than the CRUNCEP temperature data (CF > 0.97, RMSE < 4.9 °C). In terms of the simulation performance, the corrected APHRODITE precipitation and PGMFD temperature data provided the best performance. The CF and Nash-Sutcliffe (NSE) coefficients in the calibration and validation periods were 0.96 and 0.92 and 0.93 and 0.83, respectively. In addition, the combinations of PGMFD temperature data and APHRODITE, PGMFD and CRUNCEP precipitation data produced good results, with NSE ≥ 0.70 and CF ≥ 0.89. The combination of CRUNCEP temperature data and APHRODITE precipitation produced a satisfactory result, with NSE = 0.58 and CF = 0.82. The combinations of CRUNCEP temperature data and PGMFD and CRUNCEP precipitation data produced poor results.

Highlights

One of the challenges in modeling watershed hydrology is obtaining accurate weather input data [1,2], which are generally one of the most important drivers of watershed models [3,4]
An accuracy assessment was conducted by comparing the annual cycles and statistical box plots of grid-based data sets with weather station data set (WSD) based on indicator criteria
The annual cycle of maximum and minimum temperature (Figure 3) indicated that the Princeton’s Global Meteorological Forcing Dataset (PGMFD) underestimated the temperature and the CRUNCEP data set overestimated the temperature at three stations, excluding Fedchenko

Summary

Introduction

One of the challenges in modeling watershed hydrology is obtaining accurate weather input data [1,2], which are generally one of the most important drivers of watershed models [3,4]. Spatial and temporal variability are key characteristics of hydrological processes [5]. In many instances, distributed hydrological models require daily distributed meteorological data to simulate the hydrological cycle. Distributed hydrological models require spatially distributed, long-term, continuous data to simulate the impact of climate change and management practices on hydrological processes. Conventional weather stations are often sparsely distributed and cannot fully represent the climate conditions across a watershed, if large hydro climatic gradients exist [8,9,10]. Weather station records often do not cover the proposed simulation period or contain gaps

Objectives

Methods

Results

Discussion

Conclusion