Effect of baseline meteorological data selection on hydrological modelling of climate change scenarios

This study evaluates how differences in hydrological model parameterisation resulting from the choice of gridded global precipitation data sets and reference evapotranspiration (ETo) equations affects simulated climate change impacts, using the north western Himalayan Beas river catchment as a case study. Six combinations of baseline precipitation data (the Tropical Rainfall Measuring Mission (TRMM) and the Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE)) and Reference Evapotranspiration equations of differing complexity and data requirements (Penman-Monteith, Hargreaves –Samani and Priestley – Taylor) were used in the calibration of the HySim model. Although the six validated hydrological models had similar historical model performance (Nash–Sutcliffe model efficiency coefficient (NSE) from 0.64-0.70), impact response surfaces derived using a scenario neutral approach demonstrated significant deviations in the models’ responses to changes in future annual precipitation and temperature. For example, the change in Q10 varies between -6.5 % to -11.5% in the driest and coolest climate change simulation and +79% to +118% in the wettest and hottest climate change simulation among the six models. The results demonstrate that the baseline meteorological data choices made in model construction significantly condition the magnitude of simulated hydrological impacts of climate change, with important implications for impact study design.NER

A Model for Continental-Scale Water Erosion and Sediment Transport and Its Application to the Yellow River Basin

Quantifying suspended sediment discharge at large catchment scales has significant implications for various research fields such as water quality, global carbon and nutrient cycle, agriculture sustainability, and landscape evolution. There is growing evidence that climate warming is accelerating the water cycle, leading to changes in precipitation and runoff and increasing the frequency and intensity of extreme weather events, which could lead to intensive erosion and sediment discharge. However, suspended sediment discharge is still rarely represented in regional climate models because it depends not only on the sediment transport capacity based on streamflow characteristics but also on the sediment availability in the upstream basin. This thesis introduces a continental-scale Atmospheric and Hydrological-Sediment Modelling System (AHMS-SED), which overcomes the limitations of previous large-scale water erosion models. Specifically, AHMS-SED includes a complete representation of key hydrological, erosion and sediment transport processes such as runoff and sediment generation, flow and sediment routing, sediment deposition, gully erosion and river irrigation. In this thesis, we focus on developing and applying AHMS-SED in the Yellow River Basin of China, an arid and semi-arid region known for its wide distribution of loess and the highest soil erosion rate in the world. There are three key issues involving the model development and application: human perturbation (irrigation) of the water cycle, the uncertainty of precipitation forcing on the water discharge and the large-scale water erosion and sediment transport. This thesis addresses all these three issues in the following way. First, a new irrigation module is integrated into the Atmospheric and Hydrological Modelling System (AHMS). The model is calibrated and validated using in-situ and remote sensing observations. By incorporating the irrigation module into the simulation, a more realistic hydrological response was obtained near the outlet of the Yellow River Basin. Second, an evaluation of six precipitation-reanalysis products is performed based on observed precipitation and model-simulated river discharge by the AHMS for the Yellow River Basin. The hydrological model is driven with each of the precipitation-reanalysis products in two ways, one with the rainfall-runoff parameters recalibrated and the other without. Our analysis contributes to better quantifying the reliability of hydrological simulations and the improvement of future precipitation-reanalysis products. Third, a regional-scale water erosion and sediment transport model, referred to as AHMS-SED, is developed and applied to predicting continental-scale fluvial transport in the Yellow River Basin. This model couples the AHMS with the CASCade 2-Dimensional SEDiment (CASC2D-SED) and takes into account gully erosion, a process that strongly affects the sediment supply in the Chinese Loess Plateau. The AHMS-SED is then applied to simulate water erosion and sediment processes in the Yellow River Basin for a period of eight years, from 1979 to 1987. Overall, the results demonstrate the good performance of the AHMS-SED and the upland sediment discharge equation based on rainfall erosivity and gully area index. AHMS-SED is also used to predict the evolution of sediment transport in the Yellow River Basin under specific climate change scenarios. The model results indicate that changes in precipitation will have a significant impact on sediment discharge, while increased irrigation will reduce the sediment discharge from the Yellow River

Bias correction of high-resolution regional climate model precipitation output gives the best estimates of precipitation in Himalayan catchments

The need to provide accurate estimates of precipitation over catchments in the Hindu Kush, Karakoram, and Himalaya mountain ranges for hydrological and water resource systems assessments is widely recognised, as is identifying precipitation extremes for assessing hydro‐meteorological hazards. Here, we investigate the ability of bias‐corrected Weather Research and Forecasting model output at 5 km grid spacing to reproduce the spatiotemporal variability of precipitation for the Beas and Sutlej river basins in the Himalaya, measured by 44 stations spread over the period 1980 to 2012. For the Sutlej basin, we find that the raw (uncorrected) model output generally underestimated annual, monthly, and (particularly low‐intensity) daily precipitation amounts. For the Beas basin, the model performance was better, although biases still existed. It is speculated that the cause of the dry bias over the Sutlej basin is a failure of the model to represent an early‐morning maximum in precipitation during the monsoon period, which is related to excessive precipitation falling upwind. However, applying a non‐linear bias‐correction method to the model output resulted in much better results, which were superior to precipitation estimates from reanalysis and two gridded datasets. These findings highlight the difficulty in using current gridded datasets as input for hydrological modelling in Himalayan catchments, suggesting that bias‐corrected high‐resolution regional climate model output is in fact necessary. Moreover, precipitation extremes over the Beas and Sutlej basins were considerably under‐represented in the gridded datasets, suggesting that bias‐corrected regional climate model output is also necessary for hydro‐meteorological risk assessments in Himalayan catchments

A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture

Near-real-time (NRT) satellite-based rainfall estimates (SREs) are a viable option for flood/drought monitoring. However, SREs have often been associated with complex and nonlinear errors. One way to enhance the quality of SREs is to use soil moisture information. Few studies have indicated that soil moisture information can be used to improve the quality of SREs. Nowadays, satellite-based soil moisture products are becoming available at desired spatial and temporal resolutions on an NRT basis. Hence, this study proposes an integrated approach to improve NRT SRE accuracy by combining it with NRT soil moisture through a nonlinear support vector machine-based regression (SVR) model. To test this novel approach, Ashti catchment, a sub-basin of Godavari river basin, India, is chosen. Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)-based NRT SRE 3B42RT and Advanced Scatterometer-derived NRT soil moisture are considered in the present study. The performance of the 3B42RT and the corrected product are assessed using different statistical measures such as correlation coeffcient (CC), bias, and root mean square error (RMSE), for the monsoon seasons of 2012–2015. A detailed spatial analysis of these measures and their variability across different rainfall intensity classes are also presented. Overall, the results revealed significant improvement in the corrected product compared to 3B42RT (except CC) across the catchment. Particularly, for light and moderate rainfall classes, the corrected product showed the highest improvement (except CC). On the other hand, the corrected product showed limited performance for the heavy rainfall class. These results demonstrate that the proposed approach has potential to enhance the quality of NRT SRE through the use of NRT satellite-based soil moisture estimates

Comparison between statistical and dynamical downscaling of rainfall over the Gwadar‐Ormara basin, Pakistan

Abstract This paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study

Hydrological evaluation of open-access precipitation and air temperature datasets using SWAT in a poorly gauged basin in Ethiopia

Precipitation and air temperature are key drivers of watershed models. Currently there are many open-access gridded precipitation and air temperature datasets at different spatial and temporal resolutions over global or quasi-global scale. Motivated by the scarcity and substantial temporal and spatial gaps in ground measurements in Africa, this study evaluated the performance of three open-access precipitation datasets (i.e. CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), TRMM (Tropical Rainfall Measuring Mission) and CFSR (Climate Forecast System Reanalysis)) and one air temperature dataset (CFSR) in driving Soil and Water Assessment Tool (SWAT) model in simulation of daily and monthly streamflow in the upper Gilgel Abay Basin, Ethiopia. The “best” available measurements of precipitation and air temperature from sparse gauge stations were also used to drive SWAT model and the results were compared with those using open-access datasets. After a comprehensive comparison of a total of eight model scenarios with different combinations of precipitation and air temperature inputs, we draw the following conclusions: (1) using measured precipitation from even sparse available stations consistently yielded better performance in streamflow simulation than using all three open-access precipitation datasets; (2) using CFSR air temperature yielded almost identical performance in streamflow simulation to using measured air temperature from gauge stations; (3) among the three open-access precipitation, overall CHIRPS yielded best performance. These results suggested that the CHIRPS precipitation available at high spatial resolution (0.05°) together with CFSR air temperature can be a promising alternative open-access data source for streamflow simulation in this data-scarce area in the case of limited access to desirable gauge data

Evaluation of ECMWF medium-range ensemble forecasts of precipitation for river basins

Providing probabilistic forecasts using Ensemble Prediction Systems has become increasingly popular in both the meteorological and hydrological communities. Compared to conventional deterministic forecasts, probabilistic forecasts may provide more reliable forecasts of a few hours to a number of days ahead, and hence are regarded as better tools for taking uncertainties into consideration and hedging against weather risks. It is essential to evaluate performance of raw ensemble forecasts and their potential values in forecasting extreme hydro-meteorological events. This study evaluates ECMWF's medium-range ensemble forecasts of precipitation over the period 1 January 2008 to 30 September 2012 on a selected midlatitude large-scale river basin, the Huai river basin (ca. 270 000 km2) in central-east China. The evaluation unit is sub-basin in order to consider forecast performance in a hydrologically relevant way. The study finds that forecast performance varies with sub-basin properties, between flooding and non-flooding seasons, and with the forecast properties of aggregated time steps and lead times. Although the study does not evaluate any hydrological applications of the ensemble precipitation forecasts, its results have direct implications in hydrological forecasts should these ensemble precipitation forecasts be employed in hydrology

Evaluation of ECMWF medium-range ensemble forecasts of precipitation for river basins

Providing probabilistic forecasts using Ensemble Prediction Systems has become increasingly popular in both the meteorological and hydrological communities. Compared to conventional deterministic forecasts, probabilistic forecasts may provide more reliable forecasts of a few hours to a number of days ahead, and hence are regarded as better tools for taking uncertainties into consideration and hedging against weather risks. It is essential to evaluate performance of raw ensemble forecasts and their potential values in forecasting extreme hydro-meteorological events. This study evaluates ECMWF's medium-range ensemble forecasts of precipitation over the period 1 January 2008 to 30 September 2012 on a selected midlatitude large-scale river basin, the Huai river basin (ca. 270 000 km2) in central-east China. The evaluation unit is sub-basin in order to consider forecast performance in a hydrologically relevant way. The study finds that forecast performance varies with sub-basin properties, between flooding and non-flooding seasons, and with the forecast properties of aggregated time steps and lead times. Although the study does not evaluate any hydrological applications of the ensemble precipitation forecasts, its results have direct implications in hydrological forecasts should these ensemble precipitation forecasts be employed in hydrology