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This paper examines the multi-scale streamflow variability responses to precipitation over 16 headwater catchments in the Pearl River basin, South China. The long-term daily streamflow data (1952–2000), obtained using a macro-scale hydrological model, the Variable Infiltration Capacity (VIC) model, and a routing scheme, are studied. Temporal features of streamflow variability at 10 different timescales, ranging from 6 days to 8.4 years, are revealed with the Haar wavelet transform. The principal component analysis (PCA) is performed to categorize the headwater catchments with the coherent modes of multi-scale wavelet spectra. The results indicate that three distinct modes, with different variability distributions at small timescales and seasonal scales, can explain 95% of the streamflow variability. A large majority of the catchments (i.e. 12 out of 16) exhibit consistent mode feature on multi-scale variability throughout three sub-periods (1952–1968, 1969–1984, and 1985–2000). The multi-scale streamflow variability responses to precipitation are identified to be associated with the regional flood and drought tendency over the headwater catchments in southern China. [ABSTRACT FROM AUTHOR]

Summary In the past decades significant progress has been made in the fitting of hydrologic models to data. Most of this work has focused on simple, CPU-efficient, lumped hydrologic models using discharge, water table depth, soil moisture, or tracer data from relatively small river basins. In this paper, we focus on large-scale hydrologic modeling and analyze the effect of parameter and rainfall data uncertainty on simulated discharge dynamics with the global hydrologic model PCR-GLOBWB. We use three rainfall data products; the CFSR reanalysis, the ERA-Interim reanalysis, and a combined ERA-40 reanalysis and CRU dataset. Parameter uncertainty is derived from Latin Hypercube Sampling (LHS) using monthly discharge data from five of the largest river systems in the world. Our results demonstrate that the default parameterization of PCR-GLOBWB, derived from global datasets, can be improved by calibrating the model against monthly discharge observations. Yet, it is difficult to find a single parameterization of PCR-GLOBWB that works well for all of the five river basins considered herein and shows consistent performance during both the calibration and evaluation period. Still there may be possibilities for regionalization based on catchment similarities. Our simulations illustrate that parameter uncertainty constitutes only a minor part of predictive uncertainty. Thus, the apparent dichotomy between simulations of global-scale hydrologic behavior and actual data cannot be resolved by simply increasing the model complexity of PCR-GLOBWB and resolving sub-grid processes. Instead, it would be more productive to improve the characterization of global rainfall amounts at spatial resolutions of 0.5° and smaller. [ABSTRACT FROM AUTHOR]

Summary In water resources modeling, meteorological data scarcity can be compensated by various global data sets, but those data sets can differ tremendously. In the literature, hydrological models of differing complexity are proposed for estimating the water resources of semi-arid catchments, and also to evaluate rainfall data sets. The goal of this paper is to provide a joint analysis of modeling uncertainty due to different input data and increasing model complexity. Impacts of mutually concealed uncertainties on model performance and model outputs are exemplified in two data sparse semi-arid catchments in Ethiopia. We applied a semi-distributed and a fully distributed hydrological model, having different levels of complexity. Three different satellite-based rainfall data sets and two temperature products were used as model inputs. The semi-distributed model demonstrated good validation performances, while the fully distributed model was more sensitive to data uncertainties. The application of TRMM version 6 completely failed and the high-resolution CMORPH precipitation estimate outperformed TRMM version 7. In contrast, the use of high-resolution temperature data did not improve the model results. The large differences in remotely sensed input data were buffered inside the hydrological models. Therefore, data set evaluations regarding only the simulated hydrographs were less meaningful. In contrast, the investigation of parameter evolution and distributed outputs’ variability appeared to be a valuable tool to uncover the interdependencies of data and model uncertainties. We suggest this procedure to be applied by default in water resources estimations that are affected by data scarcity, but especially when data sets are evaluated using hydrological models. Our case study demonstrates that estimations of groundwater recharge and actual evapotranspiration vary largely, depending on the applied data sets and models. The joint analysis reveals large interdependencies between data and model evaluations. It shows that traditional studies focusing only on one part of uncertainty, either the input uncertainty or the uncertainty arising from the choice of model structure are limited in their explanatory power of the modeling performance, particularly in poorly gauged regions. [ABSTRACT FROM AUTHOR]

Summary There exists a widespread recognition that land use and management change should affect catchment response to precipitation (and therefore stream flow characteristics). Previous studies have shown that this affect exists at a local scale, but there is a paucity of evidence that local scale effects aggregate to detectable impacts within downstream catchments. This paper describes a novel wavelet-based analysis of hydrological model residuals to examine the effect of land-use change on the catchment hydrology across four UK catchments (the Kird, Lod, Coalburn and Wye), of which all but the Wye experienced significant changes in their land use. The HySim rainfall–runoff model was calibrated against observed long term flow series assuming a static land hydrology to allow for the effects of climatic variability. Deviations of model fit were assessed in relation to changes to catchment land hydrology. The wavelet transform was used to decompose both simulated and observed flows into different scale components and to assess changes in catchment hydrology across different temporal scales; model residuals and model performance were tested for significant changes in wavelet variance and wavelet correlation respectively. Significant changes in wavelet variance and wavelet correlation corresponded with changes in catchment land use for two of the three catchments that experience land use change, in the third there were significant changes in wavelet variation that may have resulted from land-use change, but no changes in wavelet correlation. The control catchment showed no significant features of variances and no significant change in wavelet correlation across the scales. This new approach holds great potential for separating the influences of land use and management change on catchment stream flow based on frequency scale. [ABSTRACT FROM AUTHOR]

Summary Spatial variability of climate can negatively affect catchment streamflow predictions if it is not explicitly accounted for in hydrologic models. In this paper, we examine the changes in streamflow predictability when a hydrologic model is run with spatially variable (distributed) meteorological inputs instead of spatially uniform (lumped) meteorological inputs. Both lumped and distributed versions of the EXP-HYDRO model are implemented at 41 meso-scale (500–5000 km 2 ) catchments in the Pacific Northwest region of USA. We use two complementary metrics of long-term spatial climate variability, moisture homogeneity index ( I M ) and temperature variability index ( I TV ), to analyze the performance improvement with distributed model. Results show that the distributed model performs better than the lumped model in 38 out of 41 catchments, and noticeably better (>10% improvement) in 13 catchments. Furthermore, spatial variability of moisture distribution alone is insufficient to explain the observed patterns of model performance improvement. For catchments with low moisture homogeneity ( I M < 80%), I M is a better predictor of model performance improvement than I TV ; whereas for catchments with high moisture homogeneity ( I M > 80%), I TV is a better predictor of performance improvement than I M . Based on the results, we conclude that: (1) catchments that have low homogeneity of moisture distribution are the obvious candidates for using spatially distributed meteorological inputs, and (2) catchments with a homogeneous moisture distribution benefit from spatially distributed meteorological inputs if they also have high spatial variability of precipitation phase (rain vs. snow). [ABSTRACT FROM AUTHOR]

Summary: In this paper we proposed: (1) an algorithm of glacier melt, sublimation/evaporation, accumulation, mass balance and retreat; (2) a dynamic Hydrological Response Unit approach for incorporating the algorithm into the Soil and Water Assessment Tool (SWAT) model; and (3) simulated the transient glacier retreat and its impacts on streamflow at basin scale. Application of the enhanced SWAT model in the Manas River Basin (MRB) in the Tianshan Mountains in northwest China, shows that the approach is viable as evidenced by a Nash–Sutcliff efficiency of 0.65 and a percent bias of −3.7% for daily streamflow and water balance, respectively. The results indicate that the glacier area decreased by 11% during the simulation period from 1961 to 1999, which is within the range of records from other glaciers. On average, glacier melt contributed 25% to streamflow, although glacier area accounts for only 14% of the catchment drainage area in the MRB. Glacier melt was positively correlated to temperature change (R 2 =0.70, statistical significance P <0.001) and negatively correlated to precipitation (R 2 =0.20, statistical significance P <0.005). The results indicate that glacier melt was more sensitive to temperature change than to precipitation change, implying that modeling the effects of climate change with increasing temperatures and decreasing precipitation should be further studied. [ABSTRACT FROM AUTHOR]

Summary: With the aim of calibrating a large number of catchments for a semi-distributed, process-based conceptual hydrological model, we introduce a straightforward yet robust automatic calibration procedure. Since identification of a global parameter optimum is not feasible in practical terms, the procedure presents a trade-off between computational time and algorithm complexity to identify, with reasonable effort, a parameter set that is well representative of the catchment’s dynamics. In its standard mode, the calibration combines three efficiency scores which are evaluated both for the entire calibration period and in terms of their annual and monthly variations. These scores are furthermore assessed only in their relevant value range, producing a comprehensive overall acceptability score which is used to guide an iterative parameter search algorithm. An additional set of four flood-sensitive scores is added, thereby improving results in the peak-flow range. Calibration was done for 140 mesoscale (roughly 10–1000km2) catchments in Switzerland, using the hydrological modelling system PREVAH (Precipitation–Runoff-EVApotranspiration-HRU related model) in hourly time steps. For 49 representative catchments with long gauge records, a median Nash–Sutcliffe efficiency (NSE) of 0.75 was achieved for the calibration period in standard mode. The limited loss in efficiency when moving to the validation period (median NSE: 0.72) proves the stability and representativity of the parameter sets identified, while a Monte-Carlo analysis underscores the effectiveness of our procedure. The Nash–Sutcliffe efficiencies for the additional flood calibration are slightly lower, but again almost equally high for the calibration (0.69) and the validation (0.67) period. Despite the concessions made to improve peak-flow results, the simulation’s hydrological plausibility was not compromised. The ultimate goal of our study is flood estimation in ungauged Swiss catchments through continuous simulation using PREVAH. With the extensive calibration task presented in this article, the foundation is laid for regionalisation of the tuneable model parameters, which will be addressed in the companion paper (), along with detailed flood estimation results. [Copyright &y& Elsevier]