Impacts of precipitation and topographic conditions on the model simulation in the north of China

The evaluation of hydrological models for a specific catchment is normally based on the model performance according to the selected performance criteria. However, the catchment rainfall-runoff characteristics could be used for the selection of a suitable hydrological model in study area, which, also, for the problem solve of the model application in ungauged basins. In this study, six conceptual models were applied in three semi-humid or semi-arid catchments to investigate the correlation between catchment characteristics and model structure selection. In addition, the impacts of precipitation and topography in model simulation were analyzed. The results show that runoff generation are highly impacted by catchment topographic index and land cover change, and the influence of slope for river channel is greater than mean slope for the whole catchment due to the runoff generation for partial area. For the catchments under similar climate condition, the impact of topographic features for runoff generation process is greater than the difference of precipitation. It indicates that for a specific catchment, the selection of appropriate model should base on better understanding of the rainfall-runoff relationship. The method of incorporating additional runoff generation module in the traditional model can significantly improve the accuracy of flood simulation.


INTRODUCTION
Hydrological models with different model structures are widely used around the world. The hydrologists have developed various conceptual hydrological models since the first application of the Stanford model (Singh ; Adnan et al. ). Conceptual hydrological models are effective tools to explore and understand the complex hydrological cycle processes and mechanisms, and they are also effective ways to solve the key problems in hydrology (Kavetski et al. ; Liu et al. ). Hydrological processes vary enormously across different landscapes (Winter ). For example, two general runoff generation mechanisms, saturation-excess runoff and infiltration-excess runoff, may suitable for the region with strong permeability and multiple aquifers, the series reservoir structure is suitable for the weak infiltration of bedrock, as the linear structure is suitable for the catchment with stable and smooth runoff variation (Fenicia et al. ).
Flash floods caused by rainfall of high intensity and short duration occur frequently in the semi-humid or semi-arid regions that within a size of 3,000 km 2 . For the northwest of China, due to the problems of data shortage and the complex of underlying conditions, the accuracy of flood forecasting is often too low to fulfill the needs of flood warning (Kan et al. ). A single model structure is unlikely to precisely describe a diverse range of runoff generation mechanisms (Castillo et al. ; Clark ). So far, the application of conceptual models for flood simulation in semi-humid and semi-arid regions still requires further study (Pilgrim et al. ). Therefore, it is worth investigating the application of existing models. It is also important to explore which kind of rainfallrunoff model could accommodate the runoff generation mechanisms for this region, as well as the main impact factors for model performance. In this study, six conceptual hydrological models were tested in several semi-humid or semi-arid catchments that located in Shannxi Province, China. The difference in runoff generation for spatial proximity catchments with different climate and geographic properties was investigated and compared. According to the model performance, the applicable of models in the study region will be discussed based on the simulation result, catchment characteristics, model structure and the observed data.
The objective of this work is to investigate the influence of precipitation and geographic conditions on model performance. The study catchments are described in the section Study area, followed by the models and methodology in the section Models and methodology. Results and discussion present the simulated model performances and the discussion of the results. The conclusion is summarized in Conclusion.

STUDY AREA
Three typical semi-humid and semi-arid catchments: Maduwang, Banqiao and Zhidan, which are all sub basins of the Yellow River Basin, were selected to investigate the model performance using different types of models ( Figure 1).
These three catchments are located in Shannxi province of China with varying catchment characteristics and climate conditions. The meteorological characteristics for the study catchments are listed in Table 1. The distribution of vegetation types (Table 2) is also calculated based on the global 1 km land cover data provided by the University of Maryland (Friedl et al. ).
Maduwang catchment has the largest size of 1,604 km 2 .
The annual precipitation for this catchment is about 630 mm and shows gradually increasing trend from north to south. The mountains and valleys in this area are quite steep with a good coverage of vegetation. The valleys in the hilly areas are extremely developed with large cutting depth, broken terrain and poor vegetation. The plain is flat and the soil is fertile, which is suitable for crop cultivation.
The average annual precipitation in the Banqiao River Catchment is around 730 mm with an area of 502 km 2 . The terrain is high in the northwest, low in the southeast, most hilly areas contain clayey and sand layer clay, rock type include schist and phyllite, and cultivated land is distributed in river valleys.
Zhidan catchment has only 510 mm precipitation per year, and covers a drainage area of 777 km 2 . The geographic features of Zhidan can be roughly divided into three types of landforms: valley terraces, beam-shaped gullies and earthrock mountains. The topographic terrain is distributed with mountains, canyons and barren beaches. The slope varies greatly in Zhidan catchment and the soil erosion is serious because of the poor vegetation cover.
The determination of the humid condition is usually based on water balance, while Liu suggested that the aridity index could be used for natural zonation division in Shaanxi province when the requiring data for calculating water balance is not available (La Vigna et al. ). The aridity index, which represents the ratio of long-term potential evapo-transpiration to precipitation, is about 1.4 for Maduwang, 1.6 for Banqiao and 2.0 for Zhidan, respectively.
According to Liu's study on natural zonation in the Shannxi province, based on the aridity index and annual precipitation, Maduwang and Banqiao catchments are identified as semihumid region, while Zhidan is regarded as semi-arid region due to the low precipitation and high dryness fraction.

MODELS AND METHODOLOGY
Six conceptual hydrological models are tested in the study catchments: the Sacramento model (named as M1)   ables is increasing to a specific value, the accuracy of model calibration using simplex method is generally lower than using the SCE-UA algorithm. Here only the sensitive parameters were considered to be calibrated based on historical data, thus the simplex method was selected to identify the model parameters.
The following four performance criteria were evaluated for study catchments.
The relative error of runoff depth (ΔR): The relative error of peak flow (ΔQ p ): where R sim and R obs refer to the simulated and observed runoff depth, and Q psim and Q pobs refer to the simulated and observed peak flow.
The Nash-Sutcliffe efficiency (NS) (Nash & Sutcliffe where Q sim (i) and Q obs (i) are the simulated and observed discharge at given time i, and Q obs is the average discharge over the whole period.
The difference of simulated peak flow appearance time T qpsim and the real flow appearance time T qpsim is also calculated: According to the standards of 'Forecasting norm for hydrology intelligence' in China (Fan et al. ), the accuracy of flood prediction is evaluated based on the qualification rate of runoff depth, peak flow and flood peak appearance time. Therefore, the Equations (1), (2) and (4) are transformed into a binary function in order to make the forecasting result more distinct: where QU R , QU p and QU Tqp denote the qualification rate of

RESULTS AND DISCUSSION
The hourly records of precipitation and discharge for the three catchments is only observed during the flood season. Therefore, the models were simulated based on flood events.

Result of Maduwang
For the 12 flood events from Maduwang, seven of them were used to calibrate the models and the remains were used for      Figure 4 shows the simulated discharge series of flood event 030824 for Banqiao using TOPMO-DEL. It can be seen from the curve that in spite of the big error in discharge volume, TOPMODEL is able to obtain perfect prediction of peak flow in terms of magnitude as well as the appearance time.

Result of Zhidan
For a total of 15 selected flood events, 11 floods were used for model calibration and the remains used for validation.
The simulation result (Table 5)

Results for different catchments
From the comparison of simulation results for different catchments we can conclude that for the selected models, Maduwang shows the best model performance, followed by Banqiao, and the model simulation for Zhidan could not obtain reasonable performance for most of the flood events. In general, the model performance for these three catchments is not satisfied and could not fulfil the standard requirements of flood forecasting. The poor model performance for the study area might be due to the particular runoff characteristics. Under the long-term drought condition, the runoff generation is dominated by infiltration-excess runoff at the beginning of the rainfalls. If the soil is porous and well developed, the saturation-excess runoff is likely to occur subsequently. However, if the soil is not well developed and packed together, perhaps only infiltration-excess runoff occurs during rainfall. Moreover, the sub-daily rainfall data is normally measured every 6 h for the study area, and the hourly inputs for modeling is based on the mean value of the observed sub-daily rainfall. This results in the  reduction of rainfall peak intensity. High temporal resolution of rainfall is required for infiltration-excess runoff model to accurately reproduce the discharge series. The low temporal resolution of rainfall record to some extent causes the low accuracy of model simulation results.

Impacts of topographic features for model performance
Both landscape factors and climate conditions contribute to runoff mechanisms for a specific catchment (Buda et al. ). In this work, the influence of climate conditions and underlying characteristics to the model performance are investigated. The three catchments are geographically close to each other as they are located in the same province.
The climate conditions are relatively different as mentioned in the catchment description but with the same feature of clearly dry and wet seasons. The rainfall-runoff correlation for the catchments is plotted in Figure 5. Here, p denotes areal precipitation, pa denotes antecedent precipitation and R is the observed runoff depth. According to the assumption of saturation-runoff mechanism, all the precipitation produces runoff after the soil is saturated, so the correlation curve close to the diagonal direction indicates the runoff generation is dominated by saturation-runoff mechanism. It can be detected from the figure that for Banqiao catchment, while the rainfall amount of this area is similar to that of Maduwang, the proportion of saturationrunoff is obviously lower than Maduwang. It indicates that variation in precipitation is not the only reason for the great differences in model performance.
It is well known that the spatial and temporal dynamics of runoff generation area highly depends on the landscapes.
Topographic index, which is highly related to the runoff concentration area and slope, is usually used for measuring the topographic conditions: where T s is the topographic index, α is runoff concentration area and β is the slope. Figure 6 shows the distribution of topographic index and the corresponding catchment area.  The average slope and channel gradient are estimated based on digital elevation model (DEM) data. Table 6 shows the average slope and Figure 7 shows the distribution curve for slope. The mean slope of Maduwang is much larger than the other two catchments, while the slope of Banqiao is about 1 0 higher than Zhidan. When the runoff accumulation areas are same, the larger the slope is, the smaller the topographic index is. The sorting of slope for the three catchments is consistent with the result for topographic index, indicating that the effective runoff generation area plays an important role in reflecting the topographic index of the river catchment.
Compared to Banqiao, Maduwang has a greater value in the mean of slope degree for the whole catchment, but has a smaller value if it only focuses on the slope degree for the river channel. It is found that Maduwang has rather complex terrain; the high slope degree is distributed in the upstream region that belongs to the hilly and mountainous area. Banqiao has a small catchment size with huge slope in the river channel, which implies the catchment is lack of capacity for water storage. The infiltration-excess runoff is more likely to occur in this catchment at the beginning of rainfall.

CONCLUSION
In this study, the influence of precipitation and topographic conditions on the model simulation results were tested on  Results indicate the topographic characteristics and climate conditions of a catchment strongly affect the rainfallrunoff process. Floods are relatively similar and easier to be simulated in the region with well-developed vegetation and simple terrains. The influence of slope for a river channel is higher than that mean slope for the whole catchment.
It has been detected from this work that for a particular catchment, the higher the topographic index is, the greater the possibility of saturation-excess runoff generation is.
It can be found for the study catchments that model

DATA AVAILABILITY
The precipitation and discharge data used to support the findings of this study were supplied by Shaanxi Hydrology Bureau under license and so cannot be made freely available. Requests for access to these data should be made to Long Sun (sunlongmwr@163.com).