Simulating the hydrological processes of an inland river basin can help provide the scientific guidance to the policies of water allocation among different subbasins and water resource management groups within the subbasins. However, it is difficult to simulate the hydrological processes of an inland river basin with hydrological models due to the non-consistent hydrological characteristics of the entire basin. This study presents a solution to this problem with a case study about the hydrological process simulation in an inland river basin in China, Heihe River basin. It is divided into the upper, middle, and lower reaches based on the distinctive hydrological characteristics in the Heihe River basin, and three hydrological models are selected, applied, and tested to simulate the hydrological cycling processes for each reach. The upper reach is the contributing area with the complex runoff generation processes, therefore, the hydrological informatic modeling system (HIMS) is utilized due to its combined runoff generation mechanisms. The middle reach has strong impacts of intensive human activities on the interactions of surface and subsurface flows, so a conceptual water balance model is applied to simulate the water balance process. For the lower reach, as the dissipative area with groundwater dominating the hydrological process, a groundwater modeling system with the embedment of MODFLOW model is applied to simulate the groundwater dynamics. Statistical parameters and water balance analysis prove that the three models have excellent performances in simulating the hydrological process of the three reaches. Therefore, it is an effective way to simulate the hydrological process of inland river basin with multiple hydrological models according to the characteristics of each subbasin.

It is important to simulate streamflow with hydrological models suitable for the particular study areas, as the hydrological characteristics of water cycling processes are distinctively different due to spatial heterogeneity at the watershed scale. However, most existing hydrological models cannot be customized to simulate water cycling processes of different areas due to their fixed structures and modes. This study developed a HydroInformatic Modeling System (HIMS) model with a flexible structure which had multiple equations available to describe each of the key hydrological processes. The performance of the HIMS model was evaluated with the recommended structure for semi-arid areas by comparisons with two datasets of observed streamflow: the first one of 53 Australian watersheds, the second one of the Lhasa River basin in China. Based on the first dataset, the most appropriate watersheds were identified for the HIMS model utilization with areas of 400–600 km2 and annual precipitation of 800–1200 mm. Based on the second dataset, the model performance was statistically satisfied with Nash-Sutcliffe Efficient (NSE) greater than 0.87 and Water Error (WE) within ±20% on the streamflow simulation at hourly, daily, and monthly time steps. In addition, the water balance was mostly closed with respect to precipitation, streamflow, actual evapotranspiration (ET), and soil moisture change at the annual time steps in both the periods of calibration and validation. Therefore, the HIMS model was reliable in estimating streamflow and simulating the water cycling processes for the structure of semi-arid areas. The simulated streamflow of HIMS was compared with those of the Variable Infiltration Capacity model (VIC) and Soil and Water Assessment Tool (SWAT) models and we found that the HIMS model performed better than the SWAT model, and had similar results to the VIC model with combined runoff generation mechanisms.

Water resources carrying capacity (WRCC) analysis is critically important in providing guidance to the sustainable development strategies of the YarLung Tsangpo River Basin (YTRB) due to the conflicts among the ample water resources, low development level of society, and the fragile ecological environment. This study evaluated the scheduled developing mode of YTRB in the planning years from 2016 to 2030 with a WRCC system containing three components: a hydrological informatics modeling system (HIMS), water resources carrying capacity (WRCC) model, and an index evaluation system. The averaged WRCC index is 4.29, 1.19, and 0.06 for the planning years, and 2.61, 0.98, 0.05 for the baseline years for the three sub-basins. The water deficiency problem becomes more severe in the upper sub-basin and appears in the middle sub-basin with the WRCC index greater than 1, while the water resources are not fully utilized in the lower sub-basin in the planning years, with the WRCC index far less than 1. The GDP of the three sub-basins is greater in the planning years, with 2.25 × 108, 54.60 × 108, and 3.94 × 108 dollars year−1 than those in the baseline years with 1.97 × 108, 47.71 × 108, 3.43 × 108 dollars year−1. However, GDP per capita/cubic meter keeps decreasing due to the great population growth rate and non-enhanced water use efficiency. The sustainability index is 0.04, 0.23, and 0.47 in the planning years, which is lower than the 0.04, 0.31, and 0.50 in the baseline years. Therefore, the scheduled growth rates of the population, urbanization, and GDP are a developing mode with low sustainability and are not appropriate to be continued in the planning years. Further work is needed to identify a sustainable developing mode with a decreased population growth rate, enhanced water use efficiency in the economic system, and the optimized allocation of water resources distribution in the three sub-basins with hydraulic facilities.

Non-point sources (NPS) of agricultural chemical pollution are one major reason for the water quality degradation of the Great Lakes, which impacts millions of residents in the states and provinces that are bordering them. Future climate change will further impact water quality in both direct and indirect ways by influencing the hydrological cycle and processes of nutrient transportation and transformation, but studies are still rare. This study focuses on quantifying the impacts of climate change on nutrient (Nitrogen and Phosphorus) losses from the two small watersheds (Walworth watershed and Green Lake watershed) within the Great Lakes region. Analysis focused on changes through this century (comparing the nutrient loss prediction of three future periods from 2015 to 2099 with 30 years for each period against the historical nutrient estimation data from 1985 to 2008). The effects on total phosphorus and nitrate-nitrogen losses due to changes in precipitation quantity, intensity, and frequency, as well as air temperature, are evaluated for the two small watersheds, under three special report emission scenarios (SRES A2, A1B, B1). The newly developed Water Erosion Prediction Project-Water Quality (WEPP-WQ) model is utilized to simulate nutrient losses with downscaled and bias corrected future climate forcing from two General Circulation Models (GFDL, HadCM3). For each watershed, the observed runoff and nutrient loads are used to calibrate and validate the model before the application of the WEPP-WQ model to examine potential impacts from future climate change. Total phosphorus loss is projected to increase by 28% to 89% for the Green Lake watershed and 25% to 108% for theWalworth watershed mainly due to the combined effects of increase of precipitation quantity, extreme storm events in intensity and frequency, and air temperature. Nitrate-nitrogen losses are projected to increase by 1.1% to 38% for the Green Lake watershed and 8% to 95% for the Walworth watershed with the different major influencing factors in each future periods.

Quantifying changes in potential soil erosion under projections of changing climate is important for the sustainable management of land resources, as soil loss estimates will be helpful in identifying areas susceptible to erosion, targeting future erosion control efforts, and/or conservation funding. Therefore, the macro-scale Variable Infiltration Capacity—Water Erosion Prediction Project (VIC-WEPP) soil erosion model was utilized to quantify soil losses under three climate change scenarios (A2, A1B, B1) using projections from three general circulation models (GFDL, PCM, HadCM3) for the Great Lakes region from 2000 to 2100. Soil loss was predicted to decrease throughout three future periods (2030s, 2060s, and 2090s) by 0.4–0.7 ton ha−1 year−1 (4.99–23.2%) relative to the historical period (2000s) with predicted air temperature increases of 0.68–4.34 °C and precipitation increases of 1.74–63.7 mm year−1 (0.23–8.6%). In the forested northern study domain erosion kept increasing by 0.01–0.18 ton ha−1 year−1 over three future periods due to increased precipitation of 9.7–68.3 mm year−1. The southern study domain covered by cropland and grassland had predicted soil loss decreases of 0.01–1.43 ton ha−1 year−1 due to air temperature increases of 1.75–4.79 °C and reduced precipitation in the summer. Fall and winter had greater risks of increased soil loss based on predictions for these two seasons under the A2 scenario, with the greatest cropland soil loss increase due to increased fall precipitation, and combined effects of increases in both precipitation and air temperature in the winter. Fall was identified with higher risks under the A1B scenario, while spring and summer were identified with the greatest risk of increased soil losses under the B1 scenario due to the increases in both precipitation and air temperature.

Nonpoint-source (NPS) pollutants, especially from agriculture, continue to be a primary source of water quality degradation problems. Effective land management decisions at the field scale must be made to minimize nutrient losses that could pollute streams. Existing NPS models often cannot directly estimate the impacts of different land management practices or determine the effectiveness of combined best management practices (BMPs) in a distributed way at the farm scale. In many cases, they rely on application of the Universal Soil Loss Equation (USLE) or its improved versions, which represent fields in a lumped fashion and use empirical rather than process-based modeling methodologies. In this study, a coupled Water Erosion Prediction Project and Water Quality (WEPP-WQ) model was completed, updated, improved, and evaluated for simulation of hydrology, soil erosion, and water quality. The WEPP model is a well-established process-based model that simulates runoff and erosion processes from a hillslope. The water quality components are based on those of the Soil and Water Assessment Tool (SWAT). A single overland flow element (OFE) on a hillslope is used to represent a single soil and land use management. The WEPP-WQ model was tested by comparing simulated values from the coupled model with observed nutrient and sediment concentrations in surface runoff following storm events at experimental sites near Waterloo in northeastern Indiana and at the Throckmorton Purdue Agricultural Center in west central Indiana. Time series evaluation of the WEPP-WQ model was performed with observed nutrient and sediment losses from an experimental plot near Tifton, Georgia. The model performed quite well in simulating nutrient losses for single storm events, with R-2 greater than 0.8, Nash-Sutcliffe efficiency (NSE) greater than 0.65, and percent bias (PBIAS) less than 31% for runoff, sediment, nitrate nitrogen, total nitrogen, soluble phosphorus, and total phosphorus losses. In predicting time series nutrient loss, the WEPP-WQ model simulated daily nitrate nitrogen losses adequately, with the ratio of the root mean square error to the standard deviation of measured data (RSR) less than 0.7, NSE greater than 0.55, and PBIAS within the range of +/- 40%. Comparisons between simulated soluble phosphorus, total phosphorus, and literature results were performed due to the absence of an available observational dataset. The WEPP-WQ model with a single OFE in this study provides a basic but important step for the development of WEPP-WQ models with multiple OFEs that can evaluate the effectiveness of BMPs.