Inherent properties (geology, geomorphology) interact with climate and human modification of vegetation, soils, drainage and riparian wetlands in effectuating the degree of buffering that watersheds provide . Buffering of river flow relative to the space–time dynamics of rainfall is an ecosystem service, reducing the exposure of people living on geomorphological floodplains to high-flow events, and increasing predictability and river flow in dry periods . In the absence of any vegetation and with a sealed surface, river flow will directly respond to the spatial distribution of rainfall, with only the travel time to any point of specific interest influencing the temporal pattern of river flow. Any persistence or predictability of river flow in such a situation will reflect temporal autocorrelation of rainfall, beyond statistical predictability in seasonal rainfall patterns. On the other side of the spectrum, river flow can be constant every day, beyond the theoretical condition of constant rainfall, in a watershed that provides perfect buffering, by passing all water through groundwater pools that have sufficient storage capacity at any time during the year. Both infiltration-limited (Hortonian) and saturation-induced use of more rapid flow pathways (inter and overland flows) will reduce the flow persistence and make it, at least in part, dependent on rainfall events. Separating the effects of land cover (land use), engineering and rainfall on the actual flow patterns of rivers remains a considerable challenge . It requires data, models and concepts that can serve as an effective boundary object in communication with stakeholders . There is a long tradition in using forest cover as such a boundary object, but there is only a small amount of evidence supporting this .

In Part 1 , we introduced a flow persistence parameter (Fp) that links the two, asymmetrical aspects of flow dynamics: translating rainfall excess into river flow, and gradually releasing water stored in the landscape. The direct link between these two aspects can be seen from Eq. (4) in Part 1 : Qt=FpQt-1+1-FpPt-Etx, where Qt and Qt-1 represent river flow on subsequent days, Ptx the precipitation on day t (or preceding precipitation released as snowmelt on day t) and Etx the preceding evapotranspiration since the previous precipitation event, creating storage space in the soils of the watershed. The first term on the right-hand side of the equation represents the gradual release of stored water, causing a slow decline of flow as the pools feeding this flow are gradually depleted. The second term reflects the part of fresh additions of water are partitioned over immediate river flow and the increase of stocks from which water can be gradually released. The derivation of the link depended on the long-term water balance, and thus assumed that all out- and inflows are accounted for in the watershed.

Commonly used rainfall–runoff models (including the curve number approach and SWAT models) only focus on the second term of the above equation , without link to the first. Various empirical methods for deriving “base flow” are in use, but details of the calculation procedure matter. Results in Part 1 for a number of contrasting meso-scale watersheds in Southeast Asia suggested that inter-annual variation in Fp within a given watershed correlates with both the Richards–Baker (R–B) flashiness index and the base-flow fraction of annual river flow. However, the slope of these relationships varied between watersheds. Here, in Part 2 we will further analyse the Fp results for these watersheds that were selected to represent variation in rainfall and land cover, and test the internal consistency of results based on historical data: two located in the humid and one in the subhumid tropics of Indonesia, and one in the unimodal subhumid tropics of northern Thailand.

After exploring the patterns of variation in Fp estimates derived from actual river flow records, we will quantify the sensitivity of the Fp metric to variations in rainfall intensity and its response, on a longer timescale to land cover change. To do so, we will use a model that uses basic water-balance concepts: rainfall interception, infiltration, water use by vegetation, overland flow, interflow and groundwater release, to a spatially structured watershed where travel time from sub-watersheds to any point of interest modifies the predicted river flow. In the specific model used, land cover effects on soil conditions, interception and seasonal water use have been included. After testing whether Fp values derived from model outputs match those based on empirical data where these exist, we rely on the basic logic of the model to make inference on the relative importance of modifying rainfall and land cover inputs. With the resulting temporal variation in calculated Fp values, we consider the time frame at which observed shifts in Fp can be attributed to factors other than chance (i.e. null hypotheses of random effects can be rejected with accepted chance of Type I errors).

In the discussion of Part 1 , the credibility questions on replicability of the Fp metric and its sensitivity to details of rainfall pattern vs. land cover as potential causes of variation were seen as requiring case studies in a range of contexts. Although the four case studies in Southeast Asia presented here cannot be claimed to represent the global variation in catchment behaviour (with absence of a snowpack and its dynamics as an obvious element of flow buffering not included), the diversity of responses among these four already point to challenges for any generic interpretation of the degree of flow persistence that can be achieved under natural forest cover, as well as its response to land cover change.

Where Fig. 8 in Part 1 explored the relationship in inter-annual variation between flashiness index and Fp in the actual data for the four watersheds, we can now repeat the analysis for the modelled results for each scenario. Figure 8 presents two examples with, again, evidence that the flashiness index and Fp are related, but with considerable variation between the watersheds and a lower slope for the Cidanau watershed with its downstream flow buffering.

The empirical data summarised here for (sub)humid tropical sites in Indonesia and Thailand show that values of Fp above 0.9 are scarce in the case studies provided, but values above 0.8 were found, or inferred by the model, for forested landscapes. Agroforestry landscapes generally presented Fp values above 0.7, while open-field agriculture or degraded soils led to Fp values of 0.5 or lower. Due to differences in local context, it may not be feasible to relate typical Fp values to the overall condition of a watershed, but temporal change in Fp can indicate degradation or restoration if a location-specific reference can be found. The difference between wet- and dry-season Fp can be further explored in this context. The dry-season Fp value primarily reflects the underlying geology, with potential modification by engineering and operating rules of reservoirs, the wet-season Fp is generally lower due to partial shifts to overland and interflow pathways. Where further uncertainty is introduced by the use of modelled rather than measured river flow, the lack of fit of models similar to the ones we used here would mean that scenario results are indicative of directions of change rather than a precision tool for fine-tuning combinations of engineering and land cover change as part of integrated watershed management.

The differences in relative response of the watersheds to changes in mean rainfall intensity and land cover change suggest that generalisations derived from one or a few case studies are to be interpreted cautiously. If land cover change would influence details of the rainfall generation process (arrow 10 in Fig. 1 of Part 1, ; e.g. through release of ice-nucleating bacteria, this can easily dominate over effects via interception, transpiration and soil changes.

Our results indicate an intra-annual variability of Fp values between wet and dry seasons of around 0.2 in the case studies, while inter-annual variability in either annual or seasonal Fp was generally in the 0.1 range. The difference between observed and simulated flow data as basis for Fp calculations was mostly less than 0.1. With current methods, it seems that effects of land cover change on flow persistence that shift the Fp value by about 0.1 are the limit of what can be asserted from empirical data (with shifts of that order in a single year a warning sign rather than a firmly established change). When derived from observed river flow data, Fp is suitable for monitoring change (degradation, restoration) and can be a serious candidate for monitoring performance in outcome-based ecosystem service management contracts. Choice of the part of the year for which Fp changes are used as indicator may have to depend on the seasonal patterns of rainfall.

In view of our results, the lack of robust evidence in the literature of effects of change in forest and tree cover on flood occurrence may not be a surprise; effects are subtle and most data sets contain considerable variability. Yet, such effects are consistent with current process and scaling knowledge of watersheds.

In summarising findings on the Fp metric, we can compare it with existing ones across the seven questions raised in Fig. 1 of Part 1 . Comparator metrics can be derived from various data sources, including the amount (and/or quality) of forest cover upstream, the fraction of flows that is technically controlled, direct records of river flow (over a short or longer time period), records of rainfall and/or models that combine landscape properties, climate and land cover. Tentative scoring for these metrics (Table 6) suggests that the Fp metric is an efficient tool for data-scarce environments, as it indicates aspects of hydrographs that so far required multi-annual records of river flow.

Overall, our analysis suggests that the level of flow buffering achieved depends on both land cover (including its spatial configuration and effects on soil properties) and space–time patterns of rainfall (including maximum rainfall intensity as determinant of overland flow). Generalisations on dominant influence of either, derived from one or a few case studies are to be interpreted cautiously. If land cover change would influence details of the rainfall generation process this can easily dominate over effects via interception, transpiration and soil changes. Multi-year data will generally be needed to attribute observed changes in flow buffering to degradation/restoration of watersheds, rather than specific rainfall events. With current methods, it seems that effects of land cover change on flow persistence that shift the Fp value by about 0.1 are the limit of what can be asserted from empirical data, with shifts of that order in a single year a warning sign rather than a firmly established change. When derived from observed river flow data, Fp is suitable for monitoring change (degradation, restoration) and can be a serious candidate for monitoring performance in outcome-based ecosystem service management contracts. Watershed health is here characterised through the flow pattern it generates, leaving the attribution to land cover, rainfall pattern and engineering of that pattern and of changes in pattern to further location-specific analysis, in the same way a symptom of a high body temperature can indicate health, but not diagnose the specific illness causing it.

The data sets analysed so far did not indicate that the flow persistence at high flows differed from that at lower flows within the same season, but in other circumstances this may not be the case and further care may be needed to use Fp values beyond the measurement period in which they were derived. While a major strength of the Fp method over existing procedures for parameterising curve number estimates, for example, is that the latter depends on scarce observations during extreme events and Fp can be estimated for any part of the flow record, the reliability of Fp estimates will still increase with the length of the observation period.

Further tests on the performance of the Fp metric and its standard incorporation into the output modules of river flow and watershed management models will broaden the basis for interpreting the value ranges that can be expected for well-functioning watersheds in various conditions of climate, topography, soils, vegetation and engineering interventions. Such a broader empirical base could test the possible use of Fp as a performance metric for watershed rehabilitation efforts.